diffusion - weighted imaging ( dwi ) is increasingly regarded as a promising modality in breast lesion detection and characterization [ 14 ] . adc values were demonstrated to be reduced in breast cancers compared to normal tissue and benign lesions   due to the increased cellularity . dwi is now regarded as a very useful adjunct method to dynamic contrast - enhanced ( dce ) mr imaging , which can improve the accuracy of diagnosis compared to dce mr imaging alone [ 57 ] . the histological difference between carcinomas and normal breast fibroglandular tissue lies not only in the increasing abnormal cell density , but also in the disordered organization and arrangement of fibroglandular tissues . the diffusion and adc changes detected by dwi are non - directional and can not demonstrate the 3-dimensional diffusion process resulting from the disordered structure . in normal breast tissue , the ducts , vessels , and other parenchyma around them are organized in approximately parallel patterns and have their main directions ; therefore , the water molecules within the extracellular space diffuse mainly along the axis of this space and they show anisotropy . dwi imaging can only detect the mean diffusion mobility without directional dependency , due to the limited number of gradients used in these sequences . on the other hand , the diffusion tensor imaging ( dti ) advanced from dwi can vividly exhibit the 3-dimensional diffusion process in healthy tissue and in lesions of the breast and other organs . because the number of diffusion gradients used in dti is much more than that used in dwi , a specific 3-dimensional ellipsoid tensor unit can be uniquely determined in each pixel , within which the diffusion ability in any direction can be accurately calculated . by measuring the anisotropic diffusion in different tissues , dti can provide more information about microstructure and pathophysiology than dwi , and could be helpful to describe and identify the characteristics of different kinds of tissue and even to discriminate different lesions . prior studies in anisotropy organs such as the brain , prostate , and kidney have shown its great potential [ 814 ] . similar to the brain , prostate , and kidney , the breast is also an anisotropy tissue , with parenchyma composed of ducts , vessels , and fibrous glandular tissue spreading radially from the nipple . the purpose of this study was to investigate whether the anisotropy parameters are helpful in the detection and discrimination of breast cancers , and to determine its value in predicting the risk of cancers . this study was approved by the local ethics committee , and all patients provided written informed consent before examination . between june 2012 and july 2014 , 106 female patients with breast focal lesions underwent dynamic contrast - enhanced mri and dti . inclusion criteria were : ( 1 ) focal lesions found in contrast - enhanced mri were more than 10 mm in diameter ; ( 2 ) biopsy or surgery were performed after mri with a less than 7-day interval ; ( 3 ) benign or malignant histological result . we excluded anyone with previous surgery , radiation or chemotherapy of breast cancer , lesion in bilateral breast , or incomplete examination due to artifacts . a total of 56 patients were included , with age ranging from 37 to 68 years ( median , 47 years ) . all patients were examined in prone position on the same 1.5 t mri system device ( avanto , siemens healthcare , erlangen , germany ) using a dedicated receiving bilateral breast matrix coil ( breast matrix , siemens healthcare , erlangen , germany ) . a routine axial turbo - spin echo inversion recovery sequence of fat - suppressed t2wi ( tr 5800 ms , te 56 ms , fov 275275 mm , matrix 314320 , slice thickness 6 mm with no intersection gap , nex 2 ) was first performed after tomography , followed by a dti sequence . dti was performed using an axial 2-dimensional diffusion - weighted echo planer imaging sequence ( tr 6900 ms , te 90 ms , slice thickness 5 mm with zero gap , nex 4 , fov 380 mm285 mm , matrix 144192 ) , and the diffusion gradients were applied in 6 directions with b=0 and 1 000 s / mm . finally , a dynamic contrast - enhanced sequence containing an axial t1-weighted 3d