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Hashemi Gheinani A, Kim J, You S, Adam RM. Bioinformatics in urology - molecular characterization of pathophysiology and response to treatment. Nat Rev Urol 2024; 21:214-242. [PMID: 37604982 DOI: 10.1038/s41585-023-00805-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/13/2023] [Indexed: 08/23/2023]
Abstract
The application of bioinformatics has revolutionized the practice of medicine in the past 20 years. From early studies that uncovered subtypes of cancer to broad efforts spearheaded by the Cancer Genome Atlas initiative, the use of bioinformatics strategies to analyse high-dimensional data has provided unprecedented insights into the molecular basis of disease. In addition to the identification of disease subtypes - which enables risk stratification - informatics analysis has facilitated the identification of novel risk factors and drivers of disease, biomarkers of progression and treatment response, as well as possibilities for drug repurposing or repositioning; moreover, bioinformatics has guided research towards precision and personalized medicine. Implementation of specific computational approaches such as artificial intelligence, machine learning and molecular subtyping has yet to become widespread in urology clinical practice for reasons of cost, disruption of clinical workflow and need for prospective validation of informatics approaches in independent patient cohorts. Solving these challenges might accelerate routine integration of bioinformatics into clinical settings.
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Affiliation(s)
- Ali Hashemi Gheinani
- Department of Urology, Boston Children's Hospital, Boston, MA, USA
- Department of Surgery, Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Urology, Inselspital, Bern, Switzerland
- Department for BioMedical Research, University of Bern, Bern, Switzerland
| | - Jina Kim
- Department of Urology, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Sungyong You
- Department of Urology, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Rosalyn M Adam
- Department of Urology, Boston Children's Hospital, Boston, MA, USA.
- Department of Surgery, Harvard Medical School, Boston, MA, USA.
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
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2
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Ramacciotti LS, Hershenhouse JS, Mokhtar D, Paralkar D, Kaneko M, Eppler M, Gill K, Mogoulianitis V, Duddalwar V, Abreu AL, Gill I, Cacciamani GE. Comprehensive Assessment of MRI-based Artificial Intelligence Frameworks Performance in the Detection, Segmentation, and Classification of Prostate Lesions Using Open-Source Databases. Urol Clin North Am 2024; 51:131-161. [PMID: 37945098 DOI: 10.1016/j.ucl.2023.08.003] [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] [Indexed: 11/12/2023]
Abstract
Numerous MRI-based artificial intelligence (AI) frameworks have been designed for prostate cancer lesion detection, segmentation, and classification via MRI as a result of intrareader and interreader variability that is inherent to traditional interpretation. Open-source data sets have been released with the intention of providing freely available MRIs for the testing of diverse AI frameworks in automated or semiautomated tasks. Here, an in-depth assessment of the performance of MRI-based AI frameworks for detecting, segmenting, and classifying prostate lesions using open-source databases was performed. Among 17 data sets, 12 were specific to prostate cancer detection/classification, with 52 studies meeting the inclusion criteria.
