1
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Santa-Rosario JC, Gustafson EA, Sanabria Bellassai DE, Gustafson PE, de Socarraz M. Validation and three years of clinical experience in using an artificial intelligence algorithm as a second read system for prostate cancer diagnosis-real-world experience. J Pathol Inform 2024; 15:100378. [PMID: 38868487 PMCID: PMC11166872 DOI: 10.1016/j.jpi.2024.100378] [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] [Received: 03/22/2024] [Revised: 04/23/2024] [Accepted: 04/23/2024] [Indexed: 06/14/2024] Open
Abstract
Background Prostate cancer ranks as the most frequently diagnosed cancer in men in the USA, with significant mortality rates. Early detection is pivotal for optimal patient outcomes, providing increased treatment options and potentially less invasive interventions. There remain significant challenges in prostate cancer histopathology, including the potential for missed diagnoses due to pathologist variability and subjective interpretations. Methods To address these challenges, this study investigates the ability of artificial intelligence (AI) to enhance diagnostic accuracy. The Galen™ Prostate AI algorithm was validated on a cohort of Puerto Rican men to demonstrate its efficacy in cancer detection and Gleason grading. Subsequently, the AI algorithm was integrated into routine clinical practice during a 3-year period at a CLIA certified precision pathology laboratory. Results The Galen™ Prostate AI algorithm showed a 96.7% (95% CI 95.6-97.8) specificity and a 96.6% (95% CI 93.3-98.8) sensitivity for prostate cancer detection and 82.1% specificity (95% CI 73.9-88.5) and 81.1% sensitivity (95% CI 73.7-87.2) for distinction of Gleason Grade Group 1 from Grade Group 2+. The subsequent AI integration into routine clinical use examined prostate cancer diagnoses on >122,000 slides and 9200 cases over 3 years and had an overall AI Impact ™ factor of 1.8%. Conclusions The potential of AI to be a powerful, reliable, and effective diagnostic tool for pathologists is highlighted, while the AI Impact™ in a real-world setting demonstrates the ability of AI to standardize prostate cancer diagnosis at a high level of performance across pathologists.
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Affiliation(s)
- Juan Carlos Santa-Rosario
- CorePlus Servicios Clínicos y Patológicos; Plazoleta la Cerámica, Suite 2-6 Ave. Sánchez Vilella, Esq, PR-190, Carolina, PR 00983, USA
| | - Erik A. Gustafson
- CorePlus Servicios Clínicos y Patológicos; Plazoleta la Cerámica, Suite 2-6 Ave. Sánchez Vilella, Esq, PR-190, Carolina, PR 00983, USA
| | - Dario E. Sanabria Bellassai
- CorePlus Servicios Clínicos y Patológicos; Plazoleta la Cerámica, Suite 2-6 Ave. Sánchez Vilella, Esq, PR-190, Carolina, PR 00983, USA
| | - Phillip E. Gustafson
- CorePlus Servicios Clínicos y Patológicos; Plazoleta la Cerámica, Suite 2-6 Ave. Sánchez Vilella, Esq, PR-190, Carolina, PR 00983, USA
| | - Mariano de Socarraz
- CorePlus Servicios Clínicos y Patológicos; Plazoleta la Cerámica, Suite 2-6 Ave. Sánchez Vilella, Esq, PR-190, Carolina, PR 00983, USA
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2
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Schukow CP, Allen TC. Digital and Computational Pathology Are Pathologists' Physician Extenders. Arch Pathol Lab Med 2024; 148:866-870. [PMID: 38531382 DOI: 10.5858/arpa.2023-0537-ed] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/11/2024] [Indexed: 03/28/2024]
Affiliation(s)
- Casey P Schukow
- From the Department of Pathology, Corewell Health's Beaumont Hospital, Royal Oak, Michigan
| | - Timothy Craig Allen
- From the Department of Pathology, Corewell Health's Beaumont Hospital, Royal Oak, Michigan
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3
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Maffei E, D'Antonio A, Addesso M, Pandolfo SD, Verze P, Caputo A. Exploring the landscape of urinary tract melanomas: A review for pathologists and clinicians. Urologia 2024:3915603241263215. [PMID: 39045672 DOI: 10.1177/03915603241263215] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/25/2024]
Abstract
Melanomas originating within the urinary tract represent a rare and clinically challenging subset of malignancies. Despite extensive research on cutaneous melanomas, urinary tract melanomas remain relatively unexplored, presenting diagnostic dilemmas and limited treatment consensus. In this comprehensive review, we synthesize current knowledge on the epidemiology, risk factors, clinical presentation, histopathological characteristics, and treatment strategies specific to this disease. Enhancing clinical awareness, refining diagnostic approaches, and exploring novel therapeutic interventions hold promise for improving outcomes in this challenging malignancy subset.
