Multiparametric MRI Radiomic Model for Preoperative Predicting WHO/ISUP Nuclear Grade of Clear Cell Renal Cell Carcinoma
Multiparametric MRI Radiomic Model for Preoperative Predicting WHO/ISUP Nuclear Grade of Clear Cell Renal Cell Carcinoma
复制标题
用于术前预测透明细胞肾细胞癌 WHO/ISUP 核分级的多参数 MRI 放射组学模型
DOI：
10.1002/jmri.27182
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发表时间：
2020-05-28
影响因子：
4.4
通讯作者：
Wang, Hai-yi
中科院分区：
文献类型：
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作者：
Li, Qiong;Liu, Yu-jia;Wang, Hai-yi
背景 核分级对于透明细胞肾细胞癌 (ccRCC) 患者的治疗选择和预后非常重要。目的 开发和验证基于 MRI 的放射组学模型，用于术前预测 ccRCC 的 WHO/ISUP 核分级。研究类型回顾性。人群 共有 379 名经组织学证实的 ccRCC 患者。随机分配训练队列 (n = 252) 和验证队列 (n = 127)。场强/序列预处理 3.0T 肾脏 MRI。成像序列为脂肪抑制T2WI、对比增强T1WI和弥散加权成像。评估使用选定的放射组学特征、放射组学和临床放射学特征以及仅包含临床放射学特征的模型开发了三种预测模型。受试者工作特征（ROC）曲线和曲线下面积（AUC）用于评估这些模型在预测高级别ccRCC方面的预测性能。统计测试最小绝对收缩和选择算子（LASSO）和最小冗余最大相关性（mRMR）方法分别用于放射组学特征和临床放射学特征的选择。采用多变量逻辑回归分析来开发放射组学特征的放射组学特征和临床放射学特征的临床放射学模型。 结果 在验证队列中，放射组学特征在区分高级别（3 级和 4 级）与低级别（1 级和 2 级）ccRCC 方面表现出良好的性能，敏感性、特异性和 AUC 分别为 77.3%、80.0% 和 0.842。放射组学模型结合了放射组学特征和临床放射学特征，在验证队列中显示出良好的高级别预测能力，敏感性、特异性和准确性分别为 63.6%、93.3% 和 88.2%。放射组学模型的表现明显优于临床放射学模型（P < 0.05）。数据结论 基于多参数 MRI 的放射组学模型可以预测 ccRCC 患者的 WHO/ISUP 分级，且表现令人满意，从而可以帮助医生改进治疗决策。证据级别 3 技术疗效阶段 2
Background Nuclear grade is of importance for treatment selection and prognosis in patients with clear cell renal cell carcinoma (ccRCC).Purpose To develop and validate an MRI-based radiomic model for preoperative predicting WHO/ISUP nuclear grade in ccRCC.Study Type Retrospective.Population In all, 379 patients with histologically confirmed ccRCC. Training cohort (n = 252) and validation cohort (n = 127) were randomly assigned.Field Strength/Sequence Pretreatment 3.0T renal MRI. Imaging sequences were fat-suppressed T2WI, contrast-enhanced T1WI, and diffusion weighted imaging.Assessment Three prediction models were developed using selected radiomic features, radiomic and clinicoradiologic characteristics, and a model containing only clinicoradiologic characteristics. Receiver operating characteristic (ROC) curves and area under the curve (AUC) were used to assess the predictive performance of these models in predicting high-grade ccRCC.Statistical Tests The least absolute shrinkage and selection operator (LASSO) and minimum redundancy maximum relevance (mRMR) method were used for the selection of radiomic features and clinicoradiologic characteristics, respectively. Multivariable logistic regression analysis was used to develop the radiomic signature of radiomic features and clinicoradiologic model of clinicoradiologic characteristics.Results The radiomic signature showed good performance in discriminating high-grade (grades 3 and 4) from low-grade (grades 1 and 2) ccRCC, with sensitivity, specificity, and AUC of 77.3%, 80.0%, and 0.842, respectively, in the validation cohort. The radiomic model, combining radiomic signature and clinicoradiologic characteristics, displayed good predictive ability for high-grade with sensitivity, specificity, and accuracy of 63.6%, 93.3%, and 88.2%, respectively, in the validation cohort. The radiomic model showed a significantly better performance than the clinicoradiologic model (P < 0.05).Data Conclusion Multiparametric MRI-based radiomic model can predict WHO/ISUP grade in patients with ccRCC with satisfying performance, and thus could help the physician to improve treatment decisions.Level of Evidence 3Technical Efficacy Stage 2