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Affiliation(s)
- Lorenzo Storino Ramacciotti
- USC Institute of Urology and Catherine and Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Artificial Intelligence Center at USC Urology, USC Institute of Urology, University of Southern California, Los Angeles, CA, USA; Center for Image-Guided and Focal Therapy for Prostate Cancer, Institute of Urology and Catherine and Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Jacob S Hershenhouse
- USC Institute of Urology and Catherine and Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Artificial Intelligence Center at USC Urology, USC Institute of Urology, University of Southern California, Los Angeles, CA, USA; Center for Image-Guided and Focal Therapy for Prostate Cancer, Institute of Urology and Catherine and Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Daniel Mokhtar
- USC Institute of Urology and Catherine and Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Artificial Intelligence Center at USC Urology, USC Institute of Urology, University of Southern California, Los Angeles, CA, USA; Center for Image-Guided and Focal Therapy for Prostate Cancer, Institute of Urology and Catherine and Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Divyangi Paralkar
- USC Institute of Urology and Catherine and Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Artificial Intelligence Center at USC Urology, USC Institute of Urology, University of Southern California, Los Angeles, CA, USA; Center for Image-Guided and Focal Therapy for Prostate Cancer, Institute of Urology and Catherine and Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Masatomo Kaneko
- USC Institute of Urology and Catherine and Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Artificial Intelligence Center at USC Urology, USC Institute of Urology, University of Southern California, Los Angeles, CA, USA; Center for Image-Guided and Focal Therapy for Prostate Cancer, Institute of Urology and Catherine and Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Department of Urology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Michael Eppler
- USC Institute of Urology and Catherine and Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Artificial Intelligence Center at USC Urology, USC Institute of Urology, University of Southern California, Los Angeles, CA, USA; Center for Image-Guided and Focal Therapy for Prostate Cancer, Institute of Urology and Catherine and Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Karanvir Gill
- USC Institute of Urology and Catherine and Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Artificial Intelligence Center at USC Urology, USC Institute of Urology, University of Southern California, Los Angeles, CA, USA; Center for Image-Guided and Focal Therapy for Prostate Cancer, Institute of Urology and Catherine and Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Vasileios Mogoulianitis
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, USA
| | - Vinay Duddalwar
- Department of Radiology, University of Southern California, Los Angeles, CA, USA
| | - Andre L Abreu
- USC Institute of Urology and Catherine and Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Artificial Intelligence Center at USC Urology, USC Institute of Urology, University of Southern California, Los Angeles, CA, USA; Center for Image-Guided and Focal Therapy for Prostate Cancer, Institute of Urology and Catherine and Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Department of Radiology, University of Southern California, Los Angeles, CA, USA
| | - Inderbir Gill
- USC Institute of Urology and Catherine and Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Artificial Intelligence Center at USC Urology, USC Institute of Urology, University of Southern California, Los Angeles, CA, USA; Center for Image-Guided and Focal Therapy for Prostate Cancer, Institute of Urology and Catherine and Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Giovanni E Cacciamani
- USC Institute of Urology and Catherine and Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Artificial Intelligence Center at USC Urology, USC Institute of Urology, University of Southern California, Los Angeles, CA, USA; Center for Image-Guided and Focal Therapy for Prostate Cancer, Institute of Urology and Catherine and Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Department of Radiology, University of Southern California, Los Angeles, CA, USA.
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3
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Cacciamani GE, Chen A, Gill IS, Hung AJ. Artificial intelligence and urology: ethical considerations for urologists and patients. Nat Rev Urol 2024; 21:50-59. [PMID: 37524914 DOI: 10.1038/s41585-023-00796-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/22/2023] [Indexed: 08/02/2023]
Abstract
The use of artificial intelligence (AI) in medicine and in urology specifically has increased over the past few years, during which time it has enabled optimization of patient workflow, increased diagnostic accuracy and enhanced computer analysis of radiological and pathological images. However, before further use of AI is undertaken, possible ethical issues need to be evaluated to improve understanding of this technology and to protect patients and providers. Possible ethical issues that require consideration when applying AI in clinical practice include patient safety, cybersecurity, transparency and interpretability of the data, inclusivity and equity, fostering responsibility and accountability, and the preservation of providers' decision-making and autonomy. Ethical principles for the application of AI to health care and in urology are proposed to guide urologists, patients and regulators to improve use of AI technologies and guide policy-making.
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Affiliation(s)
- Giovanni E Cacciamani
- The Catherine and Joseph Aresty Department of Urology, USC Institute of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
- AI Center at USC Urology, USC Institute of Urology, University of Southern California, Los Angeles, CA, USA.
- Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
| | - Andrew Chen
- The Catherine and Joseph Aresty Department of Urology, USC Institute of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- AI Center at USC Urology, USC Institute of Urology, University of Southern California, Los Angeles, CA, USA
| | - Inderbir S Gill
- The Catherine and Joseph Aresty Department of Urology, USC Institute of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- AI Center at USC Urology, USC Institute of Urology, University of Southern California, Los Angeles, CA, USA
| | - Andrew J Hung
- The Catherine and Joseph Aresty Department of Urology, USC Institute of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- AI Center at USC Urology, USC Institute of Urology, University of Southern California, Los Angeles, CA, USA
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4
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Scavuzzo A, Figueroa-Rodriguez P, Stefano A, Jimenez Guedulain N, Muruato Araiza S, Cendejas Gomez JDJ, Quiroz Compeaán A, Victorio Vargas DO, Jiménez-Ríos MA. CT Rendering and Radiomic Analysis in Post-Chemotherapy Retroperitoneal Lymph Node Dissection for Testicular Cancer to Anticipate Difficulties for Young Surgeons. J Imaging 2023; 9:jimaging9030071. [PMID: 36976122 PMCID: PMC10056656 DOI: 10.3390/jimaging9030071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Revised: 03/09/2023] [Accepted: 03/14/2023] [Indexed: 03/29/2023] Open
Abstract
Post-chemotherapy retroperitoneal lymph node dissection (PC-RPLND) in non-seminomatous germ-cell tumor (NSTGCTs) is a complex procedure. We evaluated whether 3D computed tomography (CT) rendering and their radiomic analysis help predict resectability by junior surgeons. The ambispective analysis was performed between 2016-2021. A prospective group (A) of 30 patients undergoing CT was segmented using the 3D Slicer software while a retrospective group (B) of 30 patients was evaluated with conventional CT (without 3D reconstruction). CatFisher's exact test showed a p-value of 0.13 for group A and 1.0 for Group B. The difference between the proportion test showed a p-value of 0.009149 (IC 0.1-0.63). The proportion of the correct classification showed a p-value of 0.645 (IC 0.55-0.87) for A, and 0.275 (IC 0.11-0.43) for Group B. Furthermore, 13 shape features were extracted: elongation, flatness, volume, sphericity, and surface area, among others. Performing a logistic regression with the entire dataset, n = 60, the results were: Accuracy: 0.7 and Precision: 0.65. Using n = 30 randomly chosen, the best result obtained was Accuracy: 0.73 and Precision: 0.83, with a p-value: 0.025 for Fisher's exact test. In conclusion, the results showed a significant difference in the prediction of resectability with conventional CT versus 3D reconstruction by junior surgeons versus experienced surgeons. Radiomic features used to elaborate an artificial intelligence model improve the prediction of resectability. The proposed model could be of great support in a university hospital, allowing it to plan the surgery and to anticipate complications.
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Affiliation(s)
- Anna Scavuzzo
- Instituto Nacional de Cancerologia, Department of Urology, Universidad Autonoma de Mexico-UNAM, Mexico City 14080, Mexico
| | - Pavel Figueroa-Rodriguez
- Instituto Nacional de Cancerologia, Department of Biomedical Engineering, Universidad Autonoma de Mexico-UNAM, Mexico City 14080, Mexico
| | - Alessandro Stefano
- Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), 90015 Cefalù, Italy
| | - Nallely Jimenez Guedulain
- Instituto Nacional de Cancerologia, Department of Urology, Universidad Autonoma de Mexico-UNAM, Mexico City 14080, Mexico
| | - Sebastian Muruato Araiza
- Instituto Nacional de Cancerologia, Department of Urology, Universidad Autonoma de Mexico-UNAM, Mexico City 14080, Mexico
| | - Jose de Jesus Cendejas Gomez
- Instituto Nacional de Cancerologia, Department of Urology, Universidad Autonoma de Mexico-UNAM, Mexico City 14080, Mexico
| | - Alejandro Quiroz Compeaán
- Instituto Nacional de Cancerologia, Department of Urology, Universidad Autonoma de Mexico-UNAM, Mexico City 14080, Mexico
| | - Dimas O Victorio Vargas
- Instituto Nacional de Cancerologia, Department of Urology, Universidad Autonoma de Mexico-UNAM, Mexico City 14080, Mexico
| | - Miguel A Jiménez-Ríos
- Instituto Nacional de Cancerologia, Department of Urology, Universidad Autonoma de Mexico-UNAM, Mexico City 14080, Mexico
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Automated Capture of Intraoperative Adverse Events Using Artificial Intelligence: A Systematic Review and Meta-Analysis. J Clin Med 2023; 12:jcm12041687. [PMID: 36836223 PMCID: PMC9963108 DOI: 10.3390/jcm12041687] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Revised: 02/08/2023] [Accepted: 02/14/2023] [Indexed: 02/22/2023] Open
Abstract
