ArticleNature communications2021
Human-interpretable image features derived from densely mapped cancer pathology slides predict diverse molecular phenotypes.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 124 papers, 1 of them a synthesis that pooled it.
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Who cites it
124 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Transforming histologic assessment: artificial intelligence in cancer diagnosis and personalized treatment.British journal of cancer · 2025Pooled it
- Explainable AI-Derived Spatial Pathological Features of Tumor, Necrosis, and Lymphocytes Identify Key Histological Signatures for Residual Cancer Burden Assessment in Breast Cancer.Diagnostics (Basel, Switzerland) · 2026Article
- FTU-Seek: Foundation Model-Guided Hard-Negative Learning for Sparse Functional Tissue Unit Segmentation.Biomedicines · 2026Article
- Review
- Artificial Intelligence and Digital Pathology for Molecular Classification of Endometrial Cancer.International journal of molecular sciences · 2026Review
- Toward comprehensive cellular characterization of H&E slides.Journal of pathology informatics · 2026Article
- TIMEL: Deep learning-statistical integration reveals spatial stromal and immune signatures of aggressive colon cancer.Journal of pathology informatics · 2026Article
- Rethinking pathology image analysis through shuffling.npj biomedical innovations · 2026Article
- Standardized, repeatable ulcerative colitis histology scoring and endpoint assessment using an automated, foundation model-based tool.medRxiv : the preprint server for health sciences · 2026Article
- Leveraging human-centered AI for clinical decision-making: a transparent, accurate rule extractor using non-dominated sorting genetic algorithm.BMC medical informatics and decision making · 2026Article
- Inflammatory marker-driven deep learning model for postoperative gastric cancer prognosis.BMC medical informatics and decision making · 2026Article
- LRF-CNN: An explainable lightweight receptive field-based CNN for colorectal cancer histopathological image classification.iScience · 2026Article
- Machine Learning-Enabled Quantification of Hepatocellular Necrosis in the Liver After Lethal Marburg and Ebola Virus Exposures.The Journal of infectious diseases · 2026Article
- LOX Inhibition Disrupts a Collagen-Integrin-MYC Axis to Suppress Progression of Invasive Lobular Carcinoma.Cancer research · 2026Article
- Integrated Collagen Architecture and Composition Improve Risk Stratification in Triple-Negative Breast Cancer.bioRxiv : the preprint server for biology · 2026Article
- An interpretable deep learning biomarker for prognostication and prediction of adjuvant chemotherapy benefit in gastric cancer.NPJ precision oncology · 2026Article
- Deep learning-driven recognition of panoramic tumor microenvironment features in H&E sections and its application.Journal for immunotherapy of cancer · 2026Review
- Interpretable Cancer Survival Prediction by Fusing Semantic Labelling of Cell Types and Whole Slide Images.Interdisciplinary sciences, computational life sciences · 2026Article
- Assessing the influence of two deep-learning assistance modes on pathologists in cancer identification.Diagnostic pathology · 2026Article
- sCellST predicts single-cell gene expression from H& E images.Nature communications · 2026Article
64 more citing papers are in PubMed but not listed here.
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Authors and funding
22 authors.
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Abstract
Computational methods have made substantial progress in improving the accuracy and throughput of pathology workflows for diagnostic, prognostic, and genomic prediction. Still, lack of interpretability remains a significant barrier to clinical integration. We present an approach for predicting clinically-relevant molecular phenotypes from whole-slide histopathology images using human-interpretable image features (HIFs). Our method leverages >1.6 million annotations from board-certified pathologists across >5700 samples to train deep learning models for cell and tissue classification that can exhaustively map whole-slide images at two and four micron-resolution. Cell- and tissue-type model outputs are combined into 607 HIFs that quantify specific and biologically-relevant characteristics across five cancer types. We demonstrate that these HIFs correlate with well-known markers of the tumor microenvironment and can predict diverse molecular signatures (AUROC 0.601-0.864), including expression of four immune checkpoint proteins and homologous recombination deficiency, with performance comparable to 'black-box' methods. Our HIF-based approach provides a comprehensive, quantitative, and interpretable window into the composition and spatial architecture of the tumor microenvironment.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.