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<p>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 <i>ρ</i> = [− 0.34, − 0.23]; all <i>p</i> &lt; . 05), while the hemodynamics-associated biomarker correlated positively with tumor size (<i>ρ</i> = [0.24, 0.32]; all <i>p</i> &lt; . 05) and Ki-67 proliferation index (<i>ρ</i> = [0.21, 0.24]; all <i>p</i> &lt; . 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.</p>

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Noninvasive molecular subtyping of breast cancer using multimodal ultrasound spatiotemporal transformer

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月4日

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  • 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

摘要

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.