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Alsaafin A, Nejat P, Shafique A, Khan J, Alfasly S, Alabtah G, Tizhoosh HR. SPLICE - Streamlining Digital Pathology Image Processing. THE AMERICAN JOURNAL OF PATHOLOGY 2024:S0002-9440(24)00238-4. [PMID: 39032601 DOI: 10.1016/j.ajpath.2024.06.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/19/2024] [Revised: 06/06/2024] [Accepted: 06/18/2024] [Indexed: 07/23/2024]
Abstract
Digital pathology and the integration of artificial intelligence (AI) models have revolutionized histopathology, opening new opportunities. With the increasing availability of Whole Slide Images (WSIs), there's a growing demand for efficient retrieval, processing, and analysis of relevant images from vast biomedical archives. However, processing WSIs presents challenges due to their large size and content complexity. Full computer digestion of WSIs is impractical, and processing all patches individually is prohibitively expensive. In this paper, we propose an unsupervised patching algorithm, Sequential Patching Lattice for Image Classification and Enquiry (SPLICE). This novel approach condenses a histopathology WSI into a compact set of representative patches, forming a "collage" of WSI while minimizing redundancy. SPLICE prioritizes patch quality and uniqueness by sequentially analyzing a WSI and selecting nonredundant representative features. We evaluated SPLICE for search and match applications, demonstrating improved accuracy, reduced computation time, and storage requirements compared to existing state-of-the-art methods. As an unsupervised method, SPLICE effectively reduces storage requirements for representing tissue images by 50%. This reduction enables numerous algorithms in computational pathology to operate much more efficiently, paving the way for accelerated adoption of digital pathology.
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Affiliation(s)
- Areej Alsaafin
- KIMIA Lab, Department of Artificial Intelligence & Informatics, Mayo Clinic, Rochester, MN, USA
| | - Peyman Nejat
- KIMIA Lab, Department of Artificial Intelligence & Informatics, Mayo Clinic, Rochester, MN, USA
| | - Abubakr Shafique
- KIMIA Lab, Department of Artificial Intelligence & Informatics, Mayo Clinic, Rochester, MN, USA
| | - Jibran Khan
- KIMIA Lab, Department of Artificial Intelligence & Informatics, Mayo Clinic, Rochester, MN, USA
| | - Saghir Alfasly
- KIMIA Lab, Department of Artificial Intelligence & Informatics, Mayo Clinic, Rochester, MN, USA
| | - Ghazal Alabtah
- KIMIA Lab, Department of Artificial Intelligence & Informatics, Mayo Clinic, Rochester, MN, USA
| | - Hamid R Tizhoosh
- KIMIA Lab, Department of Artificial Intelligence & Informatics, Mayo Clinic, Rochester, MN, USA.
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Sculthorpe D, Denton A, Rusnita D, Fadhil W, Ilyas M, Mukherjee A. Advantages of automated immunostain analyses for complex membranous immunostains: An exemplar investigating loss of E-cadherin expression in colorectal cancer. Pathol Res Pract 2024; 260:155470. [PMID: 39032383 DOI: 10.1016/j.prp.2024.155470] [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] [Revised: 06/10/2024] [Accepted: 07/14/2024] [Indexed: 07/23/2024]
Abstract
As pathology moves towards digitisation, biomarker profiling through automated image analysis provides potentially objective and time-efficient means of assessment. This study set out to determine how a complex membranous immunostain, E-cadherin, assessed using an automated digital platform fares in comparison to manual evaluation in terms of clinical correlations and prognostication. Tissue microarrays containing 1000 colorectal cancer samples, stained with clinical E-cadherin antibodies were assessed through both manual scoring and automated image analysis. Both manual and automated scores were correlated to clinicopathological and survival data. E-cadherin data generated through digital image analysis was superior to manual evaluation when investigating for clinicopathological correlations in colorectal cancer. Loss of membranous E-cadherin, assessed on automated platforms, correlated with: right sided tumours (p = <0.001), higher T-stage (p = <0.001), higher grade (p = <0.001), N2 nodal stage (p = <0.001), intramural lymphovascular invasion (p = 0.006), perineural invasion (p = 0.028), infiltrative tumour edge (p = 0.001) high tumour budding score (p = 0.038), distant metastasis (p = 0.035), and poorer 5-year (p= 0.042) survival status. Manual assessment was only correlated with higher grade tumours, though other correlations become apparent only when assessed for morphological expression pattern (circumferential, basolateral, parallel) irrespective of intensity. Digital assessment of E-cadherin is effective for prognostication of colorectal cancer and may potentially offer benefits of improved objectivity, accuracy, and economy of time. Incorporating tools to assess patterns of staining may further improve such digital assessment in the future.
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Affiliation(s)
- Declan Sculthorpe
- Translational Medical Sciences, Biodiscovery Institute, School of Medicine, University of Nottingham, Nottingham, United Kingdom.
| | - Amy Denton
- Translational Medical Sciences, Biodiscovery Institute, School of Medicine, University of Nottingham, Nottingham, United Kingdom
| | - Dewi Rusnita
- Translational Medical Sciences, Biodiscovery Institute, School of Medicine, University of Nottingham, Nottingham, United Kingdom
| | - Wakkas Fadhil
- Translational Medical Sciences, Biodiscovery Institute, School of Medicine, University of Nottingham, Nottingham, United Kingdom
| | - Mohammad Ilyas
- Translational Medical Sciences, Biodiscovery Institute, School of Medicine, University of Nottingham, Nottingham, United Kingdom; Department of Histopathology, Nottingham University Hospitals NHS Trust, Queen's Medical Centre, Nottingham, United Kingdom
| | - Abhik Mukherjee
- Translational Medical Sciences, Biodiscovery Institute, School of Medicine, University of Nottingham, Nottingham, United Kingdom; Department of Histopathology, Nottingham University Hospitals NHS Trust, Queen's Medical Centre, Nottingham, United Kingdom
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Fan F, Martinez G, DeSilvio T, Shin J, Chen Y, Jacobs J, Wang B, Ozeki T, Lafarge MW, Koelzer VH, Barisoni L, Madabhushi A, Viswanath SE, Janowczyk A. CohortFinder: an open-source tool for data-driven partitioning of digital pathology and imaging cohorts to yield robust machine-learning models. NPJ IMAGING 2024; 2:15. [PMID: 38962496 PMCID: PMC11216973 DOI: 10.1038/s44303-024-00018-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Accepted: 04/26/2024] [Indexed: 07/05/2024]
Abstract
Batch effects (BEs) refer to systematic technical differences in data collection unrelated to biological variations whose noise is shown to negatively impact machine learning (ML) model generalizability. Here we release CohortFinder (http://cohortfinder.com), an open-source tool aimed at mitigating BEs via data-driven cohort partitioning. We demonstrate CohortFinder improves ML model performance in downstream digital pathology and medical image processing tasks. CohortFinder is freely available for download at cohortfinder.com.
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Grants
- National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) / National Institute of Health (NIH)
- NephCure Kidney and the Henry E. Haller, Jr. Foundation
- Nephrotic Syndrome Study Network
- Rare Diseases Clinical Research Network
- National Center for Advancing Translational Sciences
- RDCRN Data Management and Coordinating Center (DMCC), United States
- National Institute of Neurological Disorders and Stroke
- NCATS and the NIDDK
- University of Michigan, NephCure Kidney International, Alport Syndrome Foundation, and the Halpin Foundation
- National Cancer Institute, United States
- National Heart, Lung, and Blood Institute
- National Institute of Biomedical Imaging and Bioengineering
- VA Merit Review Award
- U.S. Department of Veterans Affairs
- Development Service the Office of the Assistant Secretary of Defense for Health Affairs
- Kidney Precision Medicine Project (KPMP) Glue Grant and sponsored research agreements from Bristol Myers-Squibb, Boehringer-Ingelheim, Eli-Lilly and Astrazeneca
- National Institute of Nursing Research
- NIBIB through the CWRU Interdisciplinary Biomedical Imaging Training Program Fellowship
- DOD Peer Reviewed Cancer Research Program
- Wen Ko APT Summer Internship Program, the Ohio Third Frontier Technology Validation Fund, and the Wallace H. Coulter Foundation Program in the Department of Biomedical Engineering at Case Western Reserve University and sponsored research funding from Pfizer
- High-Performance Computing Resource in the Core Facility for Advanced Research Computing at Case Western Reserve University
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Affiliation(s)
- Fan Fan
- Emory University and Georgia Institute of Technology, Department of Biomedical Engineering, Atlanta, GA USA
| | - Georgia Martinez
- Case Western Reserve University, Department of Biomedical Engineering, Cleveland, OH USA
| | - Thomas DeSilvio
- Case Western Reserve University, Department of Biomedical Engineering, Cleveland, OH USA
| | - John Shin
- Case Western Reserve University, Department of Biomedical Engineering, Cleveland, OH USA
| | - Yijiang Chen
- Case Western Reserve University, Department of Biomedical Engineering, Cleveland, OH USA
| | - Jackson Jacobs
- Emory University and Georgia Institute of Technology, Department of Biomedical Engineering, Atlanta, GA USA
| | - Bangchen Wang
- Duke University, Department of Pathology, Division of AI & Computational Pathology, Durham, NC USA
| | - Takaya Ozeki
- University of Michigan, Department of Internal Medicine, Division of Nephrology, Ann Arbor, MI USA
| | - Maxime W. Lafarge
- University Hospital of Zurich, University of Zurich, Department of Pathology and Molecular Pathology, Zurich, Switzerland
| | - Viktor H. Koelzer
- University Hospital of Zurich, University of Zurich, Department of Pathology and Molecular Pathology, Zurich, Switzerland
| | - Laura Barisoni
- Duke University, Department of Pathology, Division of AI & Computational Pathology, Durham, NC USA
- Duke University, Department of Medicine, Division of Nephrology, Durham, NC USA
| | - Anant Madabhushi
- Emory University and Georgia Institute of Technology, Department of Biomedical Engineering, Atlanta, GA USA
- Atlanta Veterans Administration Medical Center, Atlanta, GA USA
| | - Satish E. Viswanath
- Case Western Reserve University, Department of Biomedical Engineering, Cleveland, OH USA
| | - Andrew Janowczyk
- Emory University and Georgia Institute of Technology, Department of Biomedical Engineering, Atlanta, GA USA
- University Hospital of Geneva, Department of Oncology, Division of Precision Oncology, Geneva, Switzerland
- University Hospital of Geneva, Department of Clinical Pathology, Division of Clinical Pathology, Geneva, Switzerland
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Bernard J, Sonnadara R, Saraco AN, Mitchell JP, Bak AB, Bayer I, Wainman BC. Automated grading of anatomical objective structured practical examinations using decision trees: An artificial intelligence approach. ANATOMICAL SCIENCES EDUCATION 2024; 17:967-978. [PMID: 37322819 DOI: 10.1002/ase.2305] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Revised: 05/19/2023] [Accepted: 05/22/2023] [Indexed: 06/17/2023]
Abstract
An Objective Structured Practical Examination (OSPE) is an effective and robust, but resource-intensive, means of evaluating anatomical knowledge. Since most OSPEs employ short answer or fill-in-the-blank style questions, the format requires many people familiar with the content to mark the examinations. However, the increasing prevalence of online delivery for anatomy and physiology courses could result in students losing the OSPE practice that they would receive in face-to-face learning sessions. The purpose of this study was to test the accuracy of Decision Trees (DTs) in marking OSPE questions as a first step to creating an intelligent, online OSPE tutoring system. The study used the results of the winter 2020 semester final OSPE from McMaster University's anatomy and physiology course in the Faculty of Health Sciences (HTHSCI 2FF3/2LL3/1D06) as the data set. Ninety percent of the data set was used in a 10-fold validation algorithm to train a DT for each of the 54 questions. Each DT was comprised of unique words that appeared in correct, student-written answers. The remaining 10% of the data set was marked by the generated DTs. When the answers marked by the DT were compared to the answers marked by staff and faculty, the DT achieved an average accuracy of 94.49% across all 54 questions. This suggests that machine learning algorithms such as DTs are a highly effective option for OSPE grading and are suitable for the development of an intelligent, online OSPE tutoring system.