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Affiliation(s)
| | | | - Maria Addesso
- Department of Pathology, PO Tortora, Pagani (SA), Italy
| | | | - Paolo Verze
- Department of Urology, University Hospital of Salerno, Italy
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4
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Eloy C, Bychkov A, Fraggetta F, Temprana-Salvador J, Pantanowitz L, Vielh P. How many more slides to go until we fully adopt digital cytology? Cytopathology 2024; 35:442-443. [PMID: 38736173 DOI: 10.1111/cyt.13388] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2024] [Accepted: 04/27/2024] [Indexed: 05/14/2024]
Abstract
Two‐liner/synopsis: The digital cytology hub (DCH) has been established under the umbrella of the Cytopathology journal. DCH will help bring about the crucial changes needed to make digital cytology the way of practicing cytology in our laboratories.
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Affiliation(s)
- Catarina Eloy
- Pathology Department, Medical Faculty of University of Porto & Pathology Laboratory, Institute of Molecular Pathology and Immunology of University of Porto (IPATIMUP), Porto, Portugal
| | - Andrey Bychkov
- Department of Pathology, Kameda Medical Center, Chiba, Japan
| | | | | | - Liron Pantanowitz
- Department of Pathology, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania, USA
| | - Philippe Vielh
- Department of Pathology, Medipath and American Hospital of Paris, Paris, France
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5
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Riaz IB, Harmon S, Chen Z, Naqvi SAA, Cheng L. Applications of Artificial Intelligence in Prostate Cancer Care: A Path to Enhanced Efficiency and Outcomes. Am Soc Clin Oncol Educ Book 2024; 44:e438516. [PMID: 38935882 DOI: 10.1200/edbk_438516] [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: 06/29/2024]
Abstract
The landscape of prostate cancer care has rapidly evolved. We have transitioned from the use of conventional imaging, radical surgeries, and single-agent androgen deprivation therapy to an era of advanced imaging, precision diagnostics, genomics, and targeted treatment options. Concurrently, the emergence of large language models (LLMs) has dramatically transformed the paradigm for artificial intelligence (AI). This convergence of advancements in prostate cancer management and AI provides a compelling rationale to comprehensively review the current state of AI applications in prostate cancer care. Here, we review the advancements in AI-driven applications across the continuum of the journey of a patient with prostate cancer from early interception to survivorship care. We subsequently discuss the role of AI in prostate cancer drug discovery, clinical trials, and clinical practice guidelines. In the localized disease setting, deep learning models demonstrated impressive performance in detecting and grading prostate cancer using imaging and pathology data. For biochemically recurrent diseases, machine learning approaches are being tested for improved risk stratification and treatment decisions. In advanced prostate cancer, deep learning can potentially improve prognostication and assist in clinical decision making. Furthermore, LLMs are poised to revolutionize information summarization and extraction, clinical trial design and operations, drug development, evidence synthesis, and clinical practice guidelines. Synergistic integration of multimodal data integration and human-AI integration are emerging as a key strategy to unlock the full potential of AI in prostate cancer care.
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Affiliation(s)
- Irbaz Bin Riaz
- Division of Hematology and Oncology, Department of Internal Medicine, Mayo Clinic, Phoenix, AZ
- Department of AI and Informatics, Mayo Clinic, Rochester, MN
| | - Stephanie Harmon
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD
| | - Zhijun Chen
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD
| | | | - Liang Cheng
- Department of Pathology and Laboratory Medicine, Department of Surgery (Urology), Brown University Warren Alpert Medical School, Lifespan Health, and the Legorreta Cancer Center at Brown University, Providence, RI
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6
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Caranfil E, Lami K, Uegami W, Fukuoka J. Artificial Intelligence and Lung Pathology. Adv Anat Pathol 2024:00125480-990000000-00110. [PMID: 38780094 DOI: 10.1097/pap.0000000000000448] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/25/2024]
Abstract
This manuscript provides a comprehensive overview of the application of artificial intelligence (AI) in lung pathology, particularly in the diagnosis of lung cancer. It discusses various AI models designed to support pathologists and clinicians. AI models supporting pathologists are to standardize diagnosis, score PD-L1 status, supporting tumor cellularity count, and indicating explainability for pathologic judgements. Several models predict outcomes beyond pathologic diagnosis and predict clinical outcomes like patients' survival and molecular alterations. The manuscript emphasizes the potential of AI to enhance accuracy and efficiency in pathology, while also addressing the challenges and future directions for integrating AI into clinical practice.