Intraoperative adverse events (iAEs) impact the outcomes of surgery, and yet are not routinely collected, graded, and reported. Advancements in artificial intelligence (AI) have the potential to power real-time, automatic detection of these events and disrupt the landscape of surgical safety through the prediction and mitigation of iAEs. We sought to understand the current implementation of AI in this space. A literature review was performed to PRISMA-DTA standards. Included articles were from all surgical specialties and reported the automatic identification of iAEs in real-time. Details on surgical specialty, adverse events, technology used for detecting iAEs, AI algorithm/validation, and reference standards/conventional parameters were extracted. A meta-analysis of algorithms with available data was conducted using a hierarchical summary receiver operating characteristic curve (ROC). The QUADAS-2 tool was used to assess the article risk of bias and clinical applicability. A total of 2982 studies were identified by searching PubMed, Scopus, Web of Science, and IEEE Xplore, with 13 articles included for data extraction. The AI algorithms detected bleeding (n = 7), vessel injury (n = 1), perfusion deficiencies (n = 1), thermal damage (n = 1), and EMG abnormalities (n = 1), among other iAEs. Nine of the thirteen articles described at least one validation method for the detection system; five explained using cross-validation and seven divided the dataset into training and validation cohorts. Meta-analysis showed the algorithms were both sensitive and specific across included iAEs (detection OR 14.74, CI 4.7-46.2). There was heterogeneity in reported outcome statistics and article bias risk. There is a need for standardization of iAE definitions, detection, and reporting to enhance surgical care for all patients. The heterogeneous applications of AI in the literature highlights the pluripotent nature of this technology. Applications of these algorithms across a breadth of urologic procedures should be investigated to assess the generalizability of these data.
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6
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Parwani AV, Patel A, Zhou M, Cheville JC, Tizhoosh H, Humphrey P, Reuter VE, True LD. An update on computational pathology tools for genitourinary pathology practice: A review paper from the Genitourinary Pathology Society (GUPS). J Pathol Inform 2023; 14:100177. [PMID: 36654741 PMCID: PMC9841212 DOI: 10.1016/j.jpi.2022.100177] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Revised: 12/20/2022] [Accepted: 12/20/2022] [Indexed: 12/31/2022] Open
Abstract
Machine learning has been leveraged for image analysis applications throughout a multitude of subspecialties. This position paper provides a perspective on the evolutionary trajectory of practical deep learning tools for genitourinary pathology through evaluating the most recent iterations of such algorithmic devices. Deep learning tools for genitourinary pathology demonstrate potential to enhance prognostic and predictive capacity for tumor assessment including grading, staging, and subtype identification, yet limitations in data availability, regulation, and standardization have stymied their implementation.
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Affiliation(s)
- Anil V. Parwani
- The Ohio State University, Columbus, Ohio, USA
- Corresponding author.
| | - Ankush Patel
- The Ohio State University, 2441 60th Ave SE, Mercer Island, Washington 98040, USA
| | - Ming Zhou
- Tufts University, Medford, Massachusetts, USA
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7
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Ma R, Ramaswamy A, Xu J, Trinh L, Kiyasseh D, Chu TN, Wong EY, Lee RS, Rodriguez I, DeMeo G, Desai A, Otiato MX, Roberts SI, Nguyen JH, Laca J, Liu Y, Urbanova K, Wagner C, Anandkumar A, Hu JC, Hung AJ. Surgical gestures as a method to quantify surgical performance and predict patient outcomes. NPJ Digit Med 2022; 5:187. [PMID: 36550203 PMCID: PMC9780308 DOI: 10.1038/s41746-022-00738-y] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Accepted: 11/29/2022] [Indexed: 12/24/2022] Open
Abstract
How well a surgery is performed impacts a patient's outcomes; however, objective quantification of performance remains an unsolved challenge. Deconstructing a procedure into discrete instrument-tissue "gestures" is a emerging way to understand surgery. To establish this paradigm in a procedure where performance is the most important factor for patient outcomes, we identify 34,323 individual gestures performed in 80 nerve-sparing robot-assisted radical prostatectomies from two international medical centers. Gestures are classified into nine distinct dissection gestures (e.g., hot cut) and four supporting gestures (e.g., retraction). Our primary outcome is to identify factors impacting a patient's 1-year erectile function (EF) recovery after radical prostatectomy. We find that less use of hot cut and more use of peel/push are statistically associated with better chance of 1-year EF recovery. Our results also show interactions between surgeon experience and gesture types-similar gesture selection resulted in different EF recovery rates dependent on surgeon experience. To further validate this framework, two teams independently constructe distinct machine learning models using gesture sequences vs. traditional clinical features to predict 1-year EF. In both models, gesture sequences are able to better predict 1-year EF (Team 1: AUC 0.77, 95% CI 0.73-0.81; Team 2: AUC 0.68, 95% CI 0.66-0.70) than traditional clinical features (Team 1: AUC 0.69, 95% CI 0.65-0.73; Team 2: AUC 0.65, 95% CI 0.62-0.68). Our results suggest that gestures provide a granular method to objectively indicate surgical performance and outcomes. Application of this methodology to other surgeries may lead to discoveries on methods to improve surgery.
