Ultrasound-Based Deep Learning Radiomics Nomogram for Tumor and Axillary Lymph Node Status Prediction After Neoadjuvant Chemotherapy.

Abstract

Rationale and objectives: This study aims to explore the feasibility of the deep learning radiomics nomogram (DLRN) for predicting tumor status and axillary lymph node metastasis (ALNM) after neoadjuvant chemotherapy (NAC) in patients with breast cancer. Additionally, we employ a Cox regression model for survival analysis to validate the effectiveness of the fusion algorithm.

Materials and 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 (US) images. The diagnostic performance of DLRN was evaluated by constructing ROC curves, and the clinical usefulness of models was assessed 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 area under the receiver operating characteristic curve values of 0.984 and 0.985 for the tumor and LNM, while 0.892 and 0.870, respectively, in the test cohort. The consistency indices (C-index) of the nomogram were 0.761 and 0.731, respectively, in the training and test cohorts. 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 clinical guidance for predicting the status of tumors and LNM after NAC in patients with breast cancer. This fusion model can also predict the prognosis of patients, which could help clinicians make better clinical decisions.