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Affiliation(s)
- Emanuel Caranfil
- Department of Pathology Informatics, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki
| | - Kris Lami
- Department of Pathology Informatics, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki
| | - Wataru Uegami
- Department of Pathology Informatics, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki
- Department of Pathology, Kameda Medical Center, Kamogawa, Japan
| | - Junya Fukuoka
- Department of Pathology Informatics, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki
- Department of Pathology, Kameda Medical Center, Kamogawa, Japan
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7
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Caputo A, Angeloni M, Merolla F, Vatrano S, Ferrazzi F, Fraggetta F. Digital odyssey: lessons learnt from a reverse transition from a digital to a manual pathology workflow. J Clin Pathol 2024; 77:426-429. [PMID: 38267209 DOI: 10.1136/jcp-2023-209382] [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: 12/29/2023] [Accepted: 01/12/2024] [Indexed: 01/26/2024]
Abstract
In the fully digital Caltagirone pathology laboratory, a reverse shift from a digital to a manual workflow occurred due to a server outage in September 2023. Here, insights gained from this unplanned transition are explored. Surveying the affected pathologists and technicians revealed unanimous preferences for the time-saving and error-reducing capabilities of the digital methodology. Conversely, the return to manual methods highlighted increased dissatisfaction and reduced efficiency, emphasising the superiority of digital workflows. This case study underscores that transition challenges are not inherent to digital workflows but to transitioning itself, advocating for the adoption of digital technologies in all pathology practices.
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Affiliation(s)
- Alessandro Caputo
- Department of Pathology, University Hospital of Salerno, Salerno, Italy
| | - Miriam Angeloni
- Institute of Pathology, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Francesco Merolla
- Department of Medicine and Health Sciences Vincenzo Tiberio, University of Molise, Campobasso, Italy
| | | | - Fulvia Ferrazzi
- Institute of Pathology, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
- Department of Nephropathology, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
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8
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Alhatem A, Wong T, Clark Lambert W. Revolutionizing diagnostic pathology: The emergence and impact of artificial intelligence-what doesn't kill you makes you stronger? Clin Dermatol 2024; 42:268-274. [PMID: 38181890 DOI: 10.1016/j.clindermatol.2023.12.020] [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: 01/07/2024]
Abstract
This study explored the integration and impact of artificial intelligence (AI) in diagnostic pathology, particularly dermatopathology, assessing its challenges and potential solutions for global health care enhancement. A comprehensive literature search in PubMed and Google Scholar, conducted on March 30, 2023, and using terms related to AI, pathology, and machine learning, yielded 44 relevant publications. These were analyzed under themes including the evolution of deep learning in pathology, AI's role in replacing pathologists, development challenges of diagnostic algorithms, clinical implementation hurdles, strategies for practical application in dermatopathology, and future prospects of AI in this field. The findings highlight AI's transformative potential in pathology, underscore the need for ongoing research, collaboration, and regulatory dialogue, and emphasize the importance of addressing the ethical and practical challenges in AI implementation for improved global health care outcomes.
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Affiliation(s)
- Albert Alhatem
- Department of Pathology, Immunology and Laboratory Medicine and Department of Dermatology, Rutgers-New Jersey Medical School, Newark, New Jersey, USA
| | - Trish Wong
- Department of Pathology, Immunology and Laboratory Medicine and Department of Dermatology, Rutgers-New Jersey Medical School, Newark, New Jersey, USA
| | - W Clark Lambert
- Department of Pathology, Immunology and Laboratory Medicine and Department of Dermatology, Rutgers-New Jersey Medical School, Newark, New Jersey, USA.
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9
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Zhu L, Pan J, Mou W, Deng L, Zhu Y, Wang Y, Pareek G, Hyams E, Carneiro BA, Hadfield MJ, El-Deiry WS, Yang T, Tan T, Tong T, Ta N, Zhu Y, Gao Y, Lai Y, Cheng L, Chen R, Xue W. Harnessing artificial intelligence for prostate cancer management. Cell Rep Med 2024; 5:101506. [PMID: 38593808 PMCID: PMC11031422 DOI: 10.1016/j.xcrm.2024.101506] [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: 08/30/2023] [Revised: 01/05/2024] [Accepted: 03/19/2024] [Indexed: 04/11/2024]
Abstract
Prostate cancer (PCa) is a common malignancy in males. The pathology review of PCa is crucial for clinical decision-making, but traditional pathology review is labor intensive and subjective to some extent. Digital pathology and whole-slide imaging enable the application of artificial intelligence (AI) in pathology. This review highlights the success of AI in detecting and grading PCa, predicting patient outcomes, and identifying molecular subtypes. We propose that AI-based methods could collaborate with pathologists to reduce workload and assist clinicians in formulating treatment recommendations. We also introduce the general process and challenges in developing AI pathology models for PCa. Importantly, we summarize publicly available datasets and open-source codes to facilitate the utilization of existing data and the comparison of the performance of different models to improve future studies.
