Breast cancer is the primary cause of cancer death among women globally, responsible for about 425,000 deaths in 2010. This study assessed the awareness, knowledge and practices of breast self-examination as a method of prevention and early diagnosis of breast cancer among reproductive aged women in Akatsi South district in Volta region of Ghana.This study was a cross-sectional study involving 385 women between the ages of 15–49 years. Data were collected with a structured questionnaire and variables included socio-demographic characteristics, breast cancer knowledge, breast self-examination knowledge and practice. Descriptive statistics were used to analyze and present the data and chi square test of significance was used to determine association between socio-demographic variable and practice of breast self-examination.The mean age of the women was 24.54±7.19. Only 3.1% of women had no formal education and 58.9% were single. Although 88.3% of the respondents were aware of breast cancer, 64.9% of the respondents had good or sufficient knowledge of breast cancer and only 94(37.6%) practice BSE. Over 50% of the respondents did not know how to perform BSE. There was a significant association between knowledge on breast cancer and practice of BSE (χ2 = 36.218 p = 0.000). The higher the age of a participant, the lower practice of breast self-examination and this was significant .Breast self-examination is a key strategy to early detection of breast cancer and subsequently critical for effective treatment and cure of the disease. The findings in this study have shown significant low levels of awareness and practice of breast self-examination among women in Akatsi South district of the Volta region. This pattern may be similar to other rural communities across the region. The need to create awareness and to educate women, especially rural women, on importance of breast self-examination as preventive measure for breast cancer is paramount. Over the past decade, several research findings and data sources have indicated an increasing burden of breast cancer in terms of incidence, morbidity, and mortality related to breast cancer. Breast cancer is also the primary cause of cancer death among women globally, responsible for about 425,000 deaths in 2010 . Again, This trend is alarming and indicative of the global public health threat posed by breast cancer. Of the above diagnosis, Asia and Africa had incidence of 4.8% and 5.8% respectively but mortality estimates were 57.4% and 7.2% for Asia and Africa respectively . Doing BSE is one way for a woman to know how her breasts normally feel so that she can notice any changes that do occur. This examination is critical for Ghanaian women because black women have been found to bear the greater burden of breast cancer mortality compared to other races. In order to perform BSE, the individual needs to possess the knowledge of and have the skill of doing so. BSE is important for enabling women become familiar with the feel and appearance of their breast; and help them easily and quickly detect any changes that occur . Women wThe objectives of the study were to examine awareness and knowledge of breast self-examination as well as the practice of breast self-examination among rural women in Akatsi South district of Volta region of Ghana. In addition, we wanted to explore characteristics or variables that are associated with knowledge and practice of BSE.A descriptive cross-sectional study was conducted whereby 385 reproductive aged women ages 15–49 were conveniently recruited and quantitative data were collected through a structured questionnaire in Akatsi South district of the Volta region of Ghana. Akatsi South district is predominantly rural with peasant farming and fishing being the main economic activities and an estimated population of 95,426 according the Ghana 2010 Population and Housing census. Demographically, 44,039 were males and 51,387 females constituting 46% and 54% respectively. In order to participate in the study the female had to be between the ages of 15 and 49, and living in Akatsi South district for at least five years at the time of the study. The age range of 15–49 chosen was to ensure that we covered women in reproductive ages. For the residency requirements, we wanted to adhere to the delimitation approved by GHS Ethics Review Committee. We also wanted to make sure that if any issues arose or further research was needed we could trace the participants.X2. Besides reasons provided by those who did not practice BSE, we performed X2 test of significance to determine significant predictors of BSE practice. There were both open-ended and close-ended questions on the questionnaires depending on the variable. For open-ended questions, those who were literate wrote the answers and verbal responses were made by those who could neither read nor write. The questionnaire was written in English and translated in Ewe, the predominant local language in the district, for those who were illiterate. A summary of the questions include breast cancer risk factors, symptoms, and treatment. Also awareness of BSE, skills and when to practice BSE, and whether the participants practiced BSE and when were asked.Demographic variables measured in this study included marital status, age, educational attainment, and employment status. Awareness and knowledge of breast cancer were also measured besides the two main variables of knowledge and practice of BSE. To measure awareness of breast cancer among the participants, two questions were included in the questionnaire asking whether or not they had heard of breast cancer as a disease and whether or not they considered breast cancer as a common disease among women in Ghana. The status of socio-demographic variables was important considerations because they determine how independent a woman is in a predominantly male-dominated society. Besides, age and marital status for example have been found to be associated BSE . AssociaX2 test of significance were performed to determine variables that significantly predicted BSE. For the sake of this study, good knowledge was defined as being to list at least four each of risk factors and symptoms of breast while poor knowledge was defined as not being able to describe at least four each of risk factors and symptoms breast cancer. Informed consent forms had to be signed by adult participants ages 18 and above whilst those between ages 15 and 17 had to sign assent forms after their parents/guardians approved their participation. All those who participated in the study agreed voluntarily to do so prior to recruitment into the study. The study protocol was reviewed and approved by Ghana Health Service Ethics Review Committee (GHS-ERC) with approval number GHS-ERC: 29/05/17. Descriptive statistics were used to analyze and present the data using SPSS version 20.Descriptive statistics were used to analyze and present data in percentages and frequency distribution tables and In line with the objectives of this study, a number of findings were made. These objectives included assessing knowledge of the rural women about the key symptoms and preventive measures of breast cancer, awareness and knowledge of BSE, and practice of BSE. Analyses of the socio-demographic variables included in the study are first presented. These variables included age, marital status, religious affiliation, occupation, and educational level attained by the participants. Of the 385 respondents, 296(76.9%) were aged less than 30 years, and 17(4.4%) were 40 years and above. The respondents were between the ages of 15 and 49 years, with a mean age of 24.54 (±7.19) years. Majority of the respondents (58.9%) were single and never married, 152(39.5%) were married, whilst 1.6% divorced. Majority of the respondents (56.6%) were unemployed, 36.6% reported being self-employed, and 26 (6.8%) were government employees. In terms of educational attainment, majority of the respondents (48.8%) attained high school education whilst 70 (18.2%) attained tertiary education. Only 12 (3.1%) had no formal education. Therefore, the study participants were largely a literate group.The results showed that there was high awareness of breast cancer among women at least in the Akatsi South district. Out of the 385 respondents who participated in the study, majority 340(88.3%) had heard of and were aware of breast cancer as a disease predominantly among women. In terms of prevalence of breast cancer in Ghana, 177(46.1%) of the participants believed breast cancer is most common cancer in women, 106(27.6%) believed it is not common, whilst 101(26.3%) of the respondents did not know how common breast cancer was. As to how they became aware of breast cancer, 154(40%), 115(30%), and 41(10%) of participants mentioned radio, health workers, and school respectively. Other sources of breast cancer information mentioned included friends (8%), neighbors (7%), and books/internet (5%).To measure knowledge of breast cancer, two sets of questions were presented covering risk factors for breast cancer and symptoms of breast cancer. For risk factors for breast cancer, participants were asked to list at least one risk factor that was likely to expose a woman to breast cancer. The results show that 217 out of the 385 women in this study could not list one risk factor for breast cancer and that constituted 56.3% well over of half of the study sample. Risk factors listed included family history, contraceptive use, sex, not breastfeeding, menopause, obesity or overweight and older age. Fifty-two people (12%) each mentioned family history and women who did not breastfeed as risk factors for breast cancer. Contraceptive use, being a woman, and obesity or overweight were listed by 37 (8.6%), 31 (7.2%), 18 (4.2%) were mentioned respectively. Other risk factors listed were effects of menopause and increasing age which constituted 3.2% and 2.8% respectively. The women were then asked whether breast cancer is curable. Most of them 244(63.6%) reported that they believed breast cancer is curable if detected early. Only 42(10.9%) of them believed that breast cancer is not curable but can be controlled. Like the risk factors, participants were asked to list at least one symptom of breast cancer known of or they had heard of. Pain in the breast was the most listed whereby 133 women constituting 26.8% mentioning it as a major symptom of breast cancer. This was followed by lump in the breast which was listed by 110 (22%) women. Sudden swollen or thickness of the breast, change in size or shape of breast, redness of nipple, and nipple discharge other than breast milk are correct answers and were mentioned by 87 (17.5%), 82 (16.5%), 44 (8.9%), and 41 (8.3%), respectively.Therefore, while awareness about breast cancer was relatively high in this study population (88%), knowledge was basic and not adequate with 56% not knowing any risk factor for breast cancer, knowledge of symptoms was moderate.Respondents were assessed on knowledge of breast cancer by computing knowledge variables to determine average knowledge score. Series of questions were asked with different answer choices and the number of correct answers or scores provided by a participant showed his or her knowledge level. The mean score was 0.64 (64%). Respondents who performed below the average score were classified as having poor knowledge and those who had average or above were classified as having good knowledge on breast cancer. Out of the 385 respondents interviewed, majority 65% had good knowledge about breast cancer whilst, 35.1% of them had poor knowledge about breast cancer.Age, marital status, occupation, education level and religion were cross tabulated against knowledge to determine their statistical significance. Out of the 135 respondents that had poor knowledge, majority of them 104(77.0%) were aged less than 30 years and 25(18.8%) were aged between 30 and 39 years. Only 6(4.4%) were aged above 40 years. Among the 250 respondents who had good knowledge, majority were aged less than 30 years. There was no significant relationship between a respondent age and level of knowledge .Majority (57.8%) of the respondents who had poor knowledge were single and 55(40.7%) were married. Only 2(1.5%) were divorced. Also, among those that had good knowledge, majority 149 (59.6%) were single and 97(76.8%) were married and Only 4(1.6%) were divorced. There was no significant relationship between a respondent’s knowledge and marital status .Only 4(3.0%) of the respondents who had poor knowledge were employed whilst, majority 67(49.6%) of them were unemployed and 64(47.4%) were self-employed. Among the respondents who had good knowledge, only 22(8.8%) of them were employed whilst, majority 151(60.4%) of them were unemployed and 77(30.8%) were self-employed. There was significant relationship between a respondents occupation and level of knowledge .On the other hand, majority 61(45.2%) of the respondents who reported poor knowledge attained primary/Junior High School (JHS) and 56(41.5%) attained Senior High School (SHS). Only 12(8.9%) of them attained tertiary and 6(2.4%) had no education. Also, among respondents that had good knowledge, majority 132(52.8%) of them attained SHS/SSS and 58(23.2%) attained tertiary. Only 6(2.4%) had no education and 54(21.6%) attained Primary/JHS. There was a significant association between participant level of education and knowledge . BSE knowledge and practice assessment were the major objectives of this study. This study aimed to determine the proportion of respondents who were aware of and had comprehensive knowledge of and skill to perform BSE and are practicing BSE. The findings show that knowledge of BSE was generally low among the 385 women who participated in this study. Only 165(43.3%) of the respondents knew what self-breast examination was whilst majority 217(56.7%) reported not knowing anything about it or having not heard about it. Out of the 385 respondents surveyed, majority 235 (61.0%) reported not knowing anything about BSE and were never taught how to perform it. Among those who reported they learnt about BSE, most 90(60.1%) of them were taught by a health worker, 17(11.3%) were taught by relative or friends. Fourteen (9.3%) learnt the procedure of BSE through books and 29(20.3%) learnt it through other means. Only 39(36.8%) of the respondents believes the most appropriate time for self-breast examination is between 2 and 3 days after cessation of menstruation. However, majority 49(46.2%) thought BSE can be carried out any time within the month. Seven (6.6%) of them thought the appropriate time for self-breast examination was a few days before menstruation starts.In terms of BSE practice, only 106 (27.5%) of the respondents in this study reported practicing or ever practiced breast self-examination. Overwhelming majority 279 (72.5%) reported not practicing BSE. Among those who practiced BSE, most 62(58.5%) reported performing BSE every month, 27(25.5%) examined the breast once every month and about 17(16%) examine the breast once every year. These participants provided various reasons and circumstances that hindered them from practicing BSE. Out of the 279 (72.5%) who do not perform breast self-examination, more than half 50.1% of them said they don’t know the techniques for breast self-examination. Some (17.4%) respondents also said they do not practice BSE because they don’t have breast cancer problems. Lack of privacy at home was also mentioned by 7.3% of the respondents as reason for not practicing BSE. About 3.4% of the respondents believed they did not need to self-examine their breast and another 3.4% did not feel comfortable to self-examine their breast. About 5.7% of them also gave other reasons for not practicing BSE. The results are presented in X2 was performed and significant association (χ2 = 36.218 p = 0.000) found.The age of a woman was a strong predictor of BSE practice among this study population. There was a significant association between age group and practice of breast self-examination . A higher age of a participant corresponded with a lower practice of BSE. Among respondents who practice BSE, most 70(66.0%) of them were aged less than 30 years, followed by 30–39 27(25.5%). Therefore, younger women below age 30 were more likely to perform BSE compared to women over the age of 40. Another predictor of BSE practice established among the study population was knowledge of breast cancer itself. A series of questions were asked to determine the general awareness and knowledge of breast cancer and 250 (65%) of the respondents reported good knowledge of breast cancer. Of the 250, 94 (37.6%) reported practicing BSE. On the other hand, 135 (35%) reported poor knowledge of breast cancer and of those 135 respondents, only 12 reported practicing BSE. To confirm knowledge of breast cancer as a significant predictor, a The discussion highlights the main findings of this study and how those findings compare with findings from similar studies conducted on the subject of breast cancer and breast self-examination. In this current study breast cancer awareness was high for the rural population in that 88.3% of the respondents were aware of breast cancer but only 64.9% of the respondents demonstrated adequate knowledge of symptoms and preventive measures for breast cancer. In terms of awareness of BSE, only 43.3% reported being aware of BSE in the current study. This finding of low awareness and knowledge of BSE is similar but lower compared to the findings in Nigeria  in whichAnother major objective in this study was to examine the practice of breast self-examination among the women. The findings in this study show that only 106 (27.5%) of the 385 women reported practicing or ever practiced BSE. Two hundred and seventy-nine (279) women reported they never practiced BSE which constituted 72.5% of the sample in this study. This finding is consistent with the finding of similar studies in other parts of the world whereby the practice of BSE is generally low. For example, in a study in Cameroon, only 15% of 200 women who reported basic knowledge of BSE actually practiced BSE  and in OTwo variables were found to be significantly associated with and predicted practice of BSE including knowledge of breast cancer and age of the woman. This current study reported a significant association between knowledge of breast cancer and practice of BSE (χ2 = 36.218 p = 0.000). In this current study, out of the 250 (65%) of the respondents with good knowledge about breast cancer, 94(37.6%) practiced BSE. This finding was consistent with what was reported by Dundar and colleagues who found level of breast cancer knowledge to be the only variable significantly associated with the BSE and mammography practice  . Other rThis study also showed a significant association between age group and practice of breast self-examination . As the age increased the level of participant practice of BSE was lower. Among respondents who reported practicing BSE, most 70(66.0%) were aged less than 30 years, followed by 30–39 years 2725.5%), and 40 years and above 9(8.5%). This finding is also consistent with other findings .5%, and . The assAmong those who reported not practicing BSE, many reasons were given to the low practice. For instance, half (50.1%) of the respondents said they did not know the techniques for breast self-examination. Some respondents (17.4%) also said they did not practice BSE because they didn’t have breast cancer problems. Lack of privacy at home was also mentioned by 7.3% of the respondents as reason for not practicing BSE. About 3.4% of the respondents did not think they need to self-examine their breasts and another 3.4% said they did not feel comfortable to self-examine their breast. These findings were consistent with other researchers who stated that, regarding the barriers to BSE, the majority who never practiced BSE mentioned that lack of knowledge, not having any symptoms, and being afraid of a breast cancer diagnosis were the main barriers to practicing BSE  . It is iIn conclusion, the findings in this study show an overall lack of comprehensive knowledge of breast cancer and BSE among rural women in the Volta region of Ghana. Also practice of BSE, which is an important method for early diagnosis of breast cancer especially in resource limited settings like the Volta region of Ghana, is very low. The findings did show that knowledge of breast cancer and BSE as well as age of a woman are strong predictors of BSE. The implication for public health practice is that there is the need to develop and/or adopt culturally appropriate and proven educational and skill building interventions to inform and train rural women on BSE targeting all women. There is the need to improve knowledge of breast cancer and BSE and to target all age groups in future breast cancer awareness programs. 