Evidence map›Paper›PMID 42317859›Full record

ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2026

Multi-Modal Deep Learning-Based Model to Predict Burkitt Lymphoma Recurrence.

Avery C Maytin, Jessica A Patricoski-Chavez, Ari Pelcovits, Sanjay Mishra, Adam Olszewski, Jeremy L Warner, Ece D Gamsiz Uzun

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Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Avery C MaytinCenter for Computational Molecular Biology, Brown University, Providence, RI 02912.
Jessica A Patricoski-ChavezCenter for Computational Molecular Biology, Brown University, Providence, RI 02912.
Ari PelcovitsDepartment of Medicine, Brown University, Providence, RI 02903.
Sanjay MishraDepartment of Medicine, Brown University, Providence, RI 02903.
Adam OlszewskiDepartment of Medicine, Brown University, Providence, RI 02903.
Jeremy L WarnerBrown Center for Clinical Cancer Informatics and Data Science, Providence, RI 02903.
Ece D Gamsiz UzunCenter for Computational Molecular Biology, Brown University, Providence, RI 02912.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Burkitt Lymphoma (BL) is an aggressive B-cell non-Hodgkin lymphoma. Recurrent BL has extremely poor prognosis, and does not have a standard treatment option. Although the disease pathology is well characterized, there is currently a lack of research specifically on predictive modeling of BL recurrence. We developed a deep learning (DL) model, BLIMP (Burkitt Lymphoma multI-Modal recurrence Predictor) to predict BL recurrence by combining clinical, gene expression, and mutation data, utilizing a cohort of 184 patients from a publicly available dataset. Our approach achieved an AUC of 0.788 on a held-out testing set, outperforming traditional machine learning (ML) models. Explainability analysis was performed, and features which contributed to BLIMP's predictions are consistent with known patterns of BL pathophysiology and cancer progression. These results demonstrated the effectiveness of using genomic data in DL-based models for BL recurrence prediction, suggesting promise for future applications to BL research.

Identifiers

PMID42317859
PMCPMC13274402

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.