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Affiliation(s)
- Lingxuan Zhu
- Department of Urology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China; Department of Etiology and Carcinogenesis, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China; Changping Laboratory, Beijing, China
| | - Jiahua Pan
- Department of Urology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China
| | - Weiming Mou
- Department of Urology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Longxin Deng
- Department of Urology, Shanghai Changhai Hospital, Second Military Medical University, Shanghai 200433, China
| | - Yinjie Zhu
- Department of Urology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China
| | - Yanqing Wang
- Department of Urology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China
| | - Gyan Pareek
- Department of Surgery (Urology), Brown University Warren Alpert Medical School, Providence, RI, USA; Minimally Invasive Urology Institute, Providence, RI, USA
| | - Elias Hyams
- Department of Surgery (Urology), Brown University Warren Alpert Medical School, Providence, RI, USA; Minimally Invasive Urology Institute, Providence, RI, USA
| | - Benedito A Carneiro
- The Legorreta Cancer Center at Brown University, Lifespan Cancer Institute, Providence, RI, USA
| | - Matthew J Hadfield
- The Legorreta Cancer Center at Brown University, Lifespan Cancer Institute, Providence, RI, USA
| | - Wafik S El-Deiry
- The Legorreta Cancer Center at Brown University, Laboratory of Translational Oncology and Experimental Cancer Therapeutics, Department of Pathology & Laboratory Medicine, The Warren Alpert Medical School of Brown University, The Joint Program in Cancer Biology, Brown University and Lifespan Health System, Division of Hematology/Oncology, The Warren Alpert Medical School of Brown University, Providence, RI, USA
| | - Tao Yang
- Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Tao Tan
- Faculty of Applied Sciences, Macao Polytechnic University, Address: R. de Luís Gonzaga Gomes, Macao, China
| | - Tong Tong
- College of Physics and Information Engineering, Fuzhou University, Fujian 350108, China
| | - Na Ta
- Department of Pathology, Shanghai Changhai Hospital, Second Military Medical University, Shanghai 200433, China
| | - Yan Zhu
- Department of Pathology, Shanghai Changhai Hospital, Second Military Medical University, Shanghai 200433, China
| | - Yisha Gao
- Department of Pathology, Shanghai Changhai Hospital, Second Military Medical University, Shanghai 200433, China
| | - Yancheng Lai
- Department of Urology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China; The First School of Clinical Medicine, Southern Medical University, Guangzhou, China
| | - Liang Cheng
- Department of Surgery (Urology), Brown University Warren Alpert Medical School, Providence, RI, USA; Department of Pathology and Laboratory Medicine, Department of Surgery (Urology), Brown University Warren Alpert Medical School, Lifespan Health, and the Legorreta Cancer Center at Brown University, Providence, RI, USA.
| | - Rui Chen
- Department of Urology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China.
| | - Wei Xue
- Department of Urology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China.
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10
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Wilk A, Król M, Kiełbowski K, Bakinowska E, Szumilas K, Surówka A, Kędzierska-Kapuza K. Immunolocalization of Matrix Metalloproteinases 2 and 9 and Their Inhibitors in the Hearts of Rats Treated with Immunosuppressive Drugs-An Artificial Intelligence-Based Digital Analysis. Biomedicines 2024; 12:769. [PMID: 38672125 PMCID: PMC11048150 DOI: 10.3390/biomedicines12040769] [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: 03/03/2024] [Revised: 03/26/2024] [Accepted: 03/28/2024] [Indexed: 04/28/2024] Open
Abstract
BACKGROUND Immunosuppressive agents represent a broad group of drugs, such as calcineurin inhibitors, mTOR inhibitors, and glucocorticosteroids, among others. These drugs are widely used in a number of conditions, but lifelong therapy is crucial in the case of organ recipients to prevent rejection. To further increase the safety and efficacy of these agents, their off-target mechanisms of action, as well as processes underlying the pathogenesis of adverse effects, need to be thoroughly investigated. The aim of this study was to examine the impact of various combinations of cyclosporine/tacrolimus/mycophenolate with rapamycin and steroids (CRG, TRG, MRG), on the morphology and morphometry of rats' cardiomyocytes, together with the presence of cardiac collagen and the immunoexpression of MMPs and TIMPs. METHODS Twenty-four rats were divided into four groups receiving different immunosuppressive regiments. After six months of treatment, the hearts were collected and analyzed. RESULTS Cardiomyocytes from the CRG cohorts demonstrated the most pronounced morphological alterations. In addition, chronic immunosuppression reduced the width and length of cardiac cells. However, immunosuppressive therapy did not alter the presence of cardiac collagen fibers. Nevertheless, we observed significant alterations regarding MMP/TIMP homeostasis. CONCLUSIONS Chronic immunosuppression seems to disturb the MMP/TIMP balance in aspects of immunolocalization in the hearts of rats. Further studies are required to analyze other mechanisms and pathways affected by the use of immunosuppressants.
