Radiogenomic Analysis of Papillary Thyroid Carcinoma for Prediction of Cervical Lymph Node Metastasis: A Preliminary Study - Tong, Yuyang; Sun, Peixuan; Yong, Juanjuan; Zhang, Hongbo; Huang, Yunxia; Guo, Yi; Yu, Jinhua; Zhou, Shichong; Wang, Yulong; Wang, Yu; Ji, Qinghai; Wang, Yuanyuan; et al. (Frontiers in Oncology, 2021)
Background: Papillary thyroid carcinoma (PTC) is characterized by frequent metastases to cervical lymph nodes (CLNs), and the presence of lymph node metastasis at diagnosis has a significant impact on the surgical approach. Therefore, we established a radiomic signature to predict the CLN status of PTC patients using preoperative thyroid ultrasound, and investigated the association between the radiomic features and underlying molecular characteristics of PTC tumors. Methods: In total, 270 patients were enrolled in this prospective study, and radiomic features were extracted according to multiple guidelines. A radiomic signature was built with selected features in the training cohort and validated in the validation cohort. The total protein extracted from tumor samples was analyzed with LC/MS and iTRAQ technology. Gene modules acquired by clustering were chosen for their diagnostic significance. A radiogenomic map linking radiomic features to gene modules was constructed with the Spearman correlation matrix. Genes in modules related to metastasis were extracted for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses, and a protein-protein interaction (PPI) network was built to identify the hub genes in the modules. Finally, the screened hub genes were validated by immunohistochemistry analysis. Results: The diagnostic performance of the radiomic signature was superior to the ultrasound-based method in predicting CLN status in the training and validation cohorts. Weighted gene co-expression network analysis (WGCNA) generated 16 gene modules, and a radiogenomic map with nine significant correlations between the radiomics features and gene modules was created. For example, module MEblue capturing cell adhesion and glycolysis was associated with the feature ‘minimum calcification area’, which indicated that genes active in this module were correlated with punctate calcification. Immunohistochemistry validated that LAMC1 and THBS1 were differently expressed in metastatic and non-metastatic tissues, and were associated with several radiomics features. Conclusions: The radiomic signature proposed here has the potential to noninvasively predict the CLN status in PTC patients. Merging imaging phenotypes with genomic data could allow noninvasive identification of the molecular properties of PTC tumors, which might support clinical decision making and personalized management.