首页|University Hospital Reports Findings in Glioblastomas (Glioblastoma pseudoprogression discrimination using multiparametric magnetic resonance imaging, principal component analysis, supervised and unsupervised machine learning)
University Hospital Reports Findings in Glioblastomas (Glioblastoma pseudoprogression discrimination using multiparametric magnetic resonance imaging, principal component analysis, supervised and unsupervised machine learning)
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New research on Oncology - Glioblastomas is the subject of a report. According to news reporting out of Lleida, Spain, by NewsRx editors, research stated, “One of the most frequent phenomena in the follow-up of glioblastoma is pseudoprogression, present in up to half of the cases. The clinical usefulness to discriminate this phenomenon through magnetic resonance imaging and nuclear medicine is not yet standardized, in this study we used machine learning on multiparametric magnetic resonance imaging to explore discriminators of this phenomenon.” Our news journalists obtained a quote from the research from University Hospital, “For the study, 30 patients diagnosed with IDH wild-type glioblastoma operated on at both study centers in the period 2011-2020 were selected, 15 patients correspond to early tumor progression and 15 patients to pseudo- progression, using unsupervised learning, the number of clusters and tumor segmentation has been carried out using gap-stat and k-means method, adjusting to voxel adjacency. In a second phase, a class prediction has been carried out with 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 to pseudoprogression.”
LleidaSpainEuropeCancerCyborgsEmerging TechnologiesGlioblastomasHealth and MedicineMachine LearningMagnetic ResonanceOncologySupervised Learning
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