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Affiliation(s)
- Aleksandra Wilk
- Department of Histology and Embryology, Pomeranian Medical University, 70-111 Szczecin, Poland; (A.W.); (M.K.); (E.B.)
| | - Małgorzata Król
- Department of Histology and Embryology, Pomeranian Medical University, 70-111 Szczecin, Poland; (A.W.); (M.K.); (E.B.)
| | - Kajetan Kiełbowski
- Department of Histology and Embryology, Pomeranian Medical University, 70-111 Szczecin, Poland; (A.W.); (M.K.); (E.B.)
| | - Estera Bakinowska
- Department of Histology and Embryology, Pomeranian Medical University, 70-111 Szczecin, Poland; (A.W.); (M.K.); (E.B.)
| | - Kamila Szumilas
- Department of Physiology, Pomeranian Medical University, 70-111 Szczecin, Poland;
| | - Anna Surówka
- Department of Plastic, Endocrine and General Surgery, Pomeranian Medical University, 72-010 Szczecin, Poland;
| | - Karolina Kędzierska-Kapuza
- Department of Gastroenterological Surgery and Transplantology, Center of Postgraduate Medical Education in Warsaw, 137 Wołoska St., 02-507 Warsaw, Poland;
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11
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Satturwar S, Parwani AV. Artificial Intelligence-Enabled Prostate Cancer Diagnosis and Prognosis: Current State and Future Implications. Adv Anat Pathol 2024; 31:136-144. [PMID: 38179884 DOI: 10.1097/pap.0000000000000425] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2024]
Abstract
In this modern era of digital pathology, artificial intelligence (AI)-based diagnostics for prostate cancer has become a hot topic. Multiple retrospective studies have demonstrated the benefits of AI-based diagnostic solutions for prostate cancer that includes improved prostate cancer detection, quantification, grading, interobserver concordance, cost and time savings, and a potential to reduce pathologists' workload and enhance pathology laboratory workflow. One of the major milestones is the Food and Drug Administration approval of Paige prostate AI for a second review of prostate cancer diagnosed using core needle biopsies. However, implementation of these AI tools for routine prostate cancer diagnostics is still lacking. Some of the limiting factors include costly digital pathology workflow, lack of regulatory guidelines for deployment of AI, and lack of prospective studies demonstrating the actual benefits of AI algorithms. Apart from diagnosis, AI algorithms have the potential to uncover novel insights into understanding the biology of prostate cancer and enable better risk stratification, and prognostication. This article includes an in-depth review of the current state of AI for prostate cancer diagnosis and highlights the future prospects of AI in prostate pathology for improved patient care.
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Affiliation(s)
- Swati Satturwar
- The Ohio State University, Wexner Medical Center, Columbus, OH
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12
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Mehrotra S, Sharma S, Pandey RK. A journey from omics to clinicomics in solid cancers: Success stories and challenges. ADVANCES IN PROTEIN CHEMISTRY AND STRUCTURAL BIOLOGY 2024; 139:89-139. [PMID: 38448145 DOI: 10.1016/bs.apcsb.2023.11.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/08/2024]
Abstract
The word 'cancer' encompasses a heterogenous group of distinct disease types characterized by a spectrum of pathological features, genetic alterations and response to therapies. According to the World Health Organization, cancer is the second leading cause of death worldwide, responsible for one in six deaths and hence imposes a significant burden on global healthcare systems. High-throughput omics technologies combined with advanced imaging tools, have revolutionized our ability to interrogate the molecular landscape of tumors and has provided unprecedented understanding of the disease. Yet, there is a gap between basic research discoveries and their translation into clinically meaningful therapies for improving patient care. To bridge this gap, there is a need to analyse the vast amounts of high dimensional datasets from multi-omics platforms. The integration of multi-omics data with clinical information like patient history, histological examination and imaging has led to the novel concept of clinicomics and may expedite the bench-to-bedside transition in cancer. The journey from omics to clinicomics has gained momentum with development of radiomics which involves extracting quantitative features from medical imaging data with the help of deep learning and artificial intelligence (AI) tools. These features capture detailed information about the tumor's shape, texture, intensity, and spatial distribution. Together, the related fields of multiomics, translational bioinformatics, radiomics and clinicomics may provide evidence-based recommendations tailored to the individual cancer patient's molecular profile and clinical characteristics. In this chapter, we summarize multiomics studies in solid cancers with a specific focus on breast cancer. We also review machine learning and AI based algorithms and their use in cancer diagnosis, subtyping, prognosis and predicting treatment resistance and relapse.
