Radiogenomic Analysis of Papillary Thyroid Carcinoma for Prediction of Cervical Lymph Node Metastasis: A Preliminary Study

Frontiers in Oncology

Yuanyuan Wang¹  Yi Guo¹  Jinhua Yu¹  Qinghai Ji²  Yulong Wang²  Yu Wang²  Yunxia Huang³  Shichong Zhou³  Cai Chang³  Juanjuan Yong⁴  Yuyang Tong⁵  Peixuan Sun⁷  Hongbo Zhang⁹ 

[1] Department of Electronic Engineering, Fudan University and Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention of Shanghai, Shanghai, China;Department of Head and Neck Surgery, Fudan University Shanghai Cancer Center, Shanghai, China;Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China;Department of Pathology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China;Department of Surgical Oncology, The Ohio State University, Columbus, OH, United States;Department of Ultrasound, Fudan University Shanghai Cancer Center, Shanghai, China;Diagnostic Imaging Center, Shanghai Children’s Medical Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China;Pharmaceutical Sciences Laboratory, Åbo Akademi University, Turku, Finland;Turku Biosciences Center, University of Turku and Åbo Akademi University, Turku, Finland;

关键词: radiogenomic;    papillary thyroid carcinoma;    cervical lymph node metastasis;    ultrasound;    radiomics;   

DOI  :  10.3389/fonc.2021.682998

来源: DOAJ

【 摘 要 】

BackgroundPapillary 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.MethodsIn 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.ResultsThe radiomic signature showed good performance for predicting CLN status in training and validation cohorts, with area under curve of 0.873 and 0.831 respectively. A radiogenomic map was created with nine significant correlations between radiomic features and gene modules, and two of them had higher correlation coefficient. Among these, MEmeganta representing the upregulation of telomere maintenance via telomerase and cell-cell adhesion was correlated with ‘Rectlike’ and ‘deviation ratio of tumor tissue and normal thyroid gland’ which reflect the margin and the internal echogenicity of the tumor, respectively. MEblue capturing cell-cell adhesion and glycolysis was associated with feature ‘minimum calcification area’ which measures the punctate calcification. The hub genes of the two modules were identified by protein-protein interaction network. Immunohistochemistry validated that LAMC1 and THBS1 were differently expressed in metastatic and non-metastatic tissues (p=0.003; p=0.002). And LAMC1 was associated with feature ‘Rectlike’ and ‘deviation ratio of tumor and normal thyroid gland’ (p<0.001; p<0.001); THBS1 was correlated with ‘minimum calcification area’ (p<0.001).ConclusionsThe 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.

【 授权许可】

Unknown