Ultrasound-based deep learning radiomics nomogram for comprehensive prediction of tumor, axillary lymph node status and prognosis of breast cancer patients after neoadjuvant chemotherapy

Objectives: Our study aims to explore the feasibility of the deep learning radiomics nomogram (DLRN) for predicting the status of tumors and axillary lymph node metastasis (ALNM) after neoadjuvant chemotherapy (NAC) in breast cancer patients, and employ a Cox regression model for survival analysis to validate the effectiveness of the fusion algorithm. Methods: A total of 243 patients who underwent NAC were retrospectively included between October 2014 and July 2022. The DLRN integrated clinical characteristics as well as radiomics and deep transfer learning features extracted from ultrasound images. Evaluating the diagnostic performance of DLRN by constructing ROC curve , and assessing the clinical usefulness of models by using decision curve analysis (DCA). A survival model was developed to validate the effectiveness of the fusion algorithm. Results: In the training cohort, the DLRN yielded an area under the receiver operating characteristic curve (AUC) values of 0.984 and 0.985 in the tumor and LNM, while 0.892 and 0.870, respectively, in the test cohort. The consistency index (C-index) of the nomogram was 0.761 and 0.731, respectively, in the training and test cohort.The Kaplan-Meier survival curves showed that patients in the high-risk group had significantly poorer overall survival than patients in the low-risk group (P<0.05). Conclusion: The US-based DLRN model could hold promise as a clinical guidance for predicting the status of tumor and LNM after NAC in breast cancer patients. This fusion model also can predict the prognosis of patients , which could help clinicians to make better clinical decisions.