Leveraging Representation Learning for Bi-parametric Prostate MRI to Disambiguate PI-RADS 3 and Improve Biopsy Decision Strategies
Our PI-RADS-guided deep learning RL model learns summary representations from bi-parametric prostate MR images that can provide additional information to disambiguate intermediate-risk PI-RADS 3 assessments. The resulting RL-based biopsy decision models also outperformed radiologists in avoiding benign biopsies while maintaining comparable sensitivity to csPCa for the all-PI-RADS cohort. Such AI models can easily be integrated into clinical practice to supplement radiologists' reads in general and improve biopsy yield for any equivocal decisions.
Authors
- Lavanya Umapathy (ORCID: https://orcid.org/0000-0002-7224-0930)
- Daniel K. Sodickson (ORCID: https://orcid.org/0000-0002-2436-4664)
- Tarun Dutt (ORCID: https://orcid.org/0009-0003-7683-4271)
- Angela Tong (ORCID: https://orcid.org/0000-0002-4733-3414)
- Patricia M. Johnson (ORCID: https://orcid.org/0000-0003-1547-9969)
- Hersh Chandarana (ORCID: https://orcid.org/0000-0002-6807-4589)
- Sumit Chopra (ORCID: https://orcid.org/0009-0009-6637-2230)
Institutions
- New York University (US)
Publication Details
- Journal
- Investigative Radiology
- Published
- 2025-06-30
- DOI
- https://doi.org/10.1097/rli.0000000000001218
- Citations
- 1
- Primary Topic
- Prostate Cancer Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 1.52