Ouyang Zhi-Qiang, He Shao-Nan, Zeng Yi-Zhen, Zhu Yun, Ling Bing-Bing, Sun Xue-Jin, Gu He-Yi, He Bo, Han Dan, Lu Yi
Department of Medical Imaging, Laboratory of Brain Function, First Affiliated Hospital of Kunming Medical University, Kunming, China.
Department of Radiology, Third Affiliated Hospital of Kunming Medical University, Kunming, China.
Quant Imaging Med Surg. 2023 Feb 1;13(2):1100-1114. doi: 10.21037/qims-22-689. Epub 2023 Jan 10.
The aim of this study was to develop and validate a radiomics nomogram for preoperative prediction of Ki-67 proliferative index (Ki-67 PI) expression in patients with meningioma.
A total of 280 patients from 2 independent hospital centers were enrolled. Patients from center I were randomly divided into a training cohort of 168 patients and a test cohort of 72 patients, and 40 patients from center II served as an external validation cohort. Interoperator reproducibility test, Z-score standardization, analysis of variance (ANOVA), and least absolute shrinkage and selection operator (LASSO) binary logistic regression were used to select radiomics features, which were extracted from contrast-enhanced T1-weighted imaging (CE-T1WI) imaging. The radiomics signature for predicting Ki-67 PI expression was developed and validated using 4 classifiers including logistic regression (LR), decision tree (DT), support vector machine (SVM), and adaptive boost (AdaBoost). Finally, combined radiological characteristics with radiomics signature were used to establish the nomogram to predict the risk of high Ki-67 PI expression in patients with meningioma.
Fourteen radiomics features were used to construct the radiomics signature. The radiomics nomogram that incorporated the radiomics signature and radiological characteristics showed excellent discrimination in the training, test, and validation cohorts with areas under the curve of 0.817 (95% CI: 0.753-0.881), 0.822 (95% CI: 0.727-0.916), and 0.845 (95% CI: 0.708-0.982), respectively. In addition, the calibration curve for the nomogram demonstrated good agreement between prediction and actual observation.
The proposed contrast enhanced magnetic resonance imaging (MRI)-based radiomics nomogram could be an effective tool to predict the risk of Ki-67 high expression in patients with meningioma.
本研究的目的是开发并验证一种用于术前预测脑膜瘤患者Ki-67增殖指数（Ki-67 PI）表达的影像组学列线图。
纳入来自2个独立医院中心的280例患者。中心I的患者被随机分为168例患者的训练队列和72例患者的测试队列，中心II的40例患者作为外部验证队列。采用不同操作者间再现性测试、Z分数标准化、方差分析（ANOVA）和最小绝对收缩和选择算子（LASSO）二元逻辑回归来选择影像组学特征，这些特征从对比增强T1加权成像（CE-T1WI）中提取。使用包括逻辑回归（LR）、决策树（DT）、支持向量机（SVM）和自适应增强（AdaBoost）在内的4种分类器开发并验证用于预测Ki-67 PI表达的影像组学特征。最后，将放射学特征与影像组学特征相结合，建立列线图以预测脑膜瘤患者高Ki-67 PI表达的风险。
14个影像组学特征用于构建影像组学特征。纳入影像组学特征和放射学特征的影像组学列线图在训练、测试和验证队列中显示出优异的区分度，曲线下面积分别为0.817（95%CI：0.753-0.881）、0.822（95%CI：0.727-0.916）和0.845（95%CI：0.708-0.982）。此外，列线图的校准曲线显示预测值与实际观察值之间具有良好的一致性。
所提出的基于对比增强磁共振成像（MRI）的影像组学列线图可能是预测脑膜瘤患者Ki-67高表达风险的有效工具。
