Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
570-06-206 ISMRM Abstract

Textual context in prostate MRI: Learning meaningful representations from radiology reports

Primary: Analysis Methods - Multi-Modal Learning with LLMs/VLMs
Secondary: Analysis Methods - Foundation Models
570-06-206 · Vision and Language Models in MRI · Wednesday, 13 May, 2:35 PM–3:30 PM · Traditional Posters | Exhibition Hall
Keywords: Large Language Model Representation learning Prostate cancer (PCa)
Accepted
Peter F Lais 1, Lavanya Umapathy2, Patricia M Johnson2,3, Hersh Chandarana2, Daniel K Sodickson2
1Department of Radiology, NYU Grossman School of Medicine, New York, United States of America
2Department of Radiology, Center for Advanced Imaging Innovation and Research (CAI²R), New York University Grossman School of Medicine, New York, United States of America
3Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, United States of America
Presenting Author: Peter F Lais

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References

1. Thompson JE, Moses D, Shnier R, et al. Multiparametric Magnetic Resonance Imaging Guided Diagnostic Biopsy Detects Significant Prostate Cancer and could Reduce Unnecessary Biopsies and Over Detection: A Prospective Study. J Urol. 2014;192(1):67-74. doi:10.1016/j.juro.2014.01.014 [doi]
2. Fütterer JJ, Briganti A, De Visschere P, et al. Can Clinically Significant Prostate Cancer Be Detected with Multiparametric Magnetic Resonance Imaging? A Systematic Review of the Literature. Eur Urol. 2015;68(6):1045-1053. doi:10.1016/j.eururo.2015.01.013 [doi]
3. Huang H, Mo J, Ding Z, et al. Deep Learning to Simulate Contrast-Enhanced MRI for Evaluating Suspected Prostate Cancer. Radiology. 2025;314(1):e240238. doi:10.1148/radiol.240238 [doi]
4. Umapathy L, Johnson PM, Dutt T, et al. Leveraging Representation Learning for Bi-parametric Prostate MRI to Disambiguate PI-RADS 3 and Improve Biopsy Decision Strategies. Invest Radiol. Published online June 30, 2025. doi:10.1097/RLI.0000000000001218 [doi]
5. Devlin J, Chang MW, Lee K, Toutanova K. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. arXiv. Preprint posted online May 24, 2019. doi:10.48550/arXiv.1810.04805 [doi]
6. Wolf T, Debut L, Sanh V, et al. HuggingFace’s Transformers: State-of-the-art Natural Language Processing. arXiv. Preprint posted online July 14, 2020. doi:10.48550/arXiv.1910.03771 [doi]
7. Pedregosa F, Varoquaux G, Gramfort A, et al. Scikit-learn: Machine Learning in Python. J Mach Learn Res. 2011;12(null):2825-2830.

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http://echo.ismrm.org/p/ISMRM2026/570-06-206