Catalyzing Precision Medicine: Artificial Intelligence Advancements in Prostate Cancer Diagnosis and Management
Talyshinskii A. Hameed B.M.Z. Ravinder P.P. Naik N. Randhawa P. Shah M. Rai B.P. Tokas T. Somani B.K.
May 2024Multidisciplinary Digital Publishing Institute (MDPI)
Cancers
2024#16Issue 10
Background: The aim was to analyze the current state of deep learning (DL)-based prostate cancer (PCa) diagnosis with a focus on magnetic resonance (MR) prostate reconstruction; PCa detection/stratification/reconstruction; positron emission tomography/computed tomography (PET/CT); androgen deprivation therapy (ADT); prostate biopsy; associated challenges and their clinical implications. Methods: A search of the PubMed database was conducted based on the inclusion and exclusion criteria for the use of DL methods within the abovementioned areas. Results: A total of 784 articles were found, of which, 64 were included. Reconstruction of the prostate, the detection and stratification of prostate cancer, the reconstruction of prostate cancer, and diagnosis on PET/CT, ADT, and biopsy were analyzed in 21, 22, 6, 7, 2, and 6 studies, respectively. Among studies describing DL use for MR-based purposes, datasets with magnetic field power of 3 T, 1.5 T, and 3/1.5 T were used in 18/19/5, 0/1/0, and 3/2/1 studies, respectively, of 6/7 studies analyzing DL for PET/CT diagnosis which used data from a single institution. Among the radiotracers, [68Ga]Ga-PSMA-11, [18F]DCFPyl, and [18F]PSMA-1007 were used in 5, 1, and 1 study, respectively. Only two studies that analyzed DL in the context of DT met the inclusion criteria. Both were performed with a single-institution dataset with only manual labeling of training data. Three studies, each analyzing DL for prostate biopsy, were performed with single- and multi-institutional datasets. TeUS, TRUS, and MRI were used as input modalities in two, three, and one study, respectively. Conclusion: DL models in prostate cancer diagnosis show promise but are not yet ready for clinical use due to variability in methods, labels, and evaluation criteria. Conducting additional research while acknowledging all the limitations outlined is crucial for reinforcing the utility and effectiveness of DL-based models in clinical settings.
ADT , artificial intelligence , biopsy , deep learning , MRI , PCa detection , PCa reconstruction , PET/CT , prostate cancer , prostate reconstruction
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Department of Urology and Andrology, Astana Medical University, Astana, 010000, Kazakhstan
Department of Urology, KMC Manipal Hospitals, Mangalore, 575001, India
Department of Urology, Kasturba Medical College, Mangaluru, Manipal Academy of Higher Education, Manipal, 576104, India
Department of Mechanical and Industrial Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 576104, India
Department of Mechatronics, Manipal University Jaipur, Jaipur, 303007, India
Department of Urology, Aarogyam Hospital, Ahmedabad, 380014, India
Department of Urology, Freeman Hospital, Newcastle upon Tyne, NE7 7DN, United Kingdom
Department of Urology, Medical School, University General Hospital of Heraklion, University of Crete, Heraklion, 14122, Greece
Department of Urology, University Hospital Southampton NHS Trust, Southampton, SO16 6YD, United Kingdom
Department of Urology and Andrology
Department of Urology
Department of Urology
Department of Mechanical and Industrial Engineering
Department of Mechatronics
Department of Urology
Department of Urology
Department of Urology
Department of Urology
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