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13
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Lu Z, Morita M, Yeager TS, Lyu Y, Wang SY, Wang Z, Fan G. Validation of Artificial Intelligence (AI)-Assisted Flow Cytometry Analysis for Immunological Disorders. Diagnostics (Basel) 2024; 14:420. [PMID: 38396459 PMCID: PMC10888253 DOI: 10.3390/diagnostics14040420] [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: 01/22/2024] [Revised: 02/07/2024] [Accepted: 02/13/2024] [Indexed: 02/25/2024] Open
Abstract
Flow cytometry is a vital diagnostic tool for hematologic and immunologic disorders, but manual analysis is prone to variation and time-consuming. Over the last decade, artificial intelligence (AI) has advanced significantly. In this study, we developed and validated an AI-assisted flow cytometry workflow using 379 clinical cases from 2021, employing a 3-tube, 10-color flow panel with 21 antibodies for primary immunodeficiency diseases and related immunological disorders. The AI software (DeepFlow™, version 2.1.1) is fully automated, reducing analysis time to under 5 min per case. It interacts with hematopatholoists for manual gating adjustments when necessary. Using proprietary multidimensional density-phenotype coupling algorithm, the AI model accurately classifies and enumerates T, B, and NK cells, along with important immune cell subsets, including CD4+ helper T cells, CD8+ cytotoxic T cells, CD3+/CD4-/CD8- double-negative T cells, and class-switched or non-switched B cells. Compared to manual analysis with hematopathologist-determined lymphocyte subset percentages as the gold standard, the AI model exhibited a strong correlation (r > 0.9) across lymphocyte subsets. This study highlights the accuracy and efficiency of AI-assisted flow cytometry in diagnosing immunological disorders in a clinical setting, providing a transformative approach within a concise timeframe.
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Affiliation(s)
- Zhengchun Lu
- Department of Pathology and Laboratory Medicine, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, Portland, OR 97239, USA; (Z.L.); (M.M.); (T.S.Y.); (Y.L.); (S.Y.W.)
| | - Mayu Morita
- Department of Pathology and Laboratory Medicine, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, Portland, OR 97239, USA; (Z.L.); (M.M.); (T.S.Y.); (Y.L.); (S.Y.W.)
| | - Tyler S. Yeager
- Department of Pathology and Laboratory Medicine, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, Portland, OR 97239, USA; (Z.L.); (M.M.); (T.S.Y.); (Y.L.); (S.Y.W.)
| | - Yunpeng Lyu
- Department of Pathology and Laboratory Medicine, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, Portland, OR 97239, USA; (Z.L.); (M.M.); (T.S.Y.); (Y.L.); (S.Y.W.)
| | - Sophia Y. Wang
- Department of Pathology and Laboratory Medicine, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, Portland, OR 97239, USA; (Z.L.); (M.M.); (T.S.Y.); (Y.L.); (S.Y.W.)
| | | | - Guang Fan
- Department of Pathology and Laboratory Medicine, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, Portland, OR 97239, USA; (Z.L.); (M.M.); (T.S.Y.); (Y.L.); (S.Y.W.)
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14
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Zhang C, Xu J, Tang R, Yang J, Wang W, Yu X, Shi S. Novel research and future prospects of artificial intelligence in cancer diagnosis and treatment. J Hematol Oncol 2023; 16:114. [PMID: 38012673 PMCID: PMC10680201 DOI: 10.1186/s13045-023-01514-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2023] [Accepted: 11/20/2023] [Indexed: 11/29/2023] Open
Abstract
Research into the potential benefits of artificial intelligence for comprehending the intricate biology of cancer has grown as a result of the widespread use of deep learning and machine learning in the healthcare sector and the availability of highly specialized cancer datasets. Here, we review new artificial intelligence approaches and how they are being used in oncology. We describe how artificial intelligence might be used in the detection, prognosis, and administration of cancer treatments and introduce the use of the latest large language models such as ChatGPT in oncology clinics. We highlight artificial intelligence applications for omics data types, and we offer perspectives on how the various data types might be combined to create decision-support tools. We also evaluate the present constraints and challenges to applying artificial intelligence in precision oncology. Finally, we discuss how current challenges may be surmounted to make artificial intelligence useful in clinical settings in the future.
