Exploring the Use of Artificial Intelligence in the Management of Prostate Cancer

Overview

Journal Curr Urol Rep

Publisher Current Science

Specialty Urology

Date 2023 Feb 22

PMID 36808595

Authors

Timothy N Chu

Elyssa Y Wong

Runzhuo Ma

Cherine H Yang

Istabraq S Dalieh

Andrew J Hung

Affiliations

Soon will be listed here.

Abstract

Purpose Of Review: This review aims to explore the current state of research on the use of artificial intelligence (AI) in the management of prostate cancer. We examine the various applications of AI in prostate cancer, including image analysis, prediction of treatment outcomes, and patient stratification. Additionally, the review will evaluate the current limitations and challenges faced in the implementation of AI in prostate cancer management.

Recent Findings: Recent literature has focused particularly on the use of AI in radiomics, pathomics, the evaluation of surgical skills, and patient outcomes. AI has the potential to revolutionize the future of prostate cancer management by improving diagnostic accuracy, treatment planning, and patient outcomes. Studies have shown improved accuracy and efficiency of AI models in the detection and treatment of prostate cancer, but further research is needed to understand its full potential as well as limitations.

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References

Strom P, Kartasalo K, Olsson H, Solorzano L, Delahunt B, Berney D . Artificial intelligence for diagnosis and grading of prostate cancer in biopsies: a population-based, diagnostic study. Lancet Oncol. 2020; 21(2):222-232. DOI: 10.1016/S1470-2045(19)30738-7. View

Akatsuka J, Numata Y, Morikawa H, Sekine T, Kayama S, Mikami H . A data-driven ultrasound approach discriminates pathological high grade prostate cancer. Sci Rep. 2022; 12(1):860. PMC: 8764059. DOI: 10.1038/s41598-022-04951-3. View

Lucas M, Jansen I, Dilara Savci-Heijink C, Meijer S, de Boer O, van Leeuwen T . Deep learning for automatic Gleason pattern classification for grade group determination of prostate biopsies. Virchows Arch. 2019; 475(1):77-83. PMC: 6611751. DOI: 10.1007/s00428-019-02577-x. View

Koo K, Lee K, Kim S, Min C, Min G, Lee Y . Long short-term memory artificial neural network model for prediction of prostate cancer survival outcomes according to initial treatment strategy: development of an online decision-making support system. World J Urol. 2020; 38(10):2469-2476. DOI: 10.1007/s00345-020-03080-8. View

Trinh L, Mingo S, Vanstrum E, Sanford D, Aastha , Ma R . Survival Analysis Using Surgeon Skill Metrics and Patient Factors to Predict Urinary Continence Recovery After Robot-assisted Radical Prostatectomy. Eur Urol Focus. 2021; 8(2):623-630. PMC: 8505550. DOI: 10.1016/j.euf.2021.04.001. View

Lee R, Ma R, Pham S, Maya-Silva J, Nguyen J, Aron M . Machine Learning to Delineate Surgeon and Clinical Factors That Anticipate Positive Surgical Margins After Robot-Assisted Radical Prostatectomy. J Endourol. 2022; 36(9):1192-1198. PMC: 9422786. DOI: 10.1089/end.2021.0890. View

Nouranian S, Ramezani M, Spadinger I, Morris W, Salcudean S, Abolmaesumi P . Learning-Based Multi-Label Segmentation of Transrectal Ultrasound Images for Prostate Brachytherapy. IEEE Trans Med Imaging. 2015; 35(3):921-32. DOI: 10.1109/TMI.2015.2502540. View

Woznicki P, Westhoff N, Huber T, Riffel P, Froelich M, Gresser E . Multiparametric MRI for Prostate Cancer Characterization: Combined Use of Radiomics Model with PI-RADS and Clinical Parameters. Cancers (Basel). 2020; 12(7). PMC: 7407326. DOI: 10.3390/cancers12071767. View

Schelb P, Kohl S, Radtke J, Wiesenfarth M, Kickingereder P, Bickelhaupt S . Classification of Cancer at Prostate MRI: Deep Learning versus Clinical PI-RADS Assessment. Radiology. 2019; 293(3):607-617. DOI: 10.1148/radiol.2019190938. View

10.

