Related Experiment Video
Updated: Jun 4, 2025
Computer-Aided Three-Dimensional Visualization in the Treatment of Locally Advanced Thyroid Cancer
Published on: June 9, 2023
Predicting lymph node metastasis in thyroid cancer: systematic review and meta-analysis on the CT/MRI-based radiomics
Parya Valizadeh1, Payam Jannatdoust1, Delaram J Ghadimi2
1School of Medicine, Tehran University of Medical Sciences, Tehran, Iran.
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.