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Affiliation(s)
- Chaoyi Zhang
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 270 Dong'An Road, Shanghai, 200032, People's Republic of China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
- Shanghai Pancreatic Cancer Institute, No. 399 Lingling Road, Shanghai, 200032, People's Republic of China
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, People's Republic of China
| | - Jin Xu
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 270 Dong'An Road, Shanghai, 200032, People's Republic of China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
- Shanghai Pancreatic Cancer Institute, No. 399 Lingling Road, Shanghai, 200032, People's Republic of China
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, People's Republic of China
| | - Rong Tang
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 270 Dong'An Road, Shanghai, 200032, People's Republic of China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
- Shanghai Pancreatic Cancer Institute, No. 399 Lingling Road, Shanghai, 200032, People's Republic of China
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, People's Republic of China
| | - Jianhui Yang
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 270 Dong'An Road, Shanghai, 200032, People's Republic of China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
- Shanghai Pancreatic Cancer Institute, No. 399 Lingling Road, Shanghai, 200032, People's Republic of China
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, People's Republic of China
| | - Wei Wang
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 270 Dong'An Road, Shanghai, 200032, People's Republic of China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
- Shanghai Pancreatic Cancer Institute, No. 399 Lingling Road, Shanghai, 200032, People's Republic of China
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, People's Republic of China
| | - Xianjun Yu
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 270 Dong'An Road, Shanghai, 200032, People's Republic of China.
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China.
- Shanghai Pancreatic Cancer Institute, No. 399 Lingling Road, Shanghai, 200032, People's Republic of China.
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, People's Republic of China.
| | - Si Shi
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 270 Dong'An Road, Shanghai, 200032, People's Republic of China.
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China.
- Shanghai Pancreatic Cancer Institute, No. 399 Lingling Road, Shanghai, 200032, People's Republic of China.
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, People's Republic of China.
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Lombardo R, Tema G, Nacchia A, Mancini E, Franco S, Zammitti F, Franco A, Cash H, Gravina C, Guidotti A, Gallo G, Ghezzo N, Cicione A, Tubaro A, Autorino R, De Nunzio C. Role of Perilesional Sampling of Patients Undergoing Fusion Prostate Biopsies. Life (Basel) 2023; 13:1719. [PMID: 37629576 PMCID: PMC10455324 DOI: 10.3390/life13081719] [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: 06/09/2023] [Revised: 08/04/2023] [Accepted: 08/09/2023] [Indexed: 08/27/2023] Open
Abstract
Recently, researchers have proposed perilesional sampling during prostate biopsies to avoid systematic biopsies of patients at risk of prostate cancer. The aim of our study is to evaluate the role of perilesional sampling to avoid systematic biopsies of patients undergoing fusion biopsies. A prospective cohort of patients undergoing transrectal MRI transrectal fusion biopsies were consecutively enrolled. All the patients underwent systematic biopsies (SB), targeted biopsies (TB) and perilesional biopsies within 10 mm from the lesion (PB). The detection rates of different strategies were determined. A total of 262 patients were enrolled. The median age of those enrolled was 70 years. The mean BMI was 27 kg/m2, and the mean and prostate volume was 52 mL. A PIRADS score ≥ 4 was recorded in 163/262 (40%) patients. Overall, the detection rates of cancer were 43.5% (114/262) and 35% (92/262) for csPCa. The use of the target + peri-target strategy resulted in a detection of 32.8% (86/262) of cancer cases and of 29% (76/262) of csPCa cases (Grade Group > 2). Using the target plus peri-target approach resulted in us missing 18/262 (7%) of the csPCa cases, avoiding the diagnosis of 8/262 (3%) of nsPCa cases. A biopsy strategy including lesional and perilesional sampling could avoid unnecessary prostate biopsies. However, the risk of missing significant cancers is present. Future studies should assess the cost-benefit relationship of different strategies.
