Data-driven prediction of postoperative clinical outcome using the Neurologic Assessment in Neuro-Oncology (NANO) score in glioblastoma patients - the clinical usefulness of a black-box model

• J.M. Kernbach,
• J. Ort,
• K. Hakvoort,
• G. Neuloh,
• H. Clusmann,
• D. Delev

Affiliations

J.M. Kernbach

RWTH Aachen University, Neurosurgical Artificial Intelligence Laboratory Aachen (NAILA), Aachen, Germany; RWTH Aachen University, Department of Neurosurgery, Faculty of Medicine, Aachen, Germany

J. Ort

RWTH Aachen University, Neurosurgical Artificial Intelligence Laboratory Aachen (NAILA), Aachen, Germany; RWTH Aachen University, Department of Neurosurgery, Faculty of Medicine, Aachen, Germany

K. Hakvoort

RWTH Aachen University, Neurosurgical Artificial Intelligence Laboratory Aachen (NAILA), Aachen, Germany; RWTH Aachen University, Department of Neurosurgery, Faculty of Medicine, Aachen, Germany

G. Neuloh

RWTH Aachen University, Department of Neurosurgery, Faculty of Medicine, Aachen, Germany

H. Clusmann

RWTH Aachen University, Department of Neurosurgery, Faculty of Medicine, Aachen, Germany

D. Delev

RWTH Aachen University, Neurosurgical Artificial Intelligence Laboratory Aachen (NAILA), Aachen, Germany; RWTH Aachen University, Department of Neurosurgery, Faculty of Medicine, Aachen, Germany