Predicting Survival in Patients with Brain Tumors: Current State-of-the-Art of AI Methods Applied to MRI

Abstract

Given growing clinical needs, in recent years Artificial Intelligence (AI) techniques have increasingly been used to define the best approaches for survival assessment and prediction in patients with brain tumors. Advances in computational resources, and the collection of (mainly) public databases, have promoted this rapid development. This narrative review of the current state-of-the-art aimed to survey current applications of AI in predicting survival in patients with brain tumors, with a focus on Magnetic Resonance Imaging (MRI). An extensive search was performed on PubMed and Google Scholar using a Boolean research query based on MeSH terms and restricting the search to the period between 2012 and 2022. Fifty studies were selected, mainly based on Machine Learning (ML), Deep Learning (DL), radiomics-based methods, and methods that exploit traditional imaging techniques for survival assessment. In addition, we focused on two distinct tasks related to survival assessment: the first on the classification of subjects into survival classes (short and long-term or eventually short, mid and long-term) to stratify patients in distinct groups. The second focused on quantification, in days or months, of the individual survival interval. Our survey showed excellent state-of-the-art methods for the first, with accuracy up to ∼98%. The latter task appears to be the most challenging, but state-of-the-art techniques showed promising results, albeit with limitations, with C-Index up to ∼0.91. In conclusion, according to the specific task, the available computational methods perform differently, and the choice of the best one to use is non-univocal and dependent on many aspects. Unequivocally, the use of features derived from quantitative imaging has been shown to be advantageous for AI applications, including survival prediction. This evidence from the literature motivates further research in the field of AI-powered methods for survival prediction in patients with brain tumors, in particular, using the wealth of information provided by quantitative MRI techniques.

Prediction of Survival of Glioblastoma Patients Using Local Spatial Relationships and Global Structure Awareness in FLAIR MRI Brain Images

“…Additionally, there are several good research on survival that achieve high accuracies that we can discuss more in detail and can help us have a deep understanding in the context of a broader range of methods than just the BRATS analysis. Di Noia's paper [49] The authors extract radiomic features from MRI data and use these to train machine learning models for survival prediction. The results offer insight into the potential of radiomics and MRI data for survival prediction, but it is unclear which public dataset was used.…”

The Diagnostic Efficiency of Quantitative Diffusion Weighted Imaging in Differentiating Medulloblastoma from Posterior Fossa Tumors: A Systematic Review and Meta-Analysis

“…Meanwhile, MRI-based AI technology is also used to predict the survival rate of brain tumor patients, which can provide supplementary information for improving clinical decision-making tasks. Combined with quantitative features derived from DWI, it is of great significance for AI to predict the survival assessment of brain tumor patients [ 55 ].…”

Classification of Brain Tumors Using Hybrid Feature Extraction Based on Modified Deep Learning Techniques

“…In healthcare facilities, physicians rely on radiologists to provide accurate results from their segmentation procedures. These procedures are performed manually by radiologists, which can lead to errors due to human error or misanalysis [15][16][17][18][19][20]. Manual segmentation is time-consuming and requires expertise and knowledge, especially when dealing with sensitive organs such as the brain [21,22].…”

“…Manual segmentation is time-consuming and requires expertise and knowledge, especially when dealing with sensitive organs such as the brain [21,22]. A reliable, dependable, and trustworthy automated system for image segmentation can be very helpful [18,[21][22][23][24][25].…”