26 Sep 2019PONE-D-19-20459Assessment of Knowledge And Practice Of Breast Self-Examination Among Reproductive Age Women in Akatsi South District of Volta Region of Ghana.PLOS ONEDear Dr. ADAM,Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.==============================Abstract:Background- replace “(district or township name)” with the actual location informationMethods- the software used for data analysis are probably the least important details to include. Instead include information on which variables were measured and the statistical analysis. The abstract says that women aged 15-45 were included but the rest of the paper indicates that women aged 15-49 were included.Conclusion- Your text states that breast self-examination is key to early detection, effective treatment and cure of breast cancer. This wording is too strong and I suggest tempering it to say instead that it is A key strategy.Introduction:The first two sentences of the introduction are unclear and should be clarified.The introduction is too long and repetitive and should be shortened and streamlined.In the paragraph that starts “Because of poor access…” the sentence “Korle Bu Teaching Hospital…” clarify if these hospitals are in Ghana. In the next sentence, clarify that Volta is in Ghana. In the last sentence clarify whether the five regions you refer to are in Ghana.Remove the heading in the introduction section “Awareness of breast self-examination and breast cancer”Resource is misspelled as resourseStatement “Breast self-examination is critical in resource limited settings as a strategy to reducing mortality due to late reporting and diagnosis” requires a referenceStatement “Breast selfexam (BSE), or regularly examining your breasts on your own, can be an important way to find a breast cancer early, when it's more likely to be treated successfully.” requires a referenceStatement “Breast selfexamination as a screening tool for breast cancer in developing countries is advocated in view of its cost-effectiveness.” requires a referenceThe research objective “The objectives of the study were therefore, to examine awareness and knowledge of breast selfexamination as well as the practice of breast self-examination among rural women in Akatsi South District of Volta Religion of Ghana” does not totally match the results you present. Please rewrite your objective to reflect the fact that you are exploring which characteristics are associated with knowledge and BSEMethods:Information about the setting. The district will, of course, not be familiar to readers outside Ghana. What type of setting is this? Rural? Urban? How populated?Please justify why you chose to include women ages 15-49 in your studyPlease justify why you included only women who had resided in the study area for 5 years or morePlease enumerate whether the questions asked were open ended or multiple choice. For example, was the question about risk factors for breast cancer open ended, where women had to write in answers or verbally respond without any cues? Or did they choose from a list?You refer to “good” knowledge and “poor” knowledge in your results. Please provide a justification for labeling knowledge good or poor.Please explain what variables are measured in this study  and justify why each is included. For example, is there existing evidence that religious affiliation is associated or might be associated with your two main variables in this setting?Explain what statistical analysis you performedYour methods section is missing quite a bit of information.  Please add:Results:The sentence “These socio-demographic variables were crucial in understanding the responses of the participants and the findings in this study.” should be moved to the discussion as it is a commentary on your findings.The sentence “To measure awareness of breast cancer among the participants, two questions were included in the questionnaire asking whether or not they had heard of breast cancer as a disease and whether or not they considered breast cancer as a common disease among women in Ghana.” belongs in the methods.I suggest changing the phrase “refusal to breast feed” to “did not breast feed” in the following sentence since some women might not have given birth and some women may have been unable to physically breast feed. “Fifty-two people each mentioned family history and refusal to breastfeed as risk factors for breast cancer and that constituted 12% each.”Not all of your readers will be experts on breast cancer. You should explicitly state whether the responses given by participants (e.g. list one symptom of breast cancer) were correct.Avoid the word “influence” in your results because this implies a causal association.  Also, this sentence really belongs in the methods.Please revise the title for Table 2 so that it is more informative and clearly and specifically describes the information in the tableThis sentence belongs in the methods: “Even though those who reported not practicing BSE gave various reasons for not practicing, we wanted to statistically find out the predictors of BSE in this sample and so a chi square (X 2 ) test of significance was performed.”Discussion:Please explain what you mean by “market” womenPlease provide an explanation of why age may relate to BSE. You do provide some references. Has there been any discussion or do you have any ideas about why this relationship exists?Limitations and strengths need to be added to your discussion sectionApplications of your research findings should be suggestedGeneral comments: Data are plural. Please adjust the text throughout.Avoid using the language “increase” and “decrease” throughout and instead say “higher” or “lower.” The words increase and decrease imply a longitudinal association across time. You are describing cross-sectional associations.==============================https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.We would appreciate receiving your revised manuscript by Nov 10 2019 11:59PM. 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Please do not edit.]Reviewers' comments:Reviewer's Responses to QuestionsComments to the Author1. Is the manuscript technically sound, and do the data support the conclusions?The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: YesReviewer #2: Yes**********2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: YesReviewer #2: Yes**********3. Have the authors made all data underlying the findings in their manuscript fully available?PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.The Reviewer #1: NoReviewer #2: Yes**********4. Is the manuscript presented in an intelligible fashion and written in standard English?PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.Reviewer #1: YesReviewer #2: Yes**********5. Review Comments to the AuthorPlease use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. Reviewer #1: IntroductionAuthors should refer to GLOBOCAN 2012 as reference 1. As the manuscript Ghartey et al refers to Ghanaian specific breast cancer distribution.The section on BSE of the introduction is repetitive and verbose. It would serve the authors to reduce the length and be more concise.MethodsNeed to be listed in methods:A summary of the questions asked, which language or languages (s) etc. Were the questions on scales, yes/no, categorical. What variables were collected from study participants. Where were the study participants identified?Results,Is the entire region rural or are there more urban centers within the region? Can the authors analyze based on this category.Reviewer #2: This is a timely article. The topic is good and the data connect to the discussion at hand. It is appreciated that the significant and non-significant findings are presented. Edits would include using past tense throughout and clarifying some of the statements. I have uploaded a scanned copy of my comments.