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Affiliation(s)
- Jason Bernard
- Department of Surgery, McMaster University, Hamilton, Ontario, Canada
| | - Ranil Sonnadara
- Department of Surgery, McMaster University, Hamilton, Ontario, Canada
| | - Anthony N Saraco
- Education Program in Anatomy, Faculty of Health Sciences, McMaster University, Hamilton, Ontario, Canada
| | - Josh P Mitchell
- Education Program in Anatomy, Faculty of Health Sciences, McMaster University, Hamilton, Ontario, Canada
| | - Alex B Bak
- Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Ilana Bayer
- Education Program in Anatomy, Faculty of Health Sciences, McMaster University, Hamilton, Ontario, Canada
- Department of Pathology and Molecular Medicine, McMaster University, Hamilton, Ontario, Canada
| | - Bruce C Wainman
- Education Program in Anatomy, Faculty of Health Sciences, McMaster University, Hamilton, Ontario, Canada
- Department of Pathology and Molecular Medicine, McMaster University, Hamilton, Ontario, Canada
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Dominguez-Morales JP, Duran-Lopez L, Marini N, Vicente-Diaz S, Linares-Barranco A, Atzori M, Müller H. A systematic comparison of deep learning methods for Gleason grading and scoring. Med Image Anal 2024; 95:103191. [PMID: 38728903 DOI: 10.1016/j.media.2024.103191] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Revised: 01/16/2024] [Accepted: 05/02/2024] [Indexed: 05/12/2024]
Abstract
Prostate cancer is the second most frequent cancer in men worldwide after lung cancer. Its diagnosis is based on the identification of the Gleason score that evaluates the abnormality of cells in glands through the analysis of the different Gleason patterns within tissue samples. The recent advancements in computational pathology, a domain aiming at developing algorithms to automatically analyze digitized histopathology images, lead to a large variety and availability of datasets and algorithms for Gleason grading and scoring. However, there is no clear consensus on which methods are best suited for each problem in relation to the characteristics of data and labels. This paper provides a systematic comparison on nine datasets with state-of-the-art training approaches for deep neural networks (including fully-supervised learning, weakly-supervised learning, semi-supervised learning, Additive-MIL, Attention-Based MIL, Dual-Stream MIL, TransMIL and CLAM) applied to Gleason grading and scoring tasks. The nine datasets are collected from pathology institutes and openly accessible repositories. The results show that the best methods for Gleason grading and Gleason scoring tasks are fully supervised learning and CLAM, respectively, guiding researchers to the best practice to adopt depending on the task to solve and the labels that are available.
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Affiliation(s)
- Juan P Dominguez-Morales
- Robotics and Technology of Computers Lab., ETSII-EPS, Universidad de Sevilla, Sevilla 41012, Spain; SCORE Lab, I3US. Universidad de Sevilla, Spain.
| | - Lourdes Duran-Lopez
- Robotics and Technology of Computers Lab., ETSII-EPS, Universidad de Sevilla, Sevilla 41012, Spain; SCORE Lab, I3US. Universidad de Sevilla, Spain
| | - Niccolò Marini
- Information Systems Institute, University of Applied Sciences Western Switzerland (HES-SO Valais), Technopôle 3, Sierre 3960, Switzerland; Centre Universitaire d'Informatique, University of Geneva, Carouge 1227, Switzerland
| | - Saturnino Vicente-Diaz
- Robotics and Technology of Computers Lab., ETSII-EPS, Universidad de Sevilla, Sevilla 41012, Spain; SCORE Lab, I3US. Universidad de Sevilla, Spain
| | - Alejandro Linares-Barranco
- Robotics and Technology of Computers Lab., ETSII-EPS, Universidad de Sevilla, Sevilla 41012, Spain; SCORE Lab, I3US. Universidad de Sevilla, Spain
| | - Manfredo Atzori
- Information Systems Institute, University of Applied Sciences Western Switzerland (HES-SO Valais), Technopôle 3, Sierre 3960, Switzerland; Department of Neuroscience, University of Padua, Via Giustiniani 2, Padua, 35128, Italy
| | - Henning Müller
- Information Systems Institute, University of Applied Sciences Western Switzerland (HES-SO Valais), Technopôle 3, Sierre 3960, Switzerland; Medical faculty, University of Geneva, Geneva 1211, Switzerland
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Hellen DJ, Fay ME, Lee DH, Klindt-Morgan C, Bennett A, Pachura KJ, Grakoui A, Huppert SS, Dawson PA, Lam WA, Karpen SJ. BiliQML: a supervised machine-learning model to quantify biliary forms from digitized whole slide liver histopathological images. Am J Physiol Gastrointest Liver Physiol 2024; 327:G1-G15. [PMID: 38651949 DOI: 10.1152/ajpgi.00058.2024] [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] [Received: 02/22/2024] [Revised: 04/03/2024] [Accepted: 04/09/2024] [Indexed: 04/25/2024]
Abstract
The progress of research focused on cholangiocytes and the biliary tree during development and following injury is hindered by limited available quantitative methodologies. Current techniques include two-dimensional standard histological cell-counting approaches, which are rapidly performed, error prone, and lack architectural context or three-dimensional analysis of the biliary tree in opacified livers, which introduce technical issues along with minimal quantitation. The present study aims to fill these quantitative gaps with a supervised machine-learning model (BiliQML) able to quantify biliary forms in the liver of anti-keratin 19 antibody-stained whole slide images. Training utilized 5,019 researcher-labeled biliary forms, which following feature selection, and algorithm optimization, generated an F score of 0.87. Application of BiliQML on seven separate cholangiopathy models [genetic (Afp-CRE;Pkd1l1null/Fl, Alb-CRE;Rbp-jkfl/fl, and Albumin-CRE;ROSANICD), surgical (bile duct ligation), toxicological (3,5-diethoxycarbonyl-1,4-dihydrocollidine), and therapeutic (Cyp2c70-/- with ileal bile acid transporter inhibition)] allowed for a means to validate the capabilities and utility of this platform. The results from BiliQML quantification revealed biological and pathological differences across these seven diverse models, indicating a highly sensitive, robust, and scalable methodology for the quantification of distinct biliary forms. BiliQML is the first comprehensive machine-learning platform for biliary form analysis, adding much-needed morphologic context to standard immunofluorescence-based histology, and provides clinical and basic science researchers with a novel tool for the characterization of cholangiopathies.NEW & NOTEWORTHY BiliQML is the first comprehensive machine-learning platform for biliary form analysis in whole slide histopathological images. This platform provides clinical and basic science researchers with a novel tool for the improved quantification and characterization of biliary tract disorders.
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Affiliation(s)
- Dominick J Hellen
- Division of Pediatric Gastroenterology, Hepatology, and Nutrition, Department of Pediatrics, Children's Healthcare of Atlanta and Emory University School of Medicine, Atlanta, Georgia, United States
| | - Meredith E Fay
- The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia, United States
- Department of Pediatrics, Division of Pediatric Hematology/Oncology, Aflac Cancer Center and Blood Disorders Service of Children's Healthcare of Atlanta, Emory University School of Medicine, Atlanta, Georgia, United States
| | - David H Lee
- Division of Pediatric Gastroenterology, Hepatology, and Nutrition, Department of Pediatrics, Children's Healthcare of Atlanta and Emory University School of Medicine, Atlanta, Georgia, United States
| | - Caroline Klindt-Morgan
- Division of Pediatric Gastroenterology, Hepatology, and Nutrition, Department of Pediatrics, Children's Healthcare of Atlanta and Emory University School of Medicine, Atlanta, Georgia, United States
| | - Ashley Bennett
- Division of Pediatric Gastroenterology, Hepatology, and Nutrition, Department of Pediatrics, Children's Healthcare of Atlanta and Emory University School of Medicine, Atlanta, Georgia, United States
| | - Kimberly J Pachura
- Division of Pediatric Gastroenterology, Hepatology, and Nutrition, Department of Pediatrics, Children's Healthcare of Atlanta and Emory University School of Medicine, Atlanta, Georgia, United States
| | - Arash Grakoui
- Emory National Primate Research Center, Division of Microbiology and Immunology, Emory Vaccine Center, Emory University School of Medicine, Atlanta, Georgia, United States
| | - Stacey S Huppert
- Division of Gastroenterology, Hepatology, and Nutrition, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, United States
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, United States
| | - Paul A Dawson
- Division of Pediatric Gastroenterology, Hepatology, and Nutrition, Department of Pediatrics, Children's Healthcare of Atlanta and Emory University School of Medicine, Atlanta, Georgia, United States
| | - Wilbur A Lam
- The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia, United States
- Department of Pediatrics, Division of Pediatric Hematology/Oncology, Aflac Cancer Center and Blood Disorders Service of Children's Healthcare of Atlanta, Emory University School of Medicine, Atlanta, Georgia, United States
| | - Saul J Karpen
- Division of Pediatric Gastroenterology, Hepatology, and Nutrition, Department of Pediatrics, Children's Healthcare of Atlanta and Emory University School of Medicine, Atlanta, Georgia, United States
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Bai C, Sun Y, Zhang X, Zuo Z. Assessment of AURKA expression and prognosis prediction in lung adenocarcinoma using machine learning-based pathomics signature. Heliyon 2024; 10:e33107. [PMID: 39022022 PMCID: PMC11253280 DOI: 10.1016/j.heliyon.2024.e33107] [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: 01/21/2024] [Revised: 06/07/2024] [Accepted: 06/14/2024] [Indexed: 07/20/2024] Open
Abstract
Objective This study aimed to develop quantitative feature-based models from histopathological images to assess aurora kinase A (AURKA) expression and predict the prognosis of patients with lung adenocarcinoma (LUAD). Methods A dataset of patients with LUAD was derived from the cancer genome atlas (TCGA) with information on clinical characteristics, RNA sequencing and histopathological images. The TCGA-LUAD cohort was randomly divided into training (n = 229) and testing (n = 98) sets. We extracted quantitative image features from histopathological slides of patients with LUAD using computational approaches, constructed a predictive model for AURKA expression in the training set, and estimated their predictive performance in the test set. A Cox proportional hazards model was used to assess whether the pathomic scores (PS) generated by the model independently predicted LUAD survival. Results High AURKA expression was an independent risk factor for overall survival (OS) in patients with LUAD (hazard ratio = 1.816, 95 % confidence intervals = 1.257-2.623, P = 0.001). The model based on histopathological image features had significant predictive value for AURKA expression: the area under the curve of the receiver operating characteristic curve in the training set and validation set was 0.809 and 0.739, respectively. Decision curve analysis showed that the model had clinical utility. Patients with high PS and low PS had different survival rates (P = 0.019). Multivariate analysis suggested that PS was an independent prognostic factor for LUAD (hazard ratio = 1.615, 95 % confidence intervals = 1.071-2.438, P = 0.022). Conclusion Pathomics models based on machine learning can accurately predict AURKA expression and the PS generated by the model can predict LUAD prognosis.
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Affiliation(s)
- Cuiqing Bai
- Department of Respiratory Disease, Affiliated Hospital of Jiangnan University, Wuxi, China
| | - Yan Sun
- Department of Respiratory Disease, Affiliated Hospital of Jiangnan University, Wuxi, China
| | - Xiuqin Zhang
- Department of Respiratory Disease, Affiliated Hospital of Jiangnan University, Wuxi, China
| | - Zhitong Zuo
- Department of Respiratory Disease, Affiliated Hospital of Jiangnan University, Wuxi, China
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Shephard AJ, Bashir RMS, Mahmood H, Jahanifar M, Minhas F, Raza SEA, McCombe KD, Craig SG, James J, Brooks J, Nankivell P, Mehanna H, Khurram SA, Rajpoot NM. A fully automated and explainable algorithm for predicting malignant transformation in oral epithelial dysplasia. NPJ Precis Oncol 2024; 8:137. [PMID: 38942998 PMCID: PMC11213925 DOI: 10.1038/s41698-024-00624-8] [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: 09/27/2023] [Accepted: 05/29/2024] [Indexed: 06/30/2024] Open
Abstract
Oral epithelial dysplasia (OED) is a premalignant histopathological diagnosis given to lesions of the oral cavity. Its grading suffers from significant inter-/intra-observer variability, and does not reliably predict malignancy progression, potentially leading to suboptimal treatment decisions. To address this, we developed an artificial intelligence (AI) algorithm, that assigns an Oral Malignant Transformation (OMT) risk score based on the Haematoxylin and Eosin (H&E) stained whole slide images (WSIs). Our AI pipeline leverages an in-house segmentation model to detect and segment both nuclei and epithelium. Subsequently, a shallow neural network utilises interpretable morphological and spatial features, emulating histological markers, to predict progression. We conducted internal cross-validation on our development cohort (Sheffield; n = 193 cases) and independent validation on two external cohorts (Birmingham and Belfast; n = 89 cases). On external validation, the proposed OMTscore achieved an AUROC = 0.75 (Recall = 0.92) in predicting OED progression, outperforming other grading systems (Binary: AUROC = 0.72, Recall = 0.85). Survival analyses showed the prognostic value of our OMTscore (C-index = 0.60, p = 0.02), compared to WHO (C-index = 0.64, p = 0.003) and binary grades (C-index = 0.65, p < 0.001). Nuclear analyses elucidated the presence of peri-epithelial and intra-epithelial lymphocytes in highly predictive patches of transforming cases (p < 0.001). This is the first study to propose a completely automated, explainable, and externally validated algorithm for predicting OED transformation. Our algorithm shows comparable-to-human-level performance, offering a promising solution to the challenges of grading OED in routine clinical practice.
