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Hamamoto Y, Kawamura M, Uchida H, Hiramatsu K, Katori C, Asai H, Shimizu S, Egawa S, Yoshida K. The Histological Detection of Ulcerative Colitis Using a No-Code Artificial Intelligence Model. Int J Surg Pathol 2024; 32:890-894. [PMID: 37880949 DOI: 10.1177/10668969231204955] [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: 10/27/2023]
Abstract
Ulcerative colitis (UC) is an intractable disease that affects young adults. Histological findings are essential for its diagnosis; however, the number of diagnostic pathologists is limited. Herein, we used a no-code artificial intelligence (AI) platform "Teachable Machine" to train a model that could distinguish between histological images of UC, non-UC coloproctitis, adenocarcinoma, and control. A total of 5100 histological images for training and 900 histological images for testing were prepared by pathologists. Our model showed accuracies of 0.99, 1.00, 0.99, and 0.99, for UC, non-UC coloproctitis, adenocarcinoma, and control, respectively. This is the first report in which a no-code easy AI platform has been able to comprehensively recognize the distinctive histologic patterns of UC.
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Affiliation(s)
- Yuichiro Hamamoto
- Department of Diagnostic Pathology, Kinki Central Hospital of Mutual Aid Association of Public School Teachers, Itami, Hyogo, Japan
- Faculty of Medicine Division of Medicine, Department of Pathology, Osaka University Graduate School of Medicine, Suita, Japan
| | - Michihiro Kawamura
- Department of Clinical Laboratory, Kinki Central Hospital of Mutual Aid Association of Public School Teachers, Itami, Hyogo, Japan
| | - Hiroki Uchida
- Department of Clinical Laboratory, Kinki Central Hospital of Mutual Aid Association of Public School Teachers, Itami, Hyogo, Japan
| | - Kazuhiro Hiramatsu
- Department of Clinical Laboratory, Kinki Central Hospital of Mutual Aid Association of Public School Teachers, Itami, Hyogo, Japan
| | - Chiaki Katori
- Department of Clinical Laboratory, Kinki Central Hospital of Mutual Aid Association of Public School Teachers, Itami, Hyogo, Japan
| | - Hinako Asai
- Department of Clinical Laboratory, Kinki Central Hospital of Mutual Aid Association of Public School Teachers, Itami, Hyogo, Japan
| | - Shigeki Shimizu
- Department of Clinical Laboratory, National Hospital Organization Kinki-Chuo Chest Medical Center, Kita-ku, Sakai, Osaka, Japan
| | - Satoshi Egawa
- Department of Gastroenterology, Kinki Central Hospital of Mutual Aid Association of Public School Teachers, Itami, Hyogo, Japan
| | - Kyotaro Yoshida
- Department of Clinical Laboratory, Kinki Central Hospital of Mutual Aid Association of Public School Teachers, Itami, Hyogo, Japan
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Ohara J, Maeda Y, Ogata N, Kuroki T, Misawa M, Kudo SE, Nemoto T, Yamochi T, Iacucci M. Automated Neutrophil Quantification and Histological Score Estimation in Ulcerative Colitis. Clin Gastroenterol Hepatol 2024:S1542-3565(24)00668-2. [PMID: 39059545 DOI: 10.1016/j.cgh.2024.06.040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/14/2024] [Revised: 06/27/2024] [Accepted: 06/27/2024] [Indexed: 07/28/2024]
Abstract
BACKGROUND In the management of ulcerative colitis (UC), histological remission is increasingly recognized as the ultimate goal. The absence of neutrophil infiltration is crucial for assessing remission. This study aimed to develop an artificial intelligence (AI) system capable of accurately quantifying and localizing neutrophils in UC biopsy specimens to facilitate histological assessment. METHODS Our AI system, which incorporates semantic segmentation and object detection models, was developed to identify neutrophils in hematoxylin and eosin-stained whole slide images. The system assessed the presence and location of neutrophils within either the epithelium or lamina propria and predicted components of the Nancy Histological Index and the PICaSSO Histologic Remission Index. We evaluated the system's performance against that of experienced pathologists and validated its ability to predict future clinical relapse risk in patients with clinically remitted UC. The primary outcome measure was the clinical relapse rate, defined as a partial Mayo score of ≥3. RESULTS The model accurately identified neutrophils, achieving a performance of 0.77, 0.81, and 0.79 for precision, recall, and F-score, respectively. The system's histological score predictions showed a positive correlation with the pathologists' diagnoses (Spearman's ρ = 0.68-0.80; P < .05). Among patients who relapsed, the mean number of neutrophils in the rectum was higher than in those who did not relapse. Furthermore, the study highlighted that higher AI-based PICaSSO Histologic Remission Index and Nancy Histological Index scores were associated with hazard ratios increasing from 3.2 to 5.0 for evaluating the risk of UC relapse. CONCLUSIONS The AI system's precise localization and quantification of neutrophils proved valuable for histological assessment and clinical prognosis stratification.
