To detect the HPV genotype and integration sites in patients with high-risk HPV infection at different stages of photodynamic therapy using nanopore technology and to evaluate the treatment effect.Four patients with HPV infection were selected and subjected to photodynamic therapy, and cervical exfoliated cell was sampled at before treatment, after three courses of treatment and six courses of treatment, their viral abundance and insertion sites were analyzed by nanopore technology, and pathological examinations were performed before and after treatment. In this study, we developed a novel assay that combined viral sequence enrichment and Nanopore sequencing for identification of HPV genotype and integration sites at once. The assay has obvious advantages over qPCR or NGS-based methods, as it has better sensitivity after viral sequences enrichment and can generate long-reads (kb to Mb) for better detection rate of structure variations, moreover, fast turn-around time for real-time viral sequencing and analysis.The pathological grade was reduced in all four patients after photodynamic therapy. Virus has been cleared in two cases after treatment, the virus amount reduced after treatment but not completely cleared in one case, and two type viruses were cleared and one type virus persisted after treatment in the last patient with multiple infection. Viral abundance and the number of integration sites were positively correlated. Gene enrichment analysis showed complete viral clearance in 1 patient and 3 patients required follow-up.Nanopore sequencing can effectively monitor the abundance of HPV viruses and integration sites to show the presence status of viruses, and combined with the results of gene enrichment analysis, the treatment effect can be dynamically assessed. Cervical cancer is one of the most common female malignant tumor, and the incidence rate ranks first among malignant tumor of female reproductive system. According to the data of 2020 from World Health Organization (WHO), there were about 604 thousand new cases and 342 thousand deaths worldwide every year, which seriously threatened women's lives and health. Since professor Hausen of Germany found that Human papillomavirus (HPV) was closely related to the occurrence of cervical cancer in the late 1970s, HPV detection was gradually used for cervical cancer screening. HPVs are a heterogeneous group of nonenveloped, double-stranded, circular DNA viruses of 8000 nucleotides in size that infect the basal layer of the epithelium of skin (cutaneous HPV types) and mucous tissues  . PersistAminolevulinic acid photodynamic therapy (ALA-PDT) is a combination of medicine and machinery. After exogenous ALA is given, it is selectively absorbed by proliferating active cells. After a series of enzymatic reactions, a large number of photosensitive substances protoporphyrin IX is generated in mitochondria. After irradiation with a specific wavelength light source, reactive oxygen species such as singlet oxygen and oxygen free radicals are produced, which can selectively destroy the diseased tissue and achieve the purpose of treatment. In recent years, ALA-PDT has been used to treat cervical intraepithelial neoplasia (CIN). Studies have shown that the histological remission rate of ALA-PDT in the treatment of CIN I was 75%—86% in 3–6 months; after the treatment, the histological remission rate of CIN II reached 90% in 3–12 months . Our resFour patients with cervical intraepithelial neoplasia complicated with high-risk HPV infection who were admitted to the Department of Gynecology, Beijing You'an Hospital Affiliated to Capital Medical University from January 2021 to December 2021 were selected as the research objects. Two cases’ histopathology of cervical biopsy were CIN I while the other two were CIN II. The cases aged 30–52 years  and were put into 10 ml of preservation solution. Then 5 mL cervical exfoliated cells suspension was divided into a 1.5 mL centrifuge tube and centrifuged at 12,000–15000 rpm for 3 min. The supernatant was removed and the cell pellet was extracted and purified using TIANamp Micro DNA Kit (TIANGEN BIOTECH (BEIJING) DP316) according to the instructions. The extracted DNA was quantified using Qubit3.0, and the fragment length was inspected using Agarose gel electrophoresis. DNA samples with size > 5000 bp and more than 200 ng were further processed for library construction.150 ng total DNA were sheared with ultrasound according to the optimized interruption conditions (M220 Focused-Ultrasonicator was used): Peak power: 25, Duty factor: 10, Cycles/burst: 500, Avg power: 2.5. The sheared DNA was further end-repaired and A-tailed using the NEBNext® Ultra IIEnd-prep reaction buffer and enzyme mix, and 1 × VAHTS® DNA Clean Beads  were used to remove short fragments to ensure library size larger than 500 bp, then PCR Barcoding Kit  was used for library construction according to instruction. The generated library was quantified with the Qubit dsDNA HS Assay Kit  using the Qubit 3.0 fluorometer , the size distribution of library was checked using the Agilent 2100 TapeStation . The typical size range of the library was from 0.6 to 9Kbp.The probe set of HPVs was provided by BOKE bioscience . 500 ng libraries were pre-heated with Human cot-1(Boke bioscience) and blocker , then hybridized with HPV probes using 2 × Hybridization Buffer and Hybridization Enhancer. The hybridization mix was incubated at 95℃ for 10 min, then at 65℃ for 16 h. Dynabeads™ M-270 Streptavidin (Thermo Fisher Scientific) were used for pull down of target sequences and washing steps, then the eluted fragments containing the targeted sequences were re-amplified by 30cycles to generate sequencing library. The enriched library was quantified with the Qubit dsDNA HS Assay Kit using a Qubit 3.0 fluorometer (Thermo Fisher Scientific), and library quality was checked using Agilent 2100 TapeStation . 50 ng enriched each library was pooled and attached with rapid sequencing adapters, then sequencing was performed using PromethION (FLO-PRO002) for 64 h runtime to ensure > 0.3 Gb sequence data was generated for each library.The base calling was performed using the Guppy, then the clean data was processed using Nano pack that conducted length filtering on pass reads with Q-value ≥ 7 and sequence with read-length less than 500 bp were removed. The clean reads were aligned against human genome reference (hg 19) and HPV refseq databases using a in-house bioinformatic pipeline to obtain HPV sequences and viral integration sites .P value ≤ 0.05 were regarded as the significantly enriched gene sets, and the enrichment result of the top 20 were analyzed.The reads number that detected in 12 clinical samples in this study was made into the gene expression matrix, and it was divided into two groups and named BEFORE and AFTER group. Ontology gene sets dataset of MsigDB , 19 was Changes of viral abundance from thin cytology test (TCT) samples collected before treatment, after PDT 3 times, and after PDT 6 times. The abundance of HPV16 in patient B02 were observed decreasing and completely cleared after treatment  twice  to avoid misdiagnosis, but also quantitatively detection of viral abundance and integration sites at once, moreover, with enriched reads over viral genome, we could more accurately identify viral genotype, sub-genotypes or even drug-resistant