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<p>Early prediction of response to neoadjuvant therapy (NAT) is crucial for optimizing breast cancer treatment strategies. Here, we present a novel breast self-supervised temporal learning framework (BSTNet) for predicting the pathological complete response (pCR) using longitudinal MRI. Through self-supervised pre-training, BSTNet aims to achieve model generalization across multi-timepoint and dual-timepoint scenarios while capturing dynamic tumor changes during NAT. In a multicenter cohort of 1339 patients, BSTNet demonstrated robust performance: area under the receiver operating characteristic curve (AUC) of 0.882 in internal validation (from Center 1), and 0.857 and 0.854 in external validation (Centers 2 and 3, respectively). In all three validation cohorts, subgroup analyses demonstrated consistent performance across molecular subtypes, with AUCs ranging from 0.827 to 0.886 for hormone receptor status and 0.818–0.895 for human epidermal growth factor receptor 2 status. The model maintained a stable performance across varying interim MRI timings in the external validation cohorts, with AUCs of 0.841–0.893 in Center 2 and 0.792–0.970 in Center 3. Notably, BSTNet effectively identified patients with non-pCR (specificity: 86.4%, 74.5%, and 85.1% for internal validation, Center 2, and Center 3, respectively). In conclusion, BSTNet provides a robust and generalizable deep learning framework for early pCR prediction. Its ability to effectively interpret variable longitudinal MRI data offers a powerful and practical tool to guide adaptive treatment planning in diverse clinical settings.</p>

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Longitudinal MRI-based deep learning model for predicting pathological complete response in breast cancer: a multicenter, retrospective cohort study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月5日

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  • Xu Huang,
  • Zeyan Xu,
  • Yingnan Zhao,
  • Ying Wang,
  • Yu Liu,
  • Wei Hu,
  • Ke Zhao,
  • Lisha Yao,
  • Jiahui He,
  • Yifan Yu,
  • Tianpeng Deng,
  • Lei Wu,
  • Wu Zhang,
  • Changhong Liang,
  • Zaiyi Liu

摘要

Early prediction of response to neoadjuvant therapy (NAT) is crucial for optimizing breast cancer treatment strategies. Here, we present a novel breast self-supervised temporal learning framework (BSTNet) for predicting the pathological complete response (pCR) using longitudinal MRI. Through self-supervised pre-training, BSTNet aims to achieve model generalization across multi-timepoint and dual-timepoint scenarios while capturing dynamic tumor changes during NAT. In a multicenter cohort of 1339 patients, BSTNet demonstrated robust performance: area under the receiver operating characteristic curve (AUC) of 0.882 in internal validation (from Center 1), and 0.857 and 0.854 in external validation (Centers 2 and 3, respectively). In all three validation cohorts, subgroup analyses demonstrated consistent performance across molecular subtypes, with AUCs ranging from 0.827 to 0.886 for hormone receptor status and 0.818–0.895 for human epidermal growth factor receptor 2 status. The model maintained a stable performance across varying interim MRI timings in the external validation cohorts, with AUCs of 0.841–0.893 in Center 2 and 0.792–0.970 in Center 3. Notably, BSTNet effectively identified patients with non-pCR (specificity: 86.4%, 74.5%, and 85.1% for internal validation, Center 2, and Center 3, respectively). In conclusion, BSTNet provides a robust and generalizable deep learning framework for early pCR prediction. Its ability to effectively interpret variable longitudinal MRI data offers a powerful and practical tool to guide adaptive treatment planning in diverse clinical settings.