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Affiliation(s)
- Jun Ohara
- Department of Pathology, Showa University School of Medicine, Tokyo, Japan.
| | - Yasuharu Maeda
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan; APC Microbiome Ireland, College of Medicine and Health, University College Cork, Cork, Ireland
| | - Noriyuki Ogata
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Takanori Kuroki
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Masashi Misawa
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Shin-Ei Kudo
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Tetsuo Nemoto
- Department of Diagnostic Pathology, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Toshiko Yamochi
- Department of Pathology, Showa University School of Medicine, Tokyo, Japan
| | - Marietta Iacucci
- APC Microbiome Ireland, College of Medicine and Health, University College Cork, Cork, Ireland
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Maeda Y, Kudo SE, Santacroce G, Ogata N, Misawa M, Iacucci M. Artificial intelligence-assisted colonoscopy to identify histologic remission and predict the outcomes of patients with ulcerative colitis: A systematic review. Dig Liver Dis 2024; 56:1119-1125. [PMID: 38643020 DOI: 10.1016/j.dld.2024.04.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Revised: 03/11/2024] [Accepted: 04/04/2024] [Indexed: 04/22/2024]
Abstract
This systematic review evaluated the current status of AI-assisted colonoscopy to identify histologic remission and predict the clinical outcomes of patients with ulcerative colitis. The use of artificial intelligence (AI) has increased substantially across several medical fields, including gastrointestinal endoscopy. Evidence suggests that it may be helpful to predict histologic remission and relapse, which would be beneficial because current histological diagnosis is limited by the inconvenience of obtaining biopsies and the high cost and time-intensiveness of pathological diagnosis. MEDLINE and the Cochrane Central Register of Controlled Trials were searched for studies published between January 1, 2000, and October 31, 2023. Nine studies fulfilled the selection criteria and were included; five evaluated the prediction of histologic remission, two assessed the prediction of clinical outcomes, and two evaluated both. Seven were prospective observational or cohort studies, while two were retrospective observational studies. No randomized controlled trials were identified. AI-assisted colonoscopy demonstrated sensitivity between 65 %-98 % and specificity values of 80 %-97 % for identifying histologic remission. Furthermore, it was able to predict future relapse in patients with ulcerative colitis. However, several challenges and barriers still exist to its routine clinical application, which should be overcome before the true potential of AI-assisted colonoscopy can be fully realized.
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Affiliation(s)
- Yasuharu Maeda
- Digestive Disease Center, Showa University Northern Yokohama Hospital, 35-1 Chigasaki-chuo, Tsuzuki, Yokohama 224-8503, Japan; APC Microbiome Ireland, College of Medicine and Health, University College Cork, Cork, T12 YT20, Ireland.
