Abstract

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 21^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.

Keywords

Multivariate analysis, Pediatric brain tumor, Tumor classification, Survival assessment, DWI, Diffusion-Weighted Imaging, MRS, MR Spectroscopy, PWI, Perfusion-Weighted Imaging, SVM, Support Vector Machine, LDA, Linear Discriminant Analysis, kNN, k-Nearest Neighbour, ANN, Artificial Neural Networks, KLD, Kullback-Leibler Divergence, mBm, Multi-Fractional Brownian Motion, EM, Expectation Maximization, PNET, Primitive Neuroectodermal Tumor, PTPSA, Piecewise-Triangular-Prism-Surface-Area, MD, Mean Diffusivity, FA, Fractional Anisotropy, PCA, Principal Component Analysis, mRMR, Maximum-Relevance Minimum Redundancy, DNET, Dysembryoplastic Neuroepithelial Tumor, DNT, Dysembryoplastic Neuroepithelial Tumor, ADC, Apparent Diffusion Coefficient, rADC, ADC Tumor-to-Normal-Brain Ratios, ROC, Receiver Operating Characteristic, AUC, Area Under the ROC Curve, ATCT, Apparent Transient Coefficient in Tumor, OS, Overall Survival, PFS, Progression-Free Survival, GTR, Gross Total Resection, RT, Radiation Therapy