首页|University Hospital Arnau de Vilanova Reports Findings in Glioblastomas (Glioblastoma Pseudoprogression Discrimination Using Multiparametric Magnetic Resonance Imaging, Principal Component Analysis, and Supervised and Unsupervised Machine ...)
University Hospital Arnau de Vilanova Reports Findings in Glioblastomas (Glioblastoma Pseudoprogression Discrimination Using Multiparametric Magnetic Resonance Imaging, Principal Component Analysis, and Supervised and Unsupervised Machine ...)
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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.”
LleidaSpainEuropeCancerCyborgsEmerging TechnologiesGlioblastomasHealth and MedicineMachine LearningMagnetic ResonanceOncologySupervised Learning
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