Huang Zhou-San, Xiao Xiang, Li Xiao-Dan, Mo Hai-Zhu, He Wen-Le, Deng Yao-Hong, Lu Li-Jun, Wu Yuan-Kui, Liu Hao
Department of Medical Imaging, Nanfang Hospital, Southern Medical University, Guangzhou, China.
Department of Medical Imaging, Guangdong 999 Brain Hospital, Guangzhou, China.
J Magn Reson Imaging. 2021 Nov;54(5):1541-1550. doi: 10.1002/jmri.27761. Epub 2021 Jun 4.
Preoperative, noninvasive discrimination of the craniopharyngioma subtypes is important because it influences the treatment strategy.
To develop a radiomic model based on multiparametric magnetic resonance imaging for noninvasive discrimination of pathological subtypes of craniopharyngioma.
Retrospective.
A total of 164 patients from two medical centers were enrolled in this study. Patients from the first medical center were divided into a training cohort (N = 99) and an internal validation cohort (N = 33). Patients from the second medical center were used as the external independent validation cohort (N = 32).
FIELD STRENGTH/SEQUENCE: Axial T -weighted (T -w), T -weighted (T -w), contrast-enhanced T -weighted (CET -w) on 3.0 T or 1.5 T magnetic resonance scanners.
Pathological subtypes (squamous papillary craniopharyngioma and adamantinomatous craniopharyngioma) were confirmed by surgery and hematoxylin and eosin staining. Optimal radiomic feature selection was performed by SelectKBest, the least absolute shrinkage and selection operator algorithm, and support vector machine (SVM) with a recursive feature elimination algorithm. Models based on each sequence or combinations of sequences were built using a SVM classifier and used to differentiate pathological subtypes of craniopharyngioma in the training cohort, internal validation, and external validation cohorts.
The area under the receiver operating characteristic curve (AUC) was used to assess the diagnostic performance of the radiomic models.
Seven texture features, three from T -w, two from T -w, and two from CET -w, were selected and used to construct the radiomic model. The AUC values of the radiomic model were 0.899, 0.810, and 0.920 in the training cohort, internal and external validation cohorts, respectively. The AUC values of the clinicoradiological model were 0.677, 0.655, and 0.671 in the training cohort, internal and external validation cohorts, respectively.
The model based on radiomic features from T -w, T -w, and CET -w has a high discriminatory ability for pathological subtypes of craniopharyngioma.
4 TECHNICAL EFFICACY: 2.
术前对颅咽管瘤亚型进行无创鉴别很重要，因为这会影响治疗策略。
基于多参数磁共振成像开发一种放射组学模型，用于颅咽管瘤病理亚型的无创鉴别。
回顾性研究。
本研究共纳入来自两个医疗中心的164例患者。第一个医疗中心的患者被分为训练队列（N = 99）和内部验证队列（N = 33）。第二个医疗中心的患者用作外部独立验证队列（N = 32）。
场强/序列：在3.0 T或1.5 T磁共振扫描仪上进行轴位T加权（T - w）、T加权（T - w）、对比增强T加权（CET - w）扫描。
通过手术及苏木精-伊红染色确定病理亚型（鳞状乳头型颅咽管瘤和造釉型颅咽管瘤）。采用SelectKBest、最小绝对收缩和选择算子算法以及带有递归特征消除算法的支持向量机（SVM）进行最佳放射组学特征选择。使用SVM分类器构建基于每个序列或序列组合的模型，并用于在训练队列、内部验证队列和外部验证队列中鉴别颅咽管瘤的病理亚型。
采用受试者操作特征曲线（ROC）下面积（AUC）评估放射组学模型的诊断性能。
选择了7个纹理特征，其中3个来自T - w，2个来自T - w，2个来自CET - w，并用于构建放射组学模型。放射组学模型在训练队列、内部和外部验证队列中的AUC值分别为0.899、0.810和0.920。临床放射学模型在训练队列、内部和外部验证队列中的AUC值分别为0.677、0.655和0.671。
基于T - w、T - w和CET - w放射组学特征的模型对颅咽管瘤病理亚型具有较高的鉴别能力。
4 技术效能：
