Predicting Lymph Node Metastasis in Thyroid Cancer: Systematic Review and Meta-Analysis on the Ct/Mri-Based Radiomics and Deep Learning Models Publisher Pubmed



Valizadeh P¹ ; Jannatdoust P¹ ; Ghadimi DJ² ; Bagherieh S³ ; Hassankhani A^{4, 5} ; Amoukhteh M^{4, 5} ; Adli P⁶ ; Gholamrezanezhad A⁴
Source: Clinical Imaging Published:2025

Abstract

Background: Thyroid cancer, a common endocrine malignancy, has seen increasing incidence, making lymph node metastasis (LNM) a critical factor for recurrence and survival. Radiomics and deep learning (DL) advancements offer the potential for improved LNM prediction using CT and MRI, though challenges in diagnostic accuracy remain. Methods: A systematic review and meta-analysis were conducted per established guidelines, with searches across PubMed, Scopus, Web of Science, and Embase up to February 15, 2024. Studies developing CT/MRI-based radiomics and/or DL models for preoperative LNM assessment in thyroid cancer patients were included. Data were extracted and analyzed using R software. Results: Sixteen studies were analyzed. In internal validation sets, sensitivity was 81.1 % (95 % CI: 75.6 %–85.6 %) and specificity 76.4 % (95 % CI: 68.4 %–82.9 %). Training sets showed a sensitivity of 84.4 % (95 % CI: 81.5 %–87 %) and a specificity of 84.7 % (95 % CI: 74.4 %–91.4 %). The pooled AUC was 86 % for internal validation and 87 % for training. Handcrafted radiomics had a sensitivity of 79.4 % and specificity of 69.2 %, while DL models showed 80.8 % sensitivity and 78.7 % specificity. Subgroup analysis revealed that models for papillary thyroid cancer (PTC) had a pooled specificity of 76.3 %, while those including other or unspecified cancers showed 68.3 % specificity. Despite heterogeneity, significant differences (p = 0.037) were noted between models with and without clinical data. Conclusion: Radiomics and DL models show promising potential for detecting LNM in thyroid cancer, particularly in PTC. However, study heterogeneity underscores the need for further research to optimize these imaging tools. © 2024