ArticleNPJ precision oncology2023
Deep learning generates synthetic cancer histology for explainability and education.
Article in NPJ precision oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
36 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Systematic review of generative adversarial networks (GANs) in cell microscopy: Trends, practices, and impact on image augmentation.PloS one · 2025Pooled it
- Article
- Counterfactual Diffusion Models Provide Interpretable Explanations of Artificial Intelligence Models in Pathology.Cancer research · 2026Article
- Portable Multispectral Optoelectronic System for Thyroid Cancer Detection.Sensors (Basel, Switzerland) · 2026Article
- Target identification and assessment in the era of AI.Nature reviews. Drug discovery · 2026Review
- Building a Clinically Relevant and Technically Robust Synthetic Histopathology Dataset for Breast and Gastric Cancer.Journal of medical systems · 2026Article
- Domain generalisation challenges in breast cancer molecular classification using foundation models: a cross-cohort exploratory study.Medical & biological engineering & computing · 2026Article
- Redefining oropharyngeal cancer in the HPV era: integrating precision medicine and immunotherapeutic frontiers.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Review
- Artificial intelligence-generated synthetic data for cancer research and clinical trials.Nature reviews. Cancer · 2026Review
- Article
- His-MMDM: Multi-Domain and Multi-Omics Translation of Histopathological Images with Diffusion Models.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- AI-Based Methods in Neuropathology for Diagnosis and Treatment of Brain Tumors.European journal of neurology · 2026Review
- APOLLO11: a bio-data-driven model for clinical and translational research in lung cancer.NPJ precision oncology · 2026Article
- Spatial AI in cancer: mapping immune evasion topology through multi-modal omics and deep learning.Frontiers in oncology · 2026Review
- Systems Virology at Scale.Current opinion in systems biology · 2025Article
- Building digital histology models of transcriptional tumor programs with generative deep learning for pathology-based precision medicine.Genome medicine · 2025Article
- Counterfactual Diffusion Models for Interpretable Morphology-based Explanations of Artificial Intelligence Models in Pathology.bioRxiv : the preprint server for biology · 2025Article
- A comparative study of explainability methods for whole slide classification of lymph node metastases using vision transformers.PLOS digital health · 2025Article
- Analysis of AI foundation model features decodes the histopathologic landscape of HPV-positive head and neck squamous cell carcinomas.Oral oncology · 2025Article
- Do explainable AI (XAI) methods improve the acceptance of AI in clinical practice? An evaluation of XAI methods on Gleason grading.The journal of pathology. Clinical research · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
23 authors.
Funding
Abstract
Artificial intelligence methods including deep neural networks (DNN) can provide rapid molecular classification of tumors from routine histology with accuracy that matches or exceeds human pathologists. Discerning how neural networks make their predictions remains a significant challenge, but explainability tools help provide insights into what models have learned when corresponding histologic features are poorly defined. Here, we present a method for improving explainability of DNN models using synthetic histology generated by a conditional generative adversarial network (cGAN). We show that cGANs generate high-quality synthetic histology images that can be leveraged for explaining DNN models trained to classify molecularly-subtyped tumors, exposing histologic features associated with molecular state. Fine-tuning synthetic histology through class and layer blending illustrates nuanced morphologic differences between tumor subtypes. Finally, we demonstrate the use of synthetic histology for augmenting pathologist-in-training education, showing that these intuitive visualizations can reinforce and improve understanding of histologic manifestations of tumor biology.
Identifiers
What OpenQuestion holds
Registered trials
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.