Hung A, Liu Y, Anandkumar A . Deep Learning to Automate Technical Skills Assessment in Robotic Surgery. JAMA Surg. 2021; 156(11):1059-1060. DOI: 10.1001/jamasurg.2021.3651. View

11.

Khalid S, Goldenberg M, Grantcharov T, Taati B, Rudzicz F . Evaluation of Deep Learning Models for Identifying Surgical Actions and Measuring Performance. JAMA Netw Open. 2020; 3(3):e201664. DOI: 10.1001/jamanetworkopen.2020.1664. View

12.

Ma R, Ramaswamy A, Xu J, Trinh L, Kiyasseh D, Chu T . Surgical gestures as a method to quantify surgical performance and predict patient outcomes. NPJ Digit Med. 2022; 5(1):187. PMC: 9780308. DOI: 10.1038/s41746-022-00738-y. View

13.

Baghdadi A, Hussein A, Ahmed Y, Cavuoto L, Guru K . A computer vision technique for automated assessment of surgical performance using surgeons' console-feed videos. Int J Comput Assist Radiol Surg. 2018; 14(4):697-707. DOI: 10.1007/s11548-018-1881-9. View

14.

Schuettfort V, Pradere B, Rink M, Comperat E, Shariat S . Pathomics in urology. Curr Opin Urol. 2020; 30(6):823-831. DOI: 10.1097/MOU.0000000000000813. View

15.

Kott O, Linsley D, Amin A, Karagounis A, Jeffers C, Golijanin D . Development of a Deep Learning Algorithm for the Histopathologic Diagnosis and Gleason Grading of Prostate Cancer Biopsies: A Pilot Study. Eur Urol Focus. 2019; 7(2):347-351. PMC: 7242119. DOI: 10.1016/j.euf.2019.11.003. View

16.

Ma R, Reddy S, Vanstrum E, Hung A . Innovations in Urologic Surgical Training. Curr Urol Rep. 2021; 22(4):26. PMC: 8106917. DOI: 10.1007/s11934-021-01043-z. View

17.

Winkel D, Wetterauer C, Matthias M, Lou B, Shi B, Kamen A . Autonomous Detection and Classification of PI-RADS Lesions in an MRI Screening Population Incorporating Multicenter-Labeled Deep Learning and Biparametric Imaging: Proof of Concept. Diagnostics (Basel). 2020; 10(11). PMC: 7697194. DOI: 10.3390/diagnostics10110951. View

18.

Marginean F, Arvidsson I, Simoulis A, Overgaard N, Astrom K, Heyden A . An Artificial Intelligence-based Support Tool for Automation and Standardisation of Gleason Grading in Prostate Biopsies. Eur Urol Focus. 2020; 7(5):995-1001. DOI: 10.1016/j.euf.2020.11.001. View

19.

Khosravi P, Lysandrou M, Eljalby M, Li Q, Kazemi E, Zisimopoulos P . A Deep Learning Approach to Diagnostic Classification of Prostate Cancer Using Pathology-Radiology Fusion. J Magn Reson Imaging. 2021; 54(2):462-471. PMC: 8360022. DOI: 10.1002/jmri.27599. View

20.

Wildeboer R, Mannaerts C, Van Sloun R, Budaus L, Tilki D, Wijkstra H . Automated multiparametric localization of prostate cancer based on B-mode, shear-wave elastography, and contrast-enhanced ultrasound radiomics. Eur Radiol. 2019; 30(2):806-815. PMC: 6957554. DOI: 10.1007/s00330-019-06436-w. View