| | - Shin-Ei Kudo
- Digestive Disease Center, Showa University Northern Yokohama Hospital, 35-1 Chigasaki-chuo, Tsuzuki, Yokohama 224-8503, Japan
| | - Giovanni Santacroce
- APC Microbiome Ireland, College of Medicine and Health, University College Cork, Cork, T12 YT20, Ireland
| | - Noriyuki Ogata
- Digestive Disease Center, Showa University Northern Yokohama Hospital, 35-1 Chigasaki-chuo, Tsuzuki, Yokohama 224-8503, Japan
| | - Masashi Misawa
- Digestive Disease Center, Showa University Northern Yokohama Hospital, 35-1 Chigasaki-chuo, Tsuzuki, Yokohama 224-8503, Japan
| | - Marietta Iacucci
- APC Microbiome Ireland, College of Medicine and Health, University College Cork, Cork, T12 YT20, Ireland
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Iacucci M, Santacroce G, Majumder S, Morael J, Zammarchi I, Maeda Y, Ryan D, Di Sabatino A, Rescigno M, Aburto MR, Cryan JF, Ghosh S. Opening the doors of precision medicine: novel tools to assess intestinal barrier in inflammatory bowel disease and colitis-associated neoplasia. Gut 2024:gutjnl-2023-331579. [PMID: 38851294 DOI: 10.1136/gutjnl-2023-331579] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/19/2024] [Accepted: 05/18/2024] [Indexed: 06/10/2024]
Abstract
Mounting evidence underscores the pivotal role of the intestinal barrier and its convoluted network with diet and intestinal microbiome in the pathogenesis of inflammatory bowel disease (IBD) and colitis-associated colorectal cancer (CRC). Moreover, the bidirectional association of the intestinal barrier with the liver and brain, known as the gut-brain axis, plays a crucial role in developing complications, including extraintestinal manifestations of IBD and CRC metastasis. Consequently, barrier healing represents a crucial therapeutic target in these inflammatory-dependent disorders, with barrier assessment predicting disease outcomes, response to therapy and extraintestinal manifestations.New advanced technologies are revolutionising our understanding of the barrier paradigm, enabling the accurate assessment of the intestinal barrier and aiding in unravelling the complexity of the gut-brain axis. Cutting-edge endoscopic imaging techniques, such as ultra-high magnification endocytoscopy and probe-based confocal laser endomicroscopy, are new technologies allowing real-time exploration of the 'cellular' intestinal barrier. Additionally, novel advanced spatial imaging technology platforms, including multispectral imaging, upconversion nanoparticles, digital spatial profiling, optical spectroscopy and mass cytometry, enable a deep and comprehensive assessment of the 'molecular' and 'ultrastructural' barrier. In this promising landscape, artificial intelligence plays a pivotal role in standardising and integrating these novel tools, thereby contributing to barrier assessment and prediction of outcomes.Looking ahead, this integrated and comprehensive approach holds the promise of uncovering new therapeutic targets, breaking the therapeutic ceiling in IBD. Novel molecules, dietary interventions and microbiome modulation strategies aim to restore, reinforce, or modulate the gut-brain axis. These advancements have the potential for transformative and personalised approaches to managing IBD.
