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Pak S, Park SG, Park J, Cho ST, Lee YG, Ahn H. Applications of artificial intelligence in urologic oncology. Investig Clin Urol 2024; 65:202-216. [PMID: 38714511 PMCID: PMC11076794 DOI: 10.4111/icu.20230435] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2023] [Revised: 02/24/2024] [Accepted: 03/11/2024] [Indexed: 05/10/2024] Open
Abstract
PURPOSE With the recent rising interest in artificial intelligence (AI) in medicine, many studies have explored the potential and usefulness of AI in urological diseases. This study aimed to comprehensively review recent applications of AI in urologic oncology. MATERIALS AND METHODS We searched the PubMed-MEDLINE databases for articles in English on machine learning (ML) and deep learning (DL) models related to general surgery and prostate, bladder, and kidney cancer. The search terms were a combination of keywords, including both "urology" and "artificial intelligence" with one of the following: "machine learning," "deep learning," "neural network," "renal cell carcinoma," "kidney cancer," "urothelial carcinoma," "bladder cancer," "prostate cancer," and "robotic surgery." RESULTS A total of 58 articles were included. The studies on prostate cancer were related to grade prediction, improved diagnosis, and predicting outcomes and recurrence. The studies on bladder cancer mainly used radiomics to identify aggressive tumors and predict treatment outcomes, recurrence, and survival rates. Most studies on the application of ML and DL in kidney cancer were focused on the differentiation of benign and malignant tumors as well as prediction of their grade and subtype. Most studies suggested that methods using AI may be better than or similar to existing traditional methods. CONCLUSIONS AI technology is actively being investigated in the field of urological cancers as a tool for diagnosis, prediction of prognosis, and decision-making and is expected to be applied in additional clinical areas soon. Despite technological, legal, and ethical concerns, AI will change the landscape of urological cancer management.
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Affiliation(s)
- Sahyun Pak
- Department of Urology, Kangnam Sacred Heart Hospital, Hallym University College of Medicine, Seoul, Korea
| | - Sung Gon Park
- Department of Urology, Kangnam Sacred Heart Hospital, Hallym University College of Medicine, Seoul, Korea
| | | | - Sung Tae Cho
- Department of Urology, Kangnam Sacred Heart Hospital, Hallym University College of Medicine, Seoul, Korea
| | - Young Goo Lee
- Department of Urology, Kangnam Sacred Heart Hospital, Hallym University College of Medicine, Seoul, Korea
| | - Hanjong Ahn
- Department of Urology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
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2
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Léost F, Barbet J, Beyler M, Chérel M, Delpon G, Garcion E, Lacerda S, Lepareur N, Rbah-Vidal L, Vaugier L, Visvikis D. ["New Modalities in Cancer Imaging and Therapy" XVth edition of the workshop organized by the network "Tumor Targeting, Imaging, Radiotherapies" of the Cancéropôle Grand-Ouest, 5-8 October 2022, France]. Bull Cancer 2023; 110:1322-1331. [PMID: 37880044 DOI: 10.1016/j.bulcan.2023.08.007] [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: 01/23/2023] [Revised: 05/16/2023] [Accepted: 08/13/2023] [Indexed: 10/27/2023]
Abstract
The fifteenth edition of the international workshop organized by the "Tumour Targeting and Radiotherapies network" of the Cancéropôle Grand-Ouest focused on the latest advances in internal and external radiotherapy from different disciplinary angles: chemistry, biology, physics, and medicine. The workshop covered several deliberately diverse topics: the role of artificial intelligence, new tools for imaging and external radiotherapy, theranostic aspects, molecules and contrast agents, vectors for innovative combined therapies, and the use of alpha particles in therapy.
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Affiliation(s)
- Françoise Léost
- Cancéropôle Grand-Ouest, IRS-UN, 8, quai Moncousu, 44007 Nantes cedex 1, France.
