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Abstract #2550

Deep learning-based spatial tumor phenotyping for radiogenomic prediction of the IDH-genotype in preoperative glioma

Johannes Lohmeier1, Jenny Meinhardt2, Helena Radbruch2, Winfried Brenner3, Anna Tietze1, and Marcus Makowski4
1Neuroradiology, Charité University Hospital, Berlin, Germany, 2Neuropathology, Charité University Hospital, Berlin, Germany, 3Nuclear medicine, Charité University Hospital, Berlin, Germany, 4Radiology, Hospital Rechts der Isar, Munich, Germany

Synopsis

Keywords: Tumors (Pre-Treatment), Cancer, biomarker

Motivation: The isocitrate dehydrogenase (IDH)-genotype is a central determinant for the diagnosis, therapy and management of patients with adult-type diffuse glioma.

Goal(s): To investigate spatial tumor characteristics using an U-Net-based CNN and to evaluate its diagnostic potential for predictive IDH-genotyping.

Approach: Participants with treatment-naïve glioma were investigated using an U-Net-based CNN based on a standard MRI protocol computing a compartmental tumor mass (CTM)-ratio.

Results: A total of 207 patients (IDH-mutant, 72, IDH wild-type, 135) with treatment-naïve glioma were evaluated. IDH-mutated glioma presented greater CTM-ratio (P<.001) unlike IDH wild-type glioblastoma (P<.001) – which determined the IDH-genotype with excellent diagnostic performance (AUC=0.85, P<.001).

Impact: We performed DL-based spatial mapping of treatment-naïve glioma and computed the CTM-ratio for the evaluation of spatial tumor characteristics, which enabled robust classification of the IDH-genotype – with implications for the clinical management of adult-type glioma.

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