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Reinhardt C, Briody H, MacMahon PJ. AI-accelerated prostate MRI: a systematic review. Br J Radiol 2024; 97:1234-1242. [PMID: 38718224 PMCID: PMC11186563 DOI: 10.1093/bjr/tqae093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Revised: 04/05/2024] [Accepted: 05/04/2024] [Indexed: 06/21/2024] Open
Abstract
BACKGROUND Prostate cancer ranks among the most prevalent cancers affecting men globally. While conventional MRI serves as a diagnostic tool, its extended acquisition time, associated costs, and strain on healthcare systems, underscore the necessity for more efficient methods. The emergence of AI-acceleration in prostate MRI offers promise to mitigate these challenges. METHODS A systematic review of studies looking at AI-accelerated prostate MRI was conducted, with a focus on acquisition time along with various qualitative and quantitative measurements. RESULTS Two primary findings were observed. Firstly, all studies indicated that AI-acceleration in MRI achieved notable reductions in acquisition times without compromising image quality. This efficiency offers potential clinical advantages, including reduced scan durations, improved scheduling, diminished patient discomfort, and economic benefits. Secondly, AI demonstrated a beneficial effect in reducing or maintaining artefact levels in T2-weighted images despite this accelerated acquisition time. Inconsistent results were found in all other domains, which were likely influenced by factors such as heterogeneity in methodologies, variability in AI models, and diverse radiologist profiles. These variances underscore the need for larger, more robust studies, standardization, and diverse training datasets for AI models. CONCLUSION The integration of AI-acceleration in prostate MRI thus far shows some promising results for efficient and enhanced scanning. These advancements may fill current gaps in early detection and prognosis. However, careful navigation and collaborative efforts are essential to overcome challenges and maximize the potential of this innovative and evolving field. ADVANCES IN KNOWLEDGE This article reveals overall significant reductions in acquisition time without compromised image quality in AI-accelerated prostate MRI, highlighting potential clinical and diagnostic advantages.
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Affiliation(s)
- Ciaran Reinhardt
- Department of Radiology, Mater Misericordiae Hospital, Dublin D07AX57, Ireland
| | - Hayley Briody
- Department of Radiology, Beaumont Hospital, Dublin, D09V2N0, Ireland
| | - Peter J MacMahon
- Department of Radiology, Mater Misericordiae Hospital, Dublin D07AX57, Ireland
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Ghamande SS, Cline JK, Sayyid RK, Klaassen Z. Advancing Precision Oncology With Artificial Intelligence: Ushering in the ArteraAI Prostate Test. Urology 2024; 188:20-23. [PMID: 38648952 DOI: 10.1016/j.urology.2024.04.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2023] [Revised: 03/29/2024] [Accepted: 04/09/2024] [Indexed: 04/25/2024]
Affiliation(s)
| | - Joseph K Cline
- Section of Urology, Department of Surgery, Medical College of Georgia, Augusta University, Augusta, GA
| | - Rashid K Sayyid
- Division of Urology, Department of Surgery, University of Toronto, Toronto, Canada
| | - Zachary Klaassen
- Section of Urology, Department of Surgery, Medical College of Georgia, Augusta University, Augusta, GA; Georgia Cancer Center, Augusta, GA
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Zuluaga L, Rich JM, Gupta R, Pedraza A, Ucpinar B, Okhawere KE, Saini I, Dwivedi P, Patel D, Zaytoun O, Menon M, Tewari A, Badani KK. AI-powered real-time annotations during urologic surgery: The future of training and quality metrics. Urol Oncol 2024; 42:57-66. [PMID: 38142209 DOI: 10.1016/j.urolonc.2023.11.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Revised: 10/23/2023] [Accepted: 11/02/2023] [Indexed: 12/25/2023]
Abstract
INTRODUCTION AND OBJECTIVE Real-time artificial intelligence (AI) annotation of the surgical field has the potential to automatically extract information from surgical videos, helping to create a robust surgical atlas. This content can be used for surgical education and qualitative initiatives. We demonstrate the first use of AI in urologic robotic surgery to capture live surgical video and annotate key surgical steps and safety milestones in real-time. SUMMARY BACKGROUND DATA While AI models possess the capability to generate automated annotations based on a collection of video images, the real-time implementation of such technology in urological robotic surgery to aid surgeon and training staff it is still pending to be studied. METHODS We conducted an educational symposium, which broadcasted 2 live procedures, a robotic-assisted radical prostatectomy (RARP) and a robotic-assisted partial nephrectomy (RAPN). A surgical AI platform system (Theator, Palo Alto, CA) generated real-time annotations and identified operative safety milestones. This was achieved through trained algorithms, conventional video recognition, and novel Video Transfer Network technology which captures clips in full context, enabling automatic recognition and surgical mapping in real-time. RESULTS Real-time AI annotations for procedure #1, RARP, are found in Table 1. The safety milestone annotations included the apical safety maneuver and deliberate views of structures such as the external iliac vessels and the obturator nerve. Real-time AI annotations for procedure #2, RAPN, are found in Table 1. Safety milestones included deliberate views of structures such as the gonadal vessels and the ureter. AI annotated surgical events included intraoperative ultrasound, temporary clip application and removal, hemostatic powder application, and notable hemorrhage. CONCLUSIONS For the first time, surgical intelligence successfully showcased real-time AI annotations of 2 separate urologic robotic procedures during a live telecast. These annotations may provide the technological framework for send automatic notifications to clinical or operational stakeholders. This technology is a first step in real-time intraoperative decision support, leveraging big data to improve the quality of surgical care, potentially improve surgical outcomes, and support training and education.
