Computer-aided diagnosis of papillary thyroid carcinoma based on deep learning technology.

This article examines global thyroid cancer（TC） incidence trends, with emphasis on epidemiological patterns in China, where papillary thyroid carcinoma (PTC) demonstrates disproportionately high prevalence. As the predominant histological subtype linked to adverse prognostic indicators, PTC necessitates early detection. The study systematically evaluates deep learning (DL) applications in PTC diagnostics, particularly for identifying pathological subtypes through nuclear and architectural features on histopathology. DL models exhibit enhanced accuracy in analyzing radiological modalities such as ultrasound, computed tomography (CT), and magnetic resonance imaging (MRI) and hematoxylin and eosin (H&E)-stained histopathological sections by detecting microcalcifications, irregular margins, and nuclear grooves. Current advancements encompass radio genomic correlation analysis and molecular feature prediction. However, limitations persist, including dataset heterogeneity, model interpretability constraints, and imaging standardization challenges. Future directions propose multicenter data integration, hybrid model development combining imaging with molecular biomarkers, and explainable artificial intelligence (AI) frameworks to optimize clinical decision-making. Looking ahead, this technological evolution is poised to fundamentally transform PTC diagnostics by enabling more precise preoperative risk stratification and personalized therapeutic strategies, ultimately improving patient outcomes and advancing precision oncology.