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Affiliation(s)
- Adam J Shephard
- Tissue Image Analytics Centre, Department of Computer Science, University of Warwick, Coventry, UK
| | | | - Hanya Mahmood
- School of Clinical Dentistry, University of Sheffield, Sheffield, UK
| | - Mostafa Jahanifar
- Tissue Image Analytics Centre, Department of Computer Science, University of Warwick, Coventry, UK
| | - Fayyaz Minhas
- Tissue Image Analytics Centre, Department of Computer Science, University of Warwick, Coventry, UK
| | - Shan E Ahmed Raza
- Tissue Image Analytics Centre, Department of Computer Science, University of Warwick, Coventry, UK
| | - Kris D McCombe
- Precision Medicine Centre, Patrick G. Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, UK
| | - Stephanie G Craig
- Precision Medicine Centre, Patrick G. Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, UK
| | - Jacqueline James
- Precision Medicine Centre, Patrick G. Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, UK
| | - Jill Brooks
- Institute of Head and Neck Studies and Education, Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
| | - Paul Nankivell
- Institute of Head and Neck Studies and Education, Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
| | - Hisham Mehanna
- Institute of Head and Neck Studies and Education, Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
| | - Syed Ali Khurram
- School of Clinical Dentistry, University of Sheffield, Sheffield, UK
| | - Nasir M Rajpoot
- Tissue Image Analytics Centre, Department of Computer Science, University of Warwick, Coventry, UK.
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9
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Wang Q, Akram AR, Dorward DA, Talas S, Monks B, Thum C, Hopgood JR, Javidi M, Vallejo M. Deep learning-based virtual H& E staining from label-free autofluorescence lifetime images. NPJ IMAGING 2024; 2:17. [PMID: 38948152 PMCID: PMC11213708 DOI: 10.1038/s44303-024-00021-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Accepted: 06/11/2024] [Indexed: 07/02/2024]
Abstract
Label-free autofluorescence lifetime is a unique feature of the inherent fluorescence signals emitted by natural fluorophores in biological samples. Fluorescence lifetime imaging microscopy (FLIM) can capture these signals enabling comprehensive analyses of biological samples. Despite the fundamental importance and wide application of FLIM in biomedical and clinical sciences, existing methods for analysing FLIM images often struggle to provide rapid and precise interpretations without reliable references, such as histology images, which are usually unavailable alongside FLIM images. To address this issue, we propose a deep learning (DL)-based approach for generating virtual Hematoxylin and Eosin (H&E) staining. By combining an advanced DL model with a contemporary image quality metric, we can generate clinical-grade virtual H&E-stained images from label-free FLIM images acquired on unstained tissue samples. Our experiments also show that the inclusion of lifetime information, an extra dimension beyond intensity, results in more accurate reconstructions of virtual staining when compared to using intensity-only images. This advancement allows for the instant and accurate interpretation of FLIM images at the cellular level without the complexities associated with co-registering FLIM and histology images. Consequently, we are able to identify distinct lifetime signatures of seven different cell types commonly found in the tumour microenvironment, opening up new opportunities towards biomarker-free tissue histology using FLIM across multiple cancer types.
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Affiliation(s)
- Qiang Wang
- Centre for Inflammation Research, Institute of Regeneration and Repair, The University of Edinburgh, Edinburgh, UK
- Translational Healthcare Technologies Group, Centre for Inflammation Research, Institute of Regeneration and Repair, The University of Edinburgh, Edinburgh, UK
| | - Ahsan R. Akram
- Centre for Inflammation Research, Institute of Regeneration and Repair, The University of Edinburgh, Edinburgh, UK
- Translational Healthcare Technologies Group, Centre for Inflammation Research, Institute of Regeneration and Repair, The University of Edinburgh, Edinburgh, UK
| | - David A. Dorward
- Centre for Inflammation Research, Institute of Regeneration and Repair, The University of Edinburgh, Edinburgh, UK
- Department of Pathology, Royal Infirmary of Edinburgh, Edinburgh, UK
| | - Sophie Talas
- Centre for Inflammation Research, Institute of Regeneration and Repair, The University of Edinburgh, Edinburgh, UK
- Department of Pathology, Royal Infirmary of Edinburgh, Edinburgh, UK
| | - Basil Monks
- Department of Pathology, Royal Infirmary of Edinburgh, Edinburgh, UK
| | - Chee Thum
- Department of Pathology, Royal Infirmary of Edinburgh, Edinburgh, UK
| | - James R. Hopgood
- School of Engineering, The University of Edinburgh, Edinburgh, UK
| | - Malihe Javidi
- School of Mathematical and Computer Sciences, Heriot-Watt University, Edinburgh, UK
- Department of Computer Engineering, Quchan University of Technology, Quchan, Iran
| | - Marta Vallejo
- School of Mathematical and Computer Sciences, Heriot-Watt University, Edinburgh, UK
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10
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Cazzato G, Rongioletti F. Artificial Intelligence in Dermatopathology: updates, strengths, and challenges. Clin Dermatol 2024:S0738-081X(24)00094-4. [PMID: 38909860 DOI: 10.1016/j.clindermatol.2024.06.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/25/2024]
Abstract
Artificial Intelligence (AI) has evolved to become a significant force in various domains, including medicine. We explore the role of AI in pathology, with a specific focus on dermatopathology and neoplastic dermatopathology. AI, encompassing Machine Learning (ML) and Deep Learning (DL), has demonstrated its potential in tasks ranging from diagnostic applications on Whole Slide Imaging (WSI) to predictive and prognostic functions in skin pathology. In dermatopathology, studies have assessed AI's ability to identify skin lesions, classify melanomas, and improve diagnostic accuracy. Results indicate that AI, particularly Convolutional Neural Networks (CNNs), can outperform human pathologists in terms of sensitivity and specificity. Moreover, AI aids in predicting disease outcomes, identifying aggressive tumors, and differentiating between various skin conditions. Neoplastic dermatopathology showcases AI's prowess in classifying melanocytic lesions, discriminating between melanomas and nevi, and aiding dermatopathologists in making accurate diagnoses. Studies emphasize the reproducibility and diagnostic aid that AI provides, especially in challenging cases. In inflammatory and lymphoproliferative dermatopathology, limited research exists, but studies show attempts to use AI to differentiate conditions like Mycosis Fungoides and eczema. While some results are promising, further exploration is needed in these areas. We highlight the extraordinary interest AI has garnered in the scientific community and its potential to assist clinicians and pathologists. Despite the advancements, we have stress edthe importance of collaboration between medical professionals, computer scientists, bioinformaticians, and engineers to harness AI's benefits while acknowledging its limitations and risks. The integration of AI into dermatopathology holds great promise, positioning it as a valuable tool rather than as a replacement for human expertise.
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Affiliation(s)
- Gerardo Cazzato
- Section of Molecular Pathology, Department of Precision and Regenerative Medicine and Ionian Area (DiMePRe-J), University of Bari "Aldo Moro", Bari, 70124, Italy.
| | - Franco Rongioletti
- Vita-Salute San Raffaele University, IRCCS San Raffaele Hospital, Milano, Italy
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11
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Nambiar N, Rajesh V, Nair A, Nambiar S, Nair R, Uthamanthil R, Lotodo T, Mittal S, Kussick S. An AI based, open access screening tool for early diagnosis of Burkitt lymphoma. Front Med (Lausanne) 2024; 11:1345611. [PMID: 38903819 PMCID: PMC11187324 DOI: 10.3389/fmed.2024.1345611] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Accepted: 04/08/2024] [Indexed: 06/22/2024] Open
Abstract
Burkitt Lymphoma (BL) is a highly treatable cancer. However, delayed diagnosis of BL contributes to high mortality in BL endemic regions of Africa. Lack of enough pathologists in the region is a major reason for delayed diagnosis. The work described in this paper is a proof-of-concept study to develop a targeted, open access AI tool for screening of histopathology slides in suspected BL cases. Slides were obtained from a total of 90 BL patients. 70 Tonsillectomy samples were used as controls. We fine-tuned 6 pre-trained models and evaluated the performance of all 6 models across different configurations. An ensemble-based consensus approach ensured a balanced and robust classification. The tool applies novel features to BL diagnosis including use of multiple image magnifications, thus enabling use of different magnifications of images based on the microscope/scanner available in remote clinics, composite scoring of multiple models and utilizing MIL with weak labeling and image augmentation, enabling use of relatively low sample size to achieve good performance on the inference set. The open access model allows free access to the AI tool from anywhere with an internet connection. The ultimate aim of this work is making pathology services accessible, efficient and timely in remote clinics in regions where BL is endemic. New generation of low-cost slide scanners/microscopes is expected to make slide images available immediately for the AI tool for screening and thus accelerate diagnosis by pathologists available locally or online.
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Affiliation(s)
- Nikil Nambiar
- Burkitt’s Lymphoma Fund for Africa, Seattle, WA, United States
| | - Vineeth Rajesh
- Burkitt’s Lymphoma Fund for Africa, Seattle, WA, United States
| | - Akshay Nair
- Burkitt’s Lymphoma Fund for Africa, Seattle, WA, United States
| | - Sunil Nambiar
- Burkitt’s Lymphoma Fund for Africa, Seattle, WA, United States
| | - Renjini Nair
- Burkitt’s Lymphoma Fund for Africa, Seattle, WA, United States
| | - Rajesh Uthamanthil
- Burkitt’s Lymphoma Fund for Africa, Seattle, WA, United States
- Department of Comparative Medicine, University of Washington, Burkitt’s Lymphoma Fund for Africa, Seattle, WA, United States
| | - Teresa Lotodo
- Department Of Hematology and Blood Transfusion, Moi University/MTRH/AMPATH, Eldoret, Kenya
| | - Shachi Mittal
- Department of Chemical Engineering, University of Washington, Seattle, WA, United States
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, United States
| | - Steven Kussick
- Burkitt’s Lymphoma Fund for Africa, Seattle, WA, United States
- Department of Laboratory Medicine and Pathology, University of Washington, Burkitt’s Lymphoma Fund for Africa, Seattle, WA, United States
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12
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Juan Ramon A, Parmar C, Carrasco-Zevallos OM, Csiszer C, Yip SSF, Raciti P, Stone NL, Triantos S, Quiroz MM, Crowley P, Batavia AS, Greshock J, Mansi T, Standish KA. Development and deployment of a histopathology-based deep learning algorithm for patient prescreening in a clinical trial. Nat Commun 2024; 15:4690. [PMID: 38824132 PMCID: PMC11144215 DOI: 10.1038/s41467-024-49153-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Accepted: 05/24/2024] [Indexed: 06/03/2024] Open
Abstract
Accurate identification of genetic alterations in tumors, such as Fibroblast Growth Factor Receptor, is crucial for treating with targeted therapies; however, molecular testing can delay patient care due to the time and tissue required. Successful development, validation, and deployment of an AI-based, biomarker-detection algorithm could reduce screening cost and accelerate patient recruitment. Here, we develop a deep-learning algorithm using >3000 H&E-stained whole slide images from patients with advanced urothelial cancers, optimized for high sensitivity to avoid ruling out trial-eligible patients. The algorithm is validated on a dataset of 350 patients, achieving an area under the curve of 0.75, specificity of 31.8% at 88.7% sensitivity, and projected 28.7% reduction in molecular testing. We successfully deploy the system in a non-interventional study comprising 89 global study clinical sites and demonstrate its potential to prioritize/deprioritize molecular testing resources and provide substantial cost savings in the drug development and clinical settings.
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Affiliation(s)
- Albert Juan Ramon
- Janssen R&D, LLC, a Johnson & Johnson Company. Data Science and Digital Health, San Diego, CA, USA.
| | - Chaitanya Parmar
- Janssen R&D, LLC, a Johnson & Johnson Company. Data Science and Digital Health, San Diego, CA, USA
| | | | - Carlos Csiszer
- Janssen R&D, LLC, a Johnson & Johnson Company. Data Science and Digital Health, Titusville, NJ, USA
| | - Stephen S F Yip
- Janssen R&D, LLC, a Johnson & Johnson Company. Data Science and Digital Health, Cambridge, MA, USA
| | - Patricia Raciti
- Janssen R&D, LLC, a Johnson & Johnson Company. Oncology, Spring House, PA, USA
| | - Nicole L Stone
- Janssen R&D, LLC, a Johnson & Johnson Company. Oncology, Spring House, PA, USA
| | - Spyros Triantos
- Janssen R&D, LLC, a Johnson & Johnson Company. Oncology, Spring House, PA, USA
| | - Michelle M Quiroz
- Janssen R&D, LLC, a Johnson & Johnson Company. Oncology, Spring House, PA, USA
| | - Patrick Crowley
- Janssen R&D, LLC, a Johnson & Johnson Company. Global Development, High Wycombe, UK
| | - Ashita S Batavia
- Janssen R&D, LLC, a Johnson & Johnson Company. Data Science and Digital Health, Titusville, NJ, USA
| | - Joel Greshock
- Janssen R&D, LLC, a Johnson & Johnson Company. Data Science and Digital Health, Spring House, PA, USA
| | - Tommaso Mansi
- Janssen R&D, LLC, a Johnson & Johnson Company. Data Science and Digital Health, Titusville, NJ, USA
| | - Kristopher A Standish
- Janssen R&D, LLC, a Johnson & Johnson Company. Data Science and Digital Health, San Diego, CA, USA
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13
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Longo L, Bartikoski BJ, de Souza VEG, Salvati F, Uribe‐Cruz C, Lenz G, Xavier RM, Álvares‐da‐Silva MR, Filippi‐Chiela EC. Muscle fibre morphometric analysis (MusMA) correlates with muscle function and cardiovascular risk prognosis. Int J Exp Pathol 2024; 105:100-113. [PMID: 38722178 PMCID: PMC11129960 DOI: 10.1111/iep.12504] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Revised: 03/14/2024] [Accepted: 03/22/2024] [Indexed: 05/29/2024] Open
Abstract
Morphometry of striated muscle fibres is critical for monitoring muscle health and function. Here, we evaluated functional parameters of skeletal and cardiac striated muscle in two experimental models using the Morphometric Analysis of Muscle Fibre tool (MusMA). The collagen-induced arthritis model was used to evaluate the function of skeletal striated muscle and the non-alcoholic fatty liver disease model was used for cardiac striated muscle analysis. After euthanasia, we used haeamatoxylin and eosin stained sections of skeletal and cardiac muscle to perform muscle fibre segmentation and morphometric analysis. Morphometric analysis classified muscle fibres into six subpopulations: normal, regular hypertrophic, irregular hypertrophic, irregular, irregular atrophic and regular atrophic. The percentage of atrophic fibres was associated with lower walking speed (p = 0.009) and lower body weight (p = 0.026), respectively. Fibres categorized as normal were associated with maximum grip strength (p < 0.001) and higher march speed (p < 0.001). In the evaluation of cardiac striated muscle fibres, the percentage of normal cardiomyocytes negatively correlated with cardiovascular risk markers such as the presence of abdominal adipose tissue (p = .003), miR-33a expression (p = .001) and the expression of miR-126 (p = .042) Furthermore, the percentage of atrophic cardiomyocytes correlated significantly with the Castelli risk index II (p = .014). MusMA is a simple and objective tool that allows the screening of striated muscle fibre morphometry, which can complement the diagnosis of muscle diseases while providing functional and prognostic information in basic and clinical research.
