Tan Rui, Sui Chunxiao, Wang Chao, Zhu Tao
Department of Neurosurgery, Tianjin Medical University General Hospital, Tianjin, China.
Department of Molecular Imaging and Nuclear Medicine, 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.
Front Oncol. 2024 May 13;14:1401977. doi: 10.3389/fonc.2024.1401977. eCollection 2024.
Accurate preoperative prediction of glioma is crucial for developing individualized treatment decisions and assessing prognosis. In this study, we aimed to establish and evaluate the value of integrated models by incorporating the intratumoral and peritumoral features from conventional MRI and clinical characteristics in the prediction of glioma grade.
A total of 213 glioma patients from two centers were included in the retrospective analysis, among which, 132 patients were classified as the training cohort and internal validation set, and the remaining 81 patients were zoned as the independent external testing cohort. A total of 7728 features were extracted from MRI sequences and various volumes of interest (VOIs). After feature selection, 30 radiomic models depended on five sets of machine learning classifiers, different MRI sequences, and four different combinations of predictive feature sources, including features from the intratumoral region only, features from the peritumoral edema region only, features from the fusion area including intratumoral and peritumoral edema region (VOI-fusion), and features from the intratumoral region with the addition of features from peritumoral edema region (feature-fusion), were established to select the optimal model. A nomogram based on the clinical parameter and optimal radiomic model was constructed for predicting glioma grade in clinical practice.
The intratumoral radiomic models based on contrast-enhanced T1-weighted and T2-flair sequences outperformed those based on a single MRI sequence. Moreover, the internal validation and independent external test underscored that the XGBoost machine learning classifier, incorporating features extracted from VOI-fusion, showed superior predictive efficiency in differentiating between low-grade gliomas (LGG) and high-grade gliomas (HGG), with an AUC of 0.805 in the external test. The radiomic models of VOI-fusion yielded higher prediction efficiency than those of feature-fusion. Additionally, the developed nomogram presented an optimal predictive efficacy with an AUC of 0.825 in the testing cohort.
This study systematically investigated the effect of intratumoral and peritumoral radiomics to predict glioma grading with conventional MRI. The optimal model was the XGBoost classifier coupled radiomic model based on VOI-fusion. The radiomic models that depended on VOI-fusion outperformed those that depended on feature-fusion, suggesting that peritumoral features should be rationally utilized in radiomic studies.
准确的术前胶质瘤预测对于制定个体化治疗决策和评估预后至关重要。在本研究中，我们旨在通过整合常规MRI的瘤内和瘤周特征以及临床特征来建立和评估综合模型在预测胶质瘤分级中的价值。
对来自两个中心的213例胶质瘤患者进行回顾性分析，其中132例患者被分类为训练队列和内部验证集，其余81例患者被划分为独立的外部测试队列。从MRI序列和各种感兴趣体积（VOI）中提取了总共7728个特征。经过特征选择，基于五组机器学习分类器、不同的MRI序列以及四种不同的预测特征源组合（包括仅来自瘤内区域的特征、仅来自瘤周水肿区域的特征、来自包括瘤内和瘤周水肿区域的融合区域（VOI融合）的特征以及来自瘤内区域并添加瘤周水肿区域特征的特征（特征融合））建立了30个放射组学模型，以选择最佳模型。构建了基于临床参数和最佳放射组学模型的列线图，用于临床实践中预测胶质瘤分级。
基于对比增强T1加权和T2液体衰减反转恢复序列的瘤内放射组学模型优于基于单一MRI序列的模型。此外，内部验证和独立外部测试强调，结合从VOI融合中提取的特征的XGBoost机器学习分类器在区分低级别胶质瘤（LGG）和高级别胶质瘤（HGG）方面表现出卓越的预测效率，在外部测试中的AUC为0.805。VOI融合的放射组学模型比特征融合的模型具有更高的预测效率。此外，所开发的列线图在测试队列中表现出最佳预测效能，AUC为0.825。
本研究系统地研究了瘤内和瘤周放射组学对利用常规MRI预测胶质瘤分级的影响。最佳模型是基于VOI融合的XGBoost分类器联合放射组学模型。依赖VOI融合的放射组学模型优于依赖特征融合的模型，表明在放射组学研究中应合理利用瘤周特征。
