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Related Experiment Video

Updated: Jun 16, 2026

A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules
10:26

A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules

Published on: May 19, 2023

Development of a Prognostic Predictive Model for Stage III Non-Small Cell Lung Cancer using PET-CT Radiomics Based on

Jianyang Zhang1, Yujing Hu2, Congna Tian3

  • 1Jianyang Zhang, Hebei Medical University, Shijiazhuang 050000, Hebei, China. Department of Nuclear Medicine, Hebei General Hospital, Shijiazhuang 050000, Hebei, China.

Pakistan Journal of Medical Sciences
|June 15, 2026
PubMed
Summary

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This summary is machine-generated.

A new model combining PET radiomics and metabolic tumor volume (MTVwb) accurately predicts overall survival in stage III non-small cell lung cancer (NSCLC) patients. This approach offers robust and generalizable prognostic performance for personalized risk stratification.

Area of Science:

  • Oncology
  • Radiology
  • Medical Imaging

Background:

  • Non-small cell lung cancer (NSCLC) is a leading cause of cancer death.
  • Accurate prognostic models are crucial for guiding treatment decisions in stage III NSCLC.
  • Integrating imaging-derived features with clinical data can improve prognostic accuracy.

Purpose of the Study:

  • To compare the prognostic performance of models integrating PET/CT radiomics with metabolic parameters for predicting overall survival in stage III NSCLC patients.
  • To develop and validate a robust model for survival prediction in stage III NSCLC.
  • To assess the generalizability of the developed model using multi-center retrospective data.

Main Methods:

  • Retrospective analysis of 165 stage III NSCLC patients from The Cancer Imaging Archive and an independent cohort of 48 patients.
Keywords:
18F-FDGMulticenter studyOverall survival,PET/CTRadiomicsStage III Non–small cell lung cancer

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Last Updated: Jun 16, 2026

A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules
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Published on: May 19, 2023

  • Extraction of radiomic features and metabolic parameters (including whole-body metabolic tumor volume - MTVwb) from baseline PET/CT images.
  • Development and comparison of five prediction models, with performance evaluated using AUC, Brier score, Decision Curve Analysis, and Kaplan-Meier survival analysis, and interpretation via SHAP analysis.

    • Main Results:

    • The Combined_A model, integrating PET radiomic features and MTVwb, demonstrated the best and most stable performance across training, validation, and external test cohorts (AUCs ~0.78).
    • The Combined_A model achieved the lowest Brier score (0.188) and greater net clinical benefit in the external test cohort.
    • Kaplan-Meier analysis confirmed significant survival stratification (log-rank P=0.002), with SHAP analysis identifying key prognostic features.

    Conclusions:

    • A model integrating PET radiomics with MTVwb provides robust and generalizable survival prediction for stage III NSCLC.
    • This approach shows potential for personalized risk stratification in stage III NSCLC patients.
    • Further prospective validation is warranted to confirm these findings.