Background:The current deep learning diagnosis of breast masses is mainly reflected by the diagnosis of benign and malignant lesions.In China,breast masses are divided into four categories according to the treatment m...Background:The current deep learning diagnosis of breast masses is mainly reflected by the diagnosis of benign and malignant lesions.In China,breast masses are divided into four categories according to the treatment method:inflammatory masses,adenosis,benign tumors,and malignant tumors.These categorizations are important for guiding clinical treatment.In this study,we aimed to develop a convolutional neural network(CNN)for classification of these four breast mass types using ultrasound(US)images.Methods:Taking breast biopsy or pathological examinations as the reference standard,CNNs were used to establish models for the four-way classification of 3623 breast cancer patients from 13 centers.The patients were randomly divided into training and test groups(n=1810 vs.n=1813).Separate models were created for two-dimensional(2D)images only,2D and color Doppler flow imaging(2D-CDFI),and 2D-CDFI and pulsed wave Doppler(2D-CDFI-PW)images.The performance of these three models was compared using sensitivity,specificity,area under receiver operating characteristic curve(AUC),positive(PPV)and negative predictive values(NPV),positive(LR+)and negative likelihood ratios(LR-),and the performance of the 2D model was further compared between masses of different sizes with above statistical indicators,between images from different hospitals with AUC,and with the performance of 37 radiologists.Results:The accuracies of the 2D,2D-CDFI,and 2D-CDFI-PW models on the test set were 87.9%,89.2%,and 88.7%,respectively.The AUCs for classification of benign tumors,malignant tumors,inflammatory masses,and adenosis were 0.90,0.91,0.90,and 0.89,respectively(95%confidence intervals[CIs],0.87-0.91,0.89-0.92,0.87-0.91,and 0.86-0.90).The 2D-CDFI model showed better accuracy(89.2%)on the test set than the 2D(87.9%)and 2D-CDFI-PW(88.7%)models.The 2D model showed accuracy of 81.7%on breast masses≤1 cm and 82.3%on breast masses>1 cm;there was a significant difference between the two groups(P<0.001).The accuracy of the CNN classifications for the test set(89.2%)was significantly higher than that of all the radiologists(30%).Conclusions:The CNN may have high accuracy for classification of US images of breast masses and perform significantly better than human radiologists.Trial registration:Chictr.org,ChiCTR1900021375;http://www.chictr.org.cn/showproj.aspx?proj=33139.展开更多
We wanted to determine whether shear wave elastography(SwE)could be used to evaluate the aging degree of the corpus cavernosum(CC)and to identify the histological basis of changes in SWE measurements during the aging ...We wanted to determine whether shear wave elastography(SwE)could be used to evaluate the aging degree of the corpus cavernosum(CC)and to identify the histological basis of changes in SWE measurements during the aging process.We performed a crosssectional study enrolling healthy participants of different ages.We measured the Young's modulus(YM)of the penile CCs by SWE and assessed erectile function using the International Index of Erectile Function-5(IIEF-5).Histological investigation was performed in surgically resected penile specimens from a separate group of patients to examine the smooth muscle and collagen content of the CCs.Furthermore,we measured the YM,erectile function,smooth muscle,and collagen content of the CCs in different age groups of rats.Finally,we enrolled 210 male volunteers in this study.The YM of the CC(CCYM)was positively correlated with age(r=0.949,P<0.01)and negatively correlated with erectile function(r=-0.843,P<0.01).Histological examinations showed that cCs had increased collagen content but decreased smooth muscle content with increased age.The same positive correlation between CcYM and age was also observed in the animal study.In addition,the animal study showed that older rats,with increased CcYM and decreased erectile function,had lower smooth muscle content and higher collagen content.SwE can noninvasively and quantitatively evaluate the aging degree of the Cc.Increased collagen content and decreased smooth muscle content might be the histological basis for the effect of aging on the CC and the increase in its YM.展开更多
基金This study was supported by the grants from the National Key Research and Development Program of China(No.2016YFC0104801)National Natural Science Foundation of China(No.81730050)。
文摘Background:The current deep learning diagnosis of breast masses is mainly reflected by the diagnosis of benign and malignant lesions.In China,breast masses are divided into four categories according to the treatment method:inflammatory masses,adenosis,benign tumors,and malignant tumors.These categorizations are important for guiding clinical treatment.In this study,we aimed to develop a convolutional neural network(CNN)for classification of these four breast mass types using ultrasound(US)images.Methods:Taking breast biopsy or pathological examinations as the reference standard,CNNs were used to establish models for the four-way classification of 3623 breast cancer patients from 13 centers.The patients were randomly divided into training and test groups(n=1810 vs.n=1813).Separate models were created for two-dimensional(2D)images only,2D and color Doppler flow imaging(2D-CDFI),and 2D-CDFI and pulsed wave Doppler(2D-CDFI-PW)images.The performance of these three models was compared using sensitivity,specificity,area under receiver operating characteristic curve(AUC),positive(PPV)and negative predictive values(NPV),positive(LR+)and negative likelihood ratios(LR-),and the performance of the 2D model was further compared between masses of different sizes with above statistical indicators,between images from different hospitals with AUC,and with the performance of 37 radiologists.Results:The accuracies of the 2D,2D-CDFI,and 2D-CDFI-PW models on the test set were 87.9%,89.2%,and 88.7%,respectively.The AUCs for classification of benign tumors,malignant tumors,inflammatory masses,and adenosis were 0.90,0.91,0.90,and 0.89,respectively(95%confidence intervals[CIs],0.87-0.91,0.89-0.92,0.87-0.91,and 0.86-0.90).The 2D-CDFI model showed better accuracy(89.2%)on the test set than the 2D(87.9%)and 2D-CDFI-PW(88.7%)models.The 2D model showed accuracy of 81.7%on breast masses≤1 cm and 82.3%on breast masses>1 cm;there was a significant difference between the two groups(P<0.001).The accuracy of the CNN classifications for the test set(89.2%)was significantly higher than that of all the radiologists(30%).Conclusions:The CNN may have high accuracy for classification of US images of breast masses and perform significantly better than human radiologists.Trial registration:Chictr.org,ChiCTR1900021375;http://www.chictr.org.cn/showproj.aspx?proj=33139.
文摘We wanted to determine whether shear wave elastography(SwE)could be used to evaluate the aging degree of the corpus cavernosum(CC)and to identify the histological basis of changes in SWE measurements during the aging process.We performed a crosssectional study enrolling healthy participants of different ages.We measured the Young's modulus(YM)of the penile CCs by SWE and assessed erectile function using the International Index of Erectile Function-5(IIEF-5).Histological investigation was performed in surgically resected penile specimens from a separate group of patients to examine the smooth muscle and collagen content of the CCs.Furthermore,we measured the YM,erectile function,smooth muscle,and collagen content of the CCs in different age groups of rats.Finally,we enrolled 210 male volunteers in this study.The YM of the CC(CCYM)was positively correlated with age(r=0.949,P<0.01)and negatively correlated with erectile function(r=-0.843,P<0.01).Histological examinations showed that cCs had increased collagen content but decreased smooth muscle content with increased age.The same positive correlation between CcYM and age was also observed in the animal study.In addition,the animal study showed that older rats,with increased CcYM and decreased erectile function,had lower smooth muscle content and higher collagen content.SwE can noninvasively and quantitatively evaluate the aging degree of the Cc.Increased collagen content and decreased smooth muscle content might be the histological basis for the effect of aging on the CC and the increase in its YM.