**********what does this mean?). If published, this will include your full peer review and any attached files.6. PLOS authors have the option to publish the peer review history of their article  digital diagnostic tool,  7 Nov 2019We would like to sincerely thank the reviewers for the meticulous review they conducted. We have laerned and have grown from the review and have tried to address all the comments to the best of ability. We hope that our revision makes the manuscript even more meritorious for acceptance and publication.AttachmentRebuttal Letter BSE.docxSubmitted filename: Click here for additional data file. 14 Nov 2019PONE-D-19-20459R1Assessment of Knowledge And Practice Of Breast Self-Examination Among Reproductive Age Women in Akatsi South District of Volta Region of Ghana.PLOS ONEDear Dr. ADAM,Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.We would appreciate receiving your revised manuscript by Dec 29 2019 11:59PM. When you are ready to submit your revision, log on to If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter.http://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocolsTo enhance the reproducibility of your results, we recommend that if applicable you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. For instructions see: Please include the following items when submitting your revised manuscript:A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). This letter should be uploaded as separate file and labeled 'Response to Reviewers'.A marked-up copy of your manuscript that highlights changes made to the original version. This file should be uploaded as separate file and labeled 'Revised Manuscript with Track Changes'.An unmarked version of your revised paper without tracked changes. This file should be uploaded as separate file and labeled 'Manuscript'.Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out.We look forward to receiving your revised manuscript.Kind regards,Carmella AugustAcademic EditorPLOS ONEAdditional Editor Comments (if provided):Thank you for responding to my comments on the first version of your paper. The paper is much improved. I have listed my additional concerns below, please respond and PLEASE INDICATE LINE NUMBERS WHERE YOU MADE CHANGES so that I can easily find your changes. Thank you.My comments:1. I asked you to change the conclusion in your abstract to "a key strategy" and you changed it to "key strategy." Please add the "a" so it reads "a key strategy."2. Your methods is one large paragraph. Please split the text into multiple paragraphs.3. I cannot find the revised text that you mention in your response #10 in the introduction about BSE, or regularly examining your breasts on your own... Can you please provide line numbers so that I can easily find where you made this change?4. For your response #18 in the results section of your response to me, I can't find where you made this change. Please respond and include line numbers to make it easy for me to find this change.Reviewer 1 comments:1. Please respond to reviewer 1's comments asking about analyzing your data based on geography.Reviewer 2 comments:Please review and apply edits made by reviewer 2 in the scanned file provided.Thank you.Ella Augusthttps://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email us at figures@plos.org. Please note that Supporting Information files do not need this step.While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool,  27 Nov 2019Reviewer #1: Introduction1. Authors should refer to GLOBOCAN 2012 as reference 1. As the manuscript Ghartey et al refers to Ghanaian specific breast cancer distribution.Response: Ferlay J, Soerjomataram I, Dikshit R, Eser S, Mathers C, Rebelo M, Parkin DM, Forman D, Bray F. Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. International journal of cancer. 2015 Mar 1;136(5):E359-86.The above reference has been provided as reference. Ferlay and colleagues worked on GLOBOCAN 2012 and is an appropriate reference on GLOBOCAN 2012.2. The section on BSE of the introduction is repetitive and verbose. It would serve the authors to reduce the length and be more concise.Response: The BSE section has been cut down and well synchronized.3. Methods: Need to be listed in methods:A summary of the questions asked, which language or languages (s) etc. Were the questions on scales, yes/no, categorical. What variables were collected from study participants. Where were the study participants identified?Response: The summary of the questions included breast cancer and breast self-examination knowledge and BSE practice. The variables collected from the participants are clarified in the methods section on pages 7 and 8 and lines 148 to 155.4. Results: Is the entire region rural or are there more urban centers within the region? Can the authors analyze based on this category.Response: Except for Ho, the capital town of the Volta Region of Ghana, which is urban, the rest of the region is predominantly rural. Akatsi South district is predominantly rural and only women from the district were recruited into the study. Therefore, the data analysis reflects predominantly rural women and we do not have urban data to analyze in this study. Reviewer #2: This is a timely article. The topic is good and the data connect to the discussion at hand. It is appreciated that the significant and non-significant findings are presented. Edits would include using past tense throughout and clarifying some of the statements. I have uploaded a scanned copy of my comments.Response:All the edits recommended by the reviewer 2 have been effected throughout the manuscript. We did not feel the need to track all the edits with track changes.AttachmentResponse To Reviewers BSE.docxSubmitted filename: Click here for additional data file. 10 Dec 2019Assessment of Knowledge And Practice Of Breast Self-Examination Among Reproductive Age Women in Akatsi South District of Volta Region of Ghana.PONE-D-19-20459R2Dear Dr. ADAM,We are pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it complies with all outstanding technical requirements.Within one week, you will receive an e-mail containing information on the amendments required prior to publication. When all required modifications have been addressed, you will receive a formal acceptance letter and your manuscript will proceed to our production department and be scheduled for publication.https://www.editorialmanager.com/pone/, click the "Update My Information" link at the top of the page, and update your user information. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.Shortly after the formal acceptance letter is sent, an invoice for payment will follow. To ensure an efficient production and billing process, please log into Editorial Manager at onepress@plos.org.If your institution or institutions have a press office, please notify them about your upcoming paper to enable them to help maximize its impact. If they will be preparing press materials for this manuscript, you must inform our press team as soon as possible and no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact With kind regards,Carmella AugustAcademic EditorPLOS ONEAdditional Editor Comments :Reviewers' comments: 19 Dec 2019PONE-D-19-20459R2 Assessment of Knowledge And Practice Of Breast Self-Examination Among Reproductive Age Women in Akatsi South District of Volta Region of Ghana. Dear Dr. ADAM:I am pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department. onepress@plos.org.If your institution or institutions have a press office, please notify them about your upcoming paper at this point, to enable them to help maximize its impact. If they will be preparing press materials for this manuscript, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact plosone@plos.org. For any other questions or concerns, please email Thank you for submitting your work to PLOS ONE.With kind regards,PLOS ONE Editorial Office Staffon behalf ofDr. Carmella August Academic EditorPLOS ONE


ATM and BMI‐1 genes can alter the risk of breast cancer through genotyping 6 variants among 524 breast cancer cases and 518 cancer‐free controls of Han nationality. This was an observational, hospital‐based, case–control association study. Analyses of single variant, linkage, haplotype, interaction and nomogram were performed. Risk was expressed as odds ratio (OR) and 95% confidence interval (CI). All studied variants were in the Hardy‐Weinberg equilibrium and were not linked. The mutant allele frequencies of rs1890637, rs3092856 and rs1801516 in ATM gene were significantly higher in cases than in controls . Two variants, rs1042059 and rs201024480, in BMI‐1 gene were low penetrant, with no detectable significance. After adjustment, rs189037 and rs1801516 were significantly associated with breast cancer under the additive model . In haplotype analysis, haplotypes A‐C‐G‐G  and A‐C‐A‐A in ATM gene were significantly associated with 1.98‐fold and 6.04‐fold increased risk of breast cancer . Nomogram analysis estimated that the cumulative proportion of 3 significant variants in ATM gene was about 12.5%. Our findings collectively indicated that ATM gene was a candidate gene in susceptibility to breast cancer in Han Chinese.We tested the hypothesis that genetic variation in  Recruitment was conducted in 4 hospitals of Heilongjiang province, China, that is, Daqing Oilfield General Hospital, The 2nd and 3rd Affiliated Hospitals of Qiqihar Medical University and Qiqihar Jianhua Hospital. This study was conducted during the period from January 2013 to August 2015.2.3The conduct of this study was approved by the Ethics Committee of Qiqihar Medical University. Written informed consent was obtained from all study participants at the time of recruitment. This study complied with the Declaration of Helsinki.2.4A patient was recorded to have breast cancer if he or she had either newly diagnosed or histopathologically confirmed or previously untreated breast cancer.2.5Control participants had no clinical evidence of any types of cancer except non‐melanoma skin cancer. All controls reported to have no family history of cancer. No restriction was placed on age and tumour stage at the time of recruitment.2.6In total, 1042 female participants were eligible for inclusion. There were 524 cases with breast cancer and 518 