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 Tuna; Steve H Fung; Joseph C Landolfi; Benjamin M Ellingson; Mauricio Reyes

doi:10.1093/noajnl/vdac184

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

Neurooncol Adv. 2022 Dec 20;5(1):vdac184. doi: 10.1093/noajnl/vdac184. eCollection 2023 Jan-Dec.

Authors

Aly H Abayazeed¹, Ahmed Abbassy¹, Michael Müeller^{1

2}, Michael Hill¹, Mohamed Qayati^{1

3}, Shady Mohamed^{1

3}, Mahmoud Mekhaimar³, Catalina Raymond⁴, Prachi Dubey⁵, Kambiz Nael⁶, Saurabh Rohatgi⁷, Vaishali Kapare⁸, Ashwini Kulkarni⁸, Tina Shiang⁸, Atul Kumar⁹, Nicolaus Andratschke¹⁰, Jonas Willmann¹⁰, Alexander Brawanski^{3

11}, Reordan De Jesus¹², Ibrahim Tuna¹², Steve H Fung⁵, Joseph C Landolfi¹³, Benjamin M Ellingson⁴, Mauricio Reyes²

Affiliations

¹ Biomedical Engineering group, Neosoma Inc., Groton, Massachusetts, USA (Originating Institution address:44 Farmers Row, Groton, Massachusetts, 01450), USA.
² ARTORG Biomedical Engineering group, University of Bern, Switzerland.
³ Radiology Department, University of Cairo School of Medicine, Egypt.
⁴ Brain Tumor Imaging Laboratory, University of California Los Angeles, Los Angeles, California, USA.
⁵ Radiology Department, Houston Methodist Hospital, Houston, Texas, USA.
⁶ Radiology Department, University of California Los Angeles, Los Angeles, California, USA.
⁷ Radiology Department, Massachusetts General Hospital, Boston, Massachusetts, USA.
⁸ Radiology Department, University of Massachusetts, Worcester, Massachusetts, USA.
⁹ Radiology Department, Yale School of Medicine, New Haven, Connecticut, USA.
¹⁰ Radiation Oncology Department, University of Zurich, Switzerland.
¹¹ Radiation Oncology Department, University Hospital Regensburg, Cairo Egypt and Regensburg, Germany.
¹² Radiology Department, University of Florida, Gainesville, Florida, USA.
¹³ Neurology/Neuro-oncology Department, Hackensack Meridian Health JFK Medical Center, Edison, New Jersey, USA.

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.

Keywords: RANO; artificial intelligence; glioma; machine learning; segmentation.

Abstract

Grants and funding