fast - spoiled gradient - recalled echo sequence ( tr 20 ms , te 1.19 ms , fov 300300 mm , matrix 128128 , slice thickness 2 mm ) was performed . one precontrast acquisition and 5 postcontrast acquisitions were performed before and after the contrast of gd - dtpa ( omniscan , ge ) with a dose of 0.1 mmol / kg . diffusion tensor data were post - processed and analyzed by an experienced mri physician blinded to histopathological findings , on the mr syngo station ( siemens healthcare ) using the neuro3d toolbox ( siemens healthcare , erlangen , germany ) . by browsing the contrast - enhanced subtraction images , the slice showing the lesion s maximum diameter was determined , and then the same slice was found in the axial adc map . by referring to the dce images , an roi in adc maps corresponding to the hyperintensity in dce images was drawn along the lesion margin , omitting hemorrhagic , cystic , and calcific areas . in the same patient , another roi was drawn in the contralateral healthy breast containing normal breast tissue only . then the anisotropy parameters , such as adc , fa , and eigenvalues ( e1 , e2 , e3 ) were automatically calculated , where e1 , e2 , and e3 are the maximum , intermediate , and minimum diffusion tensor eigenvalues , respectively . every lesion and normal breast tissue was measured 3 times to produce the final averaged measurements . e2 , e3 , and the maximum anisotropy index e1e3 between breast cancers and normal breast tissue in the same patient were compared by 2-tailed paired t - test . dti - derived metrics between breast cancer and benign lesions were compared by non - parametric test . multi - variant logistic analysis was performed to determine if adc , fa , eigenvalues e1 , e2 , e3 , and the maximum anisotropy index e1e3 are independent predictors in characterization of different breast lesions , and their predictive values were calculated . the full range of adc , fa , and e1 measurements were divided into equal quartiles and their values in predicting risk of breast cancer in every quartile were calculated . then , to compare diagnostic performance of adc , fa , e1 , e2 , e3 , e1e3 , and the multivariate model , receiver operating characteristic ( roc ) curve analysis was performed to calculate the area under the curve ( auc ) and 95% condence interval ( ci ) . z - tests were used to compare the difference of auc in detecting and charactering different breast lesions using medcalc 11.0 software ( medcalc software , mariakerke , belgium ) . we calculated the optimal critical value defined as the point with the maximum sum of sensitivity and specificity as well as the corresponding sensitivity and specificity . this study was approved by the local ethics committee , and all patients provided written informed consent before examination . between june 2012 and july 2014 , 106 female patients with breast focal lesions underwent dynamic contrast - enhanced mri and dti . inclusion criteria were : ( 1 ) focal lesions found in contrast - enhanced mri were more than 10 mm in diameter ; ( 2 ) biopsy or surgery were performed after mri with a less than 7-day interval ; ( 3 ) benign or malignant histological result . we excluded anyone with previous surgery , radiation or chemotherapy of breast cancer , lesion in bilateral breast , or incomplete examination due to artifacts . a total of 56 patients were included , with age ranging from 37 to 68 years ( median , 47 years ) . all patients were examined in prone position on the same 1.5 t mri system device ( avanto , siemens healthcare , erlangen , germany ) using a dedicated receiving bilateral breast matrix coil ( breast matrix , siemens healthcare , erlangen , germany ) . a routine axial turbo - spin echo inversion recovery sequence of fat - suppressed t2wi ( tr 5800 ms , te 56 ms , fov 275275 mm , matrix 314320 , slice thickness 6 mm with no intersection gap , nex 2 ) was first performed after tomography , followed by a dti sequence . dti was performed using an axial 2-dimensional diffusion - weighted echo planer imaging sequence ( tr 6900 ms , te 90 ms , slice thickness 5 mm with zero gap , nex 4 , fov 380 mm285 mm , matrix 144192 ) , and the diffusion gradients were applied in 6 directions with b=0 and 1 000 s / mm . finally , a dynamic contrast - enhanced sequence containing an axial t1-weighted 3d fast - spoiled gradient - recalled echo sequence ( tr 20 ms , te 1.19 ms , fov 300300 mm , matrix 128128 , slice thickness 2 mm ) was performed . one precontrast acquisition and 5 postcontrast acquisitions were performed before and after the contrast of gd - dtpa ( omniscan , ge ) with a dose of 0.1 mmol / kg . diffusion tensor data were post - processed and analyzed by an experienced mri physician blinded to histopathological findings , on the mr syngo station ( siemens healthcare ) using the neuro3d toolbox ( siemens healthcare , erlangen , germany ) . by browsing the contrast - enhanced subtraction images , the slice showing the lesion s maximum diameter was determined , and then the same slice was found in the axial adc map . by referring to the dce images , an roi in adc maps corresponding to the hyperintensity in dce images was drawn along the lesion margin , omitting hemorrhagic , cystic , and calcific areas . in the same patient , another roi was drawn in the contralateral healthy breast containing normal breast tissue only . then the anisotropy parameters , such as adc , fa , and eigenvalues ( e1 , e2 , e3 ) were automatically calculated , where e1 , e2 , and e3 are the maximum , intermediate , and minimum diffusion tensor eigenvalues , respectively . every lesion and normal breast tissue was measured 3 times to produce the final averaged measurements . differences of adc , fa , eigenvalues e1 , e2 , e3 , and the maximum anisotropy index e1e3 between breast cancers and normal breast tissue in the same patient were compared by 2-tailed paired t - test . dti - derived metrics between breast cancer and benign lesions were compared by non - parametric test . multi - variant logistic analysis was performed to determine if adc , fa , eigenvalues e1 , e2 , e3 , and the maximum anisotropy index e1e3 are independent predictors in characterization of different breast lesions , and their predictive values were calculated . then the best - fitting regression model combining multiple predictors was created . the full range of adc , fa , and e1 measurements were divided into equal quartiles and their values in predicting risk of breast cancer in every quartile were calculated . then , to compare diagnostic performance of adc , fa , e1 , e2 , e3 , e1e3 , and the multivariate model , receiver operating characteristic ( roc ) curve analysis was performed to calculate the area under the curve ( auc ) and 95% condence interval ( ci ) . z - tests were used to compare the difference of auc in detecting and charactering different breast lesions using medcalc 11.0 software ( medcalc software , mariakerke , belgium ) . we calculated the optimal critical value defined as the point with the maximum sum of sensitivity and specificity as well as the corresponding sensitivity and specificity . in this study , 56 lesions were identified on dce - mri in 56 women and categorized as bi - rads 3 , 4 , or 5 . adc , fa , e1 , e2 , and e3 were successfully measured in every lesion and the contralateral healthy breast tissue in carcinoma patients . adc , fa , e1 , e2 , e3 , and e1e3 were all significantly lower in breast cancers than those in normal breast tissue ( figure 2 and table 2 ) . the averaged differences of adc , fa , e1 , e2 , e3 , and e1e3 between breast cancer and normal tissue were ( 0.460.31)10 mm / s , 0.050.04 , ( 0.570.38)10 mm / s , ( 0.450.31)10 mm / s , ( 0.360.26)10 mm / s , and ( 0.230.16)10 mm / s , respectively , and the averaged reducing ratios were ( 3217)% , ( 2413)% , ( 3319)% , ( 3217)% , ( 3118)% , and ( 3720)% , respectively . the subgroups of invasive ductal cancers ( idc ) and ductal carcinoma in situ ( dcis ) were also compared to the normal tissue , and all of these 6 parameters were obviously lower in idc and dcis than in normal tissue , with a statistically significant difference ( p<0.01 ) . all of the 34 breast cancer patients and 22 benign lesions were included in this analysis . dti - derived parameters of adc , e1 , e2 , e3 , and e1e3 were all significantly lower in breast cancers than those in benign lesions ( p<0.01 ; figure 3 and table 3 ) . however , there was no difference in fa