variations.The HPV genome is divided into three regions: (i) the long control region (LCR) which contains binding sites of viral regulatory proteins (E1 and E2) and cellular transcription factors, (ii) the early region (E) that encodes for the E1, E2, E4, E5, E6 and E7 genes and (iii) the late region (L) that encodes for the L1 and L2 genes , 21. OveHPV infects the host cells and usually exists in a free state, HPV latent in the basal cell layer, where its DNA moves into the nucleus of infected cells in an independent exogenous chromosomal state. Subsequently, HPV integrates into the host chromosome, leading to genomic instability and tumorigenesis of the host. Upon integration the circular episomal HPV DNA is converted into a linear structure and inserted into the host chromosome. The disruption of viral genome can take place within the early region of viral genome (E1 and E2 genes) –34, or wHPV is often present in cervical lesion cells with a mixed infection state, for instance, 2 different genotypes of HPV were found in patient B19, with HPV58 in the integrated state and HPV52 in a free state. Integration of HPV into the host chromosome is a key factor in cervical carcinogenesis. Genes related to cervical cancer progression was found by enrichment analysis before treatment, including CTNNB1, CCR7, AKT1, SLIT2, CDK6, etc. Human SAIL4 , the new discovered proto oncogenes encode transcription factors that acted at the level of stem cells, SAIL4 protein can recognize and bind to CTNNB1 promoter region and trans‐activating CTNNB1 for accelerated expression β- Catenin, which promoted cell proliferation and the formation of tumor cervical cancer cells . InvasioThe principle of photodynamic therapy is to selectively kill actively proliferating cells. The more severe of cervical lesion, the more actively proliferating of its cells, with a greater number of HPV insertion sites and a higher HPV abundance. After photodynamic therapy, the actively proliferating cervical cells are killed and the degree of cervical lesions is significantly reduced. The integrated HPV viruses were cleared, so the HPV abundance and the number of integration sites decreased significantly in patients B02 and B19. However, in patients B15 and B16, we found that although their cervical pathological grade decreased after photodynamic therapy, their HPV abundance and the number of integration sites did not decrease accordingly, considering that the persistent HPV infection lead to new HPV integration. Especially for patient B16, the HPV59 abundance and the number of integration sites increased significantly after 3 PDT, the possible reason for this being the ineffectiveness of PDT and predicting the persistent infection of HPV and a possible exacerbation of the cervical lesions. Subsequent follow-up results also confirmed the presence of persistent infection of HPV56 and HPV59, while the cervical lesions developed from cervicitis to LSIL. Its possible explanation was the production of new HPV integration. Nanopore technology can predict the direction of disease progression earlier than TCT and pathology. Regrettably, patient B16 refused any further HPV integration site testing and pathology testing. We can still identify some genes related to cervical cancer progression from the samples of the two patients after treatment, including ATF3, GPX3, IL10, IL6, RAD51AP1, MGMT, WWOX. Activated transcription factor 3 (ATF3) was a stress-induced transcription repressor, it had been shown that ATF3 overexpression enhanced paclitaxel apoptosis in cervical cancer HeLa cells in part by stimulating TAp73, thus, it can act as a tumor suppress factor . GlutathIn this study, we developed a nanopore-sequencing based assay that provides information on HPV infection, genotype and integration all at once for patients with cervical lesions along the course of photodynamic therapy. The technique advantages of viral sequence enrichment combined with nanopore long-read sequencing contributed to more accurate prognosis determination as well as the dynamic monitoring of treatment accompaniment for a better evaluation of photodynamic therapy. That makes it a promising companion diagnostic tools and favorable supplement to traditional pathological examinations.


The following study aimed to answer the question if HPV-HNCUP and HPV-OPSCC are the same disease. Propensity score matching (PSM) was used to compare the oncological outcomes of both groups, in particular the 5-year overall survival rate (OS), the 5-year disease specific survival rate (DSS) and the 5-year progression free survival rate (PFS).Firstly, between January 1st, 2007, and March 31st, 2020 a total of 131 patients were treated with HNCUP at our Department. Out of these, 21 patients with a confirmed positive p16 status were referred to surgery followed by adjuvant therapy. Secondly, between January 1st, 2000, and January 31st, 2017, a total of 1596 patients were treated with an OPSSC at our Department. Out of these, 126 patients with a confirmed positive p16 status were referred to surgery followed by adjuvant therapy. After PSM, 84 patients with HPV-OPSCC and 21 HPV-HNCUP remained in the study for further comparison.p = 0.013). The DSS was also significantly impaired for HPV-HNCUP , in comparison with HPV-OPSCC patients . The PFS for HPV-HNCUP patients was lower  yet not significantly different to HPV-OPSCC .The OS was 63.5% (95% CI 39.4–87.6) for HPV-HNCUP and 88.9% (95% CI 90.4–100.0) for HPV-OPSCC patients and therefore, significantly lower for the first mentioned (The results presented demonstrate a significant reduced OS and DSS for HPV-HNCUP patients. Accordingly, in our study HPV-HNCUP and HPV-OPSCC are two different entities with a different oncological outcome.The online version contains supplementary material available at 10.1007/s00405-023-08115-5. We see an increasing incidence for oropharyngeal carcinoma in the last decades, a fact that is due to the increasing number of detected infections with human papilloma virus (HPV) , 2. In 2The distinction between HPV-associated and non-associated carcinomas is necessary because on the one hand the clinical aspects of the patients change, they are usually younger and do not show the noxious history typical for HPV-negative carcinomas , but it The question now arises, especially since Winter et al. and Golusinski et al. were able to show that HPV-HNCUP is often an occult HPV-OPSCC , 14, wheWe conducted this retrospective cohort study at a single tertiary referral and academic cancer center. It was approved by the local Ethics Committee  and was carried out according to the Declaration of Helsinki.All patients diagnosed with a p16 positive CUP-syndrome between January 1st, 2007, and March 31st, 2018, as well as all patients diagnosed with HPV-OPSCC between January 1st, 2000, and January 31st, 2017, were included. The following inclusion criteria were applied: surgical treatment at our institution, histologically confirmed squamous cell carcinoma, confirmed positive p16-status and complete medical and surgical record available. The association with HPV was confirmed by overexpression of the surrogate marker p16INK4a.The following exclusion criteria were applied: detection of the primary cancer during regular diagnostic work-up, histological types other than squamous cell carcinoma, negative or unknown