Longitudinal MRI‐Driven Multi‐Modality Approach for Predicting Pathological Complete Response and B Cell Infiltration in Breast Cancer

Longitudinal MRI‐Driven Multi‐Modality Approach for Predicting Pathological Complete Response and B Cell Infiltration in Breast Cancer

Abstract Accurately predicting pathological complete response (pCR) to neoadjuvant treatment (NAT) in breast cancer remains challenging due to tumor heterogeneity. This study enrolled 2279 patients across 12 centers and develops a novel multi‐modality model integrating longitudinal magnetic resonanc...

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Bibliographic Details
Main Authors: Yu‐Hong Huang, Zhen‐Yi Shi, Teng Zhu, Tian‐Han Zhou, Yi Li, Wei Li, Han Qiu, Si‐Qi Wang, Li‐Fang He, Zhi‐Yong Wu, Ying Lin, Qian Wang, Wen‐Chao Gu, Chang‐Cong Gu, Xin‐Yang Song, Yang Zhou, Dao‐Gang Guan, Kun Wang
Format: Article
Language:English
Published: Wiley 2025-03-01
Series:Advanced Science
Subjects:
artificial intelligence
breast cancer
medical imaging
multi‐omics analysis
neoadjuvant treatment
Online Access:https://doi.org/10.1002/advs.202413702
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