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Affiliation(s)
- Laura Zuluaga
- Department of Urology, Icahn School of Medicine at Mount Sinai, New York City, NY.
| | - Jordan Miller Rich
- Department of Urology, Icahn School of Medicine at Mount Sinai, New York City, NY
| | - Raghav Gupta
- Department of Urology, Icahn School of Medicine at Mount Sinai, New York City, NY
| | - Adriana Pedraza
- Department of Urology, Icahn School of Medicine at Mount Sinai, New York City, NY
| | - Burak Ucpinar
- Department of Urology, Icahn School of Medicine at Mount Sinai, New York City, NY
| | - Kennedy E Okhawere
- Department of Urology, Icahn School of Medicine at Mount Sinai, New York City, NY
| | - Indu Saini
- Department of Urology, Icahn School of Medicine at Mount Sinai, New York City, NY
| | - Priyanka Dwivedi
- Department of Urology, Icahn School of Medicine at Mount Sinai, New York City, NY
| | - Dhruti Patel
- Department of Urology, Icahn School of Medicine at Mount Sinai, New York City, NY
| | - Osama Zaytoun
- Department of Urology, Icahn School of Medicine at Mount Sinai, New York City, NY
| | - Mani Menon
- Department of Urology, Icahn School of Medicine at Mount Sinai, New York City, NY
| | - Ashutosh Tewari
- Department of Urology, Icahn School of Medicine at Mount Sinai, New York City, NY
| | - Ketan K Badani
- Department of Urology, Icahn School of Medicine at Mount Sinai, New York City, NY
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Valeri A, Nguyen TA. Research on texture images and radiomics in urology: a review of urological MR imaging applications. Curr Opin Urol 2023; 33:428-436. [PMID: 37727910 DOI: 10.1097/mou.0000000000001131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/21/2023]
Abstract
PURPOSE OF REVIEW Tumor volume and heterogenicity are associated with diagnosis and prognosis of urological cancers, and assessed by conventional imaging. Quantitative imaging, Radiomics, using advanced mathematical analysis may contain information imperceptible to the human eye, and may identify imaging-based biomarkers, a new field of research for individualized medicine. This review summarizes the recent literature on radiomics in kidney and prostate cancers and the future perspectives. RECENT FINDINGS Radiomics studies have been developed and showed promising results in diagnosis, in characterization, prognosis, treatment planning and recurrence prediction in kidney tumors and prostate cancer, but its use in guiding clinical decision-making remains limited at present due to several limitations including lack of external validations in most studies, lack of prospective studies and technical standardization. SUMMARY Future challenges, besides developing prospective and validated studies, include automated segmentation using artificial intelligence deep learning networks and hybrid radiomics integrating clinical data, combining imaging modalities and genomic features. It is anticipated that these improvements may allow identify these noninvasive, imaging-based biomarkers, to enhance precise diagnosis, improve decision-making and guide tailored treatment.
