3D convolutional neural networks for outcome prediction in glioblastoma using methionine PET and T1w MRIDownload PDF

07 Mar 2022, 21:10 (edited 04 Jun 2022)MIDL 2022 Short PapersReaders: Everyone
  • Keywords: Glioblastoma, survival analysis, 3D-CNN, biomarker
  • TL;DR: In this study, we used 3D-CNNs to develop imaging biomarker integrated with clinical data to predict time to recurrence and overall survival in patients with glioblastoma.
  • Abstract: For treatment personalization of patients with glioblastoma, we investigate three different 3D convolutional neural networks (3D-CNN) for predicting time to recurrence (TTR) and overall survival (OS) from postoperative [11C] methionine PET (MET-PET) and gadolinium-enhanced T1-weighted magnetic resonance imaging (T1c-w MRI). The 3D-DenseNet model on MET-PET integrated with age and MGMT status achieved the best performance on independent test data (Concordance-Index: TTR=0.68, OS=0.65) with significant patient stratification (p-value: TTR=0.017, OS=0.039). After prospective validation, these models may be considered for treatment personalization.
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  • Paper Type: novel methodological ideas without extensive validation
  • Primary Subject Area: Application: Radiology
  • Secondary Subject Area: Validation Study
  • Confidentiality And Author Instructions: I read the call for papers and author instructions. I acknowledge that exceeding the page limit and/or altering the latex template can result in desk rejection.
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