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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Wiley
2025-03-01
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| Online Access: | https://doi.org/10.1002/advs.202413702 |
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| author | 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 |
| author_facet | 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 |
| author_sort | Yu‐Hong Huang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-a083a41546fc4aa28cfc78e874b86fae |
| institution | OA Journals |
| issn | 2198-3844 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advanced Science |
| spelling | doaj-art-a083a41546fc4aa28cfc78e874b86fae2025-08-20T01:49:42ZengWileyAdvanced Science2198-38442025-03-011212n/an/a10.1002/advs.202413702Longitudinal MRI‐Driven Multi‐Modality Approach for Predicting Pathological Complete Response and B Cell Infiltration in Breast CancerYu‐Hong Huang0Zhen‐Yi Shi1Teng Zhu2Tian‐Han Zhou3Yi Li4Wei Li5Han Qiu6Si‐Qi Wang7Li‐Fang He8Zhi‐Yong Wu9Ying Lin10Qian Wang11Wen‐Chao Gu12Chang‐Cong Gu13Xin‐Yang Song14Yang Zhou15Dao‐Gang Guan16Kun Wang17Department of Breast Cancer Cancer Center Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences) Southern Medical University No. 106 Zhongshan Second Road, Yuexiu District Guangzhou Guangdong Province 510080 ChinaDepartment of Biochemistry and Molecular Biology School of Basic Medical Sciences Southern Medical University Guangzhou Guangdong Province 510515 ChinaDepartment of Breast Cancer Cancer Center Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences) Southern Medical University No. 106 Zhongshan Second Road, Yuexiu District Guangzhou Guangdong Province 510080 ChinaThe Department of General Surgery Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University Xihu District Hangzhou Zhejiang Province 310000 ChinaDepartment of Biochemistry and Molecular Biology School of Basic Medical Sciences Southern Medical University Guangzhou Guangdong Province 510515 ChinaDepartment of Breast Cancer Cancer Center Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences) Southern Medical University No. 106 Zhongshan Second Road, Yuexiu District Guangzhou Guangdong Province 510080 ChinaGalactophore Department Jingzhou Hospital Affiliated to Yangtze University Shashi District Jingzhou 434000 ChinaDepartment of Biostatistics Harvard T.H. Chan School of Public Health Boston MA 02115 USABreast Center Cancer Hospital of Shantou University Medical College Jinping District Shantou Guangdong Province 515000 ChinaClinical research center & Breast disease diagnosis and treatment center Shantou Central Hospital No. 114 Waima Road, Jinping District Shantou Guangdong Province 515000 ChinaBreast Disease Center, The First Affiliated Hospital Sun Yat‐sen University No. 58 Zhongshan Second Road, Yuexiu District Guangzhou Guangdong Province 510080 ChinaDepartment of Radiology The Affiliated Huaian No.1 People's Hospital of Nanjing Medical University Huaiyin District Huaian Jiangsu Province 223001 ChinaDepartment of Artificial Intelligence Medicine Graduate School of Medicine Chiba University Chiba 263‐8522 JapanDepartment of Medical Imaging The First Hospital of Qinhuangdao Haigang District Qinhuangdao Hebei Province 066000 ChinaDepartment of Radiology The First Affiliated Hospital of Jinan University No. 613 Huangpu West Road, Tianhe District Guangzhou Guangdong 510627 ChinaDepartment of Pathology The Second People's Hospital of Changzhou, The Third Affiliated Hospital of Nanjing Medical University No. 29 Xinglong Lane Changzhou Jiangsu Province 213164 ChinaDepartment of Biochemistry and Molecular Biology School of Basic Medical Sciences Southern Medical University Guangzhou Guangdong Province 510515 ChinaDepartment of Breast Cancer Cancer Center Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences) Southern Medical University No. 106 Zhongshan Second Road, Yuexiu District Guangzhou Guangdong Province 510080 ChinaAbstract 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.https://doi.org/10.1002/advs.202413702artificial intelligencebreast cancermedical imagingmulti‐omics analysisneoadjuvant treatment |
| spellingShingle | 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 Longitudinal MRI‐Driven Multi‐Modality Approach for Predicting Pathological Complete Response and B Cell Infiltration in Breast Cancer Advanced Science artificial intelligence breast cancer medical imaging multi‐omics analysis neoadjuvant treatment |
| title | Longitudinal MRI‐Driven Multi‐Modality Approach for Predicting Pathological Complete Response and B Cell Infiltration in Breast Cancer |
| title_full | Longitudinal MRI‐Driven Multi‐Modality Approach for Predicting Pathological Complete Response and B Cell Infiltration in Breast Cancer |
| title_fullStr | Longitudinal MRI‐Driven Multi‐Modality Approach for Predicting Pathological Complete Response and B Cell Infiltration in Breast Cancer |
| title_full_unstemmed | Longitudinal MRI‐Driven Multi‐Modality Approach for Predicting Pathological Complete Response and B Cell Infiltration in Breast Cancer |
| title_short | Longitudinal MRI‐Driven Multi‐Modality Approach for Predicting Pathological Complete Response and B Cell Infiltration in Breast Cancer |
| title_sort | longitudinal mri driven multi modality approach for predicting pathological complete response and b cell infiltration in breast cancer |
| topic | artificial intelligence breast cancer medical imaging multi‐omics analysis neoadjuvant treatment |
| url | https://doi.org/10.1002/advs.202413702 |
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