Shaver Madeleine M, Kohanteb Paul A, Chiou Catherine, Bardis Michelle D, Chantaduly Chanon, Bota Daniela, Filippi Christopher G, Weinberg Brent, Grinband Jack, Chow Daniel S, Chang Peter D
Department of Radiology, University of California, Irvine, Orange, CA 92868-3201, USA.
Department of Neurology, University of California, Irvine, Orange, CA 92868-3201, USA.
Cancers (Basel). 2019 Jun 14;11(6):829. doi: 10.3390/cancers11060829.
Radiographic assessment with magnetic resonance imaging (MRI) is widely used to characterize gliomas, which represent 80% of all primary malignant brain tumors. Unfortunately, glioma biology is marked by heterogeneous angiogenesis, cellular proliferation, cellular invasion, and apoptosis. This translates into varying degrees of enhancement, edema, and necrosis, making reliable imaging assessment challenging. Deep learning, a subset of machine learning artificial intelligence, has gained traction as a method, which has seen effective employment in solving image-based problems, including those in medical imaging. This review seeks to summarize current deep learning applications used in the field of glioma detection and outcome prediction and will focus on (1) pre- and post-operative tumor segmentation, (2) genetic characterization of tissue, and (3) prognostication. We demonstrate that deep learning methods of segmenting, characterizing, grading, and predicting survival in gliomas are promising opportunities that may enhance both research and clinical activities.
利用磁共振成像（MRI）进行的影像学评估被广泛用于表征神经胶质瘤，神经胶质瘤占所有原发性恶性脑肿瘤的80%。不幸的是，神经胶质瘤生物学的特征是血管生成、细胞增殖、细胞侵袭和凋亡存在异质性。这表现为不同程度的强化、水肿和坏死，使得可靠的影像学评估具有挑战性。深度学习是机器学习人工智能的一个子集，作为一种方法已受到关注，它已被有效地用于解决基于图像的问题，包括医学成像中的问题。本综述旨在总结目前在神经胶质瘤检测和预后预测领域使用的深度学习应用，并将重点关注：（1）手术前后肿瘤分割；（2）组织的基因特征；（3）预后评估。我们证明，用于神经胶质瘤分割、特征化、分级和预测生存的深度学习方法是很有前景的机会，可能会增强研究和临床活动。