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Affiliation(s)
- Larisse Longo
- Graduate Program in Gastroenterology and HepatologyUniversidade Federal do Rio Grande do SulPorto AlegreBrazil
- Experimental Hepatology and Gastroenterology Laboratory, Center for Experimental ResearchHospital de Clínicas de Porto AlegrePorto AlegreBrazil
| | - Bárbara Jonson Bartikoski
- Autoimmune Diseases Laboratory, Rheumatology ServiceHospital de Clínicas de Porto AlegrePorto AlegreBrazil
| | - Valessa Emanoele Gabriel de Souza
- Experimental Hepatology and Gastroenterology Laboratory, Center for Experimental ResearchHospital de Clínicas de Porto AlegrePorto AlegreBrazil
| | - Fernando Salvati
- Experimental Hepatology and Gastroenterology Laboratory, Center for Experimental ResearchHospital de Clínicas de Porto AlegrePorto AlegreBrazil
| | - Carolina Uribe‐Cruz
- Graduate Program in Gastroenterology and HepatologyUniversidade Federal do Rio Grande do SulPorto AlegreBrazil
- Experimental Hepatology and Gastroenterology Laboratory, Center for Experimental ResearchHospital de Clínicas de Porto AlegrePorto AlegreBrazil
- Universidad Católica de las MisionesPosadasArgentina
| | - Guido Lenz
- Department of Biophysics and Biotechnology CenterUniversidade Federal do Rio Grande do SulPorto AlegreBrazil
| | - Ricardo Machado Xavier
- Graduate Program in Gastroenterology and HepatologyUniversidade Federal do Rio Grande do SulPorto AlegreBrazil
- Graduate Program in Medical SciencesUniversidade Federal do Rio Grande do SulPorto AlegreBrazil
| | - Mário Reis Álvares‐da‐Silva
- Graduate Program in Gastroenterology and HepatologyUniversidade Federal do Rio Grande do SulPorto AlegreBrazil
- Experimental Hepatology and Gastroenterology Laboratory, Center for Experimental ResearchHospital de Clínicas de Porto AlegrePorto AlegreBrazil
- Division of GastroenterologyHospital de Clínicas de Porto AlegrePorto AlegreBrazil
| | - Eduardo Cremonese Filippi‐Chiela
- Graduate Program in Gastroenterology and HepatologyUniversidade Federal do Rio Grande do SulPorto AlegreBrazil
- Department of Morphological SciencesUniversidade Federal do Rio Grande do SulPorto AlegreBrazil
- Experimental Research ServiceHospital de Clínicas de Porto AlegrePorto AlegreBrazil
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14
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Makkithaya KN, Mazumder N, Wang WH, Chen WL, Chen MC, Lee MX, Lin CY, Yeh YJ, Tsay GJ, Chopperla S, Mahato KK, Kao FJ, Zhuo GY. Investigating cartilage-related diseases by polarization-resolved second harmonic generation (P-SHG) imaging. APL Bioeng 2024; 8:026107. [PMID: 38694891 PMCID: PMC11062753 DOI: 10.1063/5.0196676] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Accepted: 04/19/2024] [Indexed: 05/04/2024] Open
Abstract
Establishing quantitative parameters for differentiating between healthy and diseased cartilage tissues by examining collagen fibril degradation patterns facilitates the understanding of tissue characteristics during disease progression. These findings could also complement existing clinical methods used to diagnose cartilage-related diseases. In this study, cartilage samples from normal, osteoarthritis (OA), and rheumatoid arthritis (RA) tissues were prepared and analyzed using polarization-resolved second harmonic generation (P-SHG) imaging and quantitative image texture analysis. The enhanced molecular contrast obtained from this approach is expected to aid in distinguishing between healthy and diseased cartilage tissues. P-SHG image analysis revealed distinct parameters in the cartilage samples, reflecting variations in collagen fibril arrangement and organization across different pathological states. Normal tissues exhibited distinct χ33/χ31 values compared with those of OA and RA, indicating collagen type transition and cartilage erosion with chondrocyte swelling, respectively. Compared with those of normal tissues, OA samples demonstrated a higher degree of linear polarization, suggesting increased tissue birefringence due to the deposition of type-I collagen in the extracellular matrix. The distribution of the planar orientation of collagen fibrils revealed a more directional orientation in the OA samples, associated with increased type-I collagen, while the RA samples exhibited a heterogeneous molecular orientation. This study revealed that the imaging technique, the quantitative analysis of the images, and the derived parameters presented in this study could be used as a reference for disease diagnostics, providing a clear understanding of collagen fibril degradation in cartilage.
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Affiliation(s)
- Kausalya Neelavara Makkithaya
- Department of Biophysics, Manipal School of Life Sciences, Manipal Academy of Higher Education, Manipal, Karnataka 576104, India
| | - Nirmal Mazumder
- Department of Biophysics, Manipal School of Life Sciences, Manipal Academy of Higher Education, Manipal, Karnataka 576104, India
| | - Wei-Hsun Wang
- Institute of Translational Medicine and New Drug Development, China Medical University, Taichung 404328, Taiwan
| | - Wei-Liang Chen
- Center for Condensed Matter Sciences, National Taiwan University, Taipei 10617, Taiwan
| | - Ming-Chi Chen
- Institute of Translational Medicine and New Drug Development, China Medical University, Taichung 404328, Taiwan
| | - Ming-Xin Lee
- Institute of Translational Medicine and New Drug Development, China Medical University, Taichung 404328, Taiwan
| | - Chin-Yu Lin
- Department of Biomedical Sciences and Engineering, Tzu Chi University, Hualien 97004, Taiwan
| | - Yung-Ju Yeh
- Autoimmune Disease Laboratory, China Medical University Hospital, Taichung 404327, Taiwan
| | | | - Sitaram Chopperla
- Department of Orthopedics, Kasturba Medical College, Manipal Academy of Higher Education, Manipal, Karnataka 576104, India
| | - Krishna Kishore Mahato
- Department of Biophysics, Manipal School of Life Sciences, Manipal Academy of Higher Education, Manipal, Karnataka 576104, India
| | - Fu-Jen Kao
- Institute of Biophotonics, National Yang Ming Chiao Tung University, Taipei 11221, Taiwan
| | - Guan-Yu Zhuo
- Institute of Translational Medicine and New Drug Development, China Medical University, Taichung 404328, Taiwan
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15
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Galaz-Montoya JG. The advent of preventive high-resolution structural histopathology by artificial-intelligence-powered cryogenic electron tomography. Front Mol Biosci 2024; 11:1390858. [PMID: 38868297 PMCID: PMC11167099 DOI: 10.3389/fmolb.2024.1390858] [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: 02/24/2024] [Accepted: 05/08/2024] [Indexed: 06/14/2024] Open
Abstract
Advances in cryogenic electron microscopy (cryoEM) single particle analysis have revolutionized structural biology by facilitating the in vitro determination of atomic- and near-atomic-resolution structures for fully hydrated macromolecular complexes exhibiting compositional and conformational heterogeneity across a wide range of sizes. Cryogenic electron tomography (cryoET) and subtomogram averaging are rapidly progressing toward delivering similar insights for macromolecular complexes in situ, without requiring tags or harsh biochemical purification. Furthermore, cryoET enables the visualization of cellular and tissue phenotypes directly at molecular, nanometric resolution without chemical fixation or staining artifacts. This forward-looking review covers recent developments in cryoEM/ET and related technologies such as cryogenic focused ion beam milling scanning electron microscopy and correlative light microscopy, increasingly enhanced and supported by artificial intelligence algorithms. Their potential application to emerging concepts is discussed, primarily the prospect of complementing medical histopathology analysis. Machine learning solutions are poised to address current challenges posed by "big data" in cryoET of tissues, cells, and macromolecules, offering the promise of enabling novel, quantitative insights into disease processes, which may translate into the clinic and lead to improved diagnostics and targeted therapeutics.
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Affiliation(s)
- Jesús G. Galaz-Montoya
- Department of Bioengineering, James H. Clark Center, Stanford University, Stanford, CA, United States
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16
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Bian C, Ashton G, Grant M, Rodriguez VP, Martin IP, Tsakiroglou AM, Cook M, Fergie M. Integrating Spatial and Morphological Characteristics into Melanoma Prognosis: A Computational Approach. Cancers (Basel) 2024; 16:2026. [PMID: 38893146 PMCID: PMC11171264 DOI: 10.3390/cancers16112026] [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: 04/19/2024] [Revised: 05/17/2024] [Accepted: 05/24/2024] [Indexed: 06/21/2024] Open
Abstract
In this study, the prognostic value of cellular morphology and spatial configurations in melanoma has been examined, aiming to complement traditional prognostic indicators like mitotic activity and tumor thickness. Through a computational pipeline using machine learning and deep learning methods, we quantified nuclei sizes within different spatial regions and analyzed their prognostic significance using univariate and multivariate Cox models. Nuclei sizes in the invasive band demonstrated a significant hazard ratio (HR) of 1.1 (95% CI: 1.03, 1.18). Similarly, the nuclei sizes of tumor cells and Ki67 S100 co-positive cells in the invasive band achieved HRs of 1.07 (95% CI: 1.02, 1.13) and 1.09 (95% CI: 1.04, 1.16), respectively. Our findings reveal that nuclei sizes, particularly in the invasive band, are potentially prognostic factors. Correlation analyses further demonstrated a meaningful relationship between cellular morphology and tumor progression, notably showing that nuclei size within the invasive band correlates substantially with tumor thickness. These results suggest the potential of integrating spatial and morphological analyses into melanoma prognostication.
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Affiliation(s)
- Chang Bian
- The Division of Informatics, Imaging and Data Science, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester M13 9PT, UK
| | - Garry Ashton
- Cancer Research UK Manchester Institute, The University of Manchester, Manchester M20 4BX, UK
| | - Megan Grant
- Cancer Research UK Manchester Institute, The University of Manchester, Manchester M20 4BX, UK
| | - Valeria Pavet Rodriguez
- Cancer Research UK Manchester Institute, The University of Manchester, Manchester M20 4BX, UK
| | - Isabel Peset Martin
- Cancer Research UK Manchester Institute, The University of Manchester, Manchester M20 4BX, UK
| | - Anna Maria Tsakiroglou
- The Division of Informatics, Imaging and Data Science, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester M13 9PT, UK
| | - Martin Cook
- Cancer Research UK Manchester Institute, The University of Manchester, Manchester M20 4BX, UK
- Royal Surrey County Hospital, Guildford GU2 7XX, UK
| | - Martin Fergie
- The Division of Informatics, Imaging and Data Science, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester M13 9PT, UK
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17
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Lee J, Lee G, Kwak TY, Kim SW, Jin MS, Kim C, Chang H. MurSS: A Multi-Resolution Selective Segmentation Model for Breast Cancer. Bioengineering (Basel) 2024; 11:463. [PMID: 38790330 PMCID: PMC11117971 DOI: 10.3390/bioengineering11050463] [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: 03/29/2024] [Revised: 05/02/2024] [Accepted: 05/03/2024] [Indexed: 05/26/2024] Open
Abstract
Accurately segmenting cancer lesions is essential for effective personalized treatment and enhanced patient outcomes. We propose a multi-resolution selective segmentation (MurSS) model to accurately segment breast cancer lesions from hematoxylin and eosin (H&E) stained whole-slide images (WSIs). We used The Cancer Genome Atlas breast invasive carcinoma (BRCA) public dataset for training and validation. We used the Korea University Medical Center, Guro Hospital, BRCA dataset for the final test evaluation. MurSS utilizes both low- and high-resolution patches to leverage multi-resolution features using adaptive instance normalization. This enhances segmentation performance while employing a selective segmentation method to automatically reject ambiguous tissue regions, ensuring stable training. MurSS rejects 5% of WSI regions and achieves a pixel-level accuracy of 96.88% (95% confidence interval (CI): 95.97-97.62%) and mean Intersection over Union of 0.7283 (95% CI: 0.6865-0.7640). In our study, MurSS exhibits superior performance over other deep learning models, showcasing its ability to reject ambiguous areas identified by expert annotations while using multi-resolution inputs.
