Mapping 3D Heterogeneity of Thyroid Tumors Using Micro-CTbased Radiomics

Abstract

Tumor heterogeneity plays a central role in treatment resistance, disease progression, and diagnostic uncertainty. However, it may be overlooked by traditional 2D histology. Accurate 3D assessment of tumor microarchitecture is therefore essential for capturing its spatial complexity. Micro-CT, an emerging imaging modality that offers high-resolution 3D virtual histology of soft tissues, provides a promising alternative. Combined with radiomics--a computational approach for interpretable quantification of tissue phenotypes--this technique enables a deeper understanding of tumor biology beyond visual inspection. In this study, we analyzed a large cohort of thyroid tumors (418 patients) using micro-CT imaging of next-generation tissue microarrays, from which we extracted radiomics features. We achieved robust classification of (i) neoplastic versus non-neoplastic thyroid tissues, (ii) papillary versus follicular thyroid carcinoma, and (iii) BRAF V600E mutation status. Feature interpretation using Shapley additive explanations revealed key visual traits driving these classification decisions. Preliminary results also indicated radiomic patterns associated with TERT promoter mutations, suggesting the existence of potential surrogate imaging biomarkers. Overall, micro-CT radiomics shows strong potential as a complementary tool for improving diagnostic and prognostic accuracy in thyroid cancer and offers a novel platform for quantitative pathology into the 3D spatial complexity of neoplastic tissues.