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

Karl-Heinz Nenning, Johanna Gesperger, Julia Furtner, Amelie Nemc, Thomas Roetzer-Pejrimovsky, Seung-Won Choi, Christian Mitter, Stefan L Leber, Johannes Hofmanninger, Johanna Klughammer, Bekir Ergüner, Marlies Bauer, Martina Brada, Kyuha Chong, Tanisa Brandner-Kokalj, Christian F Freyschlag, Astrid Grams, Johannes Haybaeck, Selma Hoenigschnabl, Markus HoffermannSarah Iglseder, Barbara Kiesel, Melitta Kitzwoegerer, Waltraud Kleindienst, Franz Marhold, Patrizia Moser, Stefan Oberndorfer, Daniel Pinggera, Florian Scheichel, Camillo Sherif, Guenther Stockhammer, Martin Stultschnig, Claudius Thomé, Johannes Trenkler, Tadeja Urbanic-Purkart, Serge Weis, Georg Widhalm, Franz Wuertz, Matthias Preusser, Bernhard Baumann, Ingrid Simonitsch-Klupp, Do-Hyun Nam, Christoph Bock, Georg Langs, Adelheid Woehrer

Research output: Journal article (peer-reviewed)Journal article

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.

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.

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.

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.

Original languageEnglish
Article numbervdad136
Pages (from-to)vdad136
JournalNeuro-Oncology Advances
Volume5
Issue number1
DOIs
Publication statusPublished - 18 Oct 2023

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