Abstract / Summary
Abstract Objective Radiomics extracts quantitative features from medical images that may provide non-invasive biomarkers of tumor biology and heterogeneity. However, few studies have explored radiomics on micro-computed tomography (micro-CT) or its transferability to clinical CT. This study investigates the reproducibility and the potential biological correlation of radiomic features across in vivo CT, ex vivo CT, and ex vivo micro-CT in non-small-cell lung cancer (NSCLC). Materials and methods A retrospective cohort of 60 NSCLC patients with preoperative CT scans and surgical specimens was analyzed. Ex vivo CT and micro-CT images were acquired and compared with in vivo CT using standardized preprocessing, including normalization, resampling, and registration. Tumor regions of interest (ROIs) were semi-automatically segmented, and 93 radiomic features were extracted with Pyradiomics. Cross-modality reproducibility and associations with histotype were evaluated using Spearman correlation, intraclass correlation coefficient, Bland–Altman analysis, and Mann–Whitney U test. Results Normalization and volume matching improved feature repeatability, with the highest agreement observed in normalized datasets using subsampled ROIs. Four texture-based features consistently showed high reproducibility and potential biological correlation, likely reflecting intratumoral heterogeneity and variation in cell density: DependenceNonUniformity, GrayLevelNonUniformity (GLDM and GLRLM), and Coarseness (NGTDM). These features reliably distinguished adenocarcinoma from squamous cell carcinoma across modalities, confirming their stability and potential as candidate features for transferable biomarkers. Conclusion Harmonization and volume standardization are essential for cross-modality radiomic analyses. Stable texture features provide a potential transferable signature bridging micro-CT and clinical CT imaging in NSCLC, supporting their role as candidate features for further investigation in biologically oriented studies. Key Points Question Micro-CT serves as an intermediate imaging scale enabling radiomic feature extraction from ex vivo NSCLC tumors, allowing direct comparison with in vivo and ex vivo CT. Findings Intensity normalization and volume-matched subsampling between in vivo CT, ex vivo CT, and micro-CT reduced cross-modality feature variability, improving feature reproducibility. Relevance statement Normalization, volume matching, and cross-modality analysis support the identification of candidate texture features with consistent cross-scale behavior and exploratory associations with NSCLC histotype.