between malignant and benign lesions ( p>0.05 ) . a further detailed analysis was performed for idc , dics , and benign lesions ( table 3 ) . there were no differences in adc , fa , e1 , e2 , e3 , and e1e3 between idc and dcis ( p>0.05 ) . the adc , e1 , e2 , e3 , and e1e3 were all significantly lower in invasive cancer ( p<0.01 ) and dcis ( p<0.01 ) than in benign lesions , except for fa ( p>0.05 ) . because of the collinearity between adc , e1 , e2 , e3 , and e1e3 , these 5 parameters can not be combined in a single multivariate model , but 2-parameter models of adc , e1 , e2 , and e3 combined with fa all showed that adc ( p<0.01 ) , e1 ( p<0.01 ) , e2 ( p<0.01 ) , and e3 ( p<0.01 ) were the significant discriminating factors between breast cancers and benign lesions , except for fa ( p>0.05 ) . multiple logistic regression showed e1e3 ( p<0.01 ) and fa ( p<0.01 ) can be combined to produce a predicting model of y=1.9 + 29.9*fa-16.5*(e1e3 ) , which means that when fa increases by 0.1 unit or e1e3 decreases by 0.110 mm / s , the risk of a mass being malignant would increase by 20 or 5 times , respectively . to confirm the value of every parameter in predicting the risk of breast cancer , the values of adc , fa , e1 , e2 , e3 , and e1e3 of breast cancer and benign lesions were divided into equal quartiles , as shown in table 4 . adc , e1 , e2 , e3 , and e1e3 were strongly associated with breast cancers . a mass with adc , e1 , e2 , e3 , or e1e3 in the lowest quartiles was over 5090 times more likely to be a malignancy , and the odds ratios were 95.0 , 95.0 , 67.67 , 67.67 , and 47.7 , respectively . no benign lesions were found , but we did find breast cancers with adc < 0.81110 mm / s , e1 < 0.97710 mm / s , e2 < 0.80010 mm / s , or e3 the discriminating values of fa were very low , with odds ratios around 12 ( p>0.05 ) . the coefficients , odds ratios , and the corresponding p valves of each dti parameter and the combining model are shown in table 5 . the predicted risk of malignancy based on each dti parameters is shown in figure 4 . adc , e1 , e2 , e3 , and e1e3 produced aucs of 0.897 , 0.898 , 0.885 , 0.892 , and 0.882 , respectively , and fa only produced 0.607 . the multivariate model combining ( e1e3 ) and fa produced an auc of 0.897 , similar to adc ( figure 5 ) . e2 , e3 , and the combined model ( p>0.05 ) , and the difference between fa and other parameters was statistically significant ( p<0.01 ) . adc , fa , e1 , e2 , e3 , and e1e3 were all significantly lower in breast cancers than those in normal breast tissue ( figure 2 and table 2 ) . the averaged differences between cancers and normal tissue in the same patients were calculated . the averaged differences of adc , fa , e1 , e2 , e3 , and e1e3 between breast cancer and normal tissue were ( 0.460.31)10 mm / s , 0.050.04 , ( 0.570.38)10 mm / s , ( 0.450.31)10 mm / s , ( 0.360.26)10 mm / s , and ( 0.230.16)10 mm / s , respectively , and the averaged reducing ratios were ( 3217)% , ( 2413)% , ( 3319)% , ( 3217)% , ( 3118)% , and ( 3720)% , respectively . the subgroups of invasive ductal cancers ( idc ) and ductal carcinoma in situ ( dcis ) were also compared to the normal tissue , and all of these 6 parameters were obviously lower in idc and dcis than in normal tissue , with a statistically significant difference ( p<0.01 ) . all of the 34 breast cancer patients and 22 benign lesions were included in this analysis . dti - derived parameters of adc , e1 , e2 , e3 , and e1e3 were all significantly lower in breast cancers than those in benign lesions ( p<0.01 ; figure 3 and table 3 ) . however , there was no difference in fa between malignant and benign lesions ( p>0.05 ) . a further detailed analysis was performed for idc , dics , and benign lesions ( table 3 ) . there were no differences in adc , fa , e1 , e2 , e3 , and e1e3 between idc and dcis ( p>0.05 ) . the adc , e1 , e2 , e3 , and e1e3 were all significantly lower in invasive cancer ( p<0.01 ) and dcis ( p<0.01 ) than in benign lesions , except for fa ( p>0.05 ) . because of the collinearity between adc , e1 , e2 , e3 , and e1e3 , these 5 parameters can not be combined in a single multivariate model , but 2-parameter models of adc , e1 , e2 , and e3 combined with fa all