Department of Radiology, Minhang Hospital, Fudan University, 170 Xinsong Road, Shanghai, 201199, People's Republic of China.
GE Healthcare, Shanghai, People's Republic of China.
BMC Med Imaging. 2021 Feb 9;21(1):20. doi: 10.1186/s12880-021-00553-z.
To determine the predictive capability of MRI-based radiomics for extrathyroidal extension detection in papillary thyroid cancer (PTC) pre-surgically.
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.
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.
Our study indicated that MRI radiomics approach had the potential to stratify patients based on ETE in PTCs preoperatively.
旨在确定基于 MRI 的放射组学在术前预测甲状腺乳头状癌（PTC）甲状腺外延伸的能力。
本回顾性试验评估了接受多参数 MRI 检查并随后接受甲状腺手术的甲状腺结节患者。PTC 的诊断和甲状腺外延伸（ETE）特征基于病理评估。对甲状腺肿瘤进行手动分割，以提取放射组学特征。参与者按 7:3 的比例随机分配到训练和测试队列中。使用最大相关最小冗余（mRMR）算法和最小绝对收缩和选择算子（LASSO）进行放射组学特征选择。然后，通过对特征进行线性组合生成放射组学预测模型。通过分析接收器操作特征曲线评估该模型在区分 PTC 的 ETE 特征方面的性能。
本研究共评估了 132 例患者，其中 92 例和 40 例分别纳入训练组和测试组。接下来，最终保留了表现最好的 16 个特征，包括来自弥散加权（DWI）、T2 加权（T2 WI）和对比增强 T1 加权（CE-T1WI）图像的 4、7 和 5 个特征，用于构建放射组学特征。有 8 个 RLM、5 个 CM、2 个形状和 1 个 SZM 特征。放射组学预测模型在训练组和测试组中的 AUC 分别为 0.96 和 0.87。
我们的研究表明，MRI 放射组学方法有可能在术前对 PTC 患者进行 ETE 分层。
