Multivariate Analysis in Pediatric Brain Tumor

Brain tumors in children are life threatening and deserve more research to improve patient care In recent years multivariate analysis has been increasingly used in tumor classification segmentation and survival outcome assessment in childhood brain tumors This paper reviewed the studies that applied multivariate analysis to tumor classification segmentation and survival outcome assessment in pediatric brain tumors Large variations in the tumor classification results were observed in the studies of tumor classification even in similar patient populations Moderate error rate in the multivariate survival analysis model was also observed which could lead to inaccurate survival estimates and misidentification of prognostic factors To address these problems this paper analyzed the data processing chains in these multivariate analyses in detail It seems that optimizing and standardizing these data processing chains may improve tumor classification and survival analysis reduce variations and errors in classification results and survival estimates As multivariate analytic approaches data processing technologies and imaging techniques advance in the Big Data era of the st century it is anticipated that the challenges in complex imaging data processing in tumor classification will be overcome and complex data processing will be revolutionized This will make accurate automatic tumor classification segmentation for each tumor type and grade possible to early detect and treat tumors guide treatment planning monitor tumor progression and treatment effects together with advanced accurate survival assessment to guide life saving rescue and recovery planning revolutionize patient care and truly benefit children with brain tumors