p16-status or discontinued treatment as well as previous head and neck radiation.Smoking was defined as current smokers with a smoking history of at least more than 10 pack years, Alcohol consumption was defined as reported daily alcohol intake (yes/no).The treatment process for CUPs involved first of all a “no-touch”-panendoscopy followed either preferably by a core needle biopsy or in case this is not possible, a node picking of the suspicious lymph node. Afterwards, a positron emission tomography (PET) was performed. If there was still no evidence of the primary cancer, the PET was followed by a CUP-panendoscopy that included a curettage of the nasopharynx, bilateral tonsillectomy and pathological evaluation of the tongue base. Simultaneously, the respective neck dissection was performed on the side of the previously diagnosed malignant lymph node. In case of contralateral suspicious lymph nodes a bilateral neck dissection was performed.The treatment process of HPV-OPSCC involved primary ablative surgery, either through a transoral, transcervical or combined approach with or without simultaneous microvascular reconstruction and neck dissection of the appropriate nodal basis.Adjuvant treatment consisted of either radiation therapy or chemoradiation. Indications for chemoradiation were extranodal extension and more than one affected lymph node for HNCUP patients . HPV-OPSThe classification of the neck dissection was according to Robbins et al. where selective neck dissection denotes preservation of one or more groups of lymph nodes, modified radical neck dissection denotes preservation of one or more non-lymphatic structures and radical neck dissection denotes removal of the sternocleidomastoid muscle, the spinal accessory nerve, and the internal jugular vein besides the lymph node groups . Moreove2 body surface area (BSA) continuous infusion d1-5) in combination with cisplatin (100 mg/m2 BSA) or carboplatin (AUC 5) splitted to 3–5 days.Radiation techniques included 3D conformal radiation therapy, intensity-modulated radiation therapy (IMRT), or volumetric modulated arc therapy (VMAT). The dosage of radiation was 64 Gray (Gy) in the area of the affected lymph node and 56 Gy in the ipsilateral neck and possible primary sites  for HNCUP patients. The contralateral neck received 50 Gy. A dose of 70 Gy was delivered to the nasopharynx. HPV-OPSCC patients received 61 Gy in the primary tumor region and 52.3 Gy in the corresponding lymph node levels. Treatment was delivered either in single doses of 2.0 Gy sequentially in shrinking field technique or as simultaneous integrated boost using single doses of up to 2.3 Gy up to biologically equivalent cumulative doses. The standard approach for concomitant chemotherapy was two cycles of 5-Fluorouracil  and compared via independent Categorical variables are presented as absolute and relative values (N / %) and compared with the Chi-Square-Test or with the Exact Fischer-Test, whatever applicable. Survival rates were created by using the Kaplan–Meier-Method and compared by the Log-Rank-Test. Overall-, disease-specific and progression free survival values are presented as Kaplan–Meier estimates and 95% confidential intervals (CI).A Propensity Score Matching was performed to equalize both study groups regarding clinical variables that had the potential to have a confounding influence on the primary and secondary endpoints. The matching was based on the variables: gender, age, nodal stage, adjuvant treatment modality and the use of noxious agents. The PMS was conducted in R using the package and MatchIt . Specifi2-tests are reported as phi in case of nominal variables with 2 levels and Cramer’s V for higher order variables. For independent t Tests and the Mann–Whitney-U-test r was reported as an effect size. A phi/Cramer’s V/r of 0.1 displays a small effect, 0.3 a medium, and 0.5 a strong effect  for HPV-HNCUP patients and 47 d  for HPV-OPSCC patients. There was no significant difference between the two groups in any of the treatment characteristics described above . Furthermore, less lymph nodes were removed from HPV-HNCUP patients . This is due to the fact, that HPV-OPSCC patients received the neck dissection bilateral and every HPV-HNCUP patient only on the affected side.In contrast to this, patients with HPV-HNCUP received significantly higher radiation doses compared to patients with HPV-OPSCC .Regarding to the HPV-HNCUP and HPV-OPSCC group, the 5-year overall survival rate was 63.5% (95% CI 39.4–87.6) and 88.9% , in comparison with HPV-OPSCC patients  yet not significantly different compared to the HPV-OPSCC group  with HPV-HNCUP and 68 out of 84 patients (81%) with HPV-OPSCC were smokers. The data from Tribius et al. showed that smoking is a negative prognostic factor for patients with HPV-positive cancers  and hencRoss et al. showed comparable survival data for early-stage HPV-OPSCC and HPV-HNCUP and proposes to include both patients in clinical trials investigating the role of treatment deintensification . In our A limitation of our study is the retrospective study design. Nevertheless, it should be emphasized that this is a homogeneous patient population treated at a single tertiary referral cancer center. Furthermore, all patients received the same combined therapy consisting of surgical and adjuvant therapy. Accordingly, the results cannot be transferred to patients who received a single therapy modality, i.e., surgery or radiation alone, as this was beyond the scope of this study.Another limitation is the fact that the HPV status was determined only by the p16-status and not additionally by HPV-DNA. A routine determination of HPV-DNA from the specimens is not done in our clinic and subsequently there is a risk that a determination of p16INK4a alone is not sufficient to diagnose HPV-associated HNCUP, with the consequence that a possible primary is not located in the oropharynx and the corresponding tumor stage of the patients would be higher. Other risks for bias are the increased number of extranodal extension in the HPV-HNCUP cohort, the associated more radical approach to neck dissection, and the overall lower number of lymph nodes harvested. It must be taken into account that the HNCUP patients only received a median removal of 16 lymph nodes and the definition of an adequate neck dissection includes 18 lymph nodes , a numbeSince HPV-HNCUP cases are rare, prospective studies are difficult. This is also reflected in the literature. Furthermore, the existing studies often show inhomogeneous patient populations with different treatment modalities. Nevertheless, this is an important topic that should be further investigated. One way to increase the number of patients and thus the knowledge about this disease in the long term is to conduct multicenter, prospective studies.The results of this study demonstrate distinct superior oncologic outcomes of patients with a HPV-associated oropharyngeal cancer compared to matched HPV-HNCUP patients determined by propensity score matching. Accordingly, our data does not support the hypothesis of both cancers representing the same disease. Prospective multicenter studies are imperative to investigate the nature of this rare disease of carcinoma of unknown origin associated with HPV.Supplementary file1 (DOCX 13 KB)Below is the link to the electronic supplementary material.