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Affiliation(s)
- Runzhuo Ma
- grid.42505.360000 0001 2156 6853Center for Robotic Simulation & Education, Catherine & Joseph Aresty Department of Urology, USC Institute of Urology, University of Southern California, Los Angeles, CA USA
| | - Ashwin Ramaswamy
- grid.5386.8000000041936877XDepartment of Urology, Weill Cornell Medicine, New York, NY USA
| | - Jiashu Xu
- grid.42505.360000 0001 2156 6853Computer Science Department, Viterbi School of Engineering, University of Southern California, Los Angeles, CA USA
| | - Loc Trinh
- grid.42505.360000 0001 2156 6853Computer Science Department, Viterbi School of Engineering, University of Southern California, Los Angeles, CA USA
| | - Dani Kiyasseh
- grid.20861.3d0000000107068890Department of Computing & Mathematical Sciences, California Institute of Technology, Pasadena, CA USA
| | - Timothy N. Chu
- grid.42505.360000 0001 2156 6853Center for Robotic Simulation & Education, Catherine & Joseph Aresty Department of Urology, USC Institute of Urology, University of Southern California, Los Angeles, CA USA
| | - Elyssa Y. Wong
- grid.42505.360000 0001 2156 6853Center for Robotic Simulation & Education, Catherine & Joseph Aresty Department of Urology, USC Institute of Urology, University of Southern California, Los Angeles, CA USA
| | - Ryan S. Lee
- grid.42505.360000 0001 2156 6853Center for Robotic Simulation & Education, Catherine & Joseph Aresty Department of Urology, USC Institute of Urology, University of Southern California, Los Angeles, CA USA
| | - Ivan Rodriguez
- grid.42505.360000 0001 2156 6853Center for Robotic Simulation & Education, Catherine & Joseph Aresty Department of Urology, USC Institute of Urology, University of Southern California, Los Angeles, CA USA
| | - Gina DeMeo
- grid.5386.8000000041936877XDepartment of Urology, Weill Cornell Medicine, New York, NY USA
| | - Aditya Desai
- grid.42505.360000 0001 2156 6853Center for Robotic Simulation & Education, Catherine & Joseph Aresty Department of Urology, USC Institute of Urology, University of Southern California, Los Angeles, CA USA
| | - Maxwell X. Otiato
- grid.42505.360000 0001 2156 6853Center for Robotic Simulation & Education, Catherine & Joseph Aresty Department of Urology, USC Institute of Urology, University of Southern California, Los Angeles, CA USA
| | - Sidney I. Roberts
- grid.42505.360000 0001 2156 6853Center for Robotic Simulation & Education, Catherine & Joseph Aresty Department of Urology, USC Institute of Urology, University of Southern California, Los Angeles, CA USA
| | - Jessica H. Nguyen
- grid.42505.360000 0001 2156 6853Center for Robotic Simulation & Education, Catherine & Joseph Aresty Department of Urology, USC Institute of Urology, University of Southern California, Los Angeles, CA USA
| | - Jasper Laca
- grid.42505.360000 0001 2156 6853Center for Robotic Simulation & Education, Catherine & Joseph Aresty Department of Urology, USC Institute of Urology, University of Southern California, Los Angeles, CA USA
| | - Yan Liu
- grid.42505.360000 0001 2156 6853Computer Science Department, Viterbi School of Engineering, University of Southern California, Los Angeles, CA USA
| | - Katarina Urbanova
- grid.459927.40000 0000 8785 9045Department of Urology and Urologic Oncology, St. Antonius-Hospital, Gronau, Germany
| | - Christian Wagner
- grid.459927.40000 0000 8785 9045Department of Urology and Urologic Oncology, St. Antonius-Hospital, Gronau, Germany
| | - Animashree Anandkumar
- grid.20861.3d0000000107068890Department of Computing & Mathematical Sciences, California Institute of Technology, Pasadena, CA USA
| | - Jim C. Hu
- grid.5386.8000000041936877XDepartment of Urology, Weill Cornell Medicine, New York, NY USA
| | - Andrew J. Hung
- grid.42505.360000 0001 2156 6853Center for Robotic Simulation & Education, Catherine & Joseph Aresty Department of Urology, USC Institute of Urology, University of Southern California, Los Angeles, CA USA
