Calcifications and Margins as Biomarkers in Differentiating Thyroid Cancer Subtypes

Accurate preoperative differentiation of thyroid cancer subtypes is essential for optimizing treatment strategies, particularly in the context of rising incidence rates and increasing use of active surveillance. This study investigates quantitative ultrasound biomarkers based on calcification patterns and tumor margins for distinguishing papillary, follicular, and medullary thyroid carcinomas. The analysis included 110 patients with histopathologically confirmed diagnoses. Tumor boundaries were refined using an active contour algorithm, and calcifications were classified by size and spatial distribution. Significant differences were observed in contour irregularity and macrocalcification burden, particularly in follicular carcinoma. A multiparametric Random Forest model integrating margin- and calcification-related features achieved an accuracy of 84% with a macro-averaged AUC of 0.83. These findings highlight the potential of quantitative ultrasound to enhance subtype classification and support computer-aided diagnostic applications.