SynthesisThoracic cancer2025
Pretreatment CT Texture Analysis for Predicting Survival Outcomes in Advanced Nonsmall Cell Lung Cancer Patients Receiving Immunotherapy: A Systematic Review and Meta-Analysis.
Synthesis in Thoracic cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pretreatment CT Texture Analysis for Predicting Survival Outcomes in Advanced Nonsmall Cell Lung Cancer Patients Receiving Immunotherapy: A Systematic Review and Meta-Analysis.Thoracic cancer · 2025Pooled it
- Article
- Radiomics in Lung Cancer Imaging: A Narrative Review of Current Evidence.Journal of imaging · 2026Review
- Liquid Biopsy Biomarkers for Predicting and Monitoring Immunotherapy Response in Lung Cancer.Cancers · 2026Review
- Development and validation of a machine learning-based radiomics model using 45-keV virtual monoenergetic images for differentiating fat-poor angiomyolipoma from clear cell renal cell carcinoma.Translational andrology and urology · 2026Article
- Serum MIC-1, LMTK-3 and IGFBP-7 levels in the prognosis of lung cancer treated with ablation combined with chemotherapy.Journal of medical biochemistry · 2026Article
- Evolving non-invasive biomarkers in NSCLC immunotherapy: integrating liquid biopsy and multi-omics profiling for precision oncology.Frontiers in immunology · 2026Review
- 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
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundWhile established biomarkers predict immunotherapy response in advanced nonsmall cell lung cancer (NSCLC), additional noninvasive imaging biomarkers may enhance treatment selection. Pretreatment computed tomography (CT) texture analysis may provide tumor characterization to predict survival outcomes.
methodsWe conducted a systematic review and meta-analysis following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. PubMed and Cochrane Library databases were searched. Study quality was assessed using the quality in prognosis studies (QUIPS) tool. Hazard ratios (HRs) with 95% confidence intervals (CIs) were pooled using random-effects models.
resultsTen retrospective studies involving 2400 patients were included. Patients stratified as low-risk based on CT texture features demonstrated significantly improved survival outcomes compared to high-risk patients. The included studies used diverse radiomic features for risk stratification, including texture features from gray-level co-occurrence matrix (GLCM) such as entropy and dissimilarity, first-order statistical parameters including skewness and kurtosis, gray-level run-length matrix (GLRLM) features, and deep learning-derived features. Meta-analysis of five studies (n = 1102) revealed that patients stratified as low-risk based on these quantitative CT texture signatures had substantially better overall survival (OS) (p < 0.0001) with minimal heterogeneity (I
conclusionsPretreatment quantitative CT texture analysis effectively predicts survival outcomes in advanced NSCLC patients receiving immunotherapy, providing clinically meaningful risk stratification. This noninvasive imaging approach may serve as an additional tool to complement established pathological and molecular biomarkers, including liquid biopsy, for enhanced personalized treatment selection.
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