| | | | - Maryline Beyler
- Université de Brest, UMR CNRS-UBO 6521 CEMCA, 6, avenue V.-Le-Gorgeu, 29200 Brest, France
| | - Michel Chérel
- Nantes Université, Inserm, CNRS, Université d'Angers, CRCI(2)NA, Nantes, France
| | - Grégory Delpon
- Institut de cancérologie de l'Ouest, département de physique médicale, boulevard Jacques-Monod, 44800 Saint-Herblain, France; Laboratoire SUBATECH, UMR 6457 CNRS-IN2P3, IMT Atlantique, 4, rue Alfred-Kastler, 44307 Nantes cedex 3, France
| | - Emmanuel Garcion
- Université d'Angers, Inserm, CNRS, Nantes Université, CRCI(2)NA, Angers, France
| | - Sara Lacerda
- Université d'Orléans, centre de biophysique moléculaire, CNRS UPR 4301, rue Charles-Sadron, 45071 Orléans cedex 2, France
| | - Nicolas Lepareur
- Université de Rennes, Inrae, Inserm, CLCC Eugène-Marquis, institut nutrition, métabolismes et cancer (NUMECAN), UMR 1317, Rennes, France
| | - Latifa Rbah-Vidal
- Nantes Université, Inserm, CNRS, Université d'Angers, CRCI(2)NA, Nantes, France
| | - Loïg Vaugier
- Institut de cancérologie de l'Ouest, département de physique médicale, boulevard Jacques-Monod, 44800 Saint-Herblain, France; Laboratoire SUBATECH, UMR 6457 CNRS-IN2P3, IMT Atlantique, 4, rue Alfred-Kastler, 44307 Nantes cedex 3, France
| | - Dimitris Visvikis
- Inserm, LaTIM, UMR 1101, IBSAM, UBO, UBL, 22, rue Camille-Desmoulins, 29238 Brest, France
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3
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Wei L, Niraula D, Gates EDH, Fu J, Luo Y, Nyflot MJ, Bowen SR, El Naqa IM, Cui S. Artificial intelligence (AI) and machine learning (ML) in precision oncology: a review on enhancing discoverability through multiomics integration. Br J Radiol 2023; 96:20230211. [PMID: 37660402 PMCID: PMC10546458 DOI: 10.1259/bjr.20230211] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 06/15/2023] [Accepted: 06/27/2023] [Indexed: 09/05/2023] Open
Abstract
Multiomics data including imaging radiomics and various types of molecular biomarkers have been increasingly investigated for better diagnosis and therapy in the era of precision oncology. Artificial intelligence (AI) including machine learning (ML) and deep learning (DL) techniques combined with the exponential growth of multiomics data may have great potential to revolutionize cancer subtyping, risk stratification, prognostication, prediction and clinical decision-making. In this article, we first present different categories of multiomics data and their roles in diagnosis and therapy. Second, AI-based data fusion methods and modeling methods as well as different validation schemes are illustrated. Third, the applications and examples of multiomics research in oncology are demonstrated. Finally, the challenges regarding the heterogeneity data set, availability of omics data, and validation of the research are discussed. The transition of multiomics research to real clinics still requires consistent efforts in standardizing omics data collection and analysis, building computational infrastructure for data sharing and storing, developing advanced methods to improve data fusion and interpretability, and ultimately, conducting large-scale prospective clinical trials to fill the gap between study findings and clinical benefits.
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Affiliation(s)
- Lise Wei
- Department of Radiation Oncology, University of Michigan, Michigan, United States
| | - Dipesh Niraula
- Department of Radiation Oncology, Moffitt Cancer Center, Tampa, United States
| | - Evan D. H. Gates
- Department of Radiation Oncology, University of Washington, Washington, United States
| | - Jie Fu
- Department of Radiation Oncology, Stanford University, Stanford, California, United States
| | - Yi Luo
- Department of Radiation Oncology, Moffitt Cancer Center, Tampa, United States
| | - Matthew J. Nyflot
- Department of Radiation Oncology, University of Washington, Washington, United States
| | - Stephen R. Bowen
- Department of Radiation Oncology, University of Washington, Washington, United States
| | - Issam M El Naqa
- Department of Radiation Oncology, Moffitt Cancer Center, Tampa, United States
| | - Sunan Cui
- Department of Radiation Oncology, University of Washington, Washington, United States
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4
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Dubey P, Kumar S. Advancing prostate cancer detection: a comparative analysis of PCLDA-SVM and PCLDA-KNN classifiers for enhanced diagnostic accuracy. Sci Rep 2023; 13:13745. [PMID: 37612436 PMCID: PMC10447543 DOI: 10.1038/s41598-023-40906-y] [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: 05/18/2023] [Accepted: 08/18/2023] [Indexed: 08/25/2023] Open
Abstract
This investigation aimed to assess the effectiveness of different classification models in diagnosing prostate cancer using a screening dataset obtained from the National Cancer Institute's Cancer Data Access System. The dataset was first reduced using the PCLDA method, which combines Principal Component Analysis and Linear Discriminant Analysis. Two classifiers, Support Vector Machine (SVM) and k-Nearest Neighbour (KNN), were then applied to compare their performance. The results showed that the PCLDA-SVM model achieved an impressive accuracy rate of 97.99%, with a precision of 0.92, sensitivity of 92.83%, specificity of 97.65%, and F1 score of 0.93. Additionally, it demonstrated a low error rate of 0.016 and a Matthews Correlation Coefficient (MCC) and Kappa coefficient of 0.946. On the other hand, the PCLDA-KNN model also performed well, achieving an accuracy of 97.8%, precision of 0.93, sensitivity of 93.39%, specificity of 97.86%, an F1 score of 0.92, a high MCC and Kappa coefficient of 0.98, and an error rate of 0.006. In conclusion, the PCLDA-SVM method exhibited improved efficacy in diagnosing prostate cancer compared to the PCLDA-KNN model. Both models, however, showed promising results, suggesting the potential of these classifiers in prostate cancer diagnosis.
