• Wei Li, Yu-Hong Huang, Teng Zhu, Yi-Min Zhang, Xing-Xing Zheng, Ting-Feng Zhang, Ying-Yi Lin, Zhi-Yong Wu, Zai-Yi Liu, Ying Lin, Guo-Lin Ye, and Kun Wang.
• Department of Breast Cancer, The First People's Hospital of Foshan, Foshan 528000, China.
• Ann. Surg. 2024 Apr 1.

ObjectiveTo develop an artificial intelligence (AI) system for the early prediction of residual cancer burden (RCB) scores during neoadjuvant chemotherapy (NAC) in breast cancer.Summary Background DataRCB III indicates drug resistance in breast cancer, and early detection methods are lacking.MethodsThis study enrolled 1048 patients with breast cancer from four institutions, who were all receiving NAC. Magnetic resonance images were collected at the pre- and mid-NAC stages, and radiomics and deep learning features were extracted. A multitask AI system was developed to classify patients into three groups (RCB 0-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 three external validation cohorts. (EVCs, n=713).ResultsAmong the patients, 442 (42.18%) were RCB 0-I, 462 (44.08%) were RCB II and 144 (13.74%) were RCB III. Model-I achieved an area under the curve (AUC) of 0.975 in the PC and 0.923 in the EVCs for differentiating RCB III from RCB 0-II. Model-II distinguished RCB 0-I from RCB II-III, with an AUC 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.ConclusionsThe multitask AI system offers a noninvasive tool for the early prediction of RCB scores in breast cancer, supporting clinical decision-making during NAC.Copyright © 2024 The Author(s). Published by Wolters Kluwer Health, Inc.

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