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Gliomas are the most common primary brain malignancies, with differentdegrees of aggressiveness, variable prognosis and various heterogeneoushistologic sub-regions, i.e., peritumoral edematous/invaded tissue, necroticcore, active and non-enhancing core. This intrinsic heterogeneity is alsoportrayed in their radio-phenotype, as their sub-regions are depicted byvarying intensity profiles disseminated across multi-parametric magneticresonance imaging (mpMRI) scans, reflecting varying biological properties.Their heterogeneous shape, extent, and location are some of the factors thatmake these tumors difficult to resect, and in some cases inoperable. The amountof resected tumor is a factor also considered in longitudinal scans, whenevaluating the apparent tumor for potential diagnosis of progression.Furthermore, there is mounting evidence that accurate segmentation of thevarious tumor sub-regions can offer the basis for quantitative image analysistowards prediction of patient overall survival. This study assesses thestate-of-the-art machine learning (ML) methods used for brain tumor imageanalysis in mpMRI scans, during the last seven instances of the InternationalBrain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, wefocus on i) evaluating segmentations of the various glioma sub-regions inpre-operative mpMRI scans, ii) assessing potential tumor progression by virtueof longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANOcriteria, and iii) predicting the overall survival from pre-operative mpMRIscans of patients that underwent gross total resection. Finally, we investigatethe challenge of identifying the best ML algorithms for each of these tasks,considering that apart from being diverse on each instance of the challenge,the multi-institutional mpMRI BraTS dataset has also been a continuouslyevolving/growing dataset.

Identifying the Best Machine Learning Algorithms for Brain Tumor  Segmentation, Progression Assessment, and Overall Survival Prediction in the  BRATS Challenge