Zhang Rongli, Ai Qi Yong H, Wong Lun M, Green Christopher, Qamar Sahrish, So Tiffany Y, Vlantis Alexander C, King Ann D
Department of Imaging and Interventional Radiology, Faculty of Medicine, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong SAR, China.
Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China.
Cancers (Basel). 2022 Nov 25;14(23):5804. doi: 10.3390/cancers14235804.
The lack of a consistent MRI radiomic signature, partly due to the multitude of initial feature analyses, limits the widespread clinical application of radiomics for the discrimination of salivary gland tumors (SGTs). This study aimed to identify the optimal radiomics feature category and MRI sequence for characterizing SGTs, which could serve as a step towards obtaining a consensus on a radiomics signature. Preliminary radiomics models were built to discriminate malignant SGTs (n = 34) from benign SGTs (n = 57) on T1-weighted (T1WI), fat-suppressed (FS)-T2WI and contrast-enhanced (CE)-T1WI images using six feature categories. The discrimination performances of these preliminary models were evaluated using 5-fold-cross-validation with 100 repetitions and the area under the receiver operating characteristic curve (AUC). The differences between models’ performances were identified using one-way ANOVA. Results show that the best feature categories were logarithm for T1WI and CE-T1WI and exponential for FS-T2WI, with AUCs of 0.828, 0.754 and 0.819, respectively. These AUCs were higher than the AUCs obtained using all feature categories combined, which were 0.750, 0.707 and 0.774, respectively (p < 0.001). The highest AUC (0.846) was obtained using a combination of T1WI + logarithm and FS-T2WI + exponential features, which reduced the initial features by 94.0% (from 1015 × 3 to 91 × 2). CE-T1WI did not improve performance. Using one feature category rather than all feature categories combined reduced the number of initial features without compromising radiomic performance.
缺乏一致的MRI影像组学特征，部分原因是初始特征分析众多，这限制了影像组学在鉴别涎腺肿瘤（SGT）方面的广泛临床应用。本研究旨在确定用于表征SGT的最佳影像组学特征类别和MRI序列，这可作为朝着就影像组学特征达成共识迈出的一步。构建了初步的影像组学模型，以使用六种特征类别在T1加权（T1WI）、脂肪抑制（FS）-T2WI和对比增强（CE）-T1WI图像上区分恶性SGT（n = 34）和良性SGT（n = 57）。使用5折交叉验证（重复100次）和受试者操作特征曲线下面积（AUC）评估这些初步模型的鉴别性能。使用单因素方差分析确定模型性能之间的差异。结果表明，最佳特征类别是T1WI和CE-T1WI的对数以及FS-T2WI的指数，AUC分别为0.828、0.754和0.819。这些AUC高于使用所有特征类别组合获得的AUC，分别为0.750、0.707和0.774（p < 0.001）。使用T1WI + 对数和FS-T2WI + 指数特征的组合获得了最高AUC（0.846），这将初始特征减少了94.0%（从1015×3减少到91×2）。CE-T1WI并未提高性能。使用一种特征类别而非所有特征类别组合减少了初始特征数量，同时不影响影像组学性能。
