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.

Abayazeed, Aly H; Abbassy, Ahmed; Müeller, Michael; Hill, Michael; Qayati, Mohamed; Mohamed, Shady; Mekhaimar, Mahmoud; Raymond, Catalina; Dubey, Prachi; Nael, Kambiz; Rohatgi, Saurabh; Kapare, Vaishali; Kulkarni, Ashwini; Shiang, Tina; Kumar, Atul; Andratschke, Nicolaus; Willmann, Jonas; Brawanski, Alexander; De Jesus, Reordan; Tuna, Ibrahim; ... (2023). 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. Neuro-oncology advances, 5(1), vdac184. Oxford University Press 10.1093/noajnl/vdac184

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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.

Item Type:

Journal Article (Original Article)

Division/Institute:

?? C4C9B5A4EB5044C1856BB32B0E8EF1F9 ??
10 Strategic Research Centers > ARTORG Center for Biomedical Engineering Research
04 Faculty of Medicine > Department of Haematology, Oncology, Infectious Diseases, Laboratory Medicine and Hospital Pharmacy (DOLS) > Clinic of Radiation Oncology

UniBE Contributor:

Müller, Michael (A), Reyes, Mauricio

Subjects:

500 Science > 570 Life sciences; biology
600 Technology > 610 Medicine & health

ISSN:

2632-2498

Publisher:

Oxford University Press

Language:

English

Submitter:

Pubmed Import

Date Deposited:

25 Jan 2023 10:47

Last Modified:

17 Jun 2024 09:36

Publisher DOI:

10.1093/noajnl/vdac184

PubMed ID:

36685009

Uncontrolled Keywords:

RANO artificial intelligence glioma machine learning segmentation

BORIS DOI:

10.48350/177813

URI:

https://boris.unibe.ch/id/eprint/177813

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