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Affiliation(s)
- Riccardo Lombardo
- Ospedale Sant’Andrea, Sapienza University of Rome, 00185 Rome, Italy; (R.L.); (G.T.); (A.N.); (E.M.); (S.F.); (F.Z.); (A.F.); (C.G.); (A.G.); (G.G.); (N.G.); (A.C.); (A.T.)
| | - Giorgia Tema
- Ospedale Sant’Andrea, Sapienza University of Rome, 00185 Rome, Italy; (R.L.); (G.T.); (A.N.); (E.M.); (S.F.); (F.Z.); (A.F.); (C.G.); (A.G.); (G.G.); (N.G.); (A.C.); (A.T.)
| | - Antonio Nacchia
- Ospedale Sant’Andrea, Sapienza University of Rome, 00185 Rome, Italy; (R.L.); (G.T.); (A.N.); (E.M.); (S.F.); (F.Z.); (A.F.); (C.G.); (A.G.); (G.G.); (N.G.); (A.C.); (A.T.)
| | - Elisa Mancini
- Ospedale Sant’Andrea, Sapienza University of Rome, 00185 Rome, Italy; (R.L.); (G.T.); (A.N.); (E.M.); (S.F.); (F.Z.); (A.F.); (C.G.); (A.G.); (G.G.); (N.G.); (A.C.); (A.T.)
| | - Sara Franco
- Ospedale Sant’Andrea, Sapienza University of Rome, 00185 Rome, Italy; (R.L.); (G.T.); (A.N.); (E.M.); (S.F.); (F.Z.); (A.F.); (C.G.); (A.G.); (G.G.); (N.G.); (A.C.); (A.T.)
| | - Filippo Zammitti
- Ospedale Sant’Andrea, Sapienza University of Rome, 00185 Rome, Italy; (R.L.); (G.T.); (A.N.); (E.M.); (S.F.); (F.Z.); (A.F.); (C.G.); (A.G.); (G.G.); (N.G.); (A.C.); (A.T.)
| | - Antonio Franco
- Ospedale Sant’Andrea, Sapienza University of Rome, 00185 Rome, Italy; (R.L.); (G.T.); (A.N.); (E.M.); (S.F.); (F.Z.); (A.F.); (C.G.); (A.G.); (G.G.); (N.G.); (A.C.); (A.T.)
| | - Hannes Cash
- Department of Urology, University of Magdeburg, 39106 Magdeburg, Germany;
| | - Carmen Gravina
- Ospedale Sant’Andrea, Sapienza University of Rome, 00185 Rome, Italy; (R.L.); (G.T.); (A.N.); (E.M.); (S.F.); (F.Z.); (A.F.); (C.G.); (A.G.); (G.G.); (N.G.); (A.C.); (A.T.)
| | - Alessio Guidotti
- Ospedale Sant’Andrea, Sapienza University of Rome, 00185 Rome, Italy; (R.L.); (G.T.); (A.N.); (E.M.); (S.F.); (F.Z.); (A.F.); (C.G.); (A.G.); (G.G.); (N.G.); (A.C.); (A.T.)
| | - Giacomo Gallo
- Ospedale Sant’Andrea, Sapienza University of Rome, 00185 Rome, Italy; (R.L.); (G.T.); (A.N.); (E.M.); (S.F.); (F.Z.); (A.F.); (C.G.); (A.G.); (G.G.); (N.G.); (A.C.); (A.T.)
| | - Nicola Ghezzo
- Ospedale Sant’Andrea, Sapienza University of Rome, 00185 Rome, Italy; (R.L.); (G.T.); (A.N.); (E.M.); (S.F.); (F.Z.); (A.F.); (C.G.); (A.G.); (G.G.); (N.G.); (A.C.); (A.T.)
| | - Antonio Cicione
- Ospedale Sant’Andrea, Sapienza University of Rome, 00185 Rome, Italy; (R.L.); (G.T.); (A.N.); (E.M.); (S.F.); (F.Z.); (A.F.); (C.G.); (A.G.); (G.G.); (N.G.); (A.C.); (A.T.)
| | - Andrea Tubaro
- Ospedale Sant’Andrea, Sapienza University of Rome, 00185 Rome, Italy; (R.L.); (G.T.); (A.N.); (E.M.); (S.F.); (F.Z.); (A.F.); (C.G.); (A.G.); (G.G.); (N.G.); (A.C.); (A.T.)
| | - Riccardo Autorino
- Department of Urology, University of Chicago, Chicago, IL 60637, USA;
| | - Cosimo De Nunzio
- Ospedale Sant’Andrea, Sapienza University of Rome, 00185 Rome, Italy; (R.L.); (G.T.); (A.N.); (E.M.); (S.F.); (F.Z.); (A.F.); (C.G.); (A.G.); (G.G.); (N.G.); (A.C.); (A.T.)
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