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Purpose We investigate the predictive value of a comprehensive model based on preoperative ultrasound radiomics, deep migration learning, and clinical features for pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) for the breast cancer. Methods We enrolled 211 patients with pathologically confirmed the breast cancer who underwent NAC. The patients were randomly divided into the training set and the validation set in the ratio of 7:3. The deep learning and radiomics features of pre-treatment ultrasound images were extracted, and the random forest recursive elimination algorithm and the least absolute shrinkage and selection operator were used for feature screening and DL-Score and Rad-Score construction. According to multiple logistic regression, independent clinical predictors, DL-Score, and Rad-Score were selected to construct the comprehensive prediction model DLR + C. The performance of the model was evaluated in terms of its predictive effect, calibration ability, and clinical practicability. Result Compared to the clinical, radiomics (Rad-Score), and deep learning (DL-Score) models, the DLR + C accurately predicted the pCR status, with an area under the curve (AUC)of 0.906 (95% CI: 0.871–0.935) in the training set and 0.849 (95% CI: 0.799–0.887) in the validation set, with good calibration ability (Hosmer-Lemeshow: P > 0.05). Moreover, decision curve analysis confirmed that the DLR + C had the highest clinical value among all models. Conclusion The comprehensive model DLR + C based on ultrasound radiomics, deep transfer learning, and clinical features can effectively and accurately predict the pCR status of breast cancer after NAC, which is conducive to assisting clinical personalized diagnosis and treatment plan.

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Purpose: To develop and validate a predictive combined model for metastasis in patients with clear cell renal cell carcinoma (ccRCC) by integrating multimodal data. Materials and Methods: In this retrospective study, the clinical and imaging data (CT and ultrasound) of patients with ccRCC confirmed by pathology from three tertiary hospitals in different regions were collected from January 2013 to January 2023. We developed three models, including a clinical model, a radiomics model, and a combined model. The performance of the model was determined based on its discriminative power and clinical utility. The evaluation indicators included AUC value, accuracy, sensitivity, specificity, negative predictive value, positive predictive value and DCA(Decision Curve Analysis) curve. Results:A total of 251 patients were evaluated. Patients (n=166) from Shandong University Qilu Hospital (Jinan) were divided into the training cohort, of which 50 patients developed metastases; patients (n=37) from Shandong University Qilu Hospital (Qingdao) were used as testing set 1, of which 15 patients developed metastases; patients (n=48) from Changzhou Second People's Hospital were used as testing set 2, of which 13 patients developed metastases. In the training set, the combined model showed the highest performance (area under the receiver operating characteristic curve [AUC], 0.924) in predicting lymph node metastasis, while the clinical and radiomics models both had AUCs of 0.875 and 0.870, respectively. In the testing set 1, the combined model had the highest performance (AUC, 0.877) for predicting lymph node metastasis, while the AUCs of the clinical and radiomics models were 0.726 and 0.836, respectively. In the testing set 2, the combined model had the highest performance (AUC, 0.849) for predicting lymph node metastasis, while the AUCs of the clinical and radiomics models were 0.708 and 0.804, respectively. The DCA curve showed that the combined model had a significant prediction probability in predicting the risk of lymph node metastasis in ccRCC patients compared with the clinical model or the radiomics model. Conclusion: The combined model was superior to the clinical and radiomics models in predicting lymph node metastasis in ccRCC patients.