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Artificial intelligence applications in prostate cancer

Prostate Cancer and Prostatic Diseases volume 27, pages 37–45 (2024)Cite this article

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Abstract

Artificial intelligence (AI) applications have enabled remarkable advancements in healthcare delivery. These AI tools are often aimed to improve accuracy and efficiency of histopathology assessment and diagnostic imaging interpretation, risk stratification (i.e., prognostication), and prediction of therapeutic benefit for personalized treatment recommendations. To date, multiple AI algorithms have been explored for prostate cancer to address automation of clinical workflow, integration of data from multiple domains in the decision-making process, and the generation of diagnostic, prognostic, and predictive biomarkers. While many studies remain within the pre-clinical space or lack validation, the last few years have witnessed the emergence of robust AI-based biomarkers validated on thousands of patients, and the prospective deployment of clinically-integrated workflows for automated radiation therapy design. To advance the field forward, multi-institutional and multi-disciplinary collaborations are needed in order to prospectively implement interoperable and accountable AI technology routinely in clinic.

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Fig. 1: Presentation to clinical action flow in prostate cancer and AI potential.
Fig. 2: Classes of AI algorithms and their main applications in PCa.

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Acknowledgements

We thank UH Seidman Cancer Center and generous philanthropy from patients. Figures 1 and 2 were done using biorender.com

Author information

Authors and Affiliations

  1. Department of Radiation Oncology, University Hospitals Seidman Cancer Center, Case Western Reserve University, Cleveland, OH, 44106, USA

    Atallah Baydoun, Angela Y. Jia, Nicholas G. Zaorsky, Rojano Kashani, Raed Zuhour, Alex T. Price, Theodore H. Arsenault & Daniel E. Spratt

  2. Department of Medicine, University Hospitals Seidman Cancer Center, Case Western Reserve University, Cleveland, OH, 44106, USA

    Santosh Rao

  3. Department of Urology, University Hospitals Seidman Cancer Center, Case Western Reserve University, Cleveland, OH, 44106, USA

    Jonathan E. Shoag & Randy A. Vince Jr

  4. Department of Radiology, University Hospitals Cleveland Medical Center Center, Case Western Reserve University, Cleveland, OH, 44106, USA

    Leonardo Kayat Bittencourt

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Contributions

Conceptualization: AB and DES. Writing: AB and DES. Reviewing & Editing: AB, AYJ, NGZ, RK, SR, JS, RAV, LKB, RZ, AP, THA, and DES. All authors approved the final version of the paper.

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Correspondence to Daniel E. Spratt.

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AB: None. AYJ: Personal Fees from Myovant. NGZ: Support by the National Institutes of Health Grant LRP 1 L30 CA231572-01 (2018-2022) and by the American Cancer Society – Tri State CEOs Against Cancer Clinician Scientist Development Grant, CSDG-20-013-01-CCE (2020-). Remuneration from Springer Nature for his textbook, Absolute Clinical Radiation Oncology Review (2019). Remuneration from the American College of Radiation Oncology for chart review and accreditation of radiation oncology facilities nationally (2020-). RK: None. SR: Personal speaking fees from Seagen. JS: Grant support from BMS Foundation, Damon Runyon Cancer Research Foundation. Key opinon leader for Fortec Medical. RAV: None. LKB: None. RZ: None. AP: Honoraria with both ViewRay and SunNuclear. THA: None. DES: Personal fees from AstraZeneca, Blue Earth, Bayer, Boston Scientific, Janssen, Novartis, Myovant, Pfizer, Varian, Elekta, Gamma Tile.

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Baydoun, A., Jia, A.Y., Zaorsky, N.G. et al. Artificial intelligence applications in prostate cancer. Prostate Cancer Prostatic Dis 27, 37–45 (2024). https://doi.org/10.1038/s41391-023-00684-0

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