showed that adc ( p<0.01 ) , e1 ( p<0.01 ) , e2 ( p<0.01 ) , and e3 ( p<0.01 ) were the significant discriminating factors between breast cancers and benign lesions , except for fa ( p>0.05 ) . multiple logistic regression showed e1e3 ( p<0.01 ) and fa ( p<0.01 ) can be combined to produce a predicting model of y=1.9 + 29.9*fa-16.5*(e1e3 ) , which means that when fa increases by 0.1 unit or e1e3 decreases by 0.110 mm / s , the risk of a mass being malignant would increase by 20 or 5 times , respectively . to confirm the value of every parameter in predicting the risk of breast cancer , the values of adc , fa , e1 , e2 , e3 , and e1e3 of breast cancer and benign lesions were divided into equal quartiles , as shown in table 4 . adc , e1 , e2 , e3 , and e1e3 were strongly associated with breast cancers . a mass with adc , e1 , e2 , e3 , or e1e3 in the lowest quartiles was over 5090 times more likely to be a malignancy , and the odds ratios were 95.0 , 95.0 , 67.67 , 67.67 , and 47.7 , respectively . no benign lesions were found , but we did find breast cancers with adc < 0.81110 mm / s , e1 < 0.97710 mm / s , e2 < 0.80010 mm / s , or e3 the discriminating values of fa were very low , with odds ratios around 12 ( p>0.05 ) . the coefficients , odds ratios , and the corresponding p valves of each dti parameter and the combining model are shown in table 5 . the predicted risk of malignancy based on each dti parameters is shown in figure 4 . adc , e1 , e2 , e3 , and e1e3 produced aucs of 0.897 , 0.898 , 0.885 , 0.892 , and 0.882 , respectively , and fa only produced 0.607 . the multivariate model combining ( e1e3 ) and fa produced an auc of 0.897 , similar to adc ( figure 5 ) . e2 , e3 , and the combined model ( p>0.05 ) , and the difference between fa and other parameters was statistically significant ( p<0.01 ) . dwi is now widely used in breast cancer detection and characterization . developed from dwi , dti not only provides traced weighted images and adc maps , but also other parameters maps such as fa and eigenvalues , which can show the microstructures and arrangements of tissues . studies have shown that adc and fa values are useful tools in breast carcinoma detection from normal breast tissue , and that adc is very useful in characterization and differentiation of malignant breast lesions from benign ones . adc values calculated from dti were shown to be highly consistent with those from dwi . in our study , the eigenvalues e1 , e2 , and e3 , the maximum anisotropy index e1e3 , and adc and fa were included , and their potential values in breast cancers detection and differentiation were investigated and evaluated with a 1.5 t mr scanner . our results showed that adc , e1 , e2 , e3 , and e1e3 were significantly lower in cancer lesions compared to normal breast tissue . fa in breast cancers was also decreased by a lesser degree than adc , e1 , and e1e3 . in breast cancer differentiation , adc , e1 , e2 , and e3 , as malignancy predictors , were highly associated with breast cancers risk , which had much more high - differentiation efficacy than other dti parameters used in our study . the fa alone did not provide any useful information in distinguish malignant from benign breast lesions . several studies have demonstrated that adc and fa are lower in breast cancers compared with normal breast tissue and benign lesions [ 1618 ] , and this was supported by our results . we also found that eigenvalue e1 , e2 , e3 , and the maximum anisotropy index e1e3 all were significantly lower in malignant lesions . to the best of our knowledge , no investigations have ever measured the decrease in those dti metrics . in our study , we first quantitatively investigated the decreasing degree of these parameters , and found they decreased by about 2437% . studies have examined the histologic changes of breast fibroglandular tissue in different phases of the menstrual cycle and found morphologic changes in epithelial and stromal components . as a result , breast morphology and density influenced by hormonal level have demonstrated regular fluctuation during the menstrual cycle . however , previous studies that examined these changes by using dti showed that adc and other anisotropy parameters , such as fa