NS-HGlio: A generalizable and repeatable HGG segmentation and volumetric measurement AI algorithm for the longitudinal MRI assessment to inform RANO in trials and clinics

Aly H. Abayazeed, Ahmed Abbassy, Michael Müeller, Michael Hill, Mohamed Qayati, Shady Mohamed, Mahmoud Mekhaimar, Catalina Raymond, Prachi Dubey, Kambiz Nael, Saurabh Rohatgi, Vaishali Kapare, Ashwini Kulkarni, Tina Shiang, Atul Kumar, Nicolaus Andratschke, Jonas Willmann, Alexander Brawanski, Reordan De Jesus, Ibrahim TunaSteve H. Fung, Joseph C. Landolfi, Benjamin M. Ellingson, Mauricio Reyes

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

Background: Accurate and repeatable measurement of high-grade glioma (HGG) enhancing (Enh.) and T2/FLAIR hyperintensity/edema (Ed.) is required for monitoring treatment response. 3D measurements can be used to inform the modified Response Assessment in Neuro-oncology criteria. We aim to develop an HGG volumetric measurement and visualization AI algorithm that is generalizable and repeatable. Methods: A single 3D-Convoluted Neural Network, NS-HGlio, to analyze HGG on MRIs using 5-fold cross validation was developed using retrospective (557 MRIs), multicentre (38 sites) and multivendor (32 scanners) dataset divided into training (70%), validation (20%), and testing (10%). Six neuroradiologists created the ground truth (GT). Additional Internal validation (IV, three institutions) using 70 MRIs, and External validation (EV, single institution) using 40 MRIs through measuring the Dice Similarity Coefficient (DSC) of Enh., Ed., and Enh. + Ed. (WholeLesion/WL) tumor tissue and repeatability testing on 14 subjects from the TCIA MGH-QIN-GBM dataset using volume correlations between timepoints were performed. Results: IV Preoperative median DSC Enh. 0.89 (SD 0.11), Ed. 0.88 (0.28), WL 0.88 (0.11). EV Preoperative median DSC Enh. 0.82 (0.09), Ed. 0.83 (0.11), WL 0.86 (0.06). IV Postoperative median DSC Enh. 0.77 (SD 0.20), Ed 0.78. (SD 0.09), WL 0.78 (SD 0.11). EV Postoperative median DSC Enh. 0.75 (0.21), Ed 0.74 (0.12), WL 0.79 (0.07). Repeatability testing; Intraclass Correlation Coefficient of 0.95 Enh. and 0.92 Ed. Conclusion: NS-HGlio is accurate, repeatable, and generalizable. The output can be used for visualization, documentation, treatment response monitoring, radiation planning, intra-operative targeting, and estimation of Residual Tumor Volume among others.

Original languageEnglish (US)
Article numbervdac184
Pages (from-to)vdac184
JournalNeuro-Oncology Advances
Volume5
Issue number1
DOIs
StatePublished - Jan 1 2023

Keywords

  • RANO
  • artificial intelligence
  • glioma
  • machine learning
  • segmentation

ASJC Scopus subject areas

  • Surgery
  • Oncology
  • Clinical Neurology

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