Multivariate Analysis in Pediatric Brain Tumor Research

Abstract

Children's brain tumours are life-threatening, and more research is needed to improve patient care. Multivariate analysis has been more popular in recent years for tumour categorization (segmentation) and survival (outcome) assessment in children with brain tumours. This paper reviewed studies that applied multivariate analysis to pediatric brain tumor research in order to provide an overview of the field. In tumour classification investigations, large variability in tumour categorization outcomes were discovered. In addition, there was moderate error rate in the multivariate survival analysis model, which could lead to inaccurate survival estimates and misidentification of prognostic factors. To address these issues, this paper examined the data processing chains in these multivariate analyses in depth, suggesting that optimising and standardising these data processing chains could improve tumour classification and survival analysis, as well as reduce variations and errors in classification and survival estimates. In the Big Data era of the twenty-first century, as multivariate analytic approaches, data processing technologies, and imaging techniques advance, it is expected that the challenges in complex imaging data processing in tumour classification will be overcome, and complex data processing will be revolutionised. This will allow for accurate automatic tumour classification/segmentation, which will aid in the early detection and treatment of cancers, as well as the planning of therapy and monitoring of tumour progression and treatment effects. Further, with the advances of survival assessment to guide life-saving rescue and recovery planning, multivariate analytic methods and technologies will help revolutionize patient care, and truly benefit children with brain tumors.