Abstract
New research on Oncology - Glioblastomas is the subject of a report. According to news reporting originating in Lleida, Spain, by NewsRx journalists, research stated, “One of the most frequent phenomena in the follow-up of glioblastoma is pseudoprogression, present in up to half of cases. The clinical usefulness of discriminating this phenomenon through magnetic resonance imaging and nuclear medicine has not yet been standardized; in this study, we used machine learning on multiparametric magnetic resonance imaging to explore discriminators of this phenomenon.” The news reporters obtained a quote from the research from University Hospital Arnau de Vilanova, “For the study, 30 patients diagnosed with IDH wild-type glioblastoma operated on at both study centers in 2011-2020 were selected; 15 patients corresponded to early tumor progression and 15 patients to pseudoprogression. Using unsupervised learning, the number of clusters and tumor segmentation was recorded using gap-stat and k-means method, adjusting to voxel adjacency. In a second phase, a class prediction was carried out with a multinomial logistic regression supervised learning method; the outcome variables were the percentage of assignment, class overrepresentation, and degree of voxel adjacency. Unsupervised learning of the tumor in its diagnosis shows up to 14 well-differentiated tumor areas. In the supervised learning phase, there is a higher percentage of assigned classes (P ＜0.01), less overrepresentation of classes (P ＜0.01), and greater adjacency (55% vs. 33%) in cases of true tumor progression compared with pseudoprogression.”
NETL
NSTL