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, Gabe Dumbrille Search for other works by this author on: Oxford Academic Silu Zhang St Jude Children’s Reaserch Hospital , Memphis, TN, USA Search for other works by this author on: Oxford Academic Giles W Robinson St Jude Children’s Reaserch Hospital , Memphis, TN, USA Search for other works by this author on: Oxford Academic Thomas E Merchant St Jude Children’s Reaserch Hospital , Memphis, TN, USA Search for other works by this author on: Oxford Academic Asim K Bag St Jude Children’s Reaserch Hospital , Memphis, TN, USA Search for other works by this author on: Oxford Academic
Neuro-Oncology, Volume 26, Issue Supplement_4, June 2024, Page 0, https://doi.org/10.1093/neuonc/noae064.418
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18 June 2024
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Gabe Dumbrille, Silu Zhang, Giles W Robinson, Thomas E Merchant, Asim K Bag, LGG-25. ADVANCING RESPONSE ASSESSMENT OF LOW-GRADE GLIOMAS: IDENTIFYING VOLUME CORRELATIONS OF BI-PERPENDICULAR DIAMETER CHANGES WITH MACHINE LEARNING, Neuro-Oncology, Volume 26, Issue Supplement_4, June 2024, Page 0, https://doi.org/10.1093/neuonc/noae064.418
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Abstract
BACKGROUND
Assessing brain tumor response is key for effective management and clinical trial benchmarks. Traditionally, bi-perpendicular diameter (BPD) measurements have been used to estimate tumor size. However, recent studies suggest volumetric assessment yields a more accurate measure of response and estimate of tumor burden. Advances in technology enable rapid volume assessments. Integrating volumetric analysis into clinical settings is challenged by the absence of volume-based standards aligned with traditional RAPNO bi-dimensional criteria. Our goal was to correlate volume change percentages with bi-perpendicular diameter changes using machine learning.
METHODS
In this project, we compared 2 machine learning models to establish a correlation between percentage change of sum of products of BPD and percent change in volume using a generalized linear model (GLM) and an artificial neural network (ANN). We analyzed 49 patients with low grade glioma, treated with focal irradiation (NCT04065776) (n=27) or mirdametinib (NCT04923126) (n=22). We trained and validated the models at train-test split ratios of 80/20, 75/25, and 70/30, incorporating a proportionate split of tumors from each group. The models were compared on the validation data set using mean-squared error (MSE).
RESULTS
At percentage changes of +25%, -25%, -50%, and -75% in the sum of BPDs, the GLM predicts changes in volume to be +33%, -31%, -59%, and -83%, respectively. For the ANN, the predicted changes are +20%, -29%, -53%, and -78%. The ANN demonstrated a lower MSE compared to the GLM. The two models showed higher concordance in their volume predictions at negative percentage changes in the sum of BPDs: -25% (p=0.15), -50% (p=0.03), and 75% (p=.08)
CONCLUSION
We identified volume percentage changes matching BPD changes per RAPNO criteria using two machine learning methods, both agreeing on negative values. Further testing with a larger dataset is needed to reliably correlate percentage changes.
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© The Author(s) 2024. Published by Oxford University Press on behalf of the Society for Neuro-Oncology.
This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial 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 reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.
Issue Section:
Final category: Low Grade Glioma
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