Can human papillomavirus (HPV) screening results and commonly available clinical data be used to develop a high-performance prediction model for cervical cancer among women infected with high-risk HPV?In this diagnostic study of 21 720 women with high-risk HPV infection, the developed prediction model had good performance for predicting cervical intraepithelial neoplasia grade 3 or worse and grade 2 or worse, especially when HPV genotype was included in the model.These findings suggest that this prediction model may be an important tool in screening and monitoring cervical cancer, particularly in low-resource settings where high-quality and extensive cytological and colposcopic examinations are unavailable. High-risk human papillomavirus (hrHPV) is recognized as an etiologic agent for cervical cancer, and hrHPV DNA testing is recommended as the preferred method of cervical cancer screening in recent World Health Organization guidelines. Cervical cancer prediction models may be useful for screening and monitoring, particularly in low-resource settings with unavailable cytological and colposcopic examination results, but previous studies did not include women infected with hrHPV.To develop and validate a cervical cancer prediction model that includes women positive for hrHPV infection and examine whether the inclusion of HPV genotypes improves the cervical cancer prediction ability.This diagnostic study included diagnostic data from 314 587 women collected from 136 primary care centers in China between January 15, 2017, and February 28, 2018. The data set was separated geographically into data from 100 primary care centers in 6 districts for model development (training data set) and 36 centers in 3 districts for model validation. A total of 24 391 women identified with positive hrHPV test results in the cervical cancer screening program were included in the study. Data were analyzed from January 1, 2022, to July 14, 2022.Cervical intraepithelial neoplasia grade 3 or worse (CIN3+) was the primary outcome, and cervical intraepithelial neoplasia grade 2 or worse (CIN2+) was the secondary outcome. The ability of the prediction models to discriminate CIN3+ and CIN2+ was evaluated using the area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, positive likelihood ratio, and negative likelihood ratio. The calibration and clinical utility of the models were assessed using calibration plots and decision curves, respectively.After excluding women without screening outcomes, the study included 21 720 women . Of 14 553 women in the training data set, 349 (2.4%) received a diagnosis of CIN3+ and 673 (4.6%) of CIN2+. Of 7167 women in the validation set, 167 (2.3%) received a diagnosis of CIN3+ and 228 (3.2%) of CIN2+. Including HPV genotype in the model improved the AUROC by 35.9% for CIN3+ and 41.7% for CIN2+. With HPV genotype, epidemiological factors, and pelvic examination as predictors, the stacking model had an AUROC of 0.87  for predicting CIN3+. The sensitivity was 80.1%, specificity was 83.4%, positive likelihood ratio was 4.83, and negative likelihood ratio was 0.24. The model for predicting CIN2+ had an AUROC of 0.85 , with a sensitivity of 80.4%, specificity of 81.0%, positive likelihood ratio of 4.23, and negative likelihood ratio of 0.24. The decision curve analysis indicated that the stacking model provided a superior standardized net benefit when the threshold probability for clinical decision was lower than 23% for CIN3+ and lower than 17% for CIN2+.This diagnostic study found that inclusion of HPV genotypes markedly improved the ability of a stacking model to predict cervical cancer among women who tested positive for hrHPV infection. This prediction model may be an important tool for screening and monitoring cervical cancer, particularly in low-resource settings. This diagnostic study develops and validates a cervical cancer prediction model that includes human papillomavirus (HPV) screening results and commonly available clinical data and assesses whether the inclusion of HPV genotypes improves the cervical cancer prediction ability of the model among women positive for high-risk HPV infection. This lack of high-quality screening suggests that the ability to accurately predict cervical cancer based on commonly available clinical information would be of great value in low-resource settings.Well-established screening programs for early detection can lead to a clear decrease in the mortality and incidence of cervical cancer. However, the implementation of screening programs in developing countries remains a critical issue.8 have constructed prediction models for cervical cancer based on common clinical information, but the participants were from only 1 or 2 hospitals, which may not be representative of the general population, and the sample sizes were insufficient. In addition, high-risk human papillomavirus (hrHPV) is recognized as an etiologic agent for cervical cancer,9 and the different HPV genotypes are associated with different risks of cervical cancer.10 It is reasonable to assume that the inclusion of HPV genotypes in the model may improve prediction ability. However, only 2 previous studies have considered HPV genotypes in prediction models. One study considered 3 HPV genotypes  and observed a model accuracy of 74.4%,11 and the other study used HPV genotype classifications of uninfected, low risk, and high risk and observed an area under the receiver operating characteristic curve (AUROC) of 0.73.12 A more granular classification of HPV genotypes may further improve the prediction performance.Previous studies13 and the Chinese Society for Colposcopy and Cervical Pathology guidelines,14 women who test negative for hrHPV infection are considered free of the disease, whereas women who test positive for hrHPV infection require subsequent testing during cervical cancer screening. However, approximately 10% to 30% of women who test positive for hrHPV infection do not adhere to the screening procedure for the subsequent cytological or colposcopic examination, and their final cervical lesion status is unknown.16 It is important to predict cervical cancer among this high-risk subpopulation of women infected with hrHPV. But only 1 previous study developed a prediction model for this high-risk subgroup, and the model was not thoroughly evaluated or validated .11According to the American Society for Colposcopy and Cervical Pathology (ASCCP) guidelines18 Unlike traditional regression models, machine learning approaches do not make assumptions on data distribution and tend to have better predictive performance. The stacking model, also known as a stacked generalization or a super learner, is an ensemble machine learning approach that permits researchers to incorporate several different prediction algorithms, including regression models, and therefore typically performs better than its submodels.19 The stacking model has been widely used for the prediction of various diseases and has shown good performance,22 and it may be an appropriate approach to predict cervical cancer.Most previous studies that developed models to predict cervical cancer used regression