Distinction of pseudoprogression from true progression in glioblastomas using machine learning based on multiparametric magnetic resonance imaging and O⁶-methylguanine-methyltransferase promoter methylation status.

IF 3.7 Q1 CLINICAL NEUROLOGY Neuro-oncology advances Pub Date : 2024-10-03 eCollection Date: 2024-01-01 DOI:10.1093/noajnl/vdae159

Virendra Kumar Yadav, Suyash Mohan, Sumeet Agarwal, Laiz Laura de Godoy, Archith Rajan, MacLean P Nasrallah, Stephen J Bagley, Steven Brem, Laurie A Loevner, Harish Poptani, Anup Singh, Sanjeev Chawla

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Abstract

Background: It is imperative to differentiate true progression (TP) from pseudoprogression (PsP) in glioblastomas (GBMs). We sought to investigate the potential of physiologically sensitive quantitative parameters derived from diffusion and perfusion magnetic resonance imaging (MRI), and molecular signature combined with machine learning in distinguishing TP from PsP in GBMs in the present study.

Methods: GBM patients (n = 93) exhibiting contrast-enhancing lesions within 6 months after completion of standard treatment underwent 3T MRI. Final data analyses were performed on 75 patients as O⁶-methylguanine-DNA-methyltransferase (MGMT) status was available only from these patients. Subsequently, patients were classified as TP (n = 55) or PsP (n = 20) based on histological features or mRANO criteria. Quantitative parameters were computed from contrast-enhancing regions of neoplasms. PsP datasets were artificially augmented to achieve balanced class distribution in 2 groups (TP and PsP). A random forest algorithm was applied to select the optimized features. The data were randomly split into training and testing subsets in an 8:2 ratio. To develop a robust prediction model in distinguishing TP from PsP, several machine-learning classifiers were employed. The cross-validation and receiver operating characteristic (ROC) curve analyses were performed to determine the diagnostic performance.

Results: The quadratic support vector machine was found to be the best classifier in distinguishing TP from PsP with a training accuracy of 91%, cross-validation accuracy of 86%, and testing accuracy of 85%. Additionally, ROC analysis revealed an accuracy of 85%, sensitivity of 70%, and specificity of 100%.

Conclusions: Machine learning using quantitative multiparametric MRI may be a promising approach to distinguishing TP from PsP in GBMs.

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基于多参数磁共振成像和O6-甲基鸟嘌呤甲基转移酶启动子甲基化状态的机器学习区分胶质母细胞瘤的假性进展和真性进展

背景：区分胶质母细胞瘤（GBMs）的真正进展（TP）和假性进展（PsP）至关重要。在本研究中，我们试图研究从弥散和灌注磁共振成像（MRI）中得出的生理敏感定量参数以及分子特征与机器学习相结合在区分GBMs真性进展（TP）和假性进展（PsP）方面的潜力：完成标准治疗后 6 个月内出现对比增强病灶的 GBM 患者（n = 93）接受了 3T MRI 检查。由于只有这些患者的 O6-甲基鸟嘌呤-DNA-甲基转移酶（MGMT）状态可用，因此对 75 名患者进行了最终数据分析。随后，根据组织学特征或 mRANO 标准将患者分为 TP（55 人）或 PsP（20 人）。定量参数根据肿瘤的对比增强区域计算得出。PsP数据集被人为地增加，以实现两组（TP和PsP）的平衡类分布。采用随机森林算法选择优化特征。数据以 8:2 的比例随机分成训练子集和测试子集。为了建立一个能够区分 TP 和 PsP 的稳健预测模型，研究人员采用了多个机器学习分类器。交叉验证和接收者操作特征曲线（ROC）分析用于确定诊断性能：结果：二次支持向量机是区分 TP 和 PsP 的最佳分类器，其训练准确率为 91%，交叉验证准确率为 86%，测试准确率为 85%。此外，ROC 分析显示准确率为 85%，灵敏度为 70%，特异性为 100%：使用定量多参数磁共振成像进行机器学习可能是区分 GBM 中 TP 和 PsP 的一种有前途的方法。

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