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Affiliation(s)
- Marietta Iacucci
- APC Microbiome Ireland, College of Medicine and Health, University College Cork, Cork, Ireland
| | - Giovanni Santacroce
- APC Microbiome Ireland, College of Medicine and Health, University College Cork, Cork, Ireland
| | - Snehali Majumder
- APC Microbiome Ireland, College of Medicine and Health, University College Cork, Cork, Ireland
| | - Jennifer Morael
- APC Microbiome Ireland, Department of Anatomy and Neuroscience, School of Medicine, University College Cork, Cork, Ireland
| | - Irene Zammarchi
- APC Microbiome Ireland, College of Medicine and Health, University College Cork, Cork, Ireland
| | - Yasuharu Maeda
- APC Microbiome Ireland, College of Medicine and Health, University College Cork, Cork, Ireland
| | - David Ryan
- Department of Radiology, School of Medicine, University College Cork, Cork, Ireland
| | - Antonio Di Sabatino
- Department of Internal Medicine and Medical Therapeutics, University of Pavia, Pavia, Italy
- First Department of Internal Medicine, San Matteo Hospital Foundation, Pavia, Italy
| | - Maria Rescigno
- IRCSS Humanitas Research Hospital, Milan, Italy; Department of Biomedical Sciences, Humanitas University, Milan, Italy
| | - Maria R Aburto
- APC Microbiome Ireland, Department of Anatomy and Neuroscience, School of Medicine, University College Cork, Cork, Ireland
| | - John F Cryan
- APC Microbiome Ireland, Department of Anatomy and Neuroscience, School of Medicine, University College Cork, Cork, Ireland
| | - Subrata Ghosh
- APC Microbiome Ireland, College of Medicine and Health, University College Cork, Cork, Ireland
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Syed S, Boland BS, Bourke LT, Chen LA, Churchill L, Dobes A, Greene A, Heller C, Jayson C, Kostiuk B, Moss A, Najdawi F, Plung L, Rioux JD, Rosen MJ, Torres J, Zulqarnain F, Satsangi J. Challenges in IBD Research 2024: Precision Medicine. Inflamm Bowel Dis 2024; 30:S39-S54. [PMID: 38778628 DOI: 10.1093/ibd/izae084] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Indexed: 05/25/2024]
Abstract
Precision medicine is part of 5 focus areas of the Challenges in IBD Research 2024 research document, which also includes preclinical human IBD mechanisms, environmental triggers, novel technologies, and pragmatic clinical research. Building on Challenges in IBD Research 2019, the current Challenges aims to provide a comprehensive overview of current gaps in inflammatory bowel diseases (IBDs) research and deliver actionable approaches to address them with a focus on how these gaps can lead to advancements in interception, remission, and restoration for these diseases. The document is the result of multidisciplinary input from scientists, clinicians, patients, and funders, and represents a valuable resource for patient-centric research prioritization. In particular, the precision medicine section is focused on the main research gaps in elucidating how to bring the best care to the individual patient in IBD. Research gaps were identified in biomarker discovery and validation for predicting disease progression and choosing the most appropriate treatment for each patient. Other gaps were identified in making the best use of existing patient biosamples and clinical data, developing new technologies to analyze large datasets, and overcoming regulatory and payer hurdles to enable clinical use of biomarkers. To address these gaps, the Workgroup suggests focusing on thoroughly validating existing candidate biomarkers, using best-in-class data generation and analysis tools, and establishing cross-disciplinary teams to tackle regulatory hurdles as early as possible. Altogether, the precision medicine group recognizes the importance of bringing basic scientific biomarker discovery and translating it into the clinic to help improve the lives of IBD patients.
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Affiliation(s)
- Sana Syed
- Department of Pediatrics, University of Virginia, Charlottesville, VA, USA
- Patient representative for Crohn's & Colitis Foundation, New York, NY, USA
| | - Brigid S Boland
- Department of Medicine, University of California San Diego, La Jolla, CA, USA
| | - Lauren T Bourke
- Precision Medicine Drug Development, Early Respiratory and Immunology, AstraZeneca, Boston, MA, USA
| | - Lea Ann Chen
- Division of Gastroenterology, Department of Medicine, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ, USA
| | - Laurie Churchill
- Leona M. and Harry B. Helmsley Charitable Trust, New York, NY, USA
| | | | - Adam Greene
- Department of Pediatrics, University of Virginia, Charlottesville, VA, USA
| | | | | | | | - Alan Moss
