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Radiomic features define risk and are linked to DNA methylation attributes in primary CNS lymphoma.

Authors :
Nenning KH
Gesperger J
Furtner J
Nemc A
Roetzer-Pejrimovsky T
Choi SW
Mitter C
Leber SL
Hofmanninger J
Klughammer J
Ergüner B
Bauer M
Brada M
Chong K
Brandner-Kokalj T
Freyschlag CF
Grams A
Haybaeck J
Hoenigschnabl S
Hoffermann M
Iglseder S
Kiesel B
Kitzwoegerer M
Kleindienst W
Marhold F
Moser P
Oberndorfer S
Pinggera D
Scheichel F
Sherif C
Stockhammer G
Stultschnig M
Thomé C
Trenkler J
Urbanic-Purkart T
Weis S
Widhalm G
Wuertz F
Preusser M
Baumann B
Simonitsch-Klupp I
Nam DH
Bock C
Langs G
Woehrer A
Source :
Neuro-oncology advances [Neurooncol Adv] 2023 Oct 18; Vol. 5 (1), pp. vdad136. Date of Electronic Publication: 2023 Oct 18 (Print Publication: 2023).
Publication Year :
2023

Abstract

Background: The prognostic roles of clinical and laboratory markers have been exploited to model risk in patients with primary CNS lymphoma, but these approaches do not fully explain the observed variation in outcome. To date, neuroimaging or molecular information is not used. The aim of this study was to determine the utility of radiomic features to capture clinically relevant phenotypes, and to link those to molecular profiles for enhanced risk stratification.<br />Methods: In this retrospective study, we investigated 133 patients across 9 sites in Austria (2005-2018) and an external validation site in South Korea (44 patients, 2013-2016). We used T1-weighted contrast-enhanced MRI and an L1-norm regularized Cox proportional hazard model to derive a radiomic risk score. We integrated radiomic features with DNA methylation profiles using machine learning-based prediction, and validated the most relevant biological associations in tissues and cell lines.<br />Results: The radiomic risk score, consisting of 20 mostly textural features, was a strong and independent predictor of survival (multivariate hazard ratio = 6.56 [3.64-11.81]) that remained valid in the external validation cohort. Radiomic features captured gene regulatory differences such as in BCL6 binding activity, which was put forth as testable treatment target for a subset of patients.<br />Conclusions: The radiomic risk score was a robust and complementary predictor of survival and reflected characteristics in underlying DNA methylation patterns. Leveraging imaging phenotypes to assess risk and inform epigenetic treatment targets provides a concept on which to advance prognostic modeling and precision therapy for this aggressive cancer.<br />Competing Interests: M.P. has received honoraria for lectures, consultation, or advisory board participation from the following for-profit companies: Bayer, Bristol-Myers Squibb, Novartis, Gerson Lehrman Group (GLG), CMC Contrast, GlaxoSmithKline, Mundipharma, Roche, BMJ Journals, MedMedia, Astra Zeneca, AbbVie, Lilly, Medahead, Daiichi Sankyo, Sanofi, Merck Sharp & Dome, Tocagen, Adastra, Gan & Lee Pharmaceuticals, but declares no nonfinancial competing interests. G.L. holds shares in the company contextflow and has received honoraria for lectures from the following for-profit companies: Boehringer Ingelheim, Novartis, and declares no nonfinancial competing interests. All other authors declare no financial or nonfinancial conflicts of interest.<br /> (© The Author(s) 2023. Published by Oxford University Press, the Society for Neuro-Oncology and the European Association of Neuro-Oncology.)

Details

Language :
English
ISSN :
2632-2498
Volume :
5
Issue :
1
Database :
MEDLINE
Journal :
Neuro-oncology advances
Publication Type :
Academic Journal
Accession number :
38024240
Full Text :
https://doi.org/10.1093/noajnl/vdad136