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Integrated radiopathomics nomogram for predicting angiogenic microvascular patterns in NSCLC: a dual-center
Ronghua Wang1, Lin Wang2, Xinzheng Wang3
1Department of Radiology, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Third Hospital of Shanxi Medical University, Tongji Shanxi Hospital, Taiyuan, China.
Annals of Medicine
|April 17, 2026
Summary
A novel nomogram integrating CT scans, pathology slides, and clinical data accurately predicts microvascular patterns (MVPs) in non-small cell lung cancer (NSCLC). This tool enhances understanding of the NSCLC tumor microenvironment for personalized treatment strategies.
Area of Science:
- Oncology
- Radiology
- Pathology
Background:
- Non-small cell lung cancer (NSCLC) microvascular patterns (MVPs) are crucial for tumor progression.
- Accurate prediction of MVPs aids in treatment stratification.
Purpose of the Study:
- To develop and validate an integrated radiopathomics nomogram for predicting MVPs in NSCLC.
- To combine multiphase CT imaging, H&E-stained slides, and clinicopathological data.
Main Methods:
- Retrospective study of 258 surgically resected NSCLC patients from two centers.
- Radiomics and pathomics features extracted; CD34-immunohistochemistry used as reference for MVPs.
- Nomogram developed integrating Rad-score, Path-score, and independent predictors; validated internally and externally.
Main Results:
- Combined radiomics model achieved AUCs of 0.863-0.849; pathomics model AUCs of 0.878-0.833.
- Nomogram model demonstrated superior performance with AUCs of 0.911, 0.903, and 0.901 across cohorts.
- Histological grade identified as an independent predictor of non-angiogenic alveolar (NAA) MVP.
Conclusions:
- The integrated radiopathomics nomogram accurately and robustly predicts MVPs in NSCLC.
- This nomogram is a promising tool for characterizing the NSCLC tumor microenvironment.
- The findings support the potential for individualized treatment strategies in NSCLC.
