Objectives Magnetic resonance imaging (MRI) is the method of choice for imaging meningiomas. Volumetric assessment of meningiomas is highly relevant for therapy planning and monitoring. We used a multiparametric deep-learning model (DLM) on routine MRI data including images from diverse referring institutions to investigate DLM performance in automated detection and segmentation of meningiomas in comparison to manual segmentations. Methods We included 56 of 136 consecutive preoperative MRI datasets [T1/T2-weighted, T1-weighted contrast-enhanced (T1CE), FLAIR] of meningiomas that were treated surgically at the University Hospital Cologne and graded histologically as tumour grade I (n = 38) or grade II (n = 18). The DLM was trained on an independent dataset of 249 glioma cases and segmented different tumour classes as defined in the brain tumour image segmentation benchmark (BRATS benchmark). The DLM was based on the DeepMedic architecture. Results were compared to manual segmentations by two radiologists in a consensus reading in FLAIR and T1CE. T1-weighted contrast-enhanced MRI

Abbreviations: AIP = acute interstitial pneumonia, ARDS = acute respiratory distress syndrome, CTCAE = Common Terminology Criteria for Adverse Events, CTLA-4 = cytotoxic T-lymphocyte antigen-4, HP = hypersensitivity pneumonitis, ICI = immune checkpoint inhibitor, irAE = immune-related adverse event, NSIP = nonspecific interstitial pneumonia, OP = organizing pneumonia, PD-1 = programmed cell death protein 1, PD-L1 = programmed cell death ligand 1

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