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Affiliation(s)
- Joonho Lee
- Deep Bio Inc., Seoul 08380, Republic of Korea; (J.L.); (G.L.); (T.-Y.K.); (S.W.K.)
| | - Geongyu Lee
- Deep Bio Inc., Seoul 08380, Republic of Korea; (J.L.); (G.L.); (T.-Y.K.); (S.W.K.)
| | - Tae-Yeong Kwak
- Deep Bio Inc., Seoul 08380, Republic of Korea; (J.L.); (G.L.); (T.-Y.K.); (S.W.K.)
| | - Sun Woo Kim
- Deep Bio Inc., Seoul 08380, Republic of Korea; (J.L.); (G.L.); (T.-Y.K.); (S.W.K.)
| | - Min-Sun Jin
- Department of Pathology, Bucheon St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 14647, Republic of Korea;
| | - Chungyeul Kim
- Department of Pathology, Korea University Guro Hospital, Seoul 08308, Republic of Korea;
| | - Hyeyoon Chang
- Deep Bio Inc., Seoul 08380, Republic of Korea; (J.L.); (G.L.); (T.-Y.K.); (S.W.K.)
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18
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Huang C, Ding S, Li S, Liu R. LMFE: Learning-Based Multiscale Feature Engineering in Partial Discharge Detection. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024; 35:5848-5856. [PMID: 36449579 DOI: 10.1109/tnnls.2022.3222671] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
The partial discharge (PD) detection is of critical importance in the stability and continuity of power distribution operations. Although several feature engineering methods have been developed to refine and improve PD detection accuracy, they can be suboptimal due to several major issues: 1) failure in identifying fault-related pulses; 2) the lack of inner-phase temporal representation; and 3) multiscale feature integration. The aim of this article is to develop a learning-based multiscale feature engineering (LMFE) framework for PD detection of each signal in a three-phase power system, while addressing the above issues. The three-phase measurements are first preprocessed to identify the pulses together with the surrounded waveforms. Next, our feature engineering is conducted to extract the global-scale features, i.e., phase-level and measurement-level aggregations of the pulse-level information, and the local-scale features focusing on waveforms and their inner-phase temporal information. A recurrent neural network (RNN) model is trained, and intermediate features are extracted from this trained RNN model. Furthermore, these multiscale features are merged and fed into a classifier to distinguish the different patterns between faulty and nonfaulty signals. Finally, our LMFE is evaluated by analyzing the VSB ENET dataset, which shows that LMFE outperforms existing approaches and provides the state-of-the-art solution in PD detection.
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19
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Siegfried LG, Bilik SM, Burgess JL, Catanuto P, Jozic I, Pastar I, Stone RC, Tomic-Canic M. An Optimized and Advanced Algorithm for the Quantification of Immunohistochemical Biomarkers in Keratinocytes. JID INNOVATIONS 2024; 4:100270. [PMID: 38756235 PMCID: PMC11097113 DOI: 10.1016/j.xjidi.2024.100270] [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: 07/31/2023] [Revised: 01/10/2024] [Accepted: 01/12/2024] [Indexed: 05/18/2024] Open
Abstract
Advancements in pathology have given rise to software applications intended to minimize human error and improve efficacy of image analysis. Still, the subjectivity of image quantification performed manually and the limitations of the most ubiquitous tissue stain analysis software requiring parameters tuned by the observer, reveal the need for a highly accurate, automated nuclear quantification software specific to immunohistochemistry, with improved precision and efficiency compared with the methods currently in use. We present a method for the quantification of immunohistochemical biomarkers in keratinocyte nuclei proposed to overcome these limitations, contributing sensitive shape-focused segmentation, accurate nuclear detection, and automated device-independent color assessment, without observer-dependent analysis parameters.
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Affiliation(s)
- Lindsey G. Siegfried
- Wound Healing and Regenerative Medicine Research Program, Dr. Philip Frost Department of Dermatology and Cutaneous Surgery, University of Miami Miller School of Medicine, Miami, Florida, USA
| | - Sophie M. Bilik
- Wound Healing and Regenerative Medicine Research Program, Dr. Philip Frost Department of Dermatology and Cutaneous Surgery, University of Miami Miller School of Medicine, Miami, Florida, USA
| | - Jamie L. Burgess
- Wound Healing and Regenerative Medicine Research Program, Dr. Philip Frost Department of Dermatology and Cutaneous Surgery, University of Miami Miller School of Medicine, Miami, Florida, USA
| | - Paola Catanuto
- Wound Healing and Regenerative Medicine Research Program, Dr. Philip Frost Department of Dermatology and Cutaneous Surgery, University of Miami Miller School of Medicine, Miami, Florida, USA
| | - Ivan Jozic
- Wound Healing and Regenerative Medicine Research Program, Dr. Philip Frost Department of Dermatology and Cutaneous Surgery, University of Miami Miller School of Medicine, Miami, Florida, USA
| | - Irena Pastar
- Wound Healing and Regenerative Medicine Research Program, Dr. Philip Frost Department of Dermatology and Cutaneous Surgery, University of Miami Miller School of Medicine, Miami, Florida, USA
| | - Rivka C. Stone
- Wound Healing and Regenerative Medicine Research Program, Dr. Philip Frost Department of Dermatology and Cutaneous Surgery, University of Miami Miller School of Medicine, Miami, Florida, USA
| | - Marjana Tomic-Canic
- Wound Healing and Regenerative Medicine Research Program, Dr. Philip Frost Department of Dermatology and Cutaneous Surgery, University of Miami Miller School of Medicine, Miami, Florida, USA
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20
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Hagi T, Nakamura T, Yuasa H, Uchida K, Asanuma K, Sudo A, Wakabayahsi T, Morita K. Prediction of prognosis using artificial intelligence-based histopathological image analysis in patients with soft tissue sarcomas. Cancer Med 2024; 13:e7252. [PMID: 38800990 PMCID: PMC11129162 DOI: 10.1002/cam4.7252] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Revised: 04/01/2024] [Accepted: 04/28/2024] [Indexed: 05/29/2024] Open
Abstract
BACKGROUND Prompt histopathological diagnosis with accuracy is required for soft tissue sarcomas (STSs) which are still challenging. In addition, the advances in artificial intelligence (AI) along with the development of pathology slides digitization may empower the demand for the prediction of behavior of STSs. In this article, we explored the application of deep learning for prediction of prognosis from histopathological images in patients with STS. METHODS Our retrospective study included a total of 35 histopathological slides from patients with STS. We trained Inception v3 which is proposed method of convolutional neural network based survivability estimation. F1 score which identify the accuracy and area under the receiver operating characteristic curve (AUC) served as main outcome measures from a 4-fold validation. RESULTS The cohort included 35 patients with a mean age of 64 years, and the mean follow-up period was 34 months (2-66 months). Our deep learning method achieved AUC of 0.974 and an accuracy of 91.9% in predicting overall survival. Concerning with the prediction of metastasis-free survival, the accuracy was 84.2% with the AUC of 0.852. CONCLUSION AI might be used to help pathologists with accurate prognosis prediction. This study could substantially improve the clinical management of patients with STS.
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Affiliation(s)
- Tomohito Hagi
- Department of Orthopedic SurgeryMie University Graduate School of MedicineTsuJapan
| | - Tomoki Nakamura
- Department of Orthopedic SurgeryMie University Graduate School of MedicineTsuJapan
| | - Hiroto Yuasa
- Department of Oncologic PathologyMie University Graduate School of MedicineTsuJapan
| | - Katsunori Uchida
- Department of Oncologic PathologyMie University Graduate School of MedicineTsuJapan
| | - Kunihiro Asanuma
- Department of Orthopedic SurgeryMie University Graduate School of MedicineTsuJapan
| | - Akihiro Sudo
- Department of Orthopedic SurgeryMie University Graduate School of MedicineTsuJapan
| | - Tetsushi Wakabayahsi
- Department of Information EngineeringMie University Graduate School of EngineeringTsuJapan
| | - Kento Morita
- Department of Information EngineeringMie University Graduate School of EngineeringTsuJapan
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21
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Lin S, Yong J, Zhang L, Chen X, Qiao L, Pan W, Yang Y, Zhao H. Applying image features of proximal paracancerous tissues in predicting prognosis of patients with hepatocellular carcinoma. Comput Biol Med 2024; 173:108365. [PMID: 38537563 DOI: 10.1016/j.compbiomed.2024.108365] [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: 10/18/2023] [Revised: 03/19/2024] [Accepted: 03/21/2024] [Indexed: 04/17/2024]
Abstract
BACKGROUND Most of the methods using digital pathological image for predicting Hepatocellular carcinoma (HCC) prognosis have not considered paracancerous tissue microenvironment (PTME), which are potentially important for tumour initiation and metastasis. This study aimed to identify roles of image features of PTME in predicting prognosis and tumour recurrence of HCC patients. METHODS We collected whole slide images (WSIs) of 146 HCC patients from Sun Yat-sen Memorial Hospital (SYSM dataset). For each WSI, five types of regions of interests (ROIs) in PTME and tumours were manually annotated. These ROIs were used to construct a Lasso Cox survival model for predicting the prognosis of HCC patients. To make the model broadly useful, we established a deep learning method to automatically segment WSIs, and further used it to construct a prognosis prediction model. This model was tested by the samples of 225 HCC patients from the Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC). RESULTS In predicting prognosis of the HCC patients, using the image features of manually annotated ROIs in PTME achieved C-index 0.668 in the SYSM testing dataset, which is higher than the C-index 0.648 reached by the model only using image features of tumours. Integrating ROIs of PTME and tumours achieved C-index 0.693 in the SYSM testing dataset. The model using automatically segmented ROIs of PTME and tumours achieved C-index of 0.665 (95% CI: 0.556-0.774) in the TCGA-LIHC samples, which is better than the widely used methods, WSISA (0.567), DeepGraphSurv (0.593), and SeTranSurv (0.642). Finally, we found the Texture SumAverage Skew HV on immune cell infiltration and Texture related features on desmoplastic reaction are the most important features of PTME in predicting HCC prognosis. We additionally used the model in prediction HCC recurrence for patients from SYSM-training, SYSM-testing, and TCGA-LIHC datasets, indicating the important roles of PTME in the prediction. CONCLUSIONS Our results indicate image features of PTME is critical for improving the prognosis prediction of HCC. Moreover, the image features related with immune cell infiltration and desmoplastic reaction of PTME are the most important factors associated with prognosis of HCC.
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Affiliation(s)
- Siying Lin
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, 510006, China; Department of Pathology, Department of Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China
| | - Juanjuan Yong
- Department of Pathology, Department of Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China
| | - Lei Zhang
- Department of Pancreatic-Hepato-Biliary-Surgery, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510655, China
| | - Xiaolong Chen
- Department of Hepatic Surgery, Liver Transplantation, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, 510630, China
| | - Liang Qiao
- Storr Liver Centre, Westmead Institute for Medical Research, University of Sydney at Westmead Hospital, Westmead, NSW, 2145, Australia
| | - Weidong Pan
- Department of Pancreatic-Hepato-Biliary-Surgery, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510655, China
| | - Yuedong Yang
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, 510006, China.
| | - Huiying Zhao
- Department of Pathology, Department of Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China.
