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Abstract #4167

ADC-Map Based Computer Aided Radiological Diagnostics (CARD) for the Initial Differential Diagnosis of Medulloblastoma versus Pilocytic Astrocytoma – A Reproducibility Study.

Urspeter Knecht1, Nicole Porz2, Beate Sick3, Elvis Murina4, Nuno Miguel Pedrosa de Barros5, Philippe Schucht6, Evelyn Herrmann7, Jan Gralla1, Roland Wiest1, Marwan El-Koussy5, and Johannes Slotboom8

1Neuroradiology, University Hospital Bern, Bern, Switzerland, 2Neurosurgery, University Hospital Bern, Bern, Switzerland, 3Department of Biostatistics, Institute of Epidemiology, Biostatistics and Prevention, University of Zürich, Zürich, Switzerland, 4Institute for Data Analysis and Process Design, ZHAW, Switzerland, 5Neuroradiology, University Bern, University Hospital, Bern, Switzerland, 6Neurosurgery, University Hospital Bern, Bern, 7Radiooncology, University Bern, University Hospital, Bern, Switzerland, 8Neuroradiology, University Hospital Bern, Switzerland

The diagnosis of brain tumors using visual criteria is very challenging. A novel computational method for computer aided radiologic diagnostics (CARD) is described based on quantitative textural features from ADC-maps, and a machine learning algorithm (Random-Forest classification). The reproducibility of the method was examined with 3 human raters was performed, and the Fleiss'-Kappa-test revealed high inter-rater agreement of κ=0.821 (p-value<<0.001) and an intra-rater agreement of κ =0.822 (p-value<<0.001). The method significantly improves the differential diagnosis of medulloblastoma versus pilocytic-astrocytomas.

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