models.The aim of the present study was to use a multicenter large-scale data set to develop and validate a stacking machine learning model for predicting cervical cancer among women who tested positive for hrHPV infection by incorporating HPV genotypes and commonly available clinical information. In particular, we examined whether the inclusion of HPV genotypes further improved the prediction ability of the stacking model.TRIPOD) reporting guideline.23From January 15, 2017, to February 28, 2018, a cervical cancer screening program was conducted in 136 primary care centers of Xiangyang City, China. Inclusion criteria for this program were women 30 years or older who had been sexually active for more than 1 year, were not pregnant, and had no previous HPV vaccination or history of hysterectomy or pelvic radiation therapy. This diagnostic study included only women who tested positive for hrHPV infection. All women were interviewed via questionnaires and received a pelvic examination and HPV genotype testing. Women who tested positive for HPV-16/18 infection underwent colposcopy, and women who tested positive for infection with other hrHPV genotypes were referred for a ThinPrep cytological test (Hologic), followed by colposcopy for women who had abnormal ThinPrep cytological test findings. Women with normal ThinPrep cytological test findings were considered free of cervical cancer. Written informed consent was obtained from all participants. The Ethics Committee of Xiangyang Central Hospital reviewed and approved the study, and all procedures followed the ethical standards stipulated by the institution. This study followed the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis  and included 7 categories: HPV-16, HPV-18, other hrHPV genotypes, HPV-16 plus HPV-18, HPV-16 plus other hrHPV genotypes, HPV-18 plus other hrHPV genotypes, and HPV-16 plus HPV-18 plus other hrHPV genotypes.Individual data included participant’s demographic characteristics , medical history , menstrual status (whether in menopause or age at menopause), sexual behavior factors , and family history of cancer. The pelvic examination involved the visual inspection of the vulva, internal speculum examination of the vagina and cervix, and bimanual palpation of the adnexa and uterus. We also considered infection of several microorganisms in the vaginal microenvironment, including 25 Cervical intraepithelial neoplasia 2 or worse (CIN2+) was the secondary outcome because it is a treatment threshold and a commonly used outcome in published studies.18Cervical intraepithelial neoplasia 3 or worse (CIN3+) was considered the primary outcome because there is widespread agreement that detecting and treating CIN3+, an important premalignant condition of the cervix, can prevent the progression to invasive cervical cancer.Geographic validation as a form of external validation was used to assess the prediction performance of the model, with 100 primary care centers in China  used for model development and the remaining 36 primary care centers  used for model validation.Women without cervical cancer screening outcomes were excluded from the analysis. We also excluded potential predictors that had more than 50% missing values. In both the training and validation data sets, all of the remaining predictors had less than 5% missing values, and the missing data were not imputed.X–aX)/(bX–aX), where Xa is the minimum value and Xb is the maximum value, to speed the computation of machine learning models during the learning phase. The least absolute shrinkage and selection operator (LASSO) method was used to select predictors that had a potential association with the outcomes.The continuous predictors  were normalized using the equation Z =  and brought harm to women without CIN3+ (or CIN2+), the decision curve analysis quantified the standardized net benefit (SNB) among hrHPV-positive women based on the prediction model as compared with the default strategy that none receives the intervention . Using the true-positive rate (TPR), false-positive rate (FPR), clinical decision threshold probability (R), and disease prevalence (P), the SNB can be calculated as SNB = TPR − (FPR × [R/{1 − R}] × [({1 − P}/P]). Individuals would accept the intervention when their predictive probabilities were greater than R, which is affected by clinicians’ and patients’ preferences.We assessed the performance of the stacking model and its submodel in terms of discrimination, calibration, and clinical utility. The AUROC was used to reflect the discrimination of prediction models. Sensitivity, specificity, positive likelihood ratio, and negative likelihood ratio were calculated using the maximum Youden index criterion. The calibration plot was then used to examine the agreement between model predictions and observed outcomes. Finally, we evaluated the clinical utility of the prediction models using a decision curve analysis.27 A sensitivity analysis was conducted to assess whether potential misclassification moderated the results. Based on the mean incidence rates of CIN3+ and CIN2+ reported by a meta-analysis,28 we randomly sampled 0.3% and 0.8%, respectively, of women positive for other hrHPV genotypes but who had normal cytological examination results in the validation set. The outcomes of these subsets were categorized as CIN3+ (or CIN2+). Then the prediction performance of the model was reevaluated.In the cervical cancer screening program, women who were positive for other hrHPV genotypes but had normal cytological examination results were considered free of cervical cancer.P < .05 or the 95% CI of the AUROC excluding 0.5 was considered statistically significant. All statistical analyses were conducted with R, version 4.1.1 , and the stacking model was implemented using the free and open-source package h2o in R. The R code and a user guide are provided in the eAppendix in Statistical analyses were performed from January 1, 2022, to July 14, 2022. A 2-sided value of P < .001), vaginal status , and HPV genotypes , as well as in the validation data set. Gravidity  and family history of cancer  showed statistically significant differences in the training data set, whereas postcoital bleeding  showed statistically significant differences only in the validation data set. Similar differences were observed for the predictors of CIN2+ , but the improvement was not marked, with the AUROC changing from 0.85  to 0.87 . The stacking machine learning model with all predictors included had an AUROC of 0.87 , with sensitivity of 80.1%, specificity of 83.4%, positive likelihood ratio of 4.83, and negative likelihood ratio of 0.24  group . We found that the specificity of the 6 models did not change , while the AUROC  and sensitivity  slightly decreased compared with the main analysis , which may increase screening compliance. In low-resource settings, especially in areas where cytological and colposcopic examinations are unavailable, the developed prediction model using HPV genotyping may be a practical tool for cervical cancer screening.To our knowledge, this diagnostic study is the first to build a cervical cancer diagnostic prediction model for women