ArticleNPJ precision oncology2025
Computationally integrating radiology and pathology image features for predicting treatment benefit and outcome in lung cancer.
Article in NPJ precision oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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The trial behind it
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Who cites it
18 citing papers in PubMed.
- Integrated radiopathomics nomogram for predicting angiogenic microvascular patterns in NSCLC: a dual-center validation study.Annals of medicine · 2026Article
- Resistance to Immune Checkpoint Inhibitor Treatment in Non-Small Cell Lung Cancer Clinical Trials: A Perspective From Lung-MAP Investigators.Journal of clinical oncology : official journal of the American Society of Clinical Oncology · 2026Article
- Article
- Multi-regional radiomics based on planning CT and information complementarity between GTVp and GTVn: an explainable model for predicting 5-year recurrence risk in HPV-positive oropharyngeal cancer.Radiation oncology (London, England) · 2026Article
- Recent advances in artificial intelligence across interventional pulmonology: a narrative review.Journal of thoracic disease · 2026Review
- Risk stratification of PD-L1 expression in non-small cell lung cancer: a predictive model for prognosis.Journal of thoracic disease · 2026Article
- Effects of Tumor Microenvironment on Lung Cancer Stem Cells: Bidirectional Regulatory Mechanisms.Cancer medicine · 2026Review
- Mechanistic trends and therapeutic targets in Radiation-Induced apoptosis: A bibliometric and translational mapping.Radiation and environmental biophysics · 2026Review
- Artificial Intelligence in Lung Cancer: A Narrative Review of Recent Advances in Diagnosis, Biomarker Discovery, and Drug Development.Pharmaceutics · 2026Review
- Decoding the ERS-CAF immunoregulatory axis via multimodal AI and its pan-cancer prognostic and therapeutic predictive value.NPJ digital medicine · 2026Article
- The CXCR4-targeted theranostics era: a comprehensive review of a decade of progress (2015-2025).Theranostics · 2026Review
- Foundation model-enhanced multimodal radiomics for predicting response to chemo-immunotherapy in advanced lung squamous cell carcinoma.Frontiers in oncology · 2026Article
- Exploratory Analysis of HMGB1 Genetic Variants and Their Potential Association with Lung Cancer Susceptibility and Chemotherapy Response in a Chinese Population.Human heredity · 2026Article
- Applications and challenges of utilizing digital pathology and AI-enabled workflows in clinical trials.Journal of pathology informatics · 2026Review
- Inferring tumor immune microenvironment -related risk states from pretreatment H&E pathomics and clinical biomarkers to predict checkpoint inhibitor pneumonitis in advanced NSCLC: a multicenter multimodal study.Frontiers in immunology · 2026Article
- Development and validation of a pathomics model to predict SOX11 expression and prognosis in hepatocellular carcinoma.Translational cancer research · 2025Article
- Enhanced prediction of gene mutation and risk stratification in non-small-cell lung cancer through dual-pathway fusion of radiomics and pathomics.Frontiers in oncology · 2025Article
- Early prediction of immunotherapy efficacy for advanced NSCLC based on clinical and pre-treatment contrast-enhanced CT radiomics features.Frontiers in oncology · 2025Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
Lung cancer, the leading cause of cancer-related deaths globally, includes non-small cell lung cancer (NSCLC) (85% of cases) and small cell lung cancer (SCLC) (13-15%). While accurate diagnosis and treatment selection are critical, the absence of reliable predictive or prognostic biomarkers remains a significant challenge. This study explored the combined use of radiomics from CT scans and pathomics from H&E slides in three contexts: (1) predicting disease recurrence in early-stage NSCLC, (2) predicting immunotherapy response in advanced-stage NSCLC, and (3) predicting chemotherapy response in SCLC. The integrated radio-pathomic model significantly outperformed individual models. In early-stage NSCLC (N = 194), it achieved an HR of 8.35 (C-index: 0.71, p = 0.0043). In advanced-stage NSCLC (N = 35), the combined model improved predictive performance (AUC: 0.75, p = 0.042). In SCLC (N = 50), the integrated model showed an AUC of 0.78, surpassing both radiomic and pathomic models. These findings highlight the potential of combining radiomics and pathomics for improved lung cancer risk stratification and treatment prediction.
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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.