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

10.1093/noajnl/vdae090.036
vdae090.036
Final Category: Data Sciences/AI Advances
AcademicSubjects/MED00300
AcademicSubjects/MED00310
DSAI-04 AI-DRIVEN MR IMAGE FEATURES VERSUS RANO-BM CRITERIA IN DISTINGUISHING RECURRENT BRAIN METASTASES FROM RADIATION TREATMENT EFFECT: A COMPARATIVE, MULTI-INSTITUTIONAL STUDY
Um Hyemin University of Wisconsin-Madison, Madison, WI, USA

Ismail Marwa University of Wisconsin-Madison, Madison, WI, USA

Hill Virginia Northwestern University Feinberg School of Medicine, Chicago, IL, USA

Puri Sushant Johns Hopkins Hospital, Baltimore, MD, USA

Yu Jennifer Cleveland Clinic, Cleveland, OH, USA

Lu Lan Cleveland Clinic, Cleveland, OH, USA

Nayate Ameya University Hospitals, Cleveland, OH, USA

Rogers Lisa Henry Ford Health, Detroit, MI, USA

Prasanna Prateek Strony Brook University, Stony Brook, NY, USA

Bardhan Mainak Miami Cancer Institute, Miami, FL, USA

Li Chengnan University of Wisconsin-Madison, Madison, WI, USA

Basree Mustafa University of Wisconsin-Madison, Madison, WI, USA

Baschnagel Andrew University of Wisconsin-Madison, Madison, WI, USA

McMillan Alan University of Wisconsin-Madison, Madison, WI, USA

Bhatia Ankush University of Wisconsin-Madison, Madison, WI, USA

Ahluwalia Manmeet Miami Cancer Institute, Miami, FL, USA

Veronesi Michael University of Wisconsin-Madison, Madison, WI, USA

Tiwari Pallavi University of Wisconsin-Madison, Madison, WI, USA

8 2024
02 8 2024
02 8 2024
6 Suppl 1 2024 SNO/ASCO CNS Metastases Conference i12i12
© 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

A significant challenge in brain metastases (BM) management is distinguishing radiation-induced treatment effect (TE) from tumor recurrence (TR). TE mimics the appearance of TR on follow-up MRI, making radiographic diagnosis unreliable. The standardized Response Assessment in Neuro-Oncology for brain metastases (RANO-BM) is suboptimal due to high inter-reader variability. We compared the performance of artificial intelligence (AI)-driven MRI features with that of RANO-BM criteria in differentiating TE from TR. We hypothesize AI-features from routine MRI can capture the pathophysiologic differences between TE and TR, occult on structural MRI and hence overlooked in standard-of-care evaluation. A total of 261 lesions with pathologically-confirmed diagnoses in 189 patients were retrospectively analyzed. 201 lesions (111 TR,90 TE) from Cleveland Clinic and University Hospitals, Cleveland were used for training a machine learning model. 60 lesions (33 TR,27 TE) from University of Wisconsin–Madison were used for model testing. MRI (Gd-T1w, T2w, FLAIR) were preprocessed, and lesions were expertly segmented into enhancing lesion, edema, and necrosis. 856 texture features were extracted from each sub-compartment, and a random forest classifier was employed for 3-fold cross-validation. Top-performing features and RANO-BM criteria were evaluated on the test set. Results show T1 features from edema were most discriminatory in differentiating TR from TE (training-AUC=0.86, test-accuracy=71.7%, test-sensitivity=78.8%). Using RANO-BM, 9 cases were excluded due to lack of longitudinal imaging to estimate lesion growth. Additionally, since no lesions decreased in sum of longest diameter, none were classified as partial response while the remaining 51 cases were classified as stable disease (n=14, (8 TR,6 TE)) or TR (n=37, accuracy=54.1%). Interestingly, 78.6% of the stable lesions were accurately classified using our AI-model as TE or TR, missing only 3 cases (2 TR,1 TE). Our results suggest AI-driven models on clinical MRI scans may reliably distinguish TR from TE, demonstrating potential utility in clinical practice.
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