Objective:The residual cancer burden (RCB) evaluation system was used to analyze the influencing factors of the efficacy of neoadjuvant therapy in breast cancer, and to explore the prognostic value of RCB evaluation in neoadjuvant therapy.Methods:Clinicopathologic data and postoperative RCB grading of 364 breast cancer patients who underwent neoadjuvant therapy in Renmin Hospital of Wuhan University from Nov. 2019 to Nov. 2022 were collected. Chi-square test was used to analyze the relationship between RCB grading and clinicopathological parameters, and Spearman’s rank correlation analysis was performed to evaluate the correlation between RCB grading and clinicopathological characteristics. Factors influencing pathologic complete response (pCR) were analyzed by Logistic regression. Kaplan-Meier survival analysis and log-rank test were used to evaluate cumulative survival.Results:Among the 364 patients who underwent neoadjuvant therapy, 129 cases of RCB grade 0 and 235 cases of RCB gradeⅠ-Ⅲ (including 46 cases of RCB gradeⅠ, 109 cases of RCB grade Ⅱ and 80 cases of RCB grade Ⅲ) were obtained after surgery. Histological classification ( χ 2=21.757, P=0.000), estrogen receptor (ER) ( χ 2=52.837, P=0.000), progesterone receptor (PR) ( χ 2=55.658, P=0.000), human epidermal growth factor receptor-2 (HER2) ( χ2=89.040, P=0.000), Ki67 expression ( χ2=12.927, P=0.005), molecular typing ( χ 2=80.793, P=0.000) and preoperative lymph node status ( χ 2=25.764, P=0.000) were all associated with postoperative RCB grading. Further correlation analysis showed that histological grade ( r=-0.229, P=0.000), HER2 expression ( r=-0.465, P=0.000) and Ki67 expression ( r=-0.179, P=0.000) were negatively correlated with RCB grading, while ER ( r=0.352, P=0.000), PR ( r=0.379, P=0.000) and lymph node metastasis ( r=0.214, P=0.000) were positively correlated with RCB grading. Logistic regression analysis showed that high histological grade, negative expression of ER, PR and AR, positive expression of HER2, high proliferation index of Ki67 and no lymph node metastasis were favorable factors for postoperative pCR, and PR, AR and HER2 were independent predictors of postoperative pCR. Kaplan-Meier survival analysis showed significant differences in postoperative cumulative survival among patients with different RCB grades ( P=0.004) . Conclusions:Postoperative RCB grading of breast cancer is closely related to many clinicopathological features before neoadjuvant therapy and survival prognosis. Clinicopathological factors closely related to RCB grading are also important influencing factors affecting the pCR of patients with neoadjuvant therapy. Therefors, RCB grading has a high prognostic value.

Purpose To explore the artificial intelligence(AI)-assisted diagnosis system of thyroid cancer based on deep transfer learning and evaluate its clinical application value.Methods The HE sections of 682 cases thyroid disease patients(including benign lesions,papillary carcinoma,follicular carci-noma,medullary carcinoma and undifferentiated carcinoma)in the Pathology Department of the Renmin Hospital of Wuhan Uni-versity were collected,scanned into digital sections,divided into training sets and internal test sets according to the ratio of 8 ∶ 2,and the training sets were labeled at the pixel level by patholo-gists.The thyroid cancer classification model was established u-sing VGG image classification algorithm model.In the process of model training,the parameters of the breast cancer region recog-nition model were taken as the initial values,and the parameters of the thyroid cancer region recognition model were optimized through the transfer learning strategy.Then the test set and 633 intraoperative frozen HE section images of thyroid disease in Jianli County People's Hospital,Jingzhou City,Hubei Province wereused as the external test set to evaluate the performance of the established AI-assisted diagnostic model.Results In the internal test set,without the use of the breast cancer region rec-ognition model transfer learning,the accuracy of the AI-assisted diagnosis model was 0.882,and the area under the Receiver op-erating characteristic(AUC)valuewas0.938;However,inthe use of the Transfer learning model,the accuracy of the AI-assis-ted diagnosis model for was 0.926,and the AUC value was 0.956.In the external test set,the accuracy of the zero learning model was 0.872,the AUC value was 0.915,and the accuracy of the Transfer learning model was 0.905,the AUC value was 0.930.Conclusion The AI-assisted diagnosis method for thy-roid cancer established in this study has good accuracy and gen-eralization.With the continuous development of pathological AI research,transfer learning can help improve the performance and generalization ability of the model,and improve the accura-cy of the diagnostic model.