Central Inter-Disciplinary Research Facility, School of Biological Sciences, Sri Balaji Vidyapeeth (Deemed to-be University), Pillaiyarkuppam, Puducherry, India.
Central Inter-Disciplinary Research Facility, School of Biological Sciences, Sri Balaji Vidyapeeth, Mahatma Gandhi Medical College and Research Institute Campus, Pillaiyarkuppam, Puducherry, 607403, India.
Med Oncol. 2021 Apr 3;38(5):53. doi: 10.1007/s12032-021-01500-2.
Gliomas are one of the most devastating primary brain tumors which impose significant management challenges to the clinicians. The aggressive behaviour of gliomas is mainly attributed to their rapid proliferation, unravelled genomics and the blood-brain barrier which protects the tumor cells from chemotherapeutic regimens. Suspects of brain tumors are usually assessed by magnetic resonance imaging and computed tomography. These images allow surgeons to decide on the tumor grading, intra-operative pathology, feasibility of surgery, and treatment planning. All these data are compiled manually by physicians, wherein it takes time for the validation of results and concluding the treatment modality. In this context, the arrival of artificial intelligence in this era of personalized medicine, has proven promising performance in the diagnosis and management of gliomas. Starting from grading prediction till outcome evaluation, artificial intelligence-based forefronts have revolutionized oncological research. Interestingly, this approach has also been able to precisely differentiate tumor lesion from healthy tissues. However, till date, their utility in neuro-oncological field remains limited due to the issues pertaining to their reliability and transparency. Hence, to shed novel insights on the "clinical utility of this novel approach on glioma management" and to reveal "the black-boxes that have to be solved for fruitful application of artificial intelligence in neuro-oncology research", we provide in this review, a succinct description of the potential gear of artificial intelligence-based avenues in glioma treatment and the barriers that impede their rapid implementation in neuro-oncology.
神经胶质瘤是最具破坏性的原发性脑肿瘤之一，给临床医生带来了重大的治疗挑战。神经胶质瘤的侵袭性行为主要归因于其快速增殖、基因组学的改变以及血脑屏障，该屏障保护肿瘤细胞免受化疗方案的影响。脑肿瘤的疑似病例通常通过磁共振成像和计算机断层扫描进行评估。这些图像使外科医生能够决定肿瘤分级、术中病理、手术可行性和治疗计划。所有这些数据都是由医生手动汇编的，这需要时间来验证结果并确定治疗方式。在这方面，人工智能在个性化医疗时代的到来，已被证明在神经胶质瘤的诊断和治疗方面具有很有前景的性能。从分级预测到预后评估，人工智能前沿技术已经彻底改变了肿瘤学研究。有趣的是，这种方法还能够精确地区分肿瘤病变与健康组织。然而，迄今为止，由于其可靠性和透明度方面的问题，它们在神经肿瘤学领域的应用仍然有限。因此，为了深入了解“这种新方法在神经胶质瘤管理中的临床应用”，并揭示“人工智能在神经肿瘤学研究中成功应用需要解决的黑箱问题”，我们在这篇综述中简要描述了人工智能在神经胶质瘤治疗中的潜在途径，以及阻碍其在神经肿瘤学中快速应用的障碍。
