ArticlePakistan journal of medical sciences2026
Development of a Prognostic Predictive Model for Stage III Non-Small Cell Lung Cancer using PET-CT Radiomics Based on Multi-Center and Heterogeneous Data and External Validation.
Article in Pakistan journal of medical sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: To compare the prognostic performance of models integrating PET/CT radiomics with metabolic parameters for predicting overall survival in patients with stage III NSCLC using multi-center retrospective data. Methodology: In this retrospective, comparative study, we analyzed data from 165 stage III NSCLC patients (from 37 centers in The Cancer Imaging Archive) and an independent external cohort of 48 patients from Hebei General Hospital (2022-2025). Radiomic features and metabolic parameters were extracted from baseline PET/CT images. Feature selection and cross-validation were performed to construct and compare multiple prediction models. Model performance was evaluated in training, validation, and external test cohorts using discrimination (AUC), calibration (Brier score), clinical net benefit (Decision Curve Analysis), and Kaplan-Meier survival analysis. Shapley additive explanations (SHAP) were applied to interpret the final model. Results: Five prediction models were developed and compared. The model combining PET radiomic features and whole-body metabolic tumor volume (MTVwb), termed the Combined_A model, demonstrated the best and most stable performance, with AUCs of 0.782, 0.775, and 0.778 in the training, validation, and external test cohorts, respectively. In the external test cohort. Combined_A also showed the lowest Brier score (Brier score: 0.188) and greater net clinical benefit. Kaplan-Meier analysis confirmed significant survival stratification (log-rank P= 0.002). SHAP analysis identified wavelet-HHL_GLSZM_Small Area Low Gray Level Emphasis and log first order Skewness as key prognostic features. Conclusion: In this retrospective comparative study, a model integrating PET radiomics with MTVwb provided robust and generalizable survival prediction for stage III NSCLC. These findings support its potential for personalized risk stratification, warranting further prospective validation.
Indexed as
Identifiers
What OpenQuestion holds
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.