Bridging radiomics and histopathology: cross-modality consistency of radiomic features from CT to micro-CT in NSCLC.
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…
How strong is the evidence?
Evidence Hub reads this as a cohort study, done in people. Its evidence level is 3 of 5 (Moderate · trial / cohort / secondary analysis). Trials without a large randomized design, cohorts and secondary analyses give moderate evidence. Reported size: 60 participants or records.
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