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Affiliation(s)
- Priya Dubey
- Electrical and Electronics Engineering, Birla Institute of Technology, Ranchi, Jharkhand, 835215, India.
| | - Surendra Kumar
- Electrical and Electronics Engineering, Birla Institute of Technology, Ranchi, Jharkhand, 835215, India
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5
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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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6
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Chu TN, Wong EY, Ma R, Yang CH, Dalieh IS, Hung AJ. Exploring the Use of Artificial Intelligence in the Management of Prostate Cancer. Curr Urol Rep 2023; 24:231-240. [PMID: 36808595 PMCID: PMC10090000 DOI: 10.1007/s11934-023-01149-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/30/2023] [Indexed: 02/21/2023]
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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Affiliation(s)
- Timothy N Chu
- Center for Robotic Simulation & Education, Department of Urology, USC Institute of Urology, University of Southern California, Catherine & Joseph Aresty1441 Eastlake Avenue Suite 7416, Los Angeles, CA, 90089, USA
| | - Elyssa Y Wong
- Center for Robotic Simulation & Education, Department of Urology, USC Institute of Urology, University of Southern California, Catherine & Joseph Aresty1441 Eastlake Avenue Suite 7416, Los Angeles, CA, 90089, USA
| | - Runzhuo Ma
- Center for Robotic Simulation & Education, Department of Urology, USC Institute of Urology, University of Southern California, Catherine & Joseph Aresty1441 Eastlake Avenue Suite 7416, Los Angeles, CA, 90089, USA
| | - Cherine H Yang
- Center for Robotic Simulation & Education, Department of Urology, USC Institute of Urology, University of Southern California, Catherine & Joseph Aresty1441 Eastlake Avenue Suite 7416, Los Angeles, CA, 90089, USA
| | - Istabraq S Dalieh
- Center for Robotic Simulation & Education, Department of Urology, USC Institute of Urology, University of Southern California, Catherine & Joseph Aresty1441 Eastlake Avenue Suite 7416, Los Angeles, CA, 90089, USA
| | - Andrew J Hung
- Center for Robotic Simulation & Education, Department of Urology, USC Institute of Urology, University of Southern California, Catherine & Joseph Aresty1441 Eastlake Avenue Suite 7416, Los Angeles, CA, 90089, USA.
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7
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Dai X, Park JH, Yoo S, D'Imperio N, McMahon BH, Rentsch CT, Tate JP, Justice AC. Survival analysis of localized prostate cancer with deep learning. Sci Rep 2022; 12:17821. [PMID: 36280773 PMCID: PMC9592586 DOI: 10.1038/s41598-022-22118-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2022] [Accepted: 10/10/2022] [Indexed: 01/20/2023] Open
Abstract
In recent years, data-driven, deep-learning-based models have shown great promise in medical risk prediction. By utilizing the large-scale Electronic Health Record data found in the U.S. Department of Veterans Affairs, the largest integrated healthcare system in the United States, we have developed an automated, personalized risk prediction model to support the clinical decision-making process for localized prostate cancer patients. This method combines the representative power of deep learning and the analytical interpretability of parametric regression models and can implement both time-dependent and static input data. To collect a comprehensive evaluation of model performances, we calculate time-dependent C-statistics [Formula: see text] over 2-, 5-, and 10-year time horizons using either a composite outcome or prostate cancer mortality as the target event. The composite outcome combines the Prostate-Specific Antigen (PSA) test, metastasis, and prostate cancer mortality. Our longitudinal model Recurrent Deep Survival Machine (RDSM) achieved [Formula: see text] 0.85 (0.83), 0.80 (0.83), and 0.76 (0.81), while the cross-sectional model Deep Survival Machine (DSM) attained [Formula: see text] 0.85 (0.82), 0.80 (0.82), and 0.76 (0.79) for the 2-, 5-, and 10-year composite (mortality) outcomes, respectively. In addition to estimating the survival probability, our method can quantify the uncertainty associated with the prediction. The uncertainty scores show a consistent correlation with the prediction accuracy. We find PSA and prostate cancer stage information are the most important indicators in risk prediction. Our work demonstrates the utility of the data-driven machine learning model in prostate cancer risk prediction, which can play a critical role in the clinical decision system.
