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Machine learning-based differentiation between multiple sclerosis and glioma WHO II°-IV° using O-(2-[18F] fluoroethyl)-L-tyrosine positron emission tomography

Sied Kebir, Laurèl Rauschenbach, Manuel Weber, Lazaros Lazaridis, Teresa Schmidt, Kathy Keyvani, Niklas Schäfer, Asma Milia, Lale Umutlu, Daniela Pierscianek, Martin Stuschke, Michael Forsting, Ulrich Sure, Christoph Kleinschnitz, Gerald Antoch, Patrick M. Colletti, Domenico Rubello, Ken Herrmann, Ulrich Herrlinger, Björn Scheffler, Ralph A. Bundschuh, Martin Glas

2021Journal of Neuro-Oncology19 citationsDOI

Topics & Concepts

GliomaReceiver operating characteristicPositron emission tomographyMedicineMultiple sclerosisNuclear medicineSubgroup analysisOligodendrogliomaMachine learningOncologyInternal medicineComputer scienceAstrocytomaCancer researchImmunologyConfidence intervalGlioma Diagnosis and TreatmentMultiple Sclerosis Research StudiesBrain Tumor Detection and Classification
Machine learning-based differentiation between multiple sclerosis and glioma WHO II°-IV° using O-(2-[18F] fluoroethyl)-L-tyrosine positron emission tomography | Litcius