Glioblastoma is a WHO grade IV brain tumor, which leads to poor overall survival (OS) of patients. For precise surgical and treatment planning, OS prediction of glioblastoma (GBM) patients is highly desired by clinicians and oncologists. Radiomic research attempts at predicting disease prognosis, thus providing beneficial information for personalized treatment from a variety of imaging features extracted from multiple MR images. In this study, first-order, intensity-based volume and shape-based and textural radiomic features are extracted from fluid-attenuated inversion recovery (FLAIR) and T1ce MRI data. The region of interest is further decomposed with stationary wavelet transform with low-pass and high-pass filtering. Further, radiomic features are extracted on these decomposed images, which helped in acquiring the directional information. The efficiency of the proposed algorithm is evaluated on Brain Tumor Segmentation (BraTS) challenge training, validation, and test datasets. The proposed approach achieved 0.695, 0.571, and 0.558 on BraTS training, validation, and test datasets. The proposed approach secured the third position in BraTS 2018 challenge for the OS prediction task.

中文翻译：

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使用机器学习对具有放射学特征的胶质母细胞瘤进行总体生存预测

胶质母细胞瘤是 WHO IV 级脑肿瘤，导致患者的总体生存率 (OS) 较差。对于精确的手术和治疗计划，临床医生和肿瘤学家非常希望对胶质母细胞瘤 (GBM) 患者进行 OS 预测。放射组学研究试图预测疾病预后，从而从多个 MR 图像中提取的各种成像特征为个性化治疗提供有益信息。在这项研究中，从流体衰减反转恢复 (FLAIR) 和 T1ce MRI 数据中提取一阶、基于强度的体积、基于形状和纹理的放射组学特征。感兴趣的区域通过具有低通和高通滤波的平稳小波变换进一步分解。此外，在这些分解图像上提取放射组学特征，这有助于获取方向信息。在脑肿瘤分割 (BraTS) 挑战训练、验证和测试数据集上评估了所提出算法的效率。所提出的方法在 BraTS 训练、验证和测试数据集上达到了 0.695、0.571 和 0.558。所提出的方法在 OS 预测任务的 BraTS 2018 挑战中获得了第三名。