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<p>Breast cancer diagnosis, immune cell profile, and survival forecasting are important but usually done separately, limiting clinical interpretation. This work combines histopathological diagnosis, immunological microenvironment analysis, and prognostic modeling into a data-driven pipeline. The proposed system involves three phases: PatchSight Classifier uses an optimized InceptionResNetV2 network with patch-based augmentation and transfer learning to classify benign and malignant breast tissue from the BreakHis dataset; ImmuneMap Detector uses Faster R-CNN on immunohistochemistry images from the LYSTO dataset to detect and quantify tumor-infiltrating lymphocytes; and LifeSpan Prognosticator integrates diagnostic and immune features. The PatchSight Classifier outperformed VGG-16, DenseNet-121, and baseline InceptionResNetV2 models with 98.76% accuracy and 0.98 F1-score at 400× magnification. ResNet-101’s ImmuneMap Detector had 98% detection accuracy and low lymphocyte counting inaccuracy. The LifeSpan Prognosticator identified survival-influencing biomarkers with a C-index above 0.90. This comprehensive computational pathology system improves diagnostic precision, immunological assessment, and survival prediction with interpretable, high-accuracy models. We provide end-to-end decision assistance for early detection, immunological assessment, and personalized breast cancer prognosis.</p>

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PatchSight–ImmuneMap–LifeSpan as a unified AI framework for breast cancer diagnosis, immune profiling and prognostic prediction

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月30日

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  • Ahmed Kateb Jumaah Al-Nussairi,
  • Ali B. M. Ali,
  • Saleem Malik,
  • S Gopal Krishna Patro,
  • Chandrakanta Mahanty,
  • Kasim Sakran Abass,
  • Iman Basheti,
  • Adis Abebaw Dessalegn,
  • Khursheed Muzammil,
  • Sanjay Kumar

摘要

Breast cancer diagnosis, immune cell profile, and survival forecasting are important but usually done separately, limiting clinical interpretation. This work combines histopathological diagnosis, immunological microenvironment analysis, and prognostic modeling into a data-driven pipeline. The proposed system involves three phases: PatchSight Classifier uses an optimized InceptionResNetV2 network with patch-based augmentation and transfer learning to classify benign and malignant breast tissue from the BreakHis dataset; ImmuneMap Detector uses Faster R-CNN on immunohistochemistry images from the LYSTO dataset to detect and quantify tumor-infiltrating lymphocytes; and LifeSpan Prognosticator integrates diagnostic and immune features. The PatchSight Classifier outperformed VGG-16, DenseNet-121, and baseline InceptionResNetV2 models with 98.76% accuracy and 0.98 F1-score at 400× magnification. ResNet-101’s ImmuneMap Detector had 98% detection accuracy and low lymphocyte counting inaccuracy. The LifeSpan Prognosticator identified survival-influencing biomarkers with a C-index above 0.90. This comprehensive computational pathology system improves diagnostic precision, immunological assessment, and survival prediction with interpretable, high-accuracy models. We provide end-to-end decision assistance for early detection, immunological assessment, and personalized breast cancer prognosis.