controls free of cancer.2.7The following information was collected from cases with breast cancer, including age of first onset, age of menarche, menopausal age, family history of cancer, invasion depth (T1‐T4), tumour stage (I‐III) and lymph node. From controls free of cancer, age at enrolment and age of menarche were recorded.2.83‐EDTA tubes from each participant. Plasma was separated by centrifugation at 4°C and kept frozen in a deep freezer at −80°C until assayed.Venous blood samples (3 mL) were drawn into 5‐mL vacutainer tubes containing K2.9Genomic DNA was extracted from leucocytes using the phenol‐cholesterol method according to a standard procedure.2.10https://www.ncbi.nlm.nih.gov/gene/). In this study, no restriction was placed on the cut‐off point of minor allele frequency of selected variants, as available linkage analyses only identified some rare variants of genes involved in DNA repair, including ATM gene under investigation, and these variants were associated with a moderate risk of breast cancer.Four variants in ATM gene were selected, including rs189037 (A‐111G), rs3092856 (1380His>Tyr or C4138T), rs1801516 (1853Asp>Asn) and rs373759 126713G>A). Two variants were selected from BMI‐1 gene, including rs1042059 (18Cys>Tyr) and rs201024480 310Ser>Asn). The selection of these variants was based on published papers 713G>A. T0Ser>Asn.2.11Genomic sequences of 6 variants in ATM and BMI‐1 genes were amplified by polymerase chain reaction. Genotyping was determined using ligase detection reaction method.Using the same method, 60 randomly selected samples were re‐genotyped, and the results were completely identical.Genotyping was performed by laboratory workers in a manner blind to the case–control status and related characteristics of study participants.2.12t test or Mann‐Whitney U test depending on its distribution. Categorical variable expressed as percentage were compared using the χ2 test. Testing Hardy‐Weinberg equilibrium in control participants, as well as genotype and allele differences between cases and controls was completed by the χ2 test or Fisher's exact test. Breast cancer risk conferred by genetic variants was calculated using logistic regression analysis after adjusting for age and age of menarche. Effect size was indexed as odds ratio (OR) and 95% confidence interval (95% CI). In addition, given the limited sample size of this study, an internal validation was performed by evenly and randomly splitting all study participants into 2 groups, viz. training group (n = 512) and testing group (n = 512). Risk prediction for breast cancer was, respectively, conducted in both groups.Continuous variables expressed as mean (standard deviation) were compared between cases and controls using https://www.broadinstitute.org/haploview/haploview, and linkage magnitude was expressed as D prime (D’).Linkage analysis was performed using the HaploView software Release 4.2 available at https://www.r-project.org/.Haplotype analysis was performed using the HAPLO.STATS program after controlling for age and age of menarche. This program was realized using the R Project for Statistical Computing Release 3.4.3 available at http://www.multifactordimensionalityreduction.org/.Interaction analysis was performed using the open‐source multifactor dimensionality reduction (MDR) software Release 3.0.2 available at A risk nomogram calculator was produced based on baseline information and significant variants identified. The nomogram was depicted using regression modelling strategies (rms) program. This program also was realized using the R Project for Statistical Computing Release 3.4.3. Specifically, a nomogram is a 2‐dimensional diagram that allows the approximate graphical computation of a mathematical function, and its accuracy can be justified by the concordance index (C‐index).Unless otherwise indicated, analyses were performed with the STATA software Release 13.0 .33.1P < .001). In cases, mean menopausal age was 50.19 years and only a small proportion of cases had a family history of cancer (5.95%). In case of invasion depth, 49.69%, 42.77%, 3.77% and 3.77% of cases were in T1, T2, T3 and T4, respectively. Tumour stages II (49.27%) and III (46.10%) accounted for the majority of breast cancer cases, and 42.47% of cases had positive lymph nodes.This study involved 524 cases with breast cancer and 518 controls free of cancer. Table 3.2P > .01).All studied variants were consistent with the distributions predicted by the Hardy‐Weinberg equilibrium in controls . The genotype distributions differed remarkably significantly for rs3092856 and rs1801516 between cases and controls (both P < .001). The 2 variants, rs1042059 and rs201024480, in BMI‐1 gene belonged to low‐penetrance mutations, and there was no hint of significance with breast cancer risk. The genotype and allele comparisons of 6 studied variants between breast cancer cases, respectively, stratified by tumour stage, invasion depth, lymph node and controls are presented in Table Table P: .002 and .001) of breast cancer after adjusting for age and age of menarche  and rs1801516 (AA) were associated with the significant risk , whereas this association was marginally significant for rs189037 in training group  and for rs189037 and rs1801516 in testing group  (Table P: .031 and .048), and significance persisted in both training and testing groups  Table . In addips Table .3.5P < .001). A low‐penetrance haplotype A‐C‐A‐A was overrepresented in cases than in controls .Haplotype analysis was, respectively, conducted in ATM gene and BMI‐1 gene, as they are mapped on different chromosomes. The derived haplotype frequencies and risk estimates for breast cancer are presented in Table When taking the most common haplotype (G‐C‐G‐G) as the reference group, haplotypes A‐C‐G‐G and A‐C‐A‐A in ATM gene were significantly associated with 1.98‐fold and 6.04‐fold increased risk of breast cancer . Other haplotypes carried no significance in association with breast cancer.3.6The interaction of 6 studied variants is illustrated in Figure 3.7P = .011).A nomogram calculator is presented in Figure 4Our findings partly verified the hypothesis of this study, by showing that ATM gene might be a candidate gene of breast cancer in Han Chinese. In particular, we have identified 2 variants in ATM gene, rs189037 and rs1801516, which may play a dominant role, as well as a possible joint role, in susceptibility to breast cancer risk. Despite no detectable significance, this study represents the first attempt to study the association between BMI‐1 genetic variation and breast cancer.The association of ATM genetic variation with breast cancer has been widely evaluated in Western countries,Two variants in ATM gene were under wide investigations. A meta‐analysis of 9 studies indicated that ATM gene rs1801516 might not be a breast cancer‐susceptibility locus in populations of European and Amerindian origins.In further haplotype analysis, we observed that that haplotype A‐C‐A‐A carrying the mutations of both rs189037  and rs1801516  was overrepresented in cases relative to controls, and its association with breast cancer was statistically significant. This observation not only reinforced the results of our single variant analysis, but also suggested the joint contribution of different loci in ATM gene on the development of breast cancer. However, our interaction analysis of 6 studied variants in both ATM and BMI‐1 gene failed to reveal any synergistic effect. It is possible that the variants under study may be in linkage with other causative mutations in ATM gene. We agree that additional confirmation of our findings is necessary to unravel the genetic involvement of ATM gene in breast carcinogenesis.Besides the candidate ATM gene, we have assessed the association between BMI‐1 gene 2 variants and breast cancer risk. This is, to the best of our knowledge, the first attempt in the literature that has tested this association. However, as the 2 variants are low penetrant in nature, we did not detect any significant association with breast cancer in both single variant and haplotype analyses. So, we proposed that BMI‐1 gene might not be a breast cancer‐susceptibility candidate.Several limitations should be considered in this study. First, this is an observational case–control association study, and our data cannot be used to prove the cause–effect relationship between ATM gene and breast cancer risk. Second, the coverage of genetic variation in ATM and BMI‐1 genes was limited. Third, all participants were collected from 4 local hospitals, and multi‐centre confounding cannot be ruled out. Fourth, this study involved only Chinese women of Han nationality, and extrapolation of our results to the other nationalities was only speculative.Taken together, our findings indicated that ATM gene was a candidate gene in susceptibility to breast cancer in Han Chinese. This study suggests genetic heterogeneity in diverse racial groups and underscores the necessity for race‐specific variation screening. In addition, our findings might be useful for the identification of high‐risk youth women by genotyping ATM genetic variants before the cumulative exposure of obvious risk factors, and meanwhile this study offers some helpful hints in understanding the pathophysiology of breast cancer and other solid tumours.None declared.L.H.Z and H.M.P planned and designed the study, and directed its implementation; L.L.Y and F.H.W drafted the protocol; F.H.W and PC obtained statutory and ethics approvals; M.L.Z, PC and DL contributed to data acquisition; DL, P.H.L and Q.B.M. had access to all raw data; M.L.Z and Q.B.M did the data preparation, quality control and data analysis; L.H.Z and L.L.Y wrote the manuscript. Click here for additional data file.