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22
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Glass M, Ji Z, Davis R, Pavlisko EN, DiBernardo L, Carney J, Fishbein G, Luthringer D, Miller D, Mitchell R, Larsen B, Butt Y, Bois M, Maleszewski J, Halushka M, Seidman M, Lin CY, Buja M, Stone J, Dov D, Carin L, Glass C. A machine learning algorithm improves the diagnostic accuracy of the histologic component of antibody mediated rejection (AMR-H) in cardiac transplant endomyocardial biopsies. Cardiovasc Pathol 2024; 72:107646. [PMID: 38677634 DOI: 10.1016/j.carpath.2024.107646] [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: 04/03/2023] [Revised: 04/15/2024] [Accepted: 04/16/2024] [Indexed: 04/29/2024] Open
Abstract
BACKGROUND Pathologic antibody mediated rejection (pAMR) remains a major driver of graft failure in cardiac transplant patients. The endomyocardial biopsy remains the primary diagnostic tool but presents with challenges, particularly in distinguishing the histologic component (pAMR-H) defined by 1) intravascular macrophage accumulation in capillaries and 2) activated endothelial cells that expand the cytoplasm to narrow or occlude the vascular lumen. Frequently, pAMR-H is difficult to distinguish from acute cellular rejection (ACR) and healing injury. With the advent of digital slide scanning and advances in machine deep learning, artificial intelligence technology is widely under investigation in the areas of oncologic pathology, but in its infancy in transplant pathology. For the first time, we determined if a machine learning algorithm could distinguish pAMR-H from normal myocardium, healing injury and ACR. MATERIALS AND METHODS A total of 4,212 annotations (1,053 regions of normal, 1,053 pAMR-H, 1,053 healing injury and 1,053 ACR) were completed from 300 hematoxylin and eosin slides scanned using a Leica Aperio GT450 digital whole slide scanner at 40X magnification. All regions of pAMR-H were annotated from patients confirmed with a previous diagnosis of pAMR2 (>50% positive C4d immunofluorescence and/or >10% CD68 positive intravascular macrophages). Annotations were imported into a Python 3.7 development environment using the OpenSlide™ package and a convolutional neural network approach utilizing transfer learning was performed. RESULTS The machine learning algorithm showed 98% overall validation accuracy and pAMR-H was correctly distinguished from specific categories with the following accuracies: normal myocardium (99.2%), healing injury (99.5%) and ACR (99.5%). CONCLUSION Our novel deep learning algorithm can reach acceptable, and possibly surpass, performance of current diagnostic standards of identifying pAMR-H. Such a tool may serve as an adjunct diagnostic aid for improving the pathologist's accuracy and reproducibility, especially in difficult cases with high inter-observer variability. This is one of the first studies that provides evidence that an artificial intelligence machine learning algorithm can be trained and validated to diagnose pAMR-H in cardiac transplant patients. Ongoing studies include multi-institutional verification testing to ensure generalizability.
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Affiliation(s)
- Matthew Glass
- Duke Division of Artificial Intelligence and Computational Pathology, Duke University Medical Center, Durham NC, USA; Department of Anesthesiology, Duke University Medical Center, Durham NC, USA
| | - Zhicheng Ji
- Department of Biostatistics and Bioinformatics, Duke School of Medicine, Durham NC, USA
| | - Richard Davis
- Department of Pathology, Duke University Medical Center, Durham NC, USA
| | - Elizabeth N Pavlisko
- Duke Division of Artificial Intelligence and Computational Pathology, Duke University Medical Center, Durham NC, USA; Department of Pathology, Duke University Medical Center, Durham NC, USA
| | - Louis DiBernardo
- Department of Pathology, Duke University Medical Center, Durham NC, USA
| | - John Carney
- Department of Pathology, Duke University Medical Center, Durham NC, USA
| | - Gregory Fishbein
- Department of Pathology, University of California at Los Angeles, Los Angeles CA, USA
| | - Daniel Luthringer
- Department of Pathology and Laboratory Medicine, Cedars-Sinai Medical Center, Los Angeles CA, USA
| | - Dylan Miller
- Department of Pathology, Intermountain Healthcare, Salt Lake City UT, USA
| | - Richard Mitchell
- Department of Pathology, Brigham and Women's Hospital, Boston MA, USA
| | - Brandon Larsen
- Department of Pathology and Laboratory Medicine, Mayo Clinic, Phoenix AZ, USA
| | - Yasmeen Butt
- Department of Pathology and Laboratory Medicine, Mayo Clinic, Phoenix AZ, USA
| | - Melanie Bois
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester MN, USA
| | - Joseph Maleszewski
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester MN, USA
| | - Marc Halushka
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore MD, USA
| | - Michael Seidman
- Department of Pathology, University Health Network, Toronto ON, CA
| | - Chieh-Yu Lin
- Department of Pathology and Immunology, Washington University, St. Louis MO, USA
| | - Maximilian Buja
- Department of Pathology and Laboratory Medicine, The University of Texas Health Science Center at Houston, Houston TX, USA
| | - James Stone
- Department of Pathology, Massachusetts General Hospital, Boston MA, USA
| | - David Dov
- Duke Division of Artificial Intelligence and Computational Pathology, Duke University Medical Center, Durham NC, USA; Pratt School of Engineering, Department of Electrical and Computer Engineering, Duke University, Durham NC, USA
| | - Lawrence Carin
- Duke Division of Artificial Intelligence and Computational Pathology, Duke University Medical Center, Durham NC, USA; Pratt School of Engineering, Department of Electrical and Computer Engineering, Duke University, Durham NC, USA
| | - Carolyn Glass
- Duke Division of Artificial Intelligence and Computational Pathology, Duke University Medical Center, Durham NC, USA; Department of Pathology, Duke University Medical Center, Durham NC, USA.
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23
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Abd-Ellah MK, Awad AI, Khalaf AAM, Ibraheem AM. Automatic brain-tumor diagnosis using cascaded deep convolutional neural networks with symmetric U-Net and asymmetric residual-blocks. Sci Rep 2024; 14:9501. [PMID: 38664436 PMCID: PMC11045751 DOI: 10.1038/s41598-024-59566-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2023] [Accepted: 04/12/2024] [Indexed: 04/28/2024] Open
Abstract
The use of various kinds of magnetic resonance imaging (MRI) techniques for examining brain tissue has increased significantly in recent years, and manual investigation of each of the resulting images can be a time-consuming task. This paper presents an automatic brain-tumor diagnosis system that uses a CNN for detection, classification, and segmentation of glioblastomas; the latter stage seeks to segment tumors inside glioma MRI images. The structure of the developed multi-unit system consists of two stages. The first stage is responsible for tumor detection and classification by categorizing brain MRI images into normal, high-grade glioma (glioblastoma), and low-grade glioma. The uniqueness of the proposed network lies in its use of different levels of features, including local and global paths. The second stage is responsible for tumor segmentation, and skip connections and residual units are used during this step. Using 1800 images extracted from the BraTS 2017 dataset, the detection and classification stage was found to achieve a maximum accuracy of 99%. The segmentation stage was then evaluated using the Dice score, specificity, and sensitivity. The results showed that the suggested deep-learning-based system ranks highest among a variety of different strategies reported in the literature.
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Affiliation(s)
| | - Ali Ismail Awad
- College of Information Technology, United Arab Emirates University, P.O. Box 15551, Al Ain, United Arab Emirates.
- Faculty of Engineering, Al-Azhar University, P.O. Box 83513, Qena, Egypt.
| | - Ashraf A M Khalaf
- Department of Electrical Engineering, Faculty of Engineering, Minia University, Minia, 61519, Egypt
| | - Amira Mofreh Ibraheem
- Faculty of Artificial Intelligence, Egyptian Russian University, Cairo, 11829, Egypt
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24
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Jain DA, Gupta N, Sharma DP, Gupta DOP, Gupta DS, Sahoo DAK. WITHDRAWN: Histomorphometric Image Classifier of Different Grades of Oral Squamous Cell Carcinoma Using Transfer Learning and Convolutional Neural Network. JOURNAL OF STOMATOLOGY, ORAL AND MAXILLOFACIAL SURGERY 2024:101876. [PMID: 38636805 DOI: 10.1016/j.jormas.2024.101876] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Revised: 04/05/2024] [Accepted: 04/12/2024] [Indexed: 04/20/2024]
Abstract
This article has been withdrawn at the request of the authors. The Publisher apologizes for any inconvenience this may cause. The full Elsevier Policy on Article Withdrawal can be found at https://www.elsevier.com/about/policies/article-withdrawal
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Affiliation(s)
- Dr Ayushi Jain
- Department of Oral Pathology, King George's Medical University, Lucknow 226003, UP, India
| | - Nitika Gupta
- Department of Oral Pathology, King George's Medical University, Lucknow 226003, UP, India
| | - Dr Pooja Sharma
- Department of Oral Pathology, King George's Medical University, Lucknow 226003, UP, India
| | - Dr Om Prakash Gupta
- Department of General Surgery, Career Institute of Medical sciences, Lucknow 226003, UP, India
| | - Dr Shalini Gupta
- Department of Oral Pathology, King George's Medical University, Lucknow 226003, UP, India.
| | - Dr Amaresh Kumar Sahoo
- Department of Applied Sciences, Indian Institute of Information Technology Allahabad, Prayagraj 211012, UP, India.
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25
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Reigle J, Lopez-Nunez O, Drysdale E, Abuquteish D, Liu X, Putra J, Erdman L, Griffiths AM, Prasath S, Siddiqui I, Dhaliwal J. Using Deep Learning to Automate Eosinophil Counting in Pediatric Ulcerative Colitis Histopathological Images. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.04.03.24305251. [PMID: 38633803 PMCID: PMC11023647 DOI: 10.1101/2024.04.03.24305251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/19/2024]
Abstract
Background Accurate identification of inflammatory cells from mucosal histopathology images is important in diagnosing ulcerative colitis. The identification of eosinophils in the colonic mucosa has been associated with disease course. Cell counting is not only time-consuming but can also be subjective to human biases. In this study we developed an automatic eosinophilic cell counting tool from mucosal histopathology images, using deep learning. Method Four pediatric IBD pathologists from two North American pediatric hospitals annotated 530 crops from 143 standard-of-care hematoxylin and eosin (H & E) rectal mucosal biopsies. A 305/75 split was used for training/validation to develop and optimize a U-Net based deep learning model, and 150 crops were used as a test set. The U-Net model was then compared to SAU-Net, a state-of-the-art U-Net variant. We undertook post-processing steps, namely, (1) the pixel-level probability threshold, (2) the minimum number of clustered pixels to designate a cell, and (3) the connectivity. Experiments were run to optimize model parameters using AUROC and cross-entropy loss as the performance metrics. Results The F1-score was 0.86 (95%CI:0.79-0.91) (Precision: 0.77 (95%CI:0.70-0.83), Recall: 0.96 (95%CI:0.93-0.99)) to identify eosinophils as compared to an F1-score of 0.2 (95%CI:0.13-0.26) for SAU-Net (Precision: 0.38 (95%CI:0.31-0.46), Recall: 0.13 (95%CI:0.08-0.19)). The inter-rater reliability was 0.96 (95%CI:0.93-0.97). The correlation between two pathologists and the algorithm was 0.89 (95%CI:0.82-0.94) and 0.88 (95%CI:0.80-0.94) respectively. Conclusion Our results indicate that deep learning-based automated eosinophilic cell counting can obtain a robust level of accuracy with a high degree of concordance with manual expert annotations.
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26
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Zamanitajeddin N, Jahanifar M, Bilal M, Eastwood M, Rajpoot N. Social network analysis of cell networks improves deep learning for prediction of molecular pathways and key mutations in colorectal cancer. Med Image Anal 2024; 93:103071. [PMID: 38199068 DOI: 10.1016/j.media.2023.103071] [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: 03/14/2023] [Revised: 11/14/2023] [Accepted: 12/19/2023] [Indexed: 01/12/2024]
Abstract
Colorectal cancer (CRC) is a primary global health concern, and identifying the molecular pathways, genetic subtypes, and mutations associated with CRC is crucial for precision medicine. However, traditional measurement techniques such as gene sequencing are costly and time-consuming, while most deep learning methods proposed for this task lack interpretability. This study offers a new approach to enhance the state-of-the-art deep learning methods for molecular pathways and key mutation prediction by incorporating cell network information. We build cell graphs with nuclei as nodes and nuclei connections as edges of the network and leverage Social Network Analysis (SNA) measures to extract abstract, perceivable, and interpretable features that explicitly describe the cell network characteristics in an image. Our approach does not rely on precise nuclei segmentation or feature extraction, is computationally efficient, and is easily scalable. In this study, we utilize the TCGA-CRC-DX dataset, comprising 499 patients and 502 diagnostic slides from primary colorectal tumours, sourced from 36 distinct medical centres in the United States. By incorporating the SNA features alongside deep features in two multiple instance learning frameworks, we demonstrate improved performance for chromosomal instability (CIN), hypermutated tumour (HM), TP53 gene, BRAF gene, and Microsatellite instability (MSI) status prediction tasks (2.4%-4% and 7-8.8% improvement in AUROC and AUPRC on average). Additionally, our method achieves outstanding performance on MSI prediction in an external PAIP dataset (99% AUROC and 98% AUPRC), demonstrating its generalizability. Our findings highlight the discrimination power of SNA features and how they can be beneficial to deep learning models' performance and provide insights into the correlation of cell network profiles with molecular pathways and key mutations.
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Affiliation(s)
- Neda Zamanitajeddin
- Tissue Image Analytics Centre, Department of Computer Science, University of Warwick, Coventry, UK.
| | - Mostafa Jahanifar
- Tissue Image Analytics Centre, Department of Computer Science, University of Warwick, Coventry, UK
| | - Mohsin Bilal
- Tissue Image Analytics Centre, Department of Computer Science, University of Warwick, Coventry, UK
| | - Mark Eastwood
- Tissue Image Analytics Centre, Department of Computer Science, University of Warwick, Coventry, UK
| | - Nasir Rajpoot
- Tissue Image Analytics Centre, Department of Computer Science, University of Warwick, Coventry, UK; Histofy Ltd., Birmingham, UK.
