ArticleThe Lancet. Digital health2020
A prognostic model for overall survival of patients with early-stage non-small cell lung cancer: a multicentre, retrospective study.
Article in The Lancet. Digital health, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers, 2 of them syntheses that pooled it.
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Who cites it
53 citing papers in PubMed, 2 syntheses or guidelines pooled it, 79 citations in OpenAlex.
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- MuTriM: A multiscale deep learning model integrating longitudinal radiomics and pathomic features for predicting recurrence and adjuvant radiation benefit in breast cancer.European journal of cancer (Oxford, England : 1990) · 2026Article
- Will We Need a Novel Heuristic in Resectable Lung Cancer?: A Narrative Review.Current oncology (Toronto, Ont.) · 2026Review
- Artificial Intelligence inCancers · 2026Review
- A Self-Supervised Foundation Model Based on Three-Dimensional Chest CT Scans for Lung Cancer Diagnosis and Prognosis Prediction.Radiology. Imaging cancer · 2026Article
- A Computational Pathology Model to Predict Docetaxel Benefit in Localized High-Risk and Metastatic Prostate Cancer.Clinical cancer research : an official journal of the American Association for Cancer Research · 2026Article
- Stain Consistency Learning: Handling Stain Variation for Automatic Digital Pathology Segmentation.IEEE open journal of engineering in medicine and biology · 2026Article
- Review
- HistoChat: Instruction-tuning multimodal vision language assistant for colorectal histopathology on limited data.Patterns (New York, N.Y.) · 2025Article
- Progress and challenges of artificial intelligence in lung cancer clinical translation.NPJ precision oncology · 2025Review
- Computationally integrating radiology and pathology image features for predicting treatment benefit and outcome in lung cancer.NPJ precision oncology · 2025Article
- Artificial intelligence in digital pathology - time for a reality check.Nature reviews. Clinical oncology · 2025Review
- AI-based tumor-infiltrating lymphocyte scoring system for assessing HCC prognosis in patients undergoing liver resection.JHEP reports : innovation in hepatology · 2025Article
- BEEx Is an Open-Source Tool That Evaluates Batch Effects in Medical Images to Enable Multicenter Studies.Cancer research · 2025Article
- Identification of oncofetal PIWI-interacting RNAs as potential prognostic biomarkers in non-small cell lung cancer.Frontiers in genetics · 2025Article
- A novel computational pathology approach for identifying gene signatures prognostic of disease-free survival for papillary thyroid carcinomas.European journal of cancer (Oxford, England : 1990) · 2024Article
- Mapping the landscape of histomorphological cancer phenotypes using self-supervised learning on unannotated pathology slides.Nature communications · 2024Article
- Morphological diversity of cancer cells predicts prognosis across tumor types.Journal of the National Cancer Institute · 2024Article
- Deep learning-based histological predictions of chromosomal instability in colorectal cancer.American journal of cancer research · 2024Article
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Authors and funding
17 authors at 12 institutions in 3 countries.
Funding
Abstract
Background: Intratumoural heterogeneity has been previously shown to be related to clonal evolution and genetic instability and associated with tumour progression. Phenotypically, it is reflected in the diversity of appearance and morphology within cell populations. Computer-extracted features relating to tumour cellular diversity on routine tissue images might correlate with outcome. This study investigated the prognostic ability of computer-extracted features of tumour cellular diversity (CellDiv) from haematoxylin and eosin (H&E)-stained histology images of non-small cell lung carcinomas (NSCLCs). Methods: In this multicentre, retrospective study, we included 1057 patients with early-stage NSCLC with corresponding diagnostic histology slides and overall survival information from four different centres. CellDiv features quantifying local cellular morphological diversity from H&E-stained histology images were extracted from the tumour epithelium region. A Cox proportional hazards model based on CellDiv was used to construct risk scores for lung adenocarcinoma (LUAD; 270 patients) and lung squamous cell carcinoma (LUSC; 216 patients) separately using data from two of the cohorts, and was validated in the two remaining independent cohorts (comprising 236 patients with LUAD and 335 patients with LUSC). We used multivariable Cox regression analysis to examine the predictive ability of CellDiv features for 5-year overall survival, controlling for the effects of clinical and pathological parameters. We did a gene set enrichment and Gene Ontology analysis on 405 patients to identify associations with differentially expressed biological pathways implicated in lung cancer pathogenesis. Findings: For prognosis of patients with early-stage LUSC, the CellDiv LUSC model included 11 discriminative CellDiv features, whereas for patients with early-stage LUAD, the model included 23 features. In the independent validation cohorts, patients predicted to be at a higher risk by the univariable CellDiv model had significantly worse 5-year overall survival (hazard ratio 1·48 [95% CI 1·06-2·08]; p=0·022 for The Cancer Genome Atlas [TCGA] LUSC group, 2·24 [1·04-4·80]; p=0·039 for the University of Bern LUSC group, and 1·62 [1·15-2·30]; p=0·0058 for the TCGA LUAD group). The identified CellDiv features were also found to be strongly associated with apoptotic signalling and cell differentiation pathways. Interpretation: CellDiv features were strongly prognostic of 5-year overall survival in patients with early-stage NSCLC and also associated with apoptotic signalling and cell differentiation pathways. The CellDiv-based risk stratification model could potentially help to determine which patients with early-stage NSCLC might receive added benefit from adjuvant therapy. Funding: National Institue of Health and US Department of Defense.
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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.