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Affiliation(s)
- Xin Dai
- Computational Science Initiative, Brookhaven National Laboratory, Upton, NY, USA.
| | - Ji Hwan Park
- Computational Science Initiative, Brookhaven National Laboratory, Upton, NY, USA
- School of Computer Science, The University of Oklahoma, Norman, OK, USA
| | - Shinjae Yoo
- Computational Science Initiative, Brookhaven National Laboratory, Upton, NY, USA
| | - Nicholas D'Imperio
- Computational Science Initiative, Brookhaven National Laboratory, Upton, NY, USA
| | - Benjamin H McMahon
- Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, NM, USA
| | - Christopher T Rentsch
- VA Connecticut Healthcare System, West Haven, CT, USA
- Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
- Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK
| | - Janet P Tate
- VA Connecticut Healthcare System, West Haven, CT, USA
- Schools of Medicine and Public Health, Yale University, New Haven, CT, USA
| | - Amy C Justice
- VA Connecticut Healthcare System, West Haven, CT, USA
- Schools of Medicine and Public Health, Yale University, New Haven, CT, USA
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8
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Danciu I, Agasthya G, Tate JP, Chandra-Shekar M, Goethert I, Ovchinnikova OS, McMahon BH, Justice AC. In with the old, in with the new: machine learning for time to event biomedical research. J Am Med Inform Assoc 2022; 29:1737-1743. [PMID: 35920306 PMCID: PMC9471708 DOI: 10.1093/jamia/ocac106] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Revised: 05/01/2022] [Accepted: 07/29/2022] [Indexed: 11/13/2022] Open
Abstract
The predictive modeling literature for biomedical applications is dominated by biostatistical methods for survival analysis, and more recently some out of the box machine learning approaches. In this article, we show a presentation of a machine learning method appropriate for time-to-event modeling in the area of prostate cancer long-term disease progression. Using XGBoost adapted to long-term disease progression, we developed a predictive model for 118 788 patients with localized prostate cancer at diagnosis from the Department of Veterans Affairs (VA). Our model accounted for patient censoring. Harrell's c-index for our model using only features available at the time of diagnosis was 0.757 95% confidence interval [0.756, 0.757]. Our results show that machine learning methods like XGBoost can be adapted to use accelerated failure time (AFT) with censoring to model long-term risk of disease progression. The long median survival justifies and requires censoring. Overall, we show that an existing machine learning approach can be used for AFT outcome modeling in prostate cancer, and more generally for other chronic diseases with long observation times.
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Affiliation(s)
- Ioana Danciu
- Corresponding Author: Ioana Danciu, Advanced Computing for Health Sciences Group, Oak Ridge National Laboratory, 1 Bethel Valley Road, Building 5700, Oak Ridge, TN 37830, USA;
| | - Greeshma Agasthya
- Advanced Computing for Health Sciences Group, Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA
| | - Janet P Tate
- Department of Veterans Affairs Connecticut Healthcare System, West Haven, Connecticut, USA
- Yale School of Medicine, New Haven, Connecticut, USA
| | - Mayanka Chandra-Shekar
- Advanced Computing for Health Sciences Group, Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA
| | - Ian Goethert
- Advanced Computing for Health Sciences Group, Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA
| | - Olga S Ovchinnikova
- Advanced Computing for Health Sciences Group, Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA
| | - Benjamin H McMahon
- Theoretical Biology Group, Los Alamos National Laboratory, Los Alamos, New Mexico, USA
| | - Amy C Justice
- Department of Veterans Affairs Connecticut Healthcare System, West Haven, Connecticut, USA
- Yale School of Medicine, New Haven, Connecticut, USA
- Yale School of Public Health, New Haven, Connecticut, USA
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