J. N. Kather

The paper validates a low-cost, open-source workstation and computational pipeline for automated histopathology evaluation using deep learning, showing comparable performance to expensive institutional hardware.

This paper uses machine learning to identify latent transcriptional programs in squamous cell carcinomas from histology images, linking them to clinically relevant biological processes.

This paper develops a self-supervised deep learning pipeline to predict genetic mutations from histopathology images across seven cancer types in two large datasets.

This study shows deep learning can predict multi-omic biomarkers from H&E-stained cancer slides, with 50% of models achieving AUC > 0.633 across 32 cancer types.

DeepMed provides a modular, scalable AI pipeline for predicting molecular and histopathological features from histopathology images, with public benchmark data.

The paper surveys computational pathology experts to identify promising AI applications in digital pathology, finding treatment response prediction and genetic alteration detection as top priorities.

This study applies a game-theoretic model to 590 Non-Small Cell Lung Cancer patients, showing it better fits tumor dynamics than classical models and predicts treatment outcomes.

This study evaluates classical mathematical models for predicting chemotherapy and immunotherapy responses using real-world patient tumor volume data, finding the Gompertz model provides the best balance between fit and complexity.

The paper analyzes nerve fiber density and tertiary lymphoid structures in pancreatic cancer, finding that high nerve fiber density and TLS count correlate with improved survival.

This paper uses deep learning to detect the presence of HPV and EBV in cancer histology images, achieving high accuracy in predicting virus-associated tumors.

The paper shows that tumour architecture determines evolutionary patterns, identifying four onco-evolutionary modes based on spatial structure and cell dispersal.

This paper develops a dynamic tumor-immune model to predict patient response to treatment using NLR as a biomarker and simulates optimal therapies for virtual RCC patients.