Comprehensive breast cancer risk prediction models enable identifying and targeting women at high-risk, while reducing interventions in those at low-risk. Breast cancer risk prediction models used in clinical practice have low discriminatory accuracy (0.53–0.64). Machine learning (ML) offers an alternative approach to standard prediction modeling that may address current limitations and improve accuracy of those tools. The purpose of this study was to compare the discriminatory accuracy of ML-based estimates against a pair of established methods—the Breast Cancer Risk Assessment Tool (BCRAT) and Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) models.N = 1143), and a clinical sample of Swiss breast cancer patients and cancer-free women seeking genetic evaluation and/or testing (N = 2481).We quantified and compared the performance of eight different ML methods to the performance of BCRAT and BOADICEA using eight simulated datasets and two retrospective samples: a random population-based sample of U.S. breast cancer patients and their cancer-free female relatives (Predictive accuracy (AU-ROC curve) reached 88.28% using ML-Adaptive Boosting and 88.89% using ML-random forest versus 62.40% with BCRAT for the U.S. population-based sample. Predictive accuracy reached 90.17% using ML-adaptive boosting and 89.32% using ML-Markov chain Monte Carlo generalized linear mixed model versus 59.31% with BOADICEA for the Swiss clinic-based sample.There was a striking improvement in the accuracy of classification of women with and without breast cancer achieved with ML algorithms compared to the state-of-the-art model-based approaches. High-accuracy prediction techniques are important in personalized medicine because they facilitate stratification of prevention strategies and individualized clinical management. Since 2009, the U.S. Preventive Services Task Force recommends breast cancer screening with biannual mammograms for women age 50 to 74 years old . In 2013BRCA mutations, race, and number of first-degree relatives affected with breast cancer, to calculate 5-year and lifetime risk for women older than 35 years old [BRCA mutations [The Breast Cancer Risk Assessment Tool (BCRAT), also known as the Gail model, and the Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) model were developed to identify high-risk women based on known risk factors and have been integrated into clinical guidelines to help guide decision making about breast cancer risk management , 19. BCRears old . The Natutations . Clinicautations , 24.However, both models have considerable limitations. BCRAT can only be used for women above 35 years old, and only takes into account history of breast cancer in first-degree relatives , without including age at diagnosis of these relatives. It does not consider family history of ovarian cancer, which may be of crucial importance for women with hereditary breast and ovarian cancer (HBOC). The BOADICEA model does not account for risk factors associated with reproductive history and hormonal exposure and has limited utility in cases with small family history. Although both models have been validated with large cohort data, their discriminatory ability, area under the ROC (receiver operating characteristics) curve, is between 0.53 and 0.64 , 25–28. ML offers an alternative approach to standard prediction modeling that may address current limitations and improve accuracy of breast cancer prediction tools . ML techTo provide strong assessment, reliable comparison, and reproducible results, we compared ML-based estimates and estimates from BCRAT and BOADICEA model using eight synthetic simulated datasets and two actual observational datasets. In order to have fair comparisons, we used the same risk factors as BCRAT and BOADICEA models, respectively, as input for the ML algorithms in each comparison.We used simulated data to compare the performance between the different ML algorithms and determine the stability and validity of these predictions within each algorithm. We generated two sets of four simulated datasets , one set consistent with the input values of BCRAT and the other consistent with the input values of the BOADICEA model. The BCRAT and BOADICEA models rely on different risk factors, which necessitated this dichotomy. For each of the two scenarios, we generated four synthetic datasets: A. simulated data with no signal (null data); B. simulated data with artificial signals; C. simulated dataset (B) adding 20% missing values; and D. simulated dataset (C) after applying multiple imputations. We randomly masked as missing 20% of values in datasets (B) to generate datasets (C), then we applied multiple imputations to datasets (C) to generate datasets (D). The cancer outcome for simulated dataset (B) for the BCRAT was simulated based on linear aggregation effects of all variables, with an artificial effect size for each variable. Variables in the null dataset (A) had no signal—these were generated with completely random values within specific ranges. In our simulation, having certain risk factors could elevate an individual’s breast cancer risk. This relative risk  is given according to published meta-analyses for that specific risk factor. Each individual had a baseline probability randomly assigned to them. After adding each risk factor’s attribution (RR multiplied by baseline) to baseline, we set a cutoff of the final probability to classify each sample as “healthy” or “sick”. Datasets (B) for BCRAT and BOADICEA have different input variables and data structure. For example, in data used for the BOADICEA model, each individual is imbedded into a family pedigree and have two individuals as parents. We randomly set family sizes between 3 and 80 members, and the number of generations from 1 to 5 in each family, based on our observations in the Swiss clinic-based dataset. Family members’ age and age gap between the two closest generations was set according to average age for first child birth. The pedigree  dataset (B) with artificial signal for the BOADICEA model was generated with R Package “pedantics,” enabling pedigree-based genetic simulation, pedigree manipulation, characterization, and viewing . Multipln = 1143), after excluding individuals younger than 35 years old.We used baseline data from a prospective randomized trial conducted in Michigan (USA) including a statewide, randomly selected sample of young breast cancer survivors (YBCS) who were diagnosed with invasive breast cancer or ductal carcinoma in situ (DCIS) and their cancer-free female relatives , 39. TheThe oncology department at the Geneva University Hospital (HUG) has been offering genetic evaluation and testing since 1998 to breast cancer patients and cancer-free individuals. During the genetic consultation process, information about demographic and clinical characteristics, disease history, previous genetic test results, and a detailed family pedigree are recorded with “Progeny” software . InformaFor the US population-based dataset, there were less than 3% missing values among the variables used by the BCRAT model. For Swiss clinical datasets, there were about 13% missing values among the variables used by the BOADICEA model. Among those missing values, BRCA mutations, estrogen receptor, and progesterone receptor attributed the most (11%). Thus, missing values in BRCA mutation and hormone receptor testing were given a separate category of “unknown” in the analyses, in addition to “positive” and “negative.” This approach is also consistent with the flexibility of the BOADICEA models in handling missing information.n = 1143 US YBCS and cancer-free relatives and n = 2481 Swiss cancer patients and cancer-free individuals.Descriptive statistics, i.e., frequencies, percentages, means, and standard deviations, were computed describing sample characteristics for both categorical and continuous variables in the BRCAT and BOADICEA models and in ML approaches for n = 1200) and retrospective data from the U.S. population-based trial (n = 1143 women). The R package “brca” version 2.0 was used to calculate absolute lifetime risk of invasive breast cancer according to BCRAT algorithm for specific race/ethnic groups and age intervals for each individual in the datasets [Comparisons between ML versus BRCAT were based on performance assessment on five datasets: Simulated data A to D (datasets .n = 2500 women) and retrospective data from HUG with 2481 females from 2481 families including 112,587 family members. Lifetime risk predictions were generated with the web-based batch processing from the BOADICEA web application. The lifetime risk for each woman was calculated using data from all the members in her family. In simulated datasets A to D, we randomly assigned a female member in each family as the index case.Comparisons between ML versus the BOADICEA model were based on performance assessment on five datasets: Simulated data A to D , logistic regression (LOGIT), linear discriminant analysis (LDA), Markov Chain Monte Carlo generalized linear mixed model (MCMC GLMM), and quadratic discriminant analysis (QDA) . The modOne benefit of using ML approaches was the supervised classification of breast cancer patients and cancer-free controls, where controls could outnumber patients or vice versa. We rebalanced the datasets prior to ML predictions to reduce the potential for estimate bias with the R packages “unbalanced"” and “SMOTE” (Synthetic Minority Over-sampling TEchnique) , 48. Then samples of equal size (n-folds). Each algorithm used n − 1 folds for training the ML algorithm and tested its accuracy with the last fold of the data in each of the n experiments. The final error estimate of the classification was obtained by averaging the n individual error estimates. We used n = 10 folds cross-validation with 20 repetitions in this process [To ensure the reliability of ML predictions and the consistency of the forecasts, we used internal statistical n-fold cross-validation. This is an alternative strategy for validating risk estimates without a prospective dataset  and prov process .The performance of BCRAT and the BOADICEA models were evaluated using measure of the area under the receiver operating characteristic curve (AU-ROC), while for the ML techniques the performance is presented with the mean AU-ROC from 10-fold cross validations.n − 1 folds). These features were explored to ensure they are in line with both human domain knowledge and reasonable expectations. For decision tree classification methods , we ranked variable importance on variable selection frequency as a decision node. For GLM, LOGIT, LDA, QDA, and MCMC GLMM algorithms, variable importance was determined by the coefficient effect size. KNN