and e1 , remained almost unchanged during the menstrual cycle and showed low mean within - subject variations , with coefficient of variance around 12% for adc and e1 , and around 5% for fa and e1e3 . besides the influence of hormonal level , the heterogeneous fibroglandular ( especially the collagen ) fibers may also result in the reduction of signal and mimic breast carcinomas , which may in turn produce the variations in the breast . reports have shown within - subject coefficient of variance is about 4.522% for adc , 11.4% for fa , and 17.6% for e1 . however , our results suggest that the decrease in these dti parameters in breast cancer are far greater than the normal fluctuations in normal breast tissue , which means that the lower magnitude of these dti parameters in breast cancer result from the histologic difference between cancer lesions and normal tissue rather than from normal fluctuations . compared to normal breast glands , the cellularity density and microvessel density in breast cancer lesions increased significantly . although the relationship between dti anisotropy indices and breast cellularity density and microvessel density has not been reported , studies of other organs , such as the brain , lung , and prostate , have displayed a negative correlation between cellularity , microvessel density , and anisotropy parameters such as adc and fa values [ 2730 ] , which may to some extent explain the reduction of adc and fa values in breast cancers . in the cancerous tissue , more disordered structure and increased necrotic material of gland cells may also play a role . the reduction in eigenvalues may also be due to do this , and further research on this topic is needed . our study found that the dti parameters adc , e1 , e2 , e3 , and e1e3 were reduced much more than fa in breast cancer compared to normal tissue . changes in tumor cellular and microvessel density and the disorder of fibroglandular tissue may limit diffusion activity of water molecules , which , in turn , reduces e1 , e2 , and e3 . especially interesting in our results is that the degrees of reduction of e1 , e2 , and e3 in breast cancer were very close , with a mean of around 3035% , which suggests that the 3 orthogonal eigenvectors of diffusion tensor ellipsoid unit was similarly scaled down . adc , as the average of the 3 eigenvectors , was significantly reduced , while fa was less reduced . this can also be confirmed by the different situation in breast cancers versus benign lesions , in which e1 , e2 , and e3 were significantly different between caners and benign lesions , while fa values had no significant difference , and the ratios of e1 , e2 , and e3 between both were also very close , at around 0.610.64 . this suggests that the proliferation of cells in fibroglandular tissue , as well as the structural arrangement in tumors and normal tissue , have a relatively slighter influence on fa , perhaps because the diffusion distance of water molecular and the disordered microstructure is much smaller than a voxel in scale and the impact is significantly reduced in fa due to the arithmetic averages . while values of dti indices in breast cancers varied widely in previous studies ( 0.71.6 mm / s for adc , 2.260.24 for fa ) , the mean adc of breast cancers in our results ( 0.950.29 mm / s ) are comparable with previous studies , and the indices of e1 , e2 , e3 , and e1e3 in our study also agree with results in the literature . the fa in our study was 0.190.06 , which is lower than in previous reports . no significant differences were found in anisotropy indices between dcis and invasive breast cancers in our study , which were all significantly lower than in normal tissue and benign lesions . partridge et al .   