A review on the applications of artificial intelligence and big data for glioblastoma multiforme management

Abstract

Glioblastoma is known as an aggressive type of brain tumor with a very poor survival rate and resistance to different treatment methods. Considering the difficulties in studying glioblastoma, the development of alternative methods for the identification of prognostic factors in this disease seems necessary. Noteworthy, imaging, pathologic, and molecular data obtained from patients are highly valuable because of their potential for this purpose. Artificial intelligence (AI) has emerged as a powerful tool to perform highly accurate analyses and extract more detailed information from available patient data. AI is usually used for the development of prediction models for prognosis, response/resistance to treatments, and subtype identification in cancers. Today, the number of AI-aided developed algorithms is increasing in the field of glioblastoma. Challenges in the diagnosis of tumors using imaging data, prediction of genetic alterations, and prediction of overall survival are among the most popular studies related to glioblastoma. Hereby, we reviewed peer-reviewed articles in which AI methods were used for various targets in glioblastoma. Reviewing the published articles showed that the use of clinical imaging data is reasonably more popular than other assessments because of its noninvasive nature. However, the use of molecular assessments is becoming extended in this disease. In this regard, we summarized the developed algorithms and their applications for the diagnosis and prognosis of glioblastoma tumors. We also considered the accuracy rates of algorithms to shed light on the advancements of different methodologies in the included studies.