The aggressiveness and complexity of brain tumors, especially the high-grade gliomas like glioblastoma multiforme (GBM) make it very difficult to diagnose, treat, and prognose them. These tumors are known to proliferate rapidly, are heterogeneous, and are non-responsive to traditional treatments; thus, proper evaluation is important for planning successful treatment. The greater availability of multiparametric magnetic resonance imaging (MRI) data in recent years has made automated methods for brain tumor segmentation (BTS) and the prediction of survival easier. These developments have allowed scientists and practitioners to derive useful patterns from large-scale imaging data that enhance the accuracy of diagnosis and prognostic analysis. This is a systematic classification of the segmentation strategies proposed at the Brain Tumor Segmentation (BraTS) 2020 Challenge into three major categories: manual, semiautomatic, and fully automated. Manual approaches, as precise as they are, are time-intensive and prone to inter-observer error, so semi- and fully automated approaches use machine learning (ML) and deep learning (DL) algorithms to improve efficiency and reproducibility. In addition, this study analyzes the diverse models of ML and DL-based predictors of overall survival relying on radiomic features and clinical data, and their features in personalized medicine. The latest changes, especially those highlighted by the BraTS 2024 Challenge, focus on solving problems in radiotherapy planning for meningiomas in the future and on improving clinically significant segmentation methods. The main goal of this review is to present an overall description of these advances, thereby facilitating future research on improved AI-driven brain tumor analysis and better prediction of patient outcomes.
Kumari et al. (Thu,) studied this question.