Computed Tomography Radiomic Nomogram for Preoperative Prediction of Extrathyroidal Extension in Papillary Thyroid Carcinoma - Chen, Bin; Zhong, Lianzhen; Dong, Di; Zheng, Jianjun; Fang, Mengjie; Yu, Chunyao; Dai, Qi; Zhang, Liwen; Tian, Jie; Lu, Wei; Jin, Yinhua (Frontiers in Oncology, 2019)
Objectives Determining the presence of extrathyroidal extension (ETE) is important for patients with papillary thyroid carcinoma (PTC) in selecting the proper surgical approaches. This study aimed to explore a radiomic model for preoperatively prediction of ETE in patients with PTC. Methods The study included 624 PTC patients (without ETE, n = 448; with minimal ETE, n = 52; with gross ETE, n = 124) whom were divided randomly into training (n = 437) and validation (n = 187) cohorts; all data were gathered between January 2016 and November 2017. Radiomic features were extracted from computed tomography (CT) images of PTCs. Key radiomic features were identified and incorporated into a radiomic signature. Combining the radiomic signature with clinical risk factors, a radiomic nomogram was constructed using multivariable logistic regression. Delong test was used to compare different receiver operating characteristic curves. Results Five key radiomic features were incorporated into the radiomic signature, which was significantly associated with ETE (p < 0.001 for both cohorts) and slightly better than clinical model integrating significant clinical risk factors in the training cohort (AUC, 0.791 vs. 0.778; F1, 0.729 vs. 0.714) and validation cohort (AUC, 0.772 vs. 0.756; F1, 0.710 vs. 0.692). The radiomic nomogram significantly improved predictive value in the training cohort (AUC, 0.837, p < 0.001; F1, 0.766) and validation cohort (AUC, 0.812, p = 0.024; F1, 0.732). Conclusions The radiomic nomogram significantly improved the preoperative prediction of ETE in PTC patients. It indicated that radiomics could be a valuable method in PTC research.