- Crohn's & Colitis Foundation, New York, NY, USA
| | | | - Lori Plung
- Patient representative for Crohn's & Colitis Foundation, New York, NY, USA
| | - John D Rioux
- Research Center, Montreal Heart Institute, Université de Montréal, Montréal, Québec, Canada
| | - Michael J Rosen
- Division of Pediatric Gastroenterology, Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
| | - Joana Torres
- Division of Gastroenterology, Hospital Beatriz Ângelo, Hospital da Luz, Lisbon, Portugal
- Faculdade de Medicina, Universidade de Lisboa, Lisbon, Portugal
| | - Fatima Zulqarnain
- Department of Pediatrics, University of Virginia, Charlottesville, VA, USA
| | - Jack Satsangi
- Translational Gastroenterology Unit, Experimental Medicine Division, Nuffield Department of Medicine, John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom
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6
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Yilmaz F, Brickman A, Najdawi F, Yakirevich E, Egger R, Resnick MB. Advancing Artificial Intelligence Integration Into the Pathology Workflow: Exploring Opportunities in Gastrointestinal Tract Biopsies. J Transl Med 2024; 104:102043. [PMID: 38431118 DOI: 10.1016/j.labinv.2024.102043] [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: 10/30/2023] [Revised: 02/14/2024] [Accepted: 02/26/2024] [Indexed: 03/05/2024] Open
Abstract
This review aims to present a comprehensive overview of the current landscape of artificial intelligence (AI) applications in the analysis of tubular gastrointestinal biopsies. These publications cover a spectrum of conditions, ranging from inflammatory ailments to malignancies. Moving beyond the conventional diagnosis based on hematoxylin and eosin-stained whole-slide images, the review explores additional implications of AI, including its involvement in interpreting immunohistochemical results, molecular subtyping, and the identification of cellular spatial biomarkers. Furthermore, the review examines how AI can contribute to enhancing the quality and control of diagnostic processes, introducing new workflow options, and addressing the limitations and caveats associated with current AI platforms in this context.
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Affiliation(s)
- Fazilet Yilmaz
- The Warren Alpert Medical School of Brown University, Rhode Island Hospital, Providence, Rhode Island
| | - Arlen Brickman
- The Warren Alpert Medical School of Brown University, Rhode Island Hospital, Providence, Rhode Island
| | - Fedaa Najdawi
- The Warren Alpert Medical School of Brown University, Rhode Island Hospital, Providence, Rhode Island
| | - Evgeny Yakirevich
- The Warren Alpert Medical School of Brown University, Rhode Island Hospital, Providence, Rhode Island
| | | | - Murray B Resnick
- The Warren Alpert Medical School of Brown University, Rhode Island Hospital, Providence, Rhode Island.
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7
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Gu P, Mendonca O, Carter D, Dube S, Wang P, Huang X, Li D, Moore JH, McGovern DPB. AI-luminating Artificial Intelligence in Inflammatory Bowel Diseases: A Narrative Review on the Role of AI in Endoscopy, Histology, and Imaging for IBD. Inflamm Bowel Dis 2024:izae030. [PMID: 38452040 DOI: 10.1093/ibd/izae030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Indexed: 03/09/2024]
Abstract
Endoscopy, histology, and cross-sectional imaging serve as fundamental pillars in the detection, monitoring, and prognostication of inflammatory bowel disease (IBD). However, interpretation of these studies often relies on subjective human judgment, which can lead to delays, intra- and interobserver variability, and potential diagnostic discrepancies. With the rising incidence of IBD globally coupled with the exponential digitization of these data, there is a growing demand for innovative approaches to streamline diagnosis and elevate clinical decision-making. In this context, artificial intelligence (AI) technologies emerge as a timely solution to address the evolving challenges in IBD. Early studies using deep learning and radiomics approaches for endoscopy, histology, and imaging in IBD have demonstrated promising results for using AI to detect, diagnose, characterize, phenotype, and prognosticate IBD. Nonetheless, the available literature has inherent limitations and knowledge gaps that need to be addressed before AI can transition into a mainstream clinical tool for IBD. To better understand the potential value of integrating AI in IBD, we review the available literature to summarize our current understanding and identify gaps in knowledge to inform future investigations.