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27
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Šimunić I, Jagečić D, Isaković J, Dobrivojević Radmilović M, Mitrečić D. Lusca: FIJI (ImageJ) based tool for automated morphological analysis of cellular and subcellular structures. Sci Rep 2024; 14:7383. [PMID: 38548809 PMCID: PMC10978859 DOI: 10.1038/s41598-024-57650-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2024] [Accepted: 03/20/2024] [Indexed: 04/01/2024] Open
Abstract
The human body consists of diverse subcellular, cellular and supracellular structures. Neurons possess varying-sized projections that interact with different cellular structures leading to the development of highly complex morphologies. Aiming to enhance image analysis of complex biological forms including neurons using available FIJI (ImageJ) plugins, Lusca, an advanced open-source tool, was developed. Lusca utilizes machine learning for image segmentation with intensity and size thresholds. It performs particle analysis to ascertain parameters such as area/volume, quantity, and intensity, in addition to skeletonization for determining length, branching, and width. Moreover, in conjunction with colocalization measurements, it provides an extensive set of 29 morphometric parameters for both 2D and 3D analysis. This is a significant enhancement compared to other scripts that offer only 5-15 parameters. Consequently, it ensures quicker and more precise quantification by effectively eliminating noise and discerning subtle details. With three times larger execution speed, fewer false positive and negative results, and the capacity to measure various parameters, Lusca surpasses other existing open-source solutions. Its implementation of machine learning-based segmentation facilitates versatile applications for different cell types and biological structures, including mitochondria, fibres, and vessels. Lusca's automated and precise measurement capability makes it an ideal choice for diverse biological image analyses.
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Affiliation(s)
- Iva Šimunić
- Department of Histology and Embryology, University of Zagreb School of Medicine, 10000, Zagreb, Croatia.
- Laboratory for Stem Cells, Department for Regenerative Neuroscience, Croatian Institute for Brain Research, University of Zagreb School of Medicine, 10000, Zagreb, Croatia.
| | - Denis Jagečić
- Department of Histology and Embryology, University of Zagreb School of Medicine, 10000, Zagreb, Croatia
- Laboratory for Stem Cells, Department for Regenerative Neuroscience, Croatian Institute for Brain Research, University of Zagreb School of Medicine, 10000, Zagreb, Croatia
| | - Jasmina Isaković
- School of Medicine, European University Cyprus - Frankfurt Branch, 60488, Frankfurt am Main, Germany
| | - Marina Dobrivojević Radmilović
- Department of Histology and Embryology, University of Zagreb School of Medicine, 10000, Zagreb, Croatia
- Laboratory for Regenerative Neuroscience, Department for Regenerative Neuroscience, Croatian Institute for Brain Research, University of Zagreb School of Medicine, 10000, Zagreb, Croatia
| | - Dinko Mitrečić
- Department of Histology and Embryology, University of Zagreb School of Medicine, 10000, Zagreb, Croatia
- Laboratory for Stem Cells, Department for Regenerative Neuroscience, Croatian Institute for Brain Research, University of Zagreb School of Medicine, 10000, Zagreb, Croatia
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28
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Qi F, You Z, Guo J, Hong Y, Wu X, Zhang D, Li Q, Cai C. An automatic diagnosis model of otitis media with high accuracy rate using transfer learning. Front Mol Biosci 2024; 10:1250596. [PMID: 38577506 PMCID: PMC10991843 DOI: 10.3389/fmolb.2023.1250596] [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: 06/30/2023] [Accepted: 12/27/2023] [Indexed: 04/06/2024] Open
Abstract
Introduction: Chronic Suppurative Otitis Media (CSOM) and Middle Ear Cholesteatoma are two common chronic otitis media diseases that often cause confusion among physicians due to their similar location and shape in clinical CT images of the internal auditory canal. In this study, we utilized the transfer learning method combined with CT scans of the internal auditory canal to achieve accurate lesion segmentation and automatic diagnosis for patients with CSOM and middle ear cholesteatoma. Methods: We collected 1019 CT scan images and utilized the nnUnet skeleton model along with coarse grained focal segmentation labeling to pre-train on the above CT images for focal segmentation. We then fine-tuned the pre-training model for the downstream three-classification diagnosis task. Results: Our proposed algorithm model achieved a classification accuracy of 92.33% for CSOM and middle ear cholesteatoma, which is approximately 5% higher than the benchmark model. Moreover, our upstream segmentation task training resulted in a mean Intersection of Union (mIoU) of 0.569. Discussion: Our results demonstrate that using coarse-grained contour boundary labeling can significantly enhance the accuracy of downstream classification tasks. The combination of deep learning and automatic diagnosis of CSOM and internal auditory canal CT images of middle ear cholesteatoma exhibits high sensitivity and specificity.
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Affiliation(s)
- Fangyu Qi
- Department of Anesthesiology, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, China
- School of Medicine, Xiamen University, Xiamen, China
- National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China
| | - Zhiyu You
- School of Medicine, Xiamen University, Xiamen, China
- National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China
| | - Jiayang Guo
- School of Medicine, Xiamen University, Xiamen, China
- National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China
| | - Yongjun Hong
- Zhongshan Hospital Affiliated to Xiamen University, Xiamen, China
| | - Xiaolong Wu
- School of Medicine, Xiamen University, Xiamen, China
- National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China
| | | | - Qiyuan Li
- School of Medicine, Xiamen University, Xiamen, China
- National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China
| | - Chengfu Cai
- College of Otorhinolaryngology Head and Neck Surgery, Xiamen Haicang Hospital, Xiamen, China
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29
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Civit-Masot J, Luna-Perejon F, Muñoz-Saavedra L, Domínguez-Morales M, Civit A. A lightweight xAI approach to cervical cancer classification. Med Biol Eng Comput 2024:10.1007/s11517-024-03063-6. [PMID: 38507122 DOI: 10.1007/s11517-024-03063-6] [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: 05/16/2023] [Accepted: 02/24/2024] [Indexed: 03/22/2024]
Abstract
Cervical cancer is caused in the vast majority of cases by the human papilloma virus (HPV) through sexual contact and requires a specific molecular-based analysis to be detected. As an HPV vaccine is available, the incidence of cervical cancer is up to ten times higher in areas without adequate healthcare resources. In recent years, liquid cytology has been used to overcome these shortcomings and perform mass screening. In addition, classifiers based on convolutional neural networks can be developed to help pathologists diagnose the disease. However, these systems always require the final verification of a pathologist to make a final diagnosis. For this reason, explainable AI techniques are required to highlight the most significant data to the healthcare professional, as it can be used to determine the confidence in the results and the areas of the image used for classification (allowing the professional to point out the areas he/she thinks are most important and cross-check them against those detected by the system in order to create incremental learning systems). In this work, a 4-phase optimization process is used to obtain a custom deep-learning classifier for distinguishing between 4 severity classes of cervical cancer with liquid-cytology images. The final classifier obtains an accuracy over 97% for 4 classes and 100% for 2 classes with execution times under 1 s (including the final report generation). Compared to previous works, the proposed classifier obtains better accuracy results with a lower computational cost.
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Affiliation(s)
- Javier Civit-Masot
- Robotics and Computer Technology Lab, ETSII, Universidad de Sevilla, Reina Mercedes s/n, Seville, 41018, Spain.
| | - Francisco Luna-Perejon
- Robotics and Computer Technology Lab, ETSII, Universidad de Sevilla, Reina Mercedes s/n, Seville, 41018, Spain
| | - Luis Muñoz-Saavedra
- Robotics and Computer Technology Lab, ETSII, Universidad de Sevilla, Reina Mercedes s/n, Seville, 41018, Spain
| | - Manuel Domínguez-Morales
- Robotics and Computer Technology Lab, ETSII, Universidad de Sevilla, Reina Mercedes s/n, Seville, 41018, Spain
- Computer Engineering Research Institute, Universidad de Sevilla, Reina Mercedes s/n, Seville, 41018, Spain
| | - Anton Civit
- Robotics and Computer Technology Lab, ETSII, Universidad de Sevilla, Reina Mercedes s/n, Seville, 41018, Spain
- Computer Engineering Research Institute, Universidad de Sevilla, Reina Mercedes s/n, Seville, 41018, Spain
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Neto PC, Montezuma D, Oliveira SP, Oliveira D, Fraga J, Monteiro A, Monteiro J, Ribeiro L, Gonçalves S, Reinhard S, Zlobec I, Pinto IM, Cardoso JS. An interpretable machine learning system for colorectal cancer diagnosis from pathology slides. NPJ Precis Oncol 2024; 8:56. [PMID: 38443695 PMCID: PMC10914836 DOI: 10.1038/s41698-024-00539-4] [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: 07/18/2023] [Accepted: 02/08/2024] [Indexed: 03/07/2024] Open
Abstract
Considering the profound transformation affecting pathology practice, we aimed to develop a scalable artificial intelligence (AI) system to diagnose colorectal cancer from whole-slide images (WSI). For this, we propose a deep learning (DL) system that learns from weak labels, a sampling strategy that reduces the number of training samples by a factor of six without compromising performance, an approach to leverage a small subset of fully annotated samples, and a prototype with explainable predictions, active learning features and parallelisation. Noting some problems in the literature, this study is conducted with one of the largest WSI colorectal samples dataset with approximately 10,500 WSIs. Of these samples, 900 are testing samples. Furthermore, the robustness of the proposed method is assessed with two additional external datasets (TCGA and PAIP) and a dataset of samples collected directly from the proposed prototype. Our proposed method predicts, for the patch-based tiles, a class based on the severity of the dysplasia and uses that information to classify the whole slide. It is trained with an interpretable mixed-supervision scheme to leverage the domain knowledge introduced by pathologists through spatial annotations. The mixed-supervision scheme allowed for an intelligent sampling strategy effectively evaluated in several different scenarios without compromising the performance. On the internal dataset, the method shows an accuracy of 93.44% and a sensitivity between positive (low-grade and high-grade dysplasia) and non-neoplastic samples of 0.996. On the external test samples varied with TCGA being the most challenging dataset with an overall accuracy of 84.91% and a sensitivity of 0.996.
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Affiliation(s)
- Pedro C Neto
- Institute for Systems and Computer Engineering, Technology and Science (INESC TEC), R. Dr. Roberto Frias, Porto, 4200-465, Porto, Portugal.
- Faculty of Engineering, University of Porto (FEUP), R. Dr. Roberto Frias, Porto, 4200-465, Porto, Portugal.
| | - Diana Montezuma
- IMP Diagnostics, Praça do Bom Sucesso, 61, sala 808, Porto, 4150-146, Porto, Portugal.
- Cancer Biology and Epigenetics Group, Research Center of IPO Porto (CI-IPOP) / RISE@CI-IPOP (Health Research Network), Portuguese Oncology Institute of Porto (IPO Porto) / Porto Comprehensive Cancer Center (Porto.CCC), R. Dr. António Bernardino de Almeida 865, Porto, 4200-072, Porto, Portugal.
- Doctoral Programme in Medical Sciences, School of Medicine and Biomedical Sciences - University of Porto (ICBAS-UP), R. Jorge de Viterbo Ferreira 228, Porto, 4050-313, Porto, Portugal.
| | - Sara P Oliveira
- Institute for Systems and Computer Engineering, Technology and Science (INESC TEC), R. Dr. Roberto Frias, Porto, 4200-465, Porto, Portugal.
