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Neurooncol Adv
Neurooncol Adv
noa
Neuro-Oncology Advances
2632-2498
Oxford University Press US

10.1093/noajnl/vdae090.069
vdae090.069
Final Category: Neuroimaging/Radiologic Advances
AcademicSubjects/MED00300
AcademicSubjects/MED00310
NIRL-08 AUTOMATED LONGITUDINAL TRACKING OF BRAIN METASTASES INTEGRATED IN A USER-ORIENTED SOFTWARE
Emiliani Ramon D AFX Medical Inc., Montreal/QC, Canada

Chartrand Gabriel AFX Medical Inc., Montreal/QC, Canada

Son Edward AFX Medical Inc., Montreal/QC, Canada

Pawlowski Sophie A AFX Medical Inc., Montreal/QC, Canada

Rajakesari Selvan Department of Radiation Oncology, Charles-LeMoyne Hospital, Greenfield Park/QC, Canada
Department of Radiation Oncology, Centre Hospitalier de l’Université de Montréal, Montreal/QC, Canada

Cengarle-Samak Alexandre Department of Radiology, Charles-LeMoyne Hospital, Greenfield Park/QC, Canada

Lavoie Jeremi AFX Medical Inc., Montreal/QC, Canada

Ducharme Simon AFX Medical Inc., Montreal/QC, Canada
Department of Psychiatry, Douglas Mental Health University Institute, McGill University, Montreal/QC, Canada

Roberge David Department of Radiation Oncology, Centre Hospitalier de l’Université de Montréal, Montreal/QC, Canada

8 2024
02 8 2024
02 8 2024
6 Suppl 1 2024 SNO/ASCO CNS Metastases Conference i21i21
© The Author(s) 2024. Published by Oxford University Press, the Society for Neuro-Oncology and the European Association of Neuro-Oncology.
2024
https://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com

Abstract

The burden of detection and segmentation of brain metastases (BM) for treatment planning and response assessment has been found to be alleviated by machine learning methods. However, tracking individual lesions over time remains tedious and would benefit from automated assistance for complex cases. We developed a software solution combining an AI-based BM segmentation method with an automated pairing algorithm allowing to track BM across longitudinal scans. The proposed tracking method comprises two steps: identifying lesions in each scan and pairing identified findings across scans. Identification and segmentation of BM is done with our previously published neural network based algorithm. Tracking of BM is achieved by progressively assigning a lesion ID to individual findings. For each series, individual findings are co-registered using image registration to a reference series which defines the initial set of lesions. A matching function then computes a score for each possible finding-lesion pair based on diameter similarity and inter-centroid distance. Findings from highest scoring pairs are sequentially assigned their matched lesion ID, while pairs scoring under a given threshold are assigned a new lesion ID. Series are processed as such until all findings are dispatched. Assignment accuracy was assessed using adjusted rand index (ARI) on a synthetic noisy dataset simulating registration error and misdetections. Findings from 10 to 20 lesions on 5 series of dimension 100 mm per side were randomly generated for 100 synthetic patients each. With a simulated registration error range of 0-2 deg and 0-5mm, the average per patient ARI was 0.94 +/- 0.05, while an error range of 0-4 deg and 0-10mm lowered ARI to 0.87 +/-0.08. Results support the validity of our software solution for automated detection and longitudinal tracking of BM, which can alleviate the burden of follow-ups in the context of stereotactic radiosurgery.
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pmc
