Noninvasive Artificial Intelligence System for Early Predicting Residual Cancer Burden During Neoadjuvant Chemotherapy in Breast Cancer
Wei Li, Yühong Huang, Teng Zhu, Yimin Zhang, Xingxing Zheng, Tingfeng Zhang, Ying‐Yi Lin, Zhi‐Yong Wu, Zaiyi Liu, Ying Lin, Guolin Ye, Kun Wang
Abstract
OBJECTIVE: To develop an artificial intelligence (AI) system for the early prediction of residual cancer burden (RCB) scores during neoadjuvant chemotherapy (NAC) in breast cancer. BACKGROUND: RCB III indicates drug resistance in breast cancer, and early detection methods are lacking. METHODS: This study enrolled 1048 patients with breast cancer from 4 institutions, who were all receiving NAC. Magnetic resonance images were collected at the pre-NAC and mid-NAC stages, and radiomics and deep learning features were extracted. A multitask AI system was developed to classify patients into 3 groups (RCB 0 to I, II, and III ) in the primary cohort (PC, n=335). Feature selection was conducted using the Mann-Whitney U test, Spearman analysis, least absolute shrinkage and selection operator regression, and the Boruta algorithm. Single-modality models were developed, followed by model integration. The AI system was validated in 3 external validation cohorts (EVCs, n=713). RESULTS: Among the patients, 442 (42.18%) were RCB 0 to I, 462 (44.08%) were RCB II, and 144 (13.74%) were RCB III. Model I achieved an area under the curve of 0.975 in the PC and 0.923 in the EVCs for differentiating RCB III from RCB 0 to II. Model II distinguished RCB 0 to I from RCB II-III, with an area under the curve of 0.976 in the PC and 0.910 in the EVCs. Subgroup analysis confirmed that the AI system was consistent across different clinical T stages and molecular subtypes. CONCLUSIONS: The multitask AI system offers a noninvasive tool for the early prediction of RCB scores in breast cancer, supporting clinical decision-making during NAC.