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...

Full description

Saved in:
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
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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 resonance imaging (MRI) spatial habitat radiomics, transcriptomics, and single‐cell RNA sequencing for predicting pCR. By analyzing tumor subregions on multi‐timepoint MRI, the model captures dynamic intra‐tumoral heterogeneity during NAT. It shows superior performance over traditional radiomics, with areas under the curve of 0.863, 0.813, and 0.888 in the external validation, immunotherapy, and multi‐omics cohorts, respectively. Subgroup analysis shows its robustness across varying molecular subtypes and clinical stages. Transcriptomic and single‐cell RNA sequencing analysis reveals that high model scores correlate with increased immune activity, notably elevated B cell infiltration, indicating the biological basis of the imaging model. The integration of imaging and molecular data demonstrates promise in spatial habitat radiomics to monitor dynamic changes in tumor heterogeneity during NAT. In clinical practice, this study provides a noninvasive tool to accurately predict pCR, with the potential to guide treatment planning and improve breast‐conserving surgery rates. Despite promising results, the model requires prospective validation to confirm its utility across diverse patient populations and clinical settings.
ISSN:2198-3844

Similar Items