MRI-based radiomics analysis to predict preoperative lymph node metastasis in papillary thyroid carcinoma.

Abstract

The aim of the present study was to develop a magnetic resonance imaging (MRI) radiomics model and evaluate its clinical value in predicting preoperative lymph node metastasis (LNM) in patients with papillary thyroid carcinoma (PTC). Data of 129 patients with histopathologically confirmed PTC were retrospectively reviewed in our study (90 in training group and 39 in testing group). 395 radiomics features were extracted from T2 weighted imaging (T2WI), diffusion weighted imaging (DWI) and T1 weighted multiphase contrast enhancement imaging (T1C+) respectively. Minimum redundancy maximum relevance (mRMR) was used to eliminate irrelevant and redundant features and least absolute shrinkage and selection operator (LASSO), to additionally select an optimized features' subset to construct the radiomics signature. Predictive performance was validated using receiver operating characteristic curve (ROC) analysis, while decision curve analyses (DCA) were conducted to evaluate the clinical worth of the four models according to different sequences. A radiomics nomogram was built using multivariate logistic regression model. The nomogram's performance was assessed and validated in the training and validation cohorts, respectively. Seven key features were selected from T2WI, five from DWI, ten from T1C+ and seven from the combined images. The scores (Rad-scores) of patients with LNM were significantly higher than patients with non-LNM in both the training cohort and the validation cohort. The combined model performed better than the T2WI, DWI, and T1C+ models alone in both cohorts. In the training cohort, the area under the ROC (AUC) values of T2WI, DWI, T1C+ and combined features were 0.819, 0.826, 0.808, and 0.835, respectively; corresponding values in the validation cohort were 0.798, 0.798, 0.789, and 0.830. The clinical utility of the combined model was confirmed using the radiomics nomogram and DCA. MRI radiomic model based on anatomical and functional MRI images could be used as a non-invasive biomarker to identify PTC patients at high risk of LNM, which could help to develop individualized treatment strategies in clinical practice.