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<p>Breast cancer recurrence prediction is an important feature of post-treatment therapy, requiring accurate identification of both recurrence risk and time-to-event outcomes. In this paper, we offer a unified deep learning system that jointly performs survival analysis and multi-class recurrence classification, enabling full risk stratification for breast cancer patients. The proposed model includes a Transformer-based survival module to estimate time-until-recurrence, and an attention-guided classification module to differentiate between second primary cancer, low-risk, and high-risk recurrence instances. A multi-modal dataset comprising clinical, molecular, demographic, and lifestyle data is created from established sources like METABRIC, GSE2034, GSE2990, BCSC, and the Breast Cancer Coimbra dataset. The model uses cross-modal feature fusion, autoencoder-based dimensionality reduction, and attention-based feature attribution for applicability and accessibility. Experimental results show better accuracy, precision, recall, and F1-score of 99.12%, 98.75%, 99.08%, and 98.91%, outperforming standard machine learning and survival models. This unified paradigm gives doctors a powerful, interpretable tool for early intervention and personalized breast cancer treatment.</p>

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A unified multi modal transformer framework for breast cancer recurrence prediction and survival analysis

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月11日

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  • Saleem Malik,
  • S. Gopal Krishna Patro,
  • Ahmed Kateb Jumaah Al-Nussairi,
  • Chandrakanta Mahanty,
  • Mohamed Ghouse,
  • Akila Thiyagarajan,
  • Ahmed Adnan Hadi,
  • Anwar Khan,
  • Mohit Mittal,
  • Amanuel Zewude

摘要

Breast cancer recurrence prediction is an important feature of post-treatment therapy, requiring accurate identification of both recurrence risk and time-to-event outcomes. In this paper, we offer a unified deep learning system that jointly performs survival analysis and multi-class recurrence classification, enabling full risk stratification for breast cancer patients. The proposed model includes a Transformer-based survival module to estimate time-until-recurrence, and an attention-guided classification module to differentiate between second primary cancer, low-risk, and high-risk recurrence instances. A multi-modal dataset comprising clinical, molecular, demographic, and lifestyle data is created from established sources like METABRIC, GSE2034, GSE2990, BCSC, and the Breast Cancer Coimbra dataset. The model uses cross-modal feature fusion, autoencoder-based dimensionality reduction, and attention-based feature attribution for applicability and accessibility. Experimental results show better accuracy, precision, recall, and F1-score of 99.12%, 98.75%, 99.08%, and 98.91%, outperforming standard machine learning and survival models. This unified paradigm gives doctors a powerful, interpretable tool for early intervention and personalized breast cancer treatment.