with hrHPV infection. A stacking machine learning model that included epidemiological factors, pelvic examination results, and HPV genotypes predicted CIN3+ and CIN2+ well, outperforming previous models that included only common clinical information.31 and HPV-E6/E7 mRNA,33 have been considered in some cervical cancer prediction models. Although adding biomarkers as predictors may improve the prediction performance of models,34 it is difficult to popularize biomarker acquisition technology in primary care settings due to shortages of technicians and equipment and the relatively high costs. This complex and expensive technology has limited practical applications in undeveloped regions or for large-scale population screening programs.The epidemiological factors  and pelvic examination results considered in this study can be easily obtained at low cost in clinical settings. Therefore, the model can be used in low-resource settings when data on HPV genotypes are available. In addition to epidemiological factors, some biomarkers, such as vascular endothelial growth factor8 For instance, Rothberg et al17 used HPV infection or not as a predictor, van der Waal et al12 classified HPV infection into 2 categories of risks (low vs high), and Lee et al18 used hrHPV DNA load as an indicator. In our study, we used a more granular classification of HPV genotypes that included 7 categories: HPV-16, HPV-18, other hrHPV genotypes, and 4 coinfections, which are the most common HPV test results in China. Technology for HPV self-sampling is currently being promoted35 and is expected to expand the application of this prediction model based on HPV genotype in the future. Women could collect samples themselves and send the samples to specialized laboratories for testing to ensure accuracy.The present study highlighted the importance of HPV genotype in developing cervical cancer. The HPV genotype was a dominant predictor in our model. Compared with the stacking model that included only epidemiological factors and pelvic examination results, the AUROC of the model incorporating HPV genotypes increased by 35.9% for CIN3+ and 41.7% for CIN2+. We found that with the inclusion of HPV genotypes in the model, further adding epidemiological factors and pelvic examination results only slightly improved the prediction ability. Some previous studies also considered HPV infection data when constructing prediction models.36 The prediction model in our study can be used as a triage tool after HPV testing to identify groups with a particularly high risk of cervical cancer. The sensitivity of the model was 80.1% for CIN3+ and 80.4% for CIN2+. There are 2 screening strategies for HPV combined with cytology that are commonly used in China37: one approach is HPV with reflex to cytology, that is, women who test positive for HPV infection and have abnormal cytology are recommended for immediate colposcopy, whereas women with a positive HPV test but normal cytology are retested later. The other approach involves HPV genotypes and reflex cytology, in which women with an HPV-16/18 positive test result or who test positive for other hrHPV genotypes but display abnormal cytology results undergo colposcopy, whereas women positive for other hrHPV genotypes but with normal cytology are retested at intervals. A study by Wang et al37 compared these 2 screening strategies in China and found that for CIN3+ and CIN2+, the sensitivities of the first strategy were 60.1% and 55.7%, respectively, whereas the sensitivities of the second strategy were 83.2% and 81.7%, respectively. A study in the United States found that the first strategy had a sensitivity of 51.9% and 47.5% for CIN3+ and CIN2+, respectively, whereas the second strategy had a sensitivity of 72.0% and 63.6% for CIN3+ and CIN2+, respectively.38 However, the specificity of the prediction model in the present study was lower than those of the 2 screening strategies used by both of these studies. The specificities of our prediction model for CIN3+ and CIN2+ were 83.4% and 81.0%, respectively. Wang et al37 reported that the first strategy had a specificity of 98.2% and 99.0%, and the second strategy had a specificity of 98.0% and 98.8% for CIN3+ and CIN2+, respectively. In the study conducted in the United States, the specificities for CIN3+ and CIN2+ were 85.7% and 91.8% for the first strategy and 85.2% and 91.3% for the second strategy for CIN3+ and CIN2+, respectively. The higher specificity observed in those studies may be partly because sequential tests improve the specificity of the diagnosis. The aforementioned strategies calculated the specificity among all participants, including many healthy women, whereas the prediction model used in the present study focused on hrHPV-positive women had difficulties identifying true-negative participants. The prediction model in the present study did not need information on cytology, but the sensitivity of our prediction model was much higher than that of the first strategy and was similar to that of the second strategy.With the advantage of high-throughput assays and easy sampling, HPV-DNA testing is recommended as the preferred method of cervical cancer screening in recent World Health Organization guidelines.39 Using the decision curves generated in the present study, when the threshold was 4% or lower, the SNB was approximately 50% for all models. Our model had significantly higher SNB than the strategy of colposcopy for all when the threshold was above 4%. Physicians may have different threshold preferences because of different perspectives on the relative harms of missing cervical cancer vs avoiding unnecessary colposcopy. However, within reasonable clinical thresholds, the SNB for the stacking model was superior to the strategies of colposcopy for all or colposcopy for none and may be used in clinical practice.The stacking model achieved superior net benefit when the clinical decision threshold was below 23% and 17% for CIN3+ and CIN2+, respectively. The ASCCP recommends a 4% threshold, that is, women with a predicted risk of CIN3+ higher than 4% are recommended for colposcopy.40 were not collected in the present study. Third, the screening program included only women 30 years of age or older, and the risk factors for cervical cancer may differ in women younger than 30 years. Therefore, the performance of this prediction model in younger women needs to be validated in future studies.This study has limitations. First, some of the predictors were obtained through a self-reported questionnaire, which may lead to reporting bias and recall bias. Second, some factors, such as smoking and oral contraceptive use, which have been documented to potentially influence cervical cancer,This diagnostic study developed and validated a diagnostic prediction model for cervical cancer among women who test positive for hrHPV infection. Including HPV genotypes in the model markedly improved the prediction ability, suggesting that this prediction model may be an important auxiliary tool in screening for and early diagnosis of cervical cancer in low-resource settings when cytological and colposcopic examination results are unavailable.


