Karakas Asli Beril, Govsa Figen, Ozer Mehmet Asim, Biceroglu Huseyin, Eraslan Cenk, Tanir Deniz
Department of Anatomy, Faculty of Medicine, Kastamonu University, Kastamonu, 37200, Turkey.
Department of Anatomy, Faculty of Medicine, Ege University, Izmir, Turkey.
Neurosurg Rev. 2025 Apr 29;48(1):396. doi: 10.1007/s10143-025-03515-z.
Gliomas are the most common primary tumors of the central nervous system, and advances in genetics and molecular medicine have significantly transformed their classification and treatment. This study aims to predict the IDH1 genotype in gliomas using radiomics and machine learning (ML) methods. Retrospective data from 108 glioma patients were analyzed, including MRI data supported by demographic details such as age, sex, and comorbidities. Tumor segmentation was manually performed using 3D Slicer software, and 112 radiomic features were extracted with the PyRadiomics library. Feature selection using the mRMR algorithm identified 17 significant radiomic features. Various ML algorithms, including KNN, Ensemble, DT, LR, Discriminant and SVM, were applied to predict the IDH1 genotype. The KNN and Ensemble models achieved the highest sensitivity (92-100%) and specificity (100%), emerging as the most successful models. Comparative analyses demonstrated that KNN achieved an accuracy of 92.59%, sensitivity of 92.38%, specificity of 100%, precision of 100%, and an F1-score of 95.02%. Similarly, the Ensemble model achieved an accuracy of 90.74%, sensitivity of 90.65%, specificity of 100%, precision of 100%, and an F1-score of 95.13%. To evaluate their effectiveness, KNN and Ensemble models were compared with commonly used machine learning approaches in glioma classification. LR, a conventional statistical approach, exhibited lower predictive performance with an accuracy of 79.63%, while SVM, a frequently utilized ML model for radiomics-based tumor classification, achieved an accuracy of 85.19%. Our findings are consistent with previous research indicating that radiomics-based ML models achieve high accuracy in IDH1 mutation prediction, with reported performances typically exceeding 80%. These findings suggest that KNN and Ensemble models are more effective in capturing the non-linear radiomic patterns associated with IDH1 status, compared to traditional ML approaches. Our findings indicate that radiomic analyses provide comprehensive genotypic classification by assessing the entire tumor and present a safer, faster, and more patient-friendly alternative to traditional biopsies. This study highlights the potential of radiomics and ML techniques, particularly KNN, Ensemble, and SVM, as powerful tools for predicting the molecular characteristics of gliomas and developing personalized treatment strategies.
胶质瘤是中枢神经系统最常见的原发性肿瘤，遗传学和分子医学的进展显著改变了它们的分类和治疗方法。本研究旨在使用放射组学和机器学习（ML）方法预测胶质瘤中的异柠檬酸脱氢酶1（IDH1）基因型。分析了108例胶质瘤患者的回顾性数据，包括由年龄、性别和合并症等人口统计学细节支持的磁共振成像（MRI）数据。使用3D Slicer软件手动进行肿瘤分割，并使用PyRadiomics库提取112个放射组学特征。使用最小冗余最大相关（mRMR）算法进行特征选择，确定了17个显著的放射组学特征。应用包括K近邻（KNN）、集成学习、决策树（DT）、逻辑回归（LR）、判别分析和支持向量机（SVM）在内的各种ML算法来预测IDH1基因型。KNN和集成学习模型获得了最高的灵敏度（92%-100%）和特异性（100%），成为最成功的模型。比较分析表明，KNN的准确率为92.59%，灵敏度为92.38%，特异性为100%，精确率为100%，F1分数为95.02%。同样，集成学习模型的准确率为90.74%，灵敏度为90.65%，特异性为100%，精确率为100%，F1分数为95.13%。为了评估它们的有效性，将KNN和集成学习模型与胶质瘤分类中常用的机器学习方法进行了比较。LR作为一种传统的统计方法，预测性能较低，准确率为79.63%，而SVM作为一种常用于基于放射组学的肿瘤分类的ML模型，准确率为85.19%。我们的研究结果与先前的研究一致，表明基于放射组学的ML模型在IDH1突变预测中具有较高的准确率，报道的性能通常超过80%。这些结果表明，与传统的ML方法相比，KNN和集成学习模型在捕捉与IDH1状态相关的非线性放射组学模式方面更有效。我们的研究结果表明，放射组学分析通过评估整个肿瘤提供了全面的基因型分类，并且是一种比传统活检更安全、更快且对患者更友好的替代方法。本研究强调了放射组学和ML技术，特别是KNN、集成学习和SVM，作为预测胶质瘤分子特征和制定个性化治疗策略的强大工具的潜力。