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Is Artificial Intelligence Replacing Our Radiology Stars? Not Yet! EUR UROL SUPPL 2022; 48:14-16. [PMID: 36588775 PMCID: PMC9794880 DOI: 10.1016/j.euros.2022.09.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/06/2022] [Indexed: 12/23/2022] Open
Abstract
Artificial intelligence (AI) is here to stay and will change health care as we know it. The availability of big data and the increasing numbers of AI algorithms approved by the US Food and Drug Administration together will help in improving the quality of care for patients and in overcoming human fatigue barriers. In oncology practice, patients and providers rely on the interpretation of radiologists when making clinical decisions; however, there is considerable variability among readers, and in particular for prostate imaging. AI represents an emerging solution to this problem, for which it can provide a much-needed form of standardization. The diagnostic performance of AI alone in comparison to a combination of an AI framework and radiologist assessment for evaluation of prostate imaging has yet to be explored. Here, we compare the performance of radiologists alone versus a combination of radiologists aided by a modern computer-aided diagnosis (CAD) AI system. We show that the radiologist-CAD combination demonstrates superior sensitivity and specificity in comparison to both radiologists alone and AI alone. Our findings demonstrate that a radiologist + AI combination could perform best for detection of prostate cancer lesions. A hybrid technology-human system could leverage the benefits of AI in improving radiologist performance while also reducing physician workload, minimizing burnout, and enhancing the quality of patient care. Patient summary Our report demonstrates the potential of artificial intelligence (AI) for improving the interpretation of prostate scans. A combination of AI and evaluation by a radiologist has the best performance in determining the severity of prostate cancer. A hybrid system that uses both AI and radiologists could maximize the quality of care for patients while reducing physician workload and burnout.
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9
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Patel AU, Mohanty SK, Parwani AV. Applications of Digital and Computational Pathology and Artificial Intelligence in Genitourinary Pathology Diagnostics. Surg Pathol Clin 2022; 15:759-785. [PMID: 36344188 DOI: 10.1016/j.path.2022.08.001] [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] [Indexed: 06/16/2023]
Abstract
As machine learning (ML) solutions for genitourinary pathology image analysis are fostered by a progressively digitized laboratory landscape, these integrable modalities usher in a revolution in histopathological diagnosis. As technology advances, limitations stymying clinical artificial intelligence (AI) will not be extinguished without thorough validation and interrogation of ML tools by pathologists and regulatory bodies alike. ML solutions deployed in clinical settings for applications in prostate pathology yield promising results. Recent breakthroughs in clinical artificial intelligence for genitourinary pathology demonstrate unprecedented generalizability, heralding prospects for a future in which AI-driven assistive solutions may be seen as laboratory faculty, rather than novelty.
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Affiliation(s)
- Ankush Uresh Patel
- Department of Laboratory Medicine and Pathology, Mayo Clinic, 200 First Street Southwest, Rochester, MN 55905, USA
| | - Sambit K Mohanty
- Surgical and Molecular Pathology, Advanced Medical Research Institute, Plot No. 1, Near Jayadev Vatika Park, Khandagiri, Bhubaneswar, Odisha 751019. https://twitter.com/SAMBITKMohanty1
| | - Anil V Parwani
- Department of Pathology, The Ohio State University, Cooperative Human Tissue Network (CHTN) Midwestern Division Polaris Innovation Centre, 2001 Polaris Parkway Suite 1000, Columbus, OH 43240, USA.
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