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Affiliation(s)
- Phillip Gu
- F. Widjaja Inflammatory Bowel Disease Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | | | - Dan Carter
- Department of Gastroenterology, Sheba Medical Center, Tel Aviv, Israel
| | - Shishir Dube
- F. Widjaja Inflammatory Bowel Disease Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Paul Wang
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Xiuzhen Huang
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Debiao Li
- Biomedical Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Jason H Moore
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Dermot P B McGovern
- F. Widjaja Inflammatory Bowel Disease Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
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8
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Liu X, Reigle J, Prasath VBS, Dhaliwal J. Artificial intelligence image-based prediction models in IBD exhibit high risk of bias: A systematic review. Comput Biol Med 2024; 171:108093. [PMID: 38354499 DOI: 10.1016/j.compbiomed.2024.108093] [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: 09/04/2023] [Revised: 01/04/2024] [Accepted: 01/30/2024] [Indexed: 02/16/2024]
Abstract
BACKGROUND There has been an increase in the development of both machine learning (ML) and deep learning (DL) prediction models in Inflammatory Bowel Disease. We aim in this systematic review to assess the methodological quality and risk of bias of ML and DL IBD image-based prediction studies. METHODS We searched three databases, PubMed, Scopus and Embase, to identify ML and DL diagnostic or prognostic predictive models using imaging data in IBD, to Dec 31, 2022. We restricted our search to include studies that primarily used conventional imaging data, were undertaken in human participants, and published in English. Two reviewers independently reviewed the abstracts. The methodological quality of the studies was determined, and risk of bias evaluated using the prediction risk of bias assessment tool (PROBAST). RESULTS Forty studies were included, thirty-nine developed diagnostic models. Seven studies utilized ML approaches, six were retrospective and none used multicenter data for model development. Thirty-three studies utilized DL approaches, ten were prospective, and twelve multicenter studies. Overall, all studies demonstrated high risk of bias. ML studies were evaluated in 4 domains all rated as high risk of bias: participants (6/7), predictors (1/7), outcome (3/7), and analysis (7/7), and DL studies evaluated in 3 domains: participants (24/33), outcome (10/33), and analysis (18/33). The majority of image-based studies used colonoscopy images. CONCLUSION The risk of bias was high in AI IBD image-based prediction models, owing to insufficient sample size, unreported missingness and lack of an external validation cohort. Models with a high risk of bias are unlikely to be generalizable and suitable for clinical implementation.
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Affiliation(s)
- Xiaoxuan Liu
- Department of Biomedical Informatics, College of Medicine, University of Cincinnati, OH, USA; Department of Pediatrics, University of Cincinnati, College of Medicine, Cincinnati, OH, USA
| | - James Reigle
- Department of Pediatrics, University of Cincinnati, College of Medicine, Cincinnati, OH, USA; Cincinnati Children's Hospital Medical Center, Division of Gastroenterology, Hepatology and Nutrition, USA
| | - V B Surya Prasath
- Department of Biomedical Informatics, College of Medicine, University of Cincinnati, OH, USA; Department of Pediatrics, University of Cincinnati, College of Medicine, Cincinnati, OH, USA; Cincinnati Children's Hospital Medical Center, Division of Gastroenterology, Hepatology and Nutrition, USA
| | - Jasbir Dhaliwal
- Department of Biomedical Informatics, College of Medicine, University of Cincinnati, OH, USA; Department of Pediatrics, University of Cincinnati, College of Medicine, Cincinnati, OH, USA; Cincinnati Children's Hospital Medical Center, Division of Gastroenterology, Hepatology and Nutrition, USA.