used an overall weighting of the variable within the model.To understand, interpret, and gain trust in the ML techniques, we identified the salient features with the highest contribution to the accuracy of these predictions by ranking them within each cross validation using training sets  and two observational retrospective datasets from two different countries and different target samples (population-based versus clinic-based). Compared to BCRAT and the BOADICEA models, most ML techniques we tested were superior at distinguishing cancer cases from cancer-free controls. ML algorithms improved significantly the predictive accuracy of both models from less than 0.65 to about 0.90, especially when tested with real samples. ML algorithms that produced the best accuracy were ADA followed by RF using variables of BCRAT, and the MCMC GLMM using variables of the BOADICEA model. The increased predictive accuracy observed with ML algorithms was not due to additional input variables, since we used exactly the same risk factors as the BCRAT and the BOADICEA models. Rather, this was due to inherently better predictive ability of ML algorithms. With supervised learning approaches, the artificial or natural complexities of each dataset were restored and adhered to different algorithms with high accuracy. When the datasets were intentionally perturbed by introducing missing values or performing multiple imputations, the prediction performance of the ML algorithms remained stable.Using different simulated datasets allow us to control the input and assess the case-classification/prediction results relative to “ground truth.” We simulated dataset (A) as a “null” reference case-study. This helps us identify false-positive predictions, because when no signal exists in the dataset, all approaches should fail to classify the samples. In simulated datasets (B), (C), and (D), we created the artificial signals within the datasets to strongly correlate with the outcome (breast cancer yes/no). This approach allows us to test whether the machine learning algorithms we used can detect these artificial signals and provide valid and stable predictions, even when there are missing values. This helps us identify false-negative predictions.In the simulated datasets, we assigned estimations  to each risk factor based on published epidemiological data. Unfortunately, there is no available information about the underlying estimation of each risk factor used in the BCRAT and BOADICEA models. The only available information is that these estimations are derived from large cohort studies over time. Therefore, it is possible that the estimations in the simulated datasets are different from the estimations used by the BCRAT and BOADICEA models, which may explain the underperformance of the later models to predict the class in the simulated datasets. Moreover, the simulated datasets have oversimplified artificial signals, which make it relatively easier for the more general approaches of machine learning to pick up a signal and identify features in the controlled simulated data than in real datasets. Thus, the machine learning-based algorithms showed opposite trends on simulated data compared to the model-based methods. Finally, the simulated datasets were not used for a comparison between the machine learning algorithms and the BCRAT or the BOADICEA model. The main purpose of using simulated datasets was to compare predictions between different machine learning algorithms and the stability within each machine learning method.Ranking importance of variables in each model was consistent with our expectations. Biopsy testing indicated suspicious cell abnormality. Number of first-degree relatives affected with breast cancer as well as cancer age onset in a family pedigree can partially reflect the common environmental exposures, inherited information, and lifestyles. We observed variations and similarities in the importance of risk factors depending on the core algorithms in each ML approach and variable types. ADA and RF were both based on decision trees and resembled closely in variables and ranking. QDA placed more importance on categorical variables, e.g., number of first-degree relatives with breast cancer, while LDA placed more importance on continuous variable, e.g., age in both comparisons. This finding has implications for future research aiming to develop a new breast cancer risk prediction model, incorporating established and newly evaluated risk factors.https://github.com/SOCR/ML_BCP/).As firm supporters of “open-science,” we have packaged, documented, and distributed the complete end-to-end R-protocol used to generate the synthetic data and perform all data analytics reported in this manuscript. We have shared the protocol via GitHub (The inclusion-exclusion selection criteria of the US and the Swiss datasets may have influenced the association between observed variance and outcomes. In the US population-based sample, YBCS had fewer affected relatives than their cancer-free relatives. Thus, number of affected relatives was detected as an important variable but without external validity in interpretation. Interpretability of the function modeled by ML algorithms is only partially limited by the “black-box” nature of ML algorithms in our study because we included a limited number of well-established breast cancer risk factors. However, the inherent complexity of how risk factors interact with each other, their independent effect on the outcome, and how effect sizes are determined within each ML algorithm is not known.Significant strengths of the study include the novelty of the approach, i.e., applying ML algorithms in individual breast cancer risk prediction and comparing predictive accuracy with existing models. The improvement achieved with ML algorithms in accurate classification of women with and without breast cancer compared to the state-of-the-art model-based approaches was striking. We demonstrated a range of ML algorithms with cross-validations, which is lacking in other applications of ML for cancer prognosis . DifferePredictive models are essential in personalized medicine because they contribute to early identification of high-risk individuals based on known epidemiological and clinical risk factors. Accurate breast cancer risk estimates can inform clinical care and risk management across the breast cancer continuum, e.g., behavioral changes, chemoprevention, personalized screening, and risk-stratified follow-up care. Available risk prediction models have an overall accuracy less than 0.65. ML approaches offer the exciting prospect of achieving improved and more precise risk estimates. This is the first step in developing new risk prediction approaches and further explores diverse risk factors. ML algorithms are not limited to a specific number of risk factors but have the flexibility to change or incorporate additional ones. The improvement in predictive accuracy achieved in this study should be further explored and duplicated with prospective databases and additional risk factors, e.g., mammographic density, risk factors in IBIS Breast Cancer Risk Evaluation Tool, and polygenic genetic scores. Improvements in computational capacity and data management in healthcare systems can be followed by opportunities to exploit ML to enhance risk prediction of disease and survival prognosis in clinical practice .


The incidence of breast cancer has increased dramatically in China. We evaluated the clinical and epidemiologic factors associated with breast cancer, and its stage in a case‐control study of Northeast Chinese women. We also examined whether these factors were differentially distributed among molecular subtypes of breast cancer in a case‐only analysis. We identified 1118 breast cancer patients and 2284 healthy women from Cancer Hospital of Medical University between January 2014 and December 2017. Logistic regression models were used to calculate the odds ratios (ORs) and corresponding 95% confidence intervals (CIs). We found that postmenopausal women had a decreased risk of breast cancer , and tended to have breast cancer of human epidermal growth factor receptor 2 (HER2)‐overexpressing  and triple‐negative  subtypes, compared with the luminal B subtype. Women with history of abortion had an increased risk of breast cancer . Women with high breast density and high Breast Imaging Reporting and Data System (BIRADS) scores of lesions tended to have breast cancer of advanced stage, but were not differentially distributed among its molecular subtypes. In conclusion, postmenopausal women had decreased risk of breast cancer, and tended to have nonluminal subtype, while women with history of abortion had increased risk of breast cancer. Women with high breast density and BIRADS scores of lesions tended to have advanced stage breast cancer. We provide evidence on the epidemiologic factors for breast cancer and its subtypes, which may help with breast cancer risk stratification. Our study evaluated the clinical epidemiologic factors associated with breast cancer, and its stage and molecular subtypes in a case‐control study of Northeast Chinese women. We provide evidence on the epidemiologic factors associated with breast cancer stage and molecular subtypes, which may help with breast cancer risk stratification. To further investigate whether these risk factors influenced differently on stages and molecular subtypes of breast cancer among Northeast Chinese women, a case‐only analysis was conducted. In a secondary analysis, as high breast density is an established risk factor for the progression of breast cancer22.1Our study included 1118 women with primary breast cancer who were identified from the Cancer Hospital of China Medical University from January 2014 to December 2017, including 497 early stage (stage I or II) and 621 advanced stage (stage III or IV) breast cancers. The diagnoses of breast cancer were pathologically confirmed based on breast cancer surgery or core needle biopsy. A total of 2284 healthy women undergoing a breast cancer screening program in the Cancer Hospital of China Medical University, supported by the National Cancer Center of China, during the same period were included as controls. Patients with other cancers or major cardiovascular diseases were excluded. Our study was approved by the Medical Ethical Committee of Liaoning Cancer Hospital and Institute. All the patients were informed and consented to use their general characteristics and clinical data.3General characteristics of study participants, including age, menopausal status and age at menopause, personal history of abortion overall, and the history of spontaneous or induced abortion, were collected through