Radiomics based on multiparametric MRI for extrathyroidal extension feature prediction in papillary thyroid cancer.

Affiliation

Wei R(#)(1), Wang H(#)(1), Wang L(1), Hu W(1), Sun X(1), Dai Z(1), Zhu J(1), Li H(1), Ge Y(2), Song B(3).
Author information:
(1)Department of Radiology, Minhang Hospital, Fudan University, 170 Xinsong Road, Shanghai, 201199, People's Republic of China.
(2)GE Healthcare, Shanghai, People's Republic of China.
(3)Department of Radiology, Minhang Hospital, Fudan University, 170 Xinsong Road, Shanghai, 201199, People's Republic of China. [Email]
(#)Contributed equally

Abstract

BACKGROUND: To determine the predictive capability of MRI-based radiomics for extrathyroidal extension detection in papillary thyroid cancer (PTC) pre-surgically. METHODS: The present retrospective trial assessed individuals with thyroid nodules examined by multiparametric MRI and subsequently administered thyroid surgery. Diagnosis and extrathyroidal extension (ETE) feature of PTC were based on pathological assessment. The thyroid tumors underwent manual segmentation, for radiomic feature extraction. Participants were randomized to the training and testing cohorts, at a ratio of 7:3. The mRMR (maximum correlation minimum redundancy) algorithm and the least absolute shrinkage and selection operator were utilized for radiomics feature selection. Then, a radiomics predictive model was generated via a linear combination of the features. The model's performance in distinguishing the ETE feature of PTC was assessed by analyzing the receiver operating characteristic curve. RESULTS: Totally 132 patients were assessed in this study, including 92 and 40 in the training and test cohorts, respectively). Next, the 16 top-performing features, including 4, 7 and 5 from diffusion weighted (DWI), T2-weighted (T2 WI), and contrast-enhanced T1-weighted (CE-T1WI) images, respectively, were finally retained to construct the radiomics signature. There were 8 RLM, 5 CM, 2 shape, and 1 SZM features. The radiomics prediction model achieved AUCs of 0.96 and 0.87 in the training and testing sets, respectively. CONCLUSIONS: Our study indicated that MRI radiomics approach had the potential to stratify patients based on ETE in PTCs preoperatively.