ArticleNature communications2016
Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features.
Article in Nature communications, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05300113 (Artificial Intelligence Neuropathologist - Automated CNS Tumor Pathological Diagnosis Based on Deep Learning), which is not on this map. Cited by 455 papers, 4 of them syntheses that pooled it.
What it found
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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.
Artificial Intelligence Neuropathologist - Automated CNS Tumor Pathological Diagnosis Based on Deep Learning
Who cites it
455 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- AI-Based Models for Risk Prediction in MASLD: A Systematic Review.Digestive diseases and sciences · 2026Pooled it
- Machine learning models including patient-reported outcome data in oncology: a systematic literature review and analysis of their reporting quality.Journal of patient-reported outcomes · 2024Pooled it
- The value of artificial intelligence in the diagnosis of lung cancer: A systematic review and meta-analysis.PloS one · 2023Pooled it
- Can We Quantify Aging-Associated Postural Changes Using Photogrammetry? A Systematic Review.Sensors (Basel, Switzerland) · 2022Pooled it
- Using deep learning to predict survival outcome in non-surgical cervical cancer patients based on pathological images.Journal of cancer research and clinical oncology · 2023Trial
- Development and Interpretation of a Clinicopathological-Based Model for the Identification of Microsatellite Instability in Colorectal Cancer.Disease markers · 2023Trial
- Integrated radiopathomics nomogram for predicting angiogenic microvascular patterns in NSCLC: a dual-center validation study.Annals of medicine · 2026Article
- Multimodal Artificial Intelligence in Lung Cancer: From Data Integration to Precision Oncology.Cancers · 2026Review
- Article
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- Evolution of artificial intelligence in breast cancer pathology: a bibliometric analysis.Discover oncology · 2026Article
- Deep learning prediction of pathological complete response in breast cancer using Mamba architecture.NPJ digital medicine · 2026Article
- MCLAM: a cost-effective deep learning model for predicting recurrence risk in HR+/HER2- breast cancer-a multi-center study in a Chinese cohort.Journal of the National Cancer Center · 2026Article
- A bioinformatics and experimental approach identifies glycoprotein-based diagnostic and prognostic biomarkers for colon adenocarcinoma.Scientific reports · 2026Article
- Multimodal deep learning framework for recurrence risk stratification in soft tissue sarcoma: a multicenter study.NPJ precision oncology · 2026Article
- The Prognostic and Biological Value of PGF-Based H&E Pathomics in Hepatocellular Carcinoma.Liver international : official journal of the International Association for the Study of the Liver · 2026Article
- ACRNet: high-resolution whole slide images classification network based on adaptive core regions.Medical & biological engineering & computing · 2026Article
- Unlocking the prognostic power of pathomics in bladder cancer: a machine learning odyssey across multiple centers.BMC medical imaging · 2026Article
- Machine Learning-Based Pathomics Signature in Predicting MSH2 Expression and Prognosis in Gastric Cancer.Clinical and translational gastroenterology · 2026Article
- Survival risk prediction for patients with lung adenocarcinoma containing micropapillary components based on weakly supervised pathomics.Journal of thoracic disease · 2026Article
395 more citing papers are in PubMed but not listed here.
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Authors and funding
7 authors.
Funding
Abstract
Lung cancer is the most prevalent cancer worldwide, and histopathological assessment is indispensable for its diagnosis. However, human evaluation of pathology slides cannot accurately predict patients' prognoses. In this study, we obtain 2,186 haematoxylin and eosin stained histopathology whole-slide images of lung adenocarcinoma and squamous cell carcinoma patients from The Cancer Genome Atlas (TCGA), and 294 additional images from Stanford Tissue Microarray (TMA) Database. We extract 9,879 quantitative image features and use regularized machine-learning methods to select the top features and to distinguish shorter-term survivors from longer-term survivors with stage I adenocarcinoma (P<0.003) or squamous cell carcinoma (P=0.023) in the TCGA data set. We validate the survival prediction framework with the TMA cohort (P<0.036 for both tumour types). Our results suggest that automatically derived image features can predict the prognosis of lung cancer patients and thereby contribute to precision oncology. Our methods are extensible to histopathology images of other organs.
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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.