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9
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Carreras J. The pathobiology of follicular lymphoma. J Clin Exp Hematop 2023; 63:152-163. [PMID: 37518274 PMCID: PMC10628832 DOI: 10.3960/jslrt.23014] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Revised: 06/10/2023] [Accepted: 06/15/2023] [Indexed: 08/01/2023] Open
Abstract
Follicular lymphoma is one of the most frequent lymphomas. Histologically, it is characterized by a follicular (nodular) growth pattern of centrocytes and centroblasts; mixed with variable immune microenvironment cells. Clinically, it is characterized by diffuse lymphadenopathy, bone marrow involvement, and splenomegaly. It is biologically and clinically heterogeneous. In most patients it is indolent, but others have a more aggressive evolution with relapses; and transformation to diffuse large B-cell lymphoma. Tumorigenesis includes an asymptomatic preclinical phase in which premalignant B-lymphocytes with the t(14;18) chromosomal translocation acquire additional genetic alterations in the germinal centers, and clonal evolution occurs, although not all the cells progress to the tumor stage. This manuscript reviews the pathobiology and clinicopathological characteristics of follicular lymphoma. It includes a description of the physiology of the germinal center, the genetic alterations of BCL2 and BCL6, the mutational profile, the immune checkpoint, precision medicine, and highlights in the lymphoma classification. In addition, a comment and review on artificial intelligence and machine (deep) learning are made.
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Affiliation(s)
- Joaquim Carreras
- Department of Pathology, Tokai University, School of Medicine, Isehara, Kanagawa, Japan
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10
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Zulqarnain F, Rhoads SF, Syed S. Machine and deep learning in inflammatory bowel disease. Curr Opin Gastroenterol 2023; 39:294-300. [PMID: 37144491 PMCID: PMC10256313 DOI: 10.1097/mog.0000000000000945] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/06/2023]
Abstract
PURPOSE OF REVIEW The Management of inflammatory bowel disease (IBD) has evolved with the introduction and widespread adoption of biologic agents; however, the advent of artificial intelligence technologies like machine learning and deep learning presents another watershed moment in IBD treatment. Interest in these methods in IBD research has increased over the past 10 years, and they offer a promising path to better clinical outcomes for IBD patients. RECENT FINDINGS Developing new tools to evaluate IBD and inform clinical management is challenging because of the expansive volume of data and requisite manual interpretation of data. Recently, machine and deep learning models have been used to streamline diagnosis and evaluation of IBD by automating review of data from several diagnostic modalities with high accuracy. These methods decrease the amount of time that clinicians spend manually reviewing data to formulate an assessment. SUMMARY Interest in machine and deep learning is increasing in medicine, and these methods are poised to revolutionize the way that we treat IBD. Here, we highlight the recent advances in using these technologies to evaluate IBD and discuss the ways that they can be leveraged to improve clinical outcomes.
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Affiliation(s)
- Fatima Zulqarnain
- Department of Pediatrics, University of Virginia School of Medicine, Charlottesville, Virginia, USA
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11
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Najdawi F, Sucipto K, Mistry P, Hennek S, Jayson CKB, Lin M, Fahy D, Kinsey S, Wapinski I, Beck AH, Resnick MB, Khosla A, Drage MG. Artificial Intelligence Enables Quantitative Assessment of Ulcerative Colitis Histology. Mod Pathol 2023; 36:100124. [PMID: 36841434 DOI: 10.1016/j.modpat.2023.100124] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Revised: 12/23/2022] [Accepted: 01/28/2023] [Indexed: 02/17/2023]
Abstract
Ulcerative colitis is a chronic inflammatory bowel disease that is characterized by a relapsing and remitting course. Assessment of disease activity critically informs treatment decisions. In addition to endoscopic remission, histologic remission is emerging as a treatment target and a key factor in the evaluation of disease activity and therapeutic efficacy. However, manual pathologist evaluation is semiquantitative and limited in granularity. Machine learning approaches are increasingly being developed to aid pathologists in accurate and reproducible scoring of histology, enabling precise quantitation of clinically relevant features. Here, we report the development and validation of convolutional neural network models that quantify histologic features pertinent to ulcerative colitis disease activity, directly from hematoxylin and eosin-stained whole slide images. Tissue and cell model predictions were used to generate quantitative human-interpretable features to fully characterize the histology samples. Tissue and cell predictions showed comparable agreement to pathologist annotations, and the extracted slide-level human-interpretable features demonstrated strong correlations with disease severity and pathologist-assigned Nancy histological index scores. Moreover, using a random forest classifier based on 13 human-interpretable features derived from the tissue and cell models, we were able to accurately predict Nancy histological index scores, with a weighted kappa (κ = 0.91) and Spearman correlation (⍴ = 0.89, P < .001) when compared with pathologist consensus Nancy histological index scores. We were also able to predict histologic remission, based on the absence of neutrophil extravasation, with a high accuracy of 0.97. This work demonstrates the potential of computer vision to enable a standardized and robust assessment of ulcerative colitis histopathology for translational research and improved evaluation of disease activity and prognosis.