- Faculty of Engineering, University of Porto (FEUP), R. Dr. Roberto Frias, Porto, 4200-465, Porto, Portugal.
| | - Domingos Oliveira
- IMP Diagnostics, Praça do Bom Sucesso, 61, sala 808, Porto, 4150-146, Porto, Portugal
| | - João Fraga
- Department of Pathology, IPO-Porto, R. Dr. António Bernardino de Almeida 865, Porto, 4200-072, Porto, Portugal
| | - Ana Monteiro
- IMP Diagnostics, Praça do Bom Sucesso, 61, sala 808, Porto, 4150-146, Porto, Portugal
| | - João Monteiro
- IMP Diagnostics, Praça do Bom Sucesso, 61, sala 808, Porto, 4150-146, Porto, Portugal
| | - Liliana Ribeiro
- IMP Diagnostics, Praça do Bom Sucesso, 61, sala 808, Porto, 4150-146, Porto, Portugal
| | - Sofia Gonçalves
- IMP Diagnostics, Praça do Bom Sucesso, 61, sala 808, Porto, 4150-146, Porto, Portugal
| | - Stefan Reinhard
- Institute of Pathology, University of Bern, Uni Bern, Murtenstrasse 31, Bern, 3008, Bern, Switzerland
| | - Inti Zlobec
- Institute of Pathology, University of Bern, Uni Bern, Murtenstrasse 31, Bern, 3008, Bern, Switzerland
| | - Isabel M Pinto
- IMP Diagnostics, Praça do Bom Sucesso, 61, sala 808, Porto, 4150-146, Porto, Portugal
| | - Jaime S Cardoso
- Institute for Systems and Computer Engineering, Technology and Science (INESC TEC), R. Dr. Roberto Frias, Porto, 4200-465, Porto, Portugal
- Faculty of Engineering, University of Porto (FEUP), R. Dr. Roberto Frias, Porto, 4200-465, Porto, Portugal
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Lan Y, Han B, Zhai T, Xu Q, Li Z, Liu M, Xue Y, Xu H. Clinical application of machine learning-based pathomics signature of gastric atrophy. Front Oncol 2024; 14:1289265. [PMID: 38476364 PMCID: PMC10929611 DOI: 10.3389/fonc.2024.1289265] [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: 09/05/2023] [Accepted: 02/05/2024] [Indexed: 03/14/2024] Open
Abstract
Background The diagnosis of gastric atrophy is highly subjective, and we aimed to establish a model of gastric atrophy based on pathological features to improve diagnostic consistency. Methods We retrospectively collected the HE-stained pathological slides of gastric biopsies and used CellProfiler software for image segmentation and feature extraction of ten representative images for each sample. Subsequently, we employed the Least absolute shrinkage and selection operator (LASSO) to select features and different machine learning (ML) algorithms to construct the diagnostic models for gastric atrophy. Results We selected 289 gastric biopsy specimens for training, testing, and external validation. We extracted 464 pathological features and screened ten features by LASSO to establish the diagnostic model for moderate-to-severe atrophy. The range of area under the curve (AUC) for various machine learning algorithms was 0.835-1.000 in the training set, 0.786-0.949 in the testing set, and 0.689-0.818 in the external validation set. LR model had the highest AUC value, with 0.900 (95% CI: 0.852-0.947) in the training set, 0.901 (95% CI: 0.807-0.996) in the testing set, and 0.818 (95% CI: 0.714-0.923) in the external validation set. The atrophy pathological score based on the LR model was associated with endoscopic atrophy grading (Z=-2.478, P=0.013) and gastric cancer (GC) (OR=5.70, 95% CI: 2.63-12.33, P<0.001). Conclusion The ML model based on pathological features could improve the diagnostic consistency of gastric atrophy, which is also associated with endoscopic atrophy grading and GC.
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Affiliation(s)
- Yadi Lan
- Department of Gastroenterology, Shandong Provincial Hospital, Shandong University, Jinan, Shandong, China
| | - Bing Han
- Department of Gastroenterology, Shandong Provincial Hospital, Shandong University, Jinan, Shandong, China
| | - Tianyu Zhai
- Department of Gastroenterology, Shandong Provincial Hospital, Shandong University, Jinan, Shandong, China
| | - Qianqian Xu
- Department of Gastroenterology, Shandong Provincial Hospital, Shandong University, Jinan, Shandong, China
| | - Zhiwei Li
- Department of Gastroenterology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Mingyue Liu
- Department of Gastroenterology, Shandong Provincial Hospital, Shandong University, Jinan, Shandong, China
| | - Yining Xue
- Department of Gastroenterology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Hongwei Xu
- Department of Gastroenterology, Shandong Provincial Hospital, Shandong University, Jinan, Shandong, China
- Department of Gastroenterology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
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Li Y, Pillar N, Li J, Liu T, Wu D, Sun S, Ma G, de Haan K, Huang L, Zhang Y, Hamidi S, Urisman A, Keidar Haran T, Wallace WD, Zuckerman JE, Ozcan A. Virtual histological staining of unlabeled autopsy tissue. Nat Commun 2024; 15:1684. [PMID: 38396004 PMCID: PMC10891155 DOI: 10.1038/s41467-024-46077-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2023] [Accepted: 02/09/2024] [Indexed: 02/25/2024] Open
Abstract
Traditional histochemical staining of post-mortem samples often confronts inferior staining quality due to autolysis caused by delayed fixation of cadaver tissue, and such chemical staining procedures covering large tissue areas demand substantial labor, cost and time. Here, we demonstrate virtual staining of autopsy tissue using a trained neural network to rapidly transform autofluorescence images of label-free autopsy tissue sections into brightfield equivalent images, matching hematoxylin and eosin (H&E) stained versions of the same samples. The trained model can effectively accentuate nuclear, cytoplasmic and extracellular features in new autopsy tissue samples that experienced severe autolysis, such as COVID-19 samples never seen before, where the traditional histochemical staining fails to provide consistent staining quality. This virtual autopsy staining technique provides a rapid and resource-efficient solution to generate artifact-free H&E stains despite severe autolysis and cell death, also reducing labor, cost and infrastructure requirements associated with the standard histochemical staining.
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Affiliation(s)
- Yuzhu Li
- Electrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA
- Bioengineering Department, University of California, Los Angeles, CA, 90095, USA
- California NanoSystems Institute (CNSI), University of California, Los Angeles, CA, 90095, USA
| | - Nir Pillar
- Electrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA
- Bioengineering Department, University of California, Los Angeles, CA, 90095, USA
- California NanoSystems Institute (CNSI), University of California, Los Angeles, CA, 90095, USA
| | - Jingxi Li
- Electrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA
- Bioengineering Department, University of California, Los Angeles, CA, 90095, USA
- California NanoSystems Institute (CNSI), University of California, Los Angeles, CA, 90095, USA
| | - Tairan Liu
- Electrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA
- Bioengineering Department, University of California, Los Angeles, CA, 90095, USA
- California NanoSystems Institute (CNSI), University of California, Los Angeles, CA, 90095, USA
| | - Di Wu
- Computer Science Department, University of California, Los Angeles, CA, 90095, USA
| | - Songyu Sun
- Computer Science Department, University of California, Los Angeles, CA, 90095, USA
| | - Guangdong Ma
- Electrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA
- School of Physics, Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
| | - Kevin de Haan
- Electrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA
- Bioengineering Department, University of California, Los Angeles, CA, 90095, USA
- California NanoSystems Institute (CNSI), University of California, Los Angeles, CA, 90095, USA
| | - Luzhe Huang
- Electrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA
- Bioengineering Department, University of California, Los Angeles, CA, 90095, USA
- California NanoSystems Institute (CNSI), University of California, Los Angeles, CA, 90095, USA
| | - Yijie Zhang
- Electrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA
- Bioengineering Department, University of California, Los Angeles, CA, 90095, USA
- California NanoSystems Institute (CNSI), University of California, Los Angeles, CA, 90095, USA
| | - Sepehr Hamidi
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
| | - Anatoly Urisman
- Department of Pathology, University of California, San Francisco, CA, 94143, USA
| | - Tal Keidar Haran
- Department of Pathology, Hadassah Hebrew University Medical Center, Jerusalem, 91120, Israel
| | - William Dean Wallace
- Department of Pathology, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90033, USA
| | - Jonathan E Zuckerman
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
| | - Aydogan Ozcan
- Electrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA.
- Bioengineering Department, University of California, Los Angeles, CA, 90095, USA.
- California NanoSystems Institute (CNSI), University of California, Los Angeles, CA, 90095, USA.
- Department of Surgery, University of California, Los Angeles, CA, 90095, USA.
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Gouzou D, Taimori A, Haloubi T, Finlayson N, Wang Q, Hopgood JR, Vallejo M. Applications of machine learning in time-domain fluorescence lifetime imaging: a review. Methods Appl Fluoresc 2024; 12:022001. [PMID: 38055998 PMCID: PMC10851337 DOI: 10.1088/2050-6120/ad12f7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 09/25/2023] [Accepted: 12/06/2023] [Indexed: 12/08/2023]
Abstract
Many medical imaging modalities have benefited from recent advances in Machine Learning (ML), specifically in deep learning, such as neural networks. Computers can be trained to investigate and enhance medical imaging methods without using valuable human resources. In recent years, Fluorescence Lifetime Imaging (FLIm) has received increasing attention from the ML community. FLIm goes beyond conventional spectral imaging, providing additional lifetime information, and could lead to optical histopathology supporting real-time diagnostics. However, most current studies do not use the full potential of machine/deep learning models. As a developing image modality, FLIm data are not easily obtainable, which, coupled with an absence of standardisation, is pushing back the research to develop models which could advance automated diagnosis and help promote FLIm. In this paper, we describe recent developments that improve FLIm image quality, specifically time-domain systems, and we summarise sensing, signal-to-noise analysis and the advances in registration and low-level tracking. We review the two main applications of ML for FLIm: lifetime estimation and image analysis through classification and segmentation. We suggest a course of action to improve the quality of ML studies applied to FLIm. Our final goal is to promote FLIm and attract more ML practitioners to explore the potential of lifetime imaging.
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Affiliation(s)
- Dorian Gouzou
- Dorian Gouzou and Marta Vallejo are with Institute of Signals, Sensors and Systems, School of Engineering and Physical Sciences, Heriot Watt University, Edinburgh, EH14 4AS, United Kingdom
| | - Ali Taimori
- Tarek Haloubi, Ali Taimori, and James R. Hopgood are with Institute for Imaging, Data and Communication, School of Engineering, University of Edinburgh, Edinburgh, EH9 3FG, United Kingdom
| | - Tarek Haloubi
- Tarek Haloubi, Ali Taimori, and James R. Hopgood are with Institute for Imaging, Data and Communication, School of Engineering, University of Edinburgh, Edinburgh, EH9 3FG, United Kingdom
| | - Neil Finlayson
- Neil Finlayson is with Institute for Integrated Micro and Nano Systems, School of Engineering, University ofEdinburgh, Edinburgh EH9 3FF, United Kingdom
| | - Qiang Wang
- Qiang Wang is with Centre for Inflammation Research, University of Edinburgh, Edinburgh, EH16 4TJ, United Kingdom
| | - James R Hopgood
- Tarek Haloubi, Ali Taimori, and James R. Hopgood are with Institute for Imaging, Data and Communication, School of Engineering, University of Edinburgh, Edinburgh, EH9 3FG, United Kingdom
| | - Marta Vallejo
- Dorian Gouzou and Marta Vallejo are with Institute of Signals, Sensors and Systems, School of Engineering and Physical Sciences, Heriot Watt University, Edinburgh, EH14 4AS, United Kingdom
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Greenberg A, Samueli B, Farkash S, Zohar Y, Ish-Shalom S, Hagege RR, Hershkovitz D. Algorithm-assisted diagnosis of Hirschsprung's disease - evaluation of robustness and comparative image analysis on data from various labs and slide scanners. Diagn Pathol 2024; 19:26. [PMID: 38321431 PMCID: PMC10845737 DOI: 10.1186/s13000-024-01452-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2023] [Accepted: 01/25/2024] [Indexed: 02/08/2024] Open
Abstract
BACKGROUND Differences in the preparation, staining and scanning of digital pathology slides create significant pre-analytic variability. Algorithm-assisted tools must be able to contend with this variability in order to be applicable in clinical practice. In a previous study, a decision support algorithm was developed to assist in the diagnosis of Hirschsprung's disease. In the current study, we tested the robustness of this algorithm while assessing for pre-analytic factors which may affect its performance. METHODS The decision support algorithm was used on digital pathology slides obtained from four different medical centers (A-D) and scanned by three different scanner models (by Philips, Hamamatsu and 3DHISTECH). A total of 192 cases and 1782 slides were used in this study. RGB histograms were constructed to compare images from the various medical centers and scanner models and highlight the differences in color and contrast. RESULTS The algorithm was able to correctly identify ganglion cells in 99.2% of cases, from all medical centers (All scanned by the Philips slide scanner) as well as 95.5% and 100% of the slides scanned by the 3DHISTECH and Hamamatsu brand slide scanners, respectively. The total error rate for center D was lower than the other medical centers (3.9% vs 7.1%, 10.8% and 6% for centers A-C, respectively), the vast majority of errors being false positives (3.45% vs 0.45% false negatives). The other medical centers showed a higher rate of false negatives in relation to false positives (6.81% vs 0.29%, 9.8% vs 1.2% and 5.37% vs 0.63% for centers A-C, respectively). The total error rates for the Philips, Hamamatsu and 3DHISTECH brand scanners were 3.9%, 3.2% and 9.8%, respectively. RGB histograms demonstrated significant differences in pixel value distribution between the four medical centers, as well as between the 3DHISTECH brand scanner when compared to the Philips and Hamamatsu brand scanners. CONCLUSIONS The results reported in this paper suggest that the algorithm-based decision support system has sufficient robustness to be applicable for clinical practice. In addition, the novel method used in its development - Hierarchial-Contexual Analysis (HCA) may be applicable to the development of algorithm-assisted tools in other diseases, for which available datasets are limited. Validation of any given algorithm-assisted support system should nonetheless include data from as many medical centers and scanner models as possible.
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Affiliation(s)
- Ariel Greenberg
- Institute of Pathology, Tel-Aviv Sourasky Medical Center, 6 Weizmann Street, 6423906, Tel Aviv, Israel.
| | - Benzion Samueli
- Department of Pathology, Soroka University Medical Center, 76 Wingate Street, 8486614, Be'er Sheva, Israel
| | - Shai Farkash
- Department of Pathology, Emek Medical Center, Yitshak Rabin Boulevard 21, 1834111, Afula, Israel
| | - Yaniv Zohar
- Department of Pathology, Rambam Medical Center, 8 Haalia Hashnia, 3525408, Haifa, Israel
| | - Shahar Ish-Shalom
- Department of Pathology, Kaplan Medical Center, Pasternak St. P.O.B. 1, 76100, Rehