Predicting lymph node metastasis in thyroid cancer: systematic review and meta-analysis on the CT/MRI-based radiomics and deep learning models

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.