Gu Dongsheng, Xie Yongsheng, Wei Jingwei, Li Wencui, Ye Zhaoxiang, Zhu Zhongyuan, Tian Jie, Li Xubin
Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research center for Cancer, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China.
J Magn Reson Imaging. 2020 Dec;52(6):1679-1687. doi: 10.1002/jmri.27199. Epub 2020 Jun 3.
Glypican 3 (GPC3) expression has proved to be a critical risk factor related to prognosis in hepatocellular carcinoma (HCC) patients.
To investigate the performance of MRI-based radiomics signature in identifying GPC3-positive HCC.
Retrospective.
An initial cohort of 293 patients with pathologically confirmed HCC was involved in this study, and patients were randomly divided into training (195) and validation (98) cohorts.
FIELD STRENGTH/SEQUENCES: Contrast-enhanced T -weight MRI was performed with a 1.5T scanner.
A total of 853 radiomic features were extracted from the volume imaging. Univariate analysis and Fisher scoring were utilized for feature reduction. Subsequently, forward stepwise feature selection and radiomics signature building were performed based on a support vector machine (SVM). Incorporating independent risk factors, a combined nomogram was developed by multivariable logistic regression modeling.
The predictive performance of the nomogram was calculated using the area under the receive operating characteristic curve (AUC). Decision curve analysis (DCA) was applied to estimate the clinical usefulness.
The radiomics signature consisting of 10 selected features achieved good prediction efficacy (training cohort: AUC = 0.879, validation cohort: AUC = 0.871). Additionally, the combined nomogram integrating independent clinical risk factor α-fetoprotein (AFP) and radiomics signature showed improved calibration and prominent predictive performance with AUCs of 0.926 and 0.914 in the training and validation cohorts, respectively.
The proposed MR-based radiomics signature is strongly related to GPC3-positive. The combined nomogram incorporating AFP and radiomics signature may provide an effective tool for noninvasive and individualized prediction of GPC3-positive in patients with HCC. J. MAGN. RESON. IMAGING 2020;52:1679-1687.
已证明磷脂酰肌醇蛋白聚糖3（GPC3）表达是肝细胞癌（HCC）患者预后的关键危险因素。
探讨基于MRI的放射组学特征在识别GPC3阳性HCC中的表现。
回顾性研究。
本研究纳入了最初的293例经病理证实的HCC患者，并将患者随机分为训练组（195例）和验证组（98例）。
场强/序列：使用1.5T扫描仪进行对比增强T加权MRI检查。
从容积成像中提取了总共853个放射组学特征。采用单因素分析和Fisher评分进行特征降维。随后，基于支持向量机（SVM）进行向前逐步特征选择和放射组学特征构建。纳入独立危险因素，通过多变量逻辑回归建模开发了联合列线图。
使用受试者操作特征曲线下面积（AUC）计算列线图的预测性能。应用决策曲线分析（DCA）评估临床实用性。
由10个选定特征组成的放射组学特征具有良好的预测效能（训练组：AUC = 0.879，验证组：AUC = 0.871）。此外，整合独立临床危险因素甲胎蛋白（AFP）和放射组学特征的联合列线图在训练组和验证组中的校准性得到改善，预测性能突出，AUC分别为0.926和0.914。
所提出的基于MR的放射组学特征与GPC3阳性密切相关。结合AFP和放射组学特征的联合列线图可为HCC患者GPC3阳性的无创和个体化预测提供有效工具。《磁共振成像杂志》2020年；52:1679 - 1687。