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Affiliation(s)
| | | | | | | | | | - Mary Lin
- PathAI, Inc, Boston, Massachusetts
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Wang Y, Huang X, Zhou G, Han J, Xie Z, Zhang M, Li X, Wu QR, Li L, Ye Z, Chen M, Qiu Y, Zhang S. A Novel Nomogram Combining Mucus Barrier Index for Predicting Treatment Failures in Ulcerative Colitis. J Inflamm Res 2023; 16:1879-1894. [PMID: 37152865 PMCID: PMC10162100 DOI: 10.2147/jir.s410057] [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: 02/25/2023] [Accepted: 04/21/2023] [Indexed: 05/09/2023] Open
Abstract
Background Treatment failures (TFs) generally exist in the course of ulcerative colitis (UC), while early reliable predictors of TFs are still lacking. We aimed to generate nomograms for the prediction of TFs. Methods In this retrospective case-control study, the endpoint was the occurrence of TFs, which included medically associated treatment failures and surgery-associated treatment failures (colectomy). Clinical features and mucus integrity evident by goblet cells (GCs) number, expression levels of MUC2 and SLC26A3 were enrolled in the univariate analysis. Nomogram performance was evaluated by discrimination and calibration. Results We identified 256 UC patients at our center from January 2010 to June 2022. Fourteen variables for TFs and 9 for colectomy were identified by univariate analysis. Five baseline indices were incorporated into the nomogram for the prediction of TFs: area of GCs, age at diagnosis, disease duration, hemoglobin, and Mayo score. The model was presented with decent discrimination (C index of 0.822) and well calibration. In addition, the colectomy predictive nomogram was built using MUC2 intensity, age at onset, and Mayo score with a good discrimination (C index of 0.92). Conclusion Nomograms based on comprehensive factors including mucus barrier function were developed to predict TFs in UC patients with great discrimination, which may serve as practical tools aiming to identify high-risk subgroups warrant timely intervention.
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Affiliation(s)
- Ying Wang
- Department of Gastroenterology, First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong Province, People’s Republic of China
| | - Xuanzhi Huang
- Department of Gastroenterology, First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong Province, People’s Republic of China
| | - Gaoshi Zhou
- Department of Gastroenterology, First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong Province, People’s Republic of China
| | - Jing Han
- Department of Gastroenterology, First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong Province, People’s Republic of China
| | - Zhuo Xie
- Department of Gastroenterology, First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong Province, People’s Republic of China
| | - Mudan Zhang
- Department of Gastroenterology, First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong Province, People’s Republic of China
| | - Xiaoling Li
- Department of Gastroenterology, First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong Province, People’s Republic of China
| | - Qi-rui Wu
- Department of Gastroenterology, First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong Province, People’s Republic of China
| | - Li Li
- Department of Gastroenterology, First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong Province, People’s Republic of China
| | - Ziyin Ye
- Department of Pathology, First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong Province, People’s Republic of China
| | - Minhu Chen
- Department of Gastroenterology, First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong Province, People’s Republic of China
| | - Yun Qiu
- Department of Gastroenterology, First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong Province, People’s Republic of China
| | - Shenghong Zhang
- Department of Gastroenterology, First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong Province, People’s Republic of China
- Correspondence: Shenghong Zhang; Yun Qiu, Department of Gastroenterology, First Affiliated Hospital of Sun Yat-sen University, 58 Zhongshan Road 2, Guangzhou Province, 510080, People’s Republic of China, Tel/Fax +86-20-87332916, Email ;
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