Objective:To explore the value of deep learning technology based on mammography in differentiating for breast imaging reporting and data system (BI-RADS) category 3 and 4 lesions.Methods:The clinical and imaging data of 305 patients with 314 lesions assessed as BI-RADS category 3 and 4 by mammography were analyzed retrospectively in Shenzhen People′s Hospital and Shenzhen Luohu People′s Hospital from January to December 2020. All 305 patients were female, aged 21 to 83 (47±12) years. Two general radiologists (general radiologist A and general radiologist B) with 5 and 6 years of work experience and two professional breast imaging diagnostic radiologists (professional radiologist A and professional radiologist B) with 21 years of work experience and specialized breast imaging training were randomly assigned to read the imaging independently at a 1∶1 ratio, and then to read the imaging again in combination with the deep learning system. Finally, breast lesions were reclassified into BI-RADS category 3 or 4. The receiver operating characteristic curve and area under the curve (AUC) were used to evaluate the diagnostic performance, and the differences of AUCs were compared by DeLong method.Results:The AUC of general radiologist A combined with deep learning system to reclassify BI-RADS category 3 and 4 breast lesions was significantly higher than that of general radiologist A alone (AUC=0.79, 0.63, Z=2.82, P=0.005, respectively). The AUC of general radiologist B combined with deep learning system to reclassify BI-RADS category 3 and 4 breast lesions was significantly higher than that of general radiologist B (AUC=0.83, 0.64, Z=3.32, P=0.001, respectively). There was no significant difference in the AUCs between professional radiologist A combined with deep learning system and professional radiologist A, and professional radiologist B combined with deep learning system and professional radiologist B in reclassifying BI-RADS category 3 and 4 breast lesions ( P>0.05). Conclusion:The deep learning system based on mammography is more effective in assisting general radiologists to differentiate between BI-RADS category 3 and 4 lesions.

Objective:To establish the predictive models for the prognosis of ductal carcinoma in situ (DCIS) at different pathological stages, and to evaluate the predictive performance of the models.Methods:Complete data of 273 patients with confirmed DCIS at different pathological stages who underwent mammography examination in Shenzhen People′s Hospital, Peking University Shenzhen Hospital and Shenzhen Luohu People′s Hospital from November 2014 to December 2020 were retrospectively collected, including 110 cases in the DCIS+ductal carcinoma in situ with microinvasion (DCIS-MI) group and 163 cases in the invasive ductal carcinoma (IDC)-DCIS group. The clinical, imaging and pathological features were analyzed. Mammary Mammo AI fusion model and deep learning-based natural language processing (NLP) structured diagnostic report model were used for image feature extraction. Patients in each group were randomly divided into training set and validation set with a ratio of 6∶4, and the predictors were screened by univariate and multivariate logistic regression analysis. The lowest Akaike information criterion value of each group was selected to construct the final predictive model. The receiver operating characteristic (ROC) curve was drawn to evaluate the performance of each model.Results:Taking estrogen receptor (-) or human epidermal growth factor receptor 2 (3+) as the poor prognostic reference, there were 62 cases considered with poor prognosis and 48 cases with good prognosis in DCIS+DCIS-MI group; while in the IDC-DCIS group, taking the Nottingham prognostic index as the reference, 33 cases were considered with poor prognosis, 73 cases with moderate prognosis, and 57 cases with good prognosis. Four predictive factors were screened to construct the DCIS+DCIS-MI-group predictive model, including DCIS nuclear grade, calcification with suspicious morphology in mammography, DCIS pathologic subtype and DCIS with microinvasion. Five predictive factors were screened to construct the IDC-DCIS-group predictive model, including neural or vascular invasion, Ki67 level, DCIS subtype, DCIS component proportion and associated features in mammography. The area under curve (AUC) for predicting poor prognosis of DCIS+DCIS-MI was 0.92 (95%CI 0.84-1.00) in the training set and 0.90 (95%CI 0.82-0.99) in the validation set; while the AUC for predicting poor prognosis of IDC-DCIS was 0.84 (95%CI 0.76-0.93) in the training set and 0.78 (95%CI 0.64-0.91) in the validation set.Conclusion:The developed models based on deep learning combined with NLP can effectively predict the prognosis of DCIS at different pathological stages, which are beneficial to the risk stratification of patients with DCIS, providing a reference for clinical decision.

Objective:To compare the clinical value of digital breast tomosynthesis (DBT) localization and stereotactic positioning biopsy of breast lesions.Methods:This study was a cross-sectional study. Totally of 250 patients who underwent breast biopsy at Shenzhen People′s Hospital, Luohu District People′s Hospital and Peking University Shenzhen Hospital between August 2021 to October 2023 was analyzed retrospectively, including 136 cases of DBT-guided biopsy (DBT-guided group) and 114 cases of stereotactic-guided biopsy (stereotactic-guided group). The stereotactic-guided biopsy methods included core needle biopsy (CNB) and wire positioning. The DBT-guided biopsy methods included CNB, wire positioning and vacuum-assisted breast biopsy (VABB). The χ2 test or Mann-Whitney U test was used to compare the puncture success rate, operation time, localization time, puncture time, number of first valid localization phases obtained, number of exposures, and complications of different biopsy methods between 2 groups. Results:In the wire positioning biopy, the puncture success rate was 100% (33/33) in DBT-guided group and 96% (48/50) in the stereotactic-guided group, with no statistically significant difference between the two groups ( P=0.515). Compared to the stereotactic-guided group, the operation time and localization time were shorter, and the number of first valid localization phases obtained, number of exposures were fewer in the DBT-guided group( P<0.05). The incidence of complications was lower in both the DBT-guided group and the stereotactic-guided group, with no statistically significant difference ( P=0.871). In CNB, both the DBT-guided group and the stereotactic-guided group had higher puncture success rates, with no statistically significant difference ( P=0.080). Compared to the stereotactic-guided group, the operation time, localization time and puncture time were shorter, and the number of first valid localization phases obtained, number of exposures were lower in the DBT-guided group, and the difference between the two groups were statistically significant ( P<0.05). The incidence of complications was lower in both the DBT-guided group and the stereotactic-guided group, with no statistically significant difference ( P=0.627). Twenty-one cases received DBT-guided VABB, with an operation time of (19.90±3.38) min, a localization time of 6.00 (6.00, 7.00) min, a puncture time of (13.42±3.28) min, the number of first effective localization phases obtained was 1.00 (1.00, 1.00) time, the number of exposures was 4.00 (3.50, 5.00) times, and one case experienced severe pain after puncture. Conclusion:Compared with stereotactic-guided biopsy, DBT-guided biopsy can reduce operation time and exposure times, and can target more types of breast lesions, with higher clinical application value.