face‐to‐face interviews by trained nurses at the Cancer Hospital. The information on general characteristics of cases was collected at diagnosis, while information on controls was collected at enrollment.Information on breast density was obtained from the mammographic report. Breast density was classified into four categories,Major clinical and pathological characteristics of the participants were also obtained. The location of breast cancers, including upper outer quadrant (UOQ), lower outer quadrant (LOQ), upper inner quadrant (UIQ), lower inner quadrant (LIQ), or central zone, was determined based on radiographical reports. Immunohistochemistry (IHC) assays of breast tissues were conducted to determine the status of estrogen receptor (ER), progesterone receptor (PR), HER2, and Ki67. The positive ER (ER+) or PR (PR+) status was defined as the positive nuclear staining present among 1% or more of tumor cells.3.1We firstly analyzed whether the major questionnaire‐based characteristics and breast density changed the risk of breast cancer overall, breast cancer of early stage, and advanced stage, respectively, as compared with the control group. Logistic regression models were used to calculate the odds ratios (ORs) and corresponding 95% confidence intervals (CIs). In the multivariable model, analyses were conducted including age (continuous variable), menopausal status, and age at menopause , history of abortion (never or had), and breast density . For breast density, category II, instead of category I, was used as the reference due to sample size consideration. We did not adjust for history of abortion for the separate analyses of spontaneous abortion (never or had) or induced abortion (never or had).In addition to case‐control comparisons, case‐only analyses were conducted to examine whether these questionnaire‐based characteristics, breast density, and tumor location influenced differentially the risk for molecular subtypes of breast cancer, with luminal B breast cancer as the reference group. We defined molecular subtypes of breast cancer based on recognized methods,We evaluated if mammographic and ultrasound BIRADS scores would be useful in predicting the stages and molecular subtypes of breast cancer. The analyses were conducted also with early stage or luminal B breast cancer as the reference group, respectively, adjusting for the major questionnaire‐based characteristics, breast density, and tumor location.In a secondary analysis, the clinical epidemiologic factors for breast density were also examined based on all cases and controls with data on breast density (n = 3019). For this analysis, breast density category I was used as the reference.P‐value less than .05 was considered as statistically significant. All the results of statistical analyses were two‐sided.All the statistical analyses were performed using SPSS 23.0. 44.1Ptrend < .0001). Compared with breast density of category II (scattered), the multivariate‐adjusted OR (95% CI) for advanced stage breast cancer was 0.59 (0.33‐1.05) for women with category I (fatty), 2.11 (1.58‐2.80) for women with category III (heterogeneous), and 3.03 (1.69‐5.42) for women with category IV (extreme) . Relative to luminal B subtype, breast cancers in the central zone were less likely to be HER2‐overexpressing subtype , and breast cancers in the UIQ were more likely to be luminal A subtypes . History of abortion and breast density were not differentially associated with molecular subtypes of breast cancer.For the analyses of molecular subtypes Table , postmenPtrend < .0001), but were not differentially distributed among molecular subtypes.The BIRADS scores of mammography and ultrasound were also evaluated for the risk of breast cancer stages and molecular subtypes Table . Women w4.3Consistent with the direction of how these factors changing breast cancer risk, we found lower breast density for postmenopausal women and higher breast density for women with a history of abortion, as shown in Table 5Based on the Northeast Chinese women, we comprehensively evaluated the epidemiologic factors for breast cancer and its clinical stage and molecular subtypes. We found that postmenopausal women had a decreased risk of breast cancer, and tended to have breast cancer of advanced stage as well as HER2‐overexpressing and triple‐negative subtypes, while a history of abortion had an increased risk of breast cancer. Women with a high breast density and BIRADS scores of lesions tended to have advanced stage breast cancer compared with early stage breast cancer, but were not differentially distributed among the molecular subtypes of breast cancer.Previous studies have examined the association between menopausal status and risk of breast cancer. A meta‐analysis including 118 964 women with breast cancer and 306 091 women without breast cancer showed that premenopausal women had 40% higher risk of breast cancer than postmenopausal women at the age of 45 to 54 years.Interestingly, although postmenopausal women overall had a reduced risk of breast cancer, in our study, postmenopausal women tended to have late stage breast cancer relative to early stage breast cancer, and were more likely to have HER2‐overexpressing  and triple‐negative subtypes , the two breast cancer subtypes with worse prognosis.In our study, the association between a history of abortion, particularly spontaneous abortion, and increased risk of breast cancer may be biologically plausible. The theory that a full‐term pregnancy is at decreased risk of breast cancer has been generally accepted.Consistent with the direction of changing breast cancer risk, women with history of abortion had increased breast density. However, it is worth noting that although women with both spontaneous and induced abortion had higher breast density, a history of spontaneous abortion only, but not induced abortion, increased the risk of breast cancer overall. Previous studies on abortion and breast density have been sparse.Our study based on Northeast Chinese women supported that women with high breast density had elevated risk of breast cancer, particularly advanced stage breast cancer. However, breast density was not differentially distributed among molecular subtypes of breast cancer in our study, which appears inconsistent with a prior US study showing increased mammographic density for HER2‐positive breast cancer.In addition to menopausal status, history of abortion, and breast density, we further examined whether the status of ER, PR, HER2, and Ki‐67 as well as tumor location would change the odds of late stage vs early stage breast cancer. Compared with early stage breast cancer, women with ER‐, PR‐, HER2+, and high expression of Ki‐67 (≥14%) had increased odds of advanced stage breast cancer, consistent with the previous reports indicating worse clinical outcomes for these immunohistochemical categories.We placed particular emphasis on the implications of breast imaging examination and evaluated whether the BIRADS scores would be different for the clinical stage and molecular subtypes of breast cancer. As expected, women with high BIRADS scores tended to have advanced stage breast cancer compared with early stage breast cancer in our study, demonstrating potentially greater likelihood of malignant behavior of breast cancer. The results appeared similar for ultrasound and mammographic scores. However, we did not find differences in BIRADS scores among breast cancer molecular subtypes, suggesting that BIRADS scores may not serve as predictors for molecular subtypes of breast cancer.Our study was strengthened by its extensive investigations on major epidemiologic factors, breast density, tumor location, and BIRADS scores for breast cancer and its clinical stage and molecular subtypes based on the Northeast Chinese women. In addition, we explored major characteristics for breast density. Our study also had limitations. First, our study was a hospital‐based unmatched case‐control study with its intrinsic limitations. Selection bias may be a concern as the breast cancer patients were selected from the Cancer Hospital of China Medical University, and controls were selected from a breast cancer screening program in this hospital. The cases were significantly younger than controls. However, the mean age of both case and control categories was older than 50 years. We have adjusted for age  in all the statistical analyses. We believe our results of case‐control analyses were less likely to be distorted even for the known age‐related menopausal status and history of abortion, as the continuous age‐adjusted analyses were able to minimize the effects of unbalanced age distribution. However, the potential extrapolation of our findings to other settings might need caution. Second, information on postmenopausal status and age at menopause as well as history of abortion were self‐reported, which may have led to information bias, although the misclassification would tend to be nondifferential among the different clinical stages and molecular subtypes of breast cancer. Third, our study did not collect information on several important hormonal and reproductive factors as well as other host and lifestyle factors of breast cancer, such as reproductive history, breastfeeding, oral contraceptive use, parity, number of abortion, family history of breast cancer, and body mass index. Further efforts are warranted to examine how these factors may change the risk of breast cancer and its subtypes in Northeast Chinese women and to examine whether any factor may contribute to explaining the inconsistency of our findings with prior epidemiologic studies. In addition, studies are also required to examine breast cancer subtypes defined by additional clinical factors, such as axillary nodal status, mitotic index, and nuclear pleomorphism. Fourth, we were not able to examine separately the urban and rural areas, or examine different regions and ethnicity groups altogether in our study, which requires further large scale multicentered studies.In conclusion, we found that postmenopausal women had decreased risk of breast cancer, and tended to have breast cancer of HER2‐overexpressing and triple‐negative subtypes, while women with a history of abortion had increased risk of breast cancer. Women with a high breast density and mammographic and ultrasound BIRADS scores tended to have advanced stage breast cancer. Our study provides evidence on the epidemiologic factors for breast cancer stage and molecular subtypes, and may inform practitioners on the risk stratification of breast cancer in clinics.The authors declare no potential conflict of interest. Click here for additional data file. Click here for additional data file.