基于 MRI 的放射组学特征预测原发性中枢神经系统淋巴瘤 Ki-67 指数。
MRI-based radiomics signatures for preoperative prediction of Ki-67 index in primary central nervous system lymphoma.
机构信息
Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China.
Department of Nuclear Medicine, The Second Affiliated Hospital, Shandong First Medical University, Tai'an, Shandong, China.
出版信息
Eur J Radiol. 2024 Sep;178:111603. doi: 10.1016/j.ejrad.2024.111603. Epub 2024 Jul 5.
PURPOSE
The aim of this study was to develop and validate radiomics signatures based on MRI for preoperative prediction of Ki-67 proliferative index (PI) expression in primary central nervous system lymphoma (PCNSL).
METHODS
A total of 341 patients with PCNSL were retrospectively analyzed, including 286 patients in one center as the training set and 55 patients in another two centers as the external validation set. Radiomics features were extracted and selected from preoperative contrast-enhanced T1-weighted images, fluid attenuation inversion recovery to build radiomics signatures according to the Ki-67 PI. The predictive performances of the radiomics model were evaluated using four classifiers including random forest, K-Nearest Neighbors, Neural Network and Decision Tree. A combined model was built by incorporating radiomics signature, clinical variables and MRI radiological characteristics using multivariate logistic regression analysis, and a nomogram was established to predict the expression of Ki-67 individually. The predictive performances of the models were evaluated using area under receiver operating characteristic curve (AUC) and decision curve analysis (DCA).
RESULTS
Radiomics signatures were independent predictors of the expression level of Ki-67 (OR: 2.523, P < 0.001). RF radiomics models had the highest accuracy (0.934 in the training set and 0.811 in the external validation set) and F1 Score (0.920 in the training set and 0.836 in the external validation set). The clinic-radiologic-radiomics nomogram showed better predictive performance with AUCs of 0.877(95 % CI: 0.837-0.918) in the training set and 0.866(95 % CI: 0.774-0.957) in the external validation set. The calibration curve and DCA demonstrated goodness-of-fit and improved benefits in clinical practice of the nomogram.
CONCLUSIONS
Nomograms integrating MRI-based radiomics and clinical-radiological characteristics could effectively predict Ki-67 PI in primary PCNSL.
目的
本研究旨在开发基于 MRI 的放射组学特征，用于预测原发性中枢神经系统淋巴瘤（PCNSL）术前 Ki-67 增殖指数（PI）的表达。
方法
回顾性分析了 341 例 PCNSL 患者，其中 286 例来自一个中心作为训练集，55 例来自另外两个中心作为外部验证集。从术前增强 T1 加权图像和液体衰减反转恢复（FLAIR）中提取放射组学特征，根据 Ki-67 PI 构建放射组学特征。采用随机森林（RF）、K-近邻（KNN）、神经网络（NN）和决策树（DT）等 4 种分类器评估放射组学模型的预测性能。采用多元逻辑回归分析将放射组学特征、临床变量和 MRI 影像学特征相结合建立联合模型，建立预测 Ki-67 表达的列线图。采用受试者工作特征曲线（ROC）下面积（AUC）和决策曲线分析（DCA）评估模型的预测性能。
结果
放射组学特征是 Ki-67 表达水平的独立预测因子（OR：2.523，P<0.001）。RF 放射组学模型在训练集和外部验证集的准确性最高（分别为 0.934 和 0.811），F1 评分最高（分别为 0.920 和 0.836）。临床-放射-放射组学列线图具有更好的预测性能，在训练集和外部验证集的 AUC 分别为 0.877（95%CI：0.837-0.918）和 0.866（95%CI：0.774-0.957）。校准曲线和 DCA 表明，该列线图具有良好的拟合度，并能提高临床实践的获益。
结论
整合基于 MRI 的放射组学和临床影像学特征的列线图可有效预测原发性 PCNSL 的 Ki-67 PI。
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