Noninvasive molecular subtyping of breast cancer using multimodal ultrasound spatiotemporal transformer

Tao Chen; Qinghua Niu; Zichen An; Chenhui Wang; Zhihao Chen; Lianfang Du; Chao Jia; Chunxiao Li; Jiying Gu; Ting Sun; Yuxiang Wang; Fan Li; Hongming Shan

doi:10.1038/s41746-026-02699-y

Noninvasive molecular subtyping of breast cancer using multimodal ultrasound spatiotemporal transformer

NPJ Digit Med. 2026 May 4. doi: 10.1038/s41746-026-02699-y. Online ahead of print.

Authors

Tao Chen^#^{1

2

3}, Qinghua Niu^#^{4

5}, Zichen An^#⁶, Chenhui Wang^{1

2

3}, Zhihao Chen^{1

2

3}, Lianfang Du⁵, Chao Jia⁵, Chunxiao Li⁵, Jiying Gu⁷, Ting Sun⁸, Yuxiang Wang⁹, Fan Li¹⁰, Hongming Shan^{11

12

13}

Affiliations

¹ Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.
² MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China.
³ MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Shanghai, China.
⁴ Department of Ultrasound, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
⁵ Department of Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
⁶ School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China.
⁷ Department of Ultrasound, Shidong Hospital, Shanghai, China.
⁸ Department of Ultrasound, Qingdao Hospital, University of Health and Rehabilitation Sciences (Qingdao Municipal Hospital), Qingdao, China.
⁹ Department of Ultrasound, Shanxi Province Cancer Hospital/ Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan, China.
¹⁰ Department of Ultrasound, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. medicineli@sjtu.edu.cn.
¹¹ Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China. hmshan@fudan.edu.cn.
¹² MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China. hmshan@fudan.edu.cn.
¹³ MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Shanghai, China. hmshan@fudan.edu.cn.

^# Contributed equally.

PMID: 42082579
DOI: 10.1038/s41746-026-02699-y

Abstract

Molecular subtyping is essential for guiding systemic therapy in breast cancer but currently requires invasive biopsy. Conventional B-mode ultrasound offers rich anatomical information, yet lacks the functional dynamics needed to capture the comprehensive biology of tumors. Here, we present the first multimodal ultrasound spatiotemporal transformer, MUST-Sub, which integrates paired B-mode morphological features with contrast-enhanced ultrasound (CEUS) hemodynamic patterns to classify Luminal, HER2-enriched, and triple-negative subtypes. Training on a retrospective development cohort, and validated on internal, prospective, and multicenter external cohorts, MUST-Sub achieved macro-average areas under the receiver operating characteristic curve (AUCs) of 0.94, 0.90, and 0.92, respectively, and Luminal versus non-Luminal AUCs of 0.92, 0.88, and 0.91, outperforming B-mode-only deep learning baselines. MUST-Sub also produced interpretable quantitative biomarkers derived from spatiotemporal attention: the morphology-associated biomarker showed inverse correlations with tumor size (Spearman ρ = [- 0.34, - 0.23]; all p < . 05), while the hemodynamics-associated biomarker correlated positively with tumor size (ρ = [0.24, 0.32]; all p < . 05) and Ki-67 proliferation index (ρ = [0.21, 0.24]; all p < . 05). These results suggest that multimodal ultrasound with spatiotemporal modeling can serve as a promising adjunctive approach for non-invasive pre-biopsy molecular phenotyping of breast cancer.

Grants and funding

No. 62471148/National Natural Science Foundation of China