Work

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

Year: 2026

Type: article

Abstract: 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.

Source: Discover Oncology

DOI: 10.1007/s12672-026-04491-6