reported that while the differences in fa values between dics and invasive breast cancers have no statistical significance , the adc value of invasive breast cancers were significantly lower than dcis , which is quite different from our results . the reasons for the difference in adc values between the 2 studies may lie in the variations among individual lesions . dcis has great variation in grade and cellularity , and some high - grade dcis has relatively higher cellularity , which may affect the measured dti parameters . the results of adc and fa in our study for dics and invasive cancer were all lower than the results of partridge , which , from another perspective , may confirm the reason mentioned early . comparisons between benign and malignant tumors showed no significant difference in fa values between the 2 different type of lesions , while adc , e1 , e2 , e3 , and e1e3 , as predictors for breast cancer , were significantly lower in breast cancer than in benign lesions , which is consistent with the findings of partridge   and cakir , but opposite to the results of tsougos . our results suggest that adc , e1 , e2 , e3 , and e1e3 all help to distinguish breast cancers from benign lesions . the roc curves show that adc and e1 have the highest discriminative values , with a slightly larger auc than e1e3 , but no significant difference was found among the auc of those 5 parameters . although the regression model combining fa and e1e3 has similar diagnostic efficiency as adc , the computational complexity of the model restricts its use in clinical settings . for the discrimination between malignant and benign lesions , the sensitivity and specificity of adc was 82.4% and 90.9% , respectively , which are comparable to the results of baltzer . in addition , in our study the performance of e1 and e2 was very close to adc in sensitivity and specificity . in our study , the reduction of adc , as well as e1 , e2 , e3 , and e1e3 , our results show that the adc values of benign lesions , as well as e1 , e2 , e3 and e1e3 , are mostly in the higher range , while the corresponding parameters for malignant lesions are mostly in the lower values . in fact , we did not observe a single malignant lesion when adc < 0.56 mm / s or e1 < 0.97 mm / s or e2 < 0.8 mm / s or e3 < 0.6 mm / s , and only 1 lesion with e1e3 less than 0.29 mm / s . with the reduction of adc , e1 , e2 , e3 , and e1e3 from the values that most benign lesions are in , the risk of breast cancer increases rapidly . if the ranges in which most benign lesions are concentrated are regarded as reference groups , when the adc , e1 , e2 , or e3 is located in the lowest group ( which strongly suggests breast cancer ) , the odds ratio reached about 97 or 67 , which suggests that the risk of breast cancer was more than 90 or 70 times higher than in the reference group . due to the impact of cyclical changes in hormone levels , the dti indices of tissues may be affected , especially in the 2 week , which means the detection and differentiation of breast cancers by using adc , e1 , e2 , e3 , and e1e3 may be influenced . fortunately , research shows that normal breast tissue has very slight fluctuations in adc , e1 , e2 , e3 , and e1e3 values during the menstrual cycle , and no statistical significance was observed . therefore , the relatively stable mammary glands in dti parameters provide a reliable indication that these dti parameters have utility in breast lesions characterization . first , to ensure the accuracy of measurement , the smaller lesions were excluded because the margin of small lesions can not be accurately defined , which may have influenced the values of dti indices . therefore , the dti measurements in this study are not representative of all tumor lesions . fibroadenomas are relatively common in benign lesions ; therefore , the dti parameters in the benign group may reflect the characteristics of fibroadenomas to a relative larger degree , so further studies with expanded sample sizes are needed . we investigated the diffusion tensor indices of breast cancers , and found that adc , e1 , e2 , e3 , and e1e3 were significantly lower in cancer lesions compared to normal breast tissue and benign lesions . adc , e1 , e2 , e3 , and e1e3 of a breast mass were highly associated with the risk of breast cancer in a reverse relationship , while the fa is useless in breast cancer risk prediction . our results show the potential value of dti indices in breast cancer . in previous studies , dwi and adc were combined with contrast - enhanced mri to detect and differentiate breast cancers , so further studies are also required to assess the added value of dti indices in breast cancers as complementary tools to contrast - enhanced mri .