Elastography-based AI model can predict axillary status after neoadjuvant chemotherapy in breast cancer with nodal involvement: A prospective, multicenter, diagnostic study

Abstract

Objective: To develop a model for accurate prediction of axillary lymph node (LN) status after neoadjuvant chemotherapy (NAC) in breast cancer patients with nodal involvement.

Methods: Between October 2018 and February 2024, 671 breast cancer patients with biopsy-proven LN metastasis who received NAC followed by axillary LN dissection were enrolled in this prospective, multicenter study. Preoperative ultrasound (US) images, including B-mode ultrasound (BUS) and shear wave elastography (SWE), were obtained. The included patients were randomly divided at a ratio of 8:2 into a training set and an independent test set, with five-fold cross-validation applied to training set. We first identified clinicopathological characteristics and conventional US features significantly associated with the axillary LN response and developed corresponding prediction models. We then constructed deep learning radiomics (DLR) models based on BUS and SWE data. Models performances were compared, and a combination model was developed using significant clinicopathological data and interpreted US features with the SWE-based DLR model. Discrimination, calibration and clinical utility of this model were analyzed using receiver operating characteristic curve, calibration curve and decision curve, respectively.

Results: Axillary pathologic complete response (pCR) was achieved in 52.41% of patients. In the test cohort, the clinicopathologic model had an accuracy of 71.30%, while radiologists' diagnoses ranged from 64.26% to 71.11%, indicating limited to moderate predictive ability for the axillary response to NAC. The SWE-based DLR model, with an accuracy of 80.81%, significantly outperformed the BUS-based DLR model, which scored 59.57%. The combination DLR model boasted an accuracy of 88.70% and a false-negative rate of 8.82%. It demonstrated strong discriminatory ability (AUC, 0.95), precise calibration (p value obtained by Hosmer-Lemeshow goodness-of-fit test, 0.68), and practical clinical utility (probability threshold, 2.5-97.5%).

Conclusions: The combination SWE-based DLR model can predict the axillary status after NAC in patients with node-positive breast cancer, and thus, may inform clinical decision-making to help avoid unnecessary axillary LN dissection.