Baid Ujjwal, Rane Swapnil U, Talbar Sanjay, Gupta Sudeep, Thakur Meenakshi H, Moiyadi Aliasgar, Mahajan Abhishek
Department of Electronics and Telecommunication Engineering, Shri Guru Gobind Singhji Institute of Engineering and Technology, Nanded, India.
Department of Pathology, Tata Memorial Centre, ACTREC, HBNI, Navi-Mumbai, India.
Front Comput Neurosci. 2020 Aug 4;14:61. doi: 10.3389/fncom.2020.00061. eCollection 2020.
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.
胶质母细胞瘤是一种世界卫生组织IV级脑肿瘤，会导致患者的总体生存率（OS）较低。为了进行精确的手术和治疗规划，临床医生和肿瘤学家非常希望能够预测胶质母细胞瘤（GBM）患者的OS。放射组学研究试图预测疾病预后，从而从多个MR图像中提取的各种影像特征为个性化治疗提供有益信息。在本研究中，从液体衰减反转恢复（FLAIR）和T1ce MRI数据中提取了基于强度的一阶体积、形状和纹理放射组学特征。通过低通和高通滤波的平稳小波变换对感兴趣区域进行进一步分解。此外，在这些分解后的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算法的效率。所提方法在BraTS训练、验证和测试数据集上分别取得了0.695、0.571和0.558的成绩。在所提方法在2018年BraTS挑战的OS预测任务中获得了第三名。