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Affiliation(s)
- Antoine Valeri
- Urology Department, CHU Brest
- Faculté de Médecine et des Sciences de la Santé, Université de Brest
- LaTIM, INSERM, UMR 1101, CHU Brest, Brest
- CeRePP, Paris, France
| | - Truong An Nguyen
- Urology Department, CHU Brest
- Faculté de Médecine et des Sciences de la Santé, Université de Brest
- LaTIM, INSERM, UMR 1101, CHU Brest, Brest
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Reis LO. ChatGPT for medical applications and urological science. Int Braz J Urol 2023; 49:652-656. [PMID: 37338818 PMCID: PMC10482461 DOI: 10.1590/s1677-5538.ibju.2023.0112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2023] [Accepted: 04/30/2023] [Indexed: 06/21/2023] Open
Affiliation(s)
- Leonardo O. Reis
- Universidade Estadual de CampinasFaculdade de Ciências MédicasDepartamento de UrologiaSão PauloCampinasBrasilUroScience e Departamento de Urologia, Faculdade de Ciências Médicas, Universidade Estadual de Campinas - UNICAMP, Campinas, São Paulo, Brasil
- Pontifícia Universidade Católica de CampinasFaculdade de Ciências da VidaDepartamento de ImunoncologiaSão PauloCampinasBrasilDepartamento de Imunoncologia, Faculdade de Ciências da Vida, Pontifícia Universidade Católica de Campinas, PUC-Campinas, Campinas, São Paulo, Brasil
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Kiełb P, Kowalczyk K, Gurwin A, Nowak Ł, Krajewski W, Sosnowski R, Szydełko T, Małkiewicz B. Novel Histopathological Biomarkers in Prostate Cancer: Implications and Perspectives. Biomedicines 2023; 11:1552. [PMID: 37371647 DOI: 10.3390/biomedicines11061552] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Revised: 05/12/2023] [Accepted: 05/24/2023] [Indexed: 06/29/2023] Open
Abstract
Prostate cancer (PCa) is the second most frequently diagnosed cancer in men. Despite the significant progress in cancer diagnosis and treatment over the last few years, the approach to disease detection and therapy still does not include histopathological biomarkers. The dissemination of PCa is strictly related to the creation of a premetastatic niche, which can be detected by altered levels of specific biomarkers. To date, the risk factors for biochemical recurrence include lymph node status, prostate-specific antigen (PSA), PSA density (PSAD), body mass index (BMI), pathological Gleason score, seminal vesicle invasion, extraprostatic extension, and intraductal carcinoma. In the future, biomarkers might represent another prognostic factor, as discussed in many studies. In this review, we focus on histopathological biomarkers (particularly CD169 macrophages, neuropilin-1, cofilin-1, interleukin-17, signal transducer and activator of transcription protein 3 (STAT3), LIM domain kinase 1 (LIMK1), CD15, AMACR, prostate-specific membrane antigen (PSMA), Appl1, Sortilin, Syndecan-1, and p63) and their potential application in decision making regarding the prognosis and treatment of PCa patients. We refer to studies that found a correlation between the levels of biomarkers and tumor characteristics as well as clinical outcomes. We also hypothesize about the potential use of histopathological markers as a target for novel immunotherapeutic drugs or targeted radionuclide therapy, which may be used as adjuvant therapy in the future.
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Affiliation(s)
- Paweł Kiełb
- University Center of Excellence in Urology, Department of Minimally Invasive and Robotic Urology, Wrocław Medical University, 50-556 Wroclaw, Poland
| | - Kamil Kowalczyk
- University Center of Excellence in Urology, Department of Minimally Invasive and Robotic Urology, Wrocław Medical University, 50-556 Wroclaw, Poland
| | - Adam Gurwin
- University Center of Excellence in Urology, Department of Minimally Invasive and Robotic Urology, Wrocław Medical University, 50-556 Wroclaw, Poland
| | - Łukasz Nowak
- University Center of Excellence in Urology, Department of Minimally Invasive and Robotic Urology, Wrocław Medical University, 50-556 Wroclaw, Poland
| | - Wojciech Krajewski
- University Center of Excellence in Urology, Department of Minimally Invasive and Robotic Urology, Wrocław Medical University, 50-556 Wroclaw, Poland
| | - Roman Sosnowski
- Department of Urogenital Cancer, Maria Skłodowska-Curie National Research Institute of Oncology, 02-781 Warsaw, Poland
| | - Tomasz Szydełko
- University Center of Excellence in Urology, Department of Minimally Invasive and Robotic Urology, Wrocław Medical University, 50-556 Wroclaw, Poland
| | - Bartosz Małkiewicz
- University Center of Excellence in Urology, Department of Minimally Invasive and Robotic Urology, Wrocław Medical University, 50-556 Wroclaw, Poland
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