TRAIN: 14.6 ± 1.6 MJ·day−1, EITRAIN: 7.7 ± 0.9 MJ·day−1, p ≤ 0.001, d = 5.1) and −50%  were observed throughout TRAIN  and COMP , respectively. Carbohydrate intakes were below the lower range of sports nutrition recommendations in both TRAIN  and COMP . For protein , intakes met the lower range of recommendations. Fat intake exceeded recommendations of the percentage of total EI (COMP: 39 ± 5%). Accordingly, the dietary strategies of international female R7s players may warrant optimization, as carbohydrate and fat intakes were less than optimal when compared to current performance-based sports nutrition guidelines.To understand the energy balance of international female rugby sevens (R7s) players in applied environments, this study estimated the energy intakes (EI) and total daily estimated energy expenditures (TDEE) during a five-day training camp (TRAIN) and phase of competition preparation (COMP) of equal duration. Tri-axial accelerometer devices were worn throughout both scenarios to estimate TDEE, whereas EI was estimated via self-reported food diaries. Energy deficits of −47% (TDEE It is most frequently played in tournaments that are contested under nearly identical laws as the 15-person code [y ≥4 m·s− acceleraUnderstanding the EB of athletes enables practitioners to implement appropriate interventions that can improve both health and performance outcomes and allow for the manipulation of body composition . Data reAcknowledging that alternative methods such as direct calorimetry  and doubUsing an observational approach, EB was estimated by the collection of EE (accelerometer-derived) and EI (via FD) data within professional international female R7s players during (a) a training camp (TRAIN) and (b) preparation for an international-standard competition (COMP).Twelve international R7s players  competing on behalf of a single National team were initially invited to participate in both phases (TRAIN and COMP) of the study. In TRAIN, 11 female players  participated, whereas in COMP, 8 female players participated ; both over a five-day period. The difference in numbers recruited during TRAIN and COMP represents player availability during the training camp  and variations in squad size selected for travel to the tournament competition. Five players completed both scenarios, with TRAIN being followed by COMP and seven days separating the data collection phases.The study obtained ethical approval from the School of Social and Health Sciences Ethics Committee at Leeds Trinity University, United Kingdom , and informed consent was sought from participants prior to study involvement.Participant stature and body mass (BM) were collected at baseline and repeated after the completion of training and competition preparation. Both stature and BM were collected as part of morning player monitoring protocols when participants reported for training and medical screening. In line with established squad protocols, players were asked to report prior to breakfast (following an overnight fast) in a hydrated state in both scenarios. Stature was measured via a portable stadiometer  to the nearest 1 mm. BM was measured via calibrated weighing scales  to the nearest 0.1 kg.Participants were required to wear a wrist-worn tri-axial accelerometer device  on their dominant wrist to estimate EE. Actigraph wGT3X-BT devices were selected due to their reliability and accuracy at lower intensity expenditures ,24; a peAccelerometer devices were initialized with participant characteristics before the data collection periods, and the sampling frequency was set at 100 Hz. During training, participants were instructed to wear the accelerometer device for five consecutive days during TRAIN. During COMP, participants were instructed to wear the accelerometer device for five consecutive days immediately preceding a competitive match played as part of an international tournament . In both scenarios, participants were instructed to wear the devices at all times, aside from during periods of exposure to water.Once the respective data collection periods were completed, participant EE data was combined with corresponding resting energy expenditure (REE) values resulting from BM-derived estimates of REE  as per tDietary intake was estimated via five-day FD collected throughout both activity scenarios using validated methods . ParticiAn overview of both training and competition schedules can be seen in t-tests throughout both TRAIN and COMP. Differences between mean macronutrient intakes and the lower ranges of current sports nutrition recommendations for CHO and PRO and the upper range of current recommendations for FAT were assessed using one-sample t-tests as per the methods of Marsh et al. [t-tests. For players who completed both trials (n = 5), TDEE, EI, EB, and macronutrient data were compared using a paired samples t-test. BM data collected at baseline and post-testing were analyzed via two-way ANOVA as a function of trial and time. Mauchly’s test of sphericity was consulted, and the Greenhouse–Geisser correction was applied if the assumption of sphericity was violated. Effect sizes (ES) were calculated in accordance with Cohen’s d ES principles [p ≤ 0.05 denoted significance.All data were checked for normality using the Shapiro–Wilks test. Within-player differences between the components of EB (estimated TDEE and EI) and BM changes were analyzed using paired samples h et al. . Differeinciples . An alphA total of 11 participants were recruited for TRAIN, and 8 participants for the COMP. A total of five participants completed both scenarios. All players completed all training sessions outlined in TRAIN: 14.6 ± 1.6 MJ·day−1, EITRAIN: 7.7 ± 0.9 MJ·day−1, p ≤ 0.001, d = 5.5) were observed  was significantly lower than sports nutrition recommendations . Mean TRAIN PRO intake (PROTRAIN: 1.7 ± 0.4 g·kg−1 BM) exceeded the lower range of sports nutrition recommendations , whereas FAT intakes  in TRAIN were equal to the upper range of recommended values . Mean macronutrient intakes during TRAIN are presented in Energy deficits of −47% , whereas potassium and iron in TRAIN were lower than nutrition recommendations  were observed  did not meet current sports nutrition recommendations . Mean COMP PRO intake (PROCOMP: 1.5 ± 0.1 g·kg−1 BM) was significantly higher than the minimum range of sports nutrition recommendations . Mean COMP FAT intakes  exceeded the upper range of sports nutrition recommendations .Energy deficits of −50% . Similarly, a non-significant difference of −7% for EI was observed between TRAIN and COMP conditions . A 22% difference for EB was observed between conditions . For BM, reductions (0.6 ± 0.4 kg) were observed post-activity  = 25.78, p ≤ 0.05, partial ŋ2 = 0.763) that were comparable between trials  = 0.067, p ≥ 0.05, partial ŋ2 = 0.008). Mean macronutrient intakes did not differ between conditions . A breakdown of activity-related EE for both activity scenarios can be seen in For players that completed both scenarios, a non-significant difference for TDEE was observed between conditions  to the upper range of current sport nutrition guidelines  but exceeded the upper limit of recommendations in COMP . Accordingly, as optimal performance during competition preparation and the realization of training-induced adaptations are influenced by EB, female rugby R7s should consider their overall EB, together with the macronutrient composition of their EI, during both training and competition preparation scenarios.The primary objective of this study was to provide an overview of EB within TRAIN and COMP scenarios by estimating TDEE and EI in international female R7s players. Energy deficits of −47% and −50% were observed throughout both TRAIN and COMP scenarios; findings that were confirmed in the sample of players completing both trials. Secondary findings highlighted that CHO intakes were lower than sports nutrition guidelines in both activity types . PRO intakes exceeded the lower range of current sport nutrition recommendations in both scenarios . FAT intake within TRAIN was similar  [COMP exceeded that of female touch rugby players (10.9 ± 0.8 MJ·day−1) [−1) [−1) [n = 5) highlighted post-activity losses of 0.6 kg, which, although not statistically significant, demonstrated moderate ES. Such losses may be explained by the observed energy deficits seen between scenarios and/or potential sweat losses during each scenario. That said, the lack of difference between pre-training and pre-competition BM may be explained by the fact that participants may have entered into a phase of positive EB and hydration status between data collection phases. Although we observed mass losses over the assessment period, such data should be interpreted with caution given the cross-sectional design of this study. Accordingly, further longitudinal data would need to be ascertained to confirm longer-term health risks that may be associated with RED-s.Using an estimated method, this study highlighted that TDEEJ·day−1)  and femaJ·day−1)  during t10.9 ± 0. MJ·day−1J·day−1)  reported−1) [−1)  and touc10.9 ± 0. MJ·day−1−1) [−1) , with lo−1) [−1)  as well −1·day−1 in both scenarios. The investigation indicates differences between EI and EE in an acute phase of training and competition preparation scenarios, and the potential for longer-term low energy availability (LEA) concerns  warrants potential consideration. Loucks et al. [−1·day−1 may be undertaken over short periods or as part of a well-constructed weight-loss program [Despite not being able to assess FFM in either TRAIN or COMP scenarios, our descriptive findings  indicates et al.  proposed program . The datWhile a mismatch between EE and EI is often touted as a root cause of LEA and RED-s, it is unclear why certain athletes appear to be more likely to under-fuel and ultimately predispose themselves to LEA and RED-s . It is p−1 BM; Black et al. [−1 of CHO when working towards the 6 g·kg−1·day−1 recommendations. Accordingly, while likely beneficial, achieving the intakes of published sports nutrition guidelines relative to BM may be challenging [The observed CHO intakes may be sub-optimal to fully replete players’ glycogen reserves following rugby-specific activity, possibly compromising training and match-play performance and recovery ,14, partk et al. ), with ok et al.  and lowek et al.  suggestellenging . However−1·day−1) or moderate/high  CHO diet in the 36 h preceding match-play. That said, differences in the anthropometric characteristics of the participants in these studies versus the cohort recruited for our study mean that caution should be exercised when comparing findings. Therefore, while no studies have investigated changes in muscle glycogen concentrations during single, or repeated, bouts of R7s training or match-play, it is unclear whether the effects of nutritional intakes observed here are optimal for performance and recovery; a statement that may be pertinent when seeking to enhance R7s performance.R7s tournaments require multiple matches that incorporate repeated sprint activities performed with as little as three hours of recovery between competitive bouts . Observa−1 BM, COMP: 1.5 ± 0.1 g·kg−1 BM) was meeting the minimum sports nutrition recommendations. However, as higher daily PRO intakes of 1.8–2.7 g·kg−1 BM have been reported during periods of energy deficit [−1 BM), it may be pertinent to comment that nutrition strategies for international female R7s players should seek to increase PRO intake while also increasing CHO intake to optimize performance and maximize training adaptations to facilitate optimal performance during competition preparation and subsequent tournament scenarios.Lower PRO intakes observed during COMP versus TRAIN may be due to limited or inappropriate food choices when on tour and/or during competition . Sub-opt deficit , and witTotal daily FAT intake in TRAIN exceeded current authoritative recommendations . During −1) [Micronutrient analysis within both TRAIN and COMP scenarios indicated that female R7s players consumed higher dietary intakes of calcium and iodine in TRAIN and higher dietary intakes of selenium and vitamin C in COMP than current Scientific Advisory Committee on Nutrition (SACN) recommendations. Both vitamins A and E have higher dietary intakes in both scenarios. Calcium is a known mediator of energy metabolism and muscle contraction ,44, and −1) ,45. Give−1) ,47 in fe−1) , with so−1) . As our −1) and sports nutrition recommendations with regards to daily intakes for athletes who may be deficient (≥18 mg·d−1) [−1). Given the associated roles of dietary iron within exercise physiology , roles that, in female athlete cohorts, become of greater importance given often lower serum values due to menstrual cycles [Interestingly, iron intakes were lower ~27% than both SACN recommendations (14.8 mg·d mg·d−1) ,45. DespTo the authors’ best knowledge, this is the first study to estimate EE and EI within international female R7s players. However, this study is not without its limitations. Firstly, due to competition regulations, wrist-worn devices during competitive matches are strictly prohibited, and in keeping with the practices of the team, devices were not worn during the tournament competition days. This is an important consideration when considering nutritional strategies in such environments; however, research in this field using such devices during competition is limited. Walker et al.  used ineValidated self-reported food diaries were used as a method of estimating EI in a manner that agreed with the practices of the team , but theAcknowledging the potential limitations of using estimated means of EE and EI, these findings indicate that EE exceeded EI in both training and competition preparation scenarios in international-standard female R7s players. CHO and FAT intakes in both activity types were sub-optimal when compared to the current sports nutrition recommendations, findings that may have implications for the optimized realization of training adaptations and performance. Further research into TDEE and EI within intermittent team sports within applied settings, particularly in female cohorts, is warranted to support the findings of this study. Notably, researchers working within such populations may wish to consider alternative methods of EE measurement  to substantiate our findings. Moreover, all methods of estimating EI are not without their limitations, but researchers may wish to adopt differing applied methods of EI that decrease participant burden while enabling the accuracy of EI within an applied, team-sport setting. Practitioners should also consider both competition and training demands when seeking to optimize nutritional strategies within elite female R7s. A focus on player education to allow for the manipulation of macronutrients, particularly the reduction in the total EI percentage of fat and increased consumption of CHO in line with current authoritative guidelines, may be of benefit.