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

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.

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Publication Details

Journal
Advanced Science
Published
2025-02-07
DOI
https://doi.org/10.1002/advs.202413702
Citations
24
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
32.64

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article

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

Zhong‐Song Shi, Tianhan Zhou, Daogang Guan, Wenchao Gu et al.
24 citations
Advanced Science
Radiomics and Machine Learning in Medical Imaging
32.64
article

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

Zhong‐Song Shi, Tianhan Zhou, Daogang Guan, Wenchao Gu, Teng Zhu, Xing Song, Ying Lin, Qian Wang, Siqi Wang, Yi Li, Kun Wang, Wei Li, Lifang He, Han Qiu, Zhi‐Yong Wu, Yang Zhou, Yu‐Hong Huang, Chengyuan Gu
article en
24 citations

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.

Advanced ScienceVol. 12(12)
Zhejiang Chinese Medical University (CN), Harvard University (US), Sun Yat-sen University (CN), Chiba University (JP), Yangtze University (CN), Shantou University (CN), Cancer Hospital of Shantou University Medical College (CN), Huaian First People’s Hospital (CN), Nanjing Traditional Chinese Medicine Hospital (CN), Cell Technology (China) (HK), Shantou Central Hospital (CN), Guangdong Academy of Medical Sciences (CN), Changzhou No.2 People's Hospital (CN), Guangdong Provincial People's Hospital (CN), First Affiliated Hospital of Jinan University (CN), First Hospital of Qinhuangdao (CN), Southern Medical University (CN), Nanjing Medical University (CN)
National Natural Science Foundation of China, Fundo para o Desenvolvimento das Ciências e da Tecnologia, High-level Hospital Construction Project of Guangdong Provincial People's Hospital, Basic and Applied Basic Research Foundation of Guangdong Province
Good health and well-being
Openalex Percentile: Top 0%
Radiomics and Machine Learning in Medical Imaging
32.64
DOIOpen Access PDFOpenAlex
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