ArticleCurrent medical science2025
Integrating CT Radiomics and Clinical Information to Predict Prognosis of Advanced NSCLC Patients Receiving Chemoimmunotherapy.
Article in Current medical science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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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
1 citing paper in PubMed.
- Machine learning models predict survival in unresectable stage III non-small cell lung cancer: Surveillance, Epidemiology, and End Results and Chinese cohort study.Journal of thoracic disease · 2026Article
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Authors and funding
11 authors.
Funding
Abstract
objectiveThis study aimed to develop an effective predictive tool that combines radiomics and clinical information to predict the survival outcomes of patients with advanced non-small cell lung cancer (NSCLC) undergoing chemoimmunotherapy.
methodsData were collected from 201 patients with advanced NSCLC who received first-line chemoimmunotherapy across three institutions: those from Centers I & II (n = 164) were randomly split in a 7:3 ratio into training (n = 115) and validation (n = 49) cohorts, and those form Center III (n = 37) were designated as the external test cohort. The analysis was conducted using CT images and clinical data obtained before and after induction chemoimmunotherapy. We developed multiple intratumoral and peritumoral radiomics-based models, along with clinical prediction model that integrated patients' baseline clinicopathological characteristics with plasma biomarker profiles, to predict progression-free survival (PFS). Based on expectations derived from prior established models, a stepwise backward elimination approach was utilized to select candidate submodels for the combined model construction. This combined model was internally validated using time-dependent ROC curves in training and validation sets and externally validated in the external test set.
resultsThe combined model was constructed by integrating four candidate sub-models (DeltaSub, Clinical, P4mm, and Habitat) selected through the stepwise regression analysis. The combined model demonstrated superior performance compared to conventional models that utilized only clinical features, as well as Classical-Pre, Classical-Post, delta intratumor feature-based, and peritumor feature-based models. The combined model demonstrated satisfactory predictive performance across all three datasets, achieving a C-index of 0.849 (95% CI: 0.812-0.885) in the training set, 0.744 (95% CI: 0.664-0.842) in the validation set, and 0.731 (95% CI: 0.639-0.824) in the external test set for PFS.
conclusionsWe developed a novel radiomic-clinical model to predict PFS for advanced NSCLC patients treated with first-line chemoimmunotherapy. This model enhanced survival assessment through comprehensive feature integration.
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