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Tumor Grade and Overall Survival Prediction of Gliomas Using Radiomics
Scientific Programming ( IF 1.672 ) Pub Date : 2021-04-23 , DOI: 10.1155/2021/9913466
Jianming Ye 1 , He Huang 2 , Weiwei Jiang 2 , Xiaomei Xu 2 , Chun Xie 1 , Bo Lu 3 , Xiangcai Wang 1 , Xiaobo Lai 2
Affiliation  

Glioma is one of the most common and deadly malignant brain tumors originating from glial cells. For personalized treatment, an accurate preoperative prognosis for glioma patients is highly desired. Recently, various machine learning-based approaches have been developed to predict the prognosis based on preoperative magnetic resonance imaging (MRI) radiomics, which extract quantitative features from radiographic images. However, major challenges remain for methodologic developments to optimize feature extraction and provide rapid information flow in clinical settings. This study investigates two machine learning-based prognosis prediction tasks using radiomic features extracted from preoperative multimodal MRI brain data: (i) prediction of tumor grade (higher-grade vs. lower-grade gliomas) from preoperative MRI scans and (ii) prediction of patient overall survival (OS) in higher-grade gliomas (<12 months vs. > 12 months) from preoperative MRI scans. Specifically, these two tasks utilize the conventional machine learning-based models built with various classifiers. Moreover, feature selection methods are applied to increase model performance and decrease computational costs. In the experiments, models are evaluated in terms of their predictive performance and stability using a bootstrap approach. Experimental results show that classifier choice and feature selection technique plays a significant role in model performance and stability for both tasks; a variability analysis indicates that classification method choice is the most dominant source of performance variation for both tasks.

中文翻译:

放射学对胶质瘤的肿瘤分级和总体生存预测

胶质瘤是起源于神经胶质细胞的最常见和致命的恶性脑肿瘤之一。对于个性化治疗,高度期望神经胶质瘤患者的准确术前预后。最近,已经开发出了多种基于机器学习的方法,以基于术前磁共振成像(MRI)放射线学来预测预后,该放射线学从放射线图像中提取定量特征。但是,方法学开发面临的主要挑战仍然是如何优化特征提取并在临床环境中提供快速信息流。这项研究使用从术前多模态MRI脑数据中提取的放射学特征研究了两种基于机器学习的预后预测任务:(i)肿瘤等级的预测(更高等级与更高等级之间的比较)。低级神经胶质瘤)从术前MRI扫描和(ii)术前MRI扫描预测高级别神经胶质瘤(<12个月对大于12个月)的患者总体生存率(OS)。具体而言,这两个任务利用了由各种分类器构建的基于传统机器学习的模型。而且,特征选择方法被应用于提高模型性能并降低计算成本。在实验中,使用自举方法对模型的预测性能和稳定性进行了评估。实验结果表明,分类器选择和特征选择技术在两个任务的模型性能和稳定性中都起着重要作用。变异性分析表明,分类方法的选择是两项任务绩效变化的最主要来源。
更新日期:2021-04-23
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