Immune response and mesenchymal transition of papillary thyroid carcinoma reflected in ultrasonography features assessed by radiologists and deep learning.

INTRODUCTION: Ultrasonography (US) features of papillary thyroid cancers (PTCs) are used to select nodules for biopsy due to their association with tumor behavior. However, the molecular biological mechanisms that lead to the characteristic US features of PTCs are largely unknown. OBJECTIVES: This study aimed to investigate the molecular biological mechanisms behind US features assessed by radiologists and three convolutional neural networks (CNN) through transcriptome analysis. METHODS: Transcriptome data from 273 PTC tissue samples were generated and differentially expressed genes (DEGs) were identified according to US feature. Pathway enrichment analyses were also conducted by gene set enrichment analysis (GSEA) and ClusterProfiler according to assessments made by radiologists and three CNNs - CNN1 (ResNet50), CNN2 (ResNet101) and CNN3 (VGG16). Signature gene scores for PTCs were calculated by single-sample GSEA (ssGSEA). RESULTS: Individual suspicious US features consistently suggested an upregulation of genes related to immune response and epithelial-mesenchymal transition (EMT). Likewise, PTCs assessed as positive by radiologists and three CNNs showed the coordinate enrichment of similar gene sets with abundant immune and stromal components. However, PTCs assessed as positive by radiologists had the highest number of DEGs, and those assessed as positive by CNN3 had more diverse DEGs and gene sets compared to CNN1 or CNN2. The percentage of PTCs assessed as positive or negative concordantly by radiologists and three CNNs was 85.6% (231/273) and 7.1% (3/273), respectively. CONCLUSION: US features assessed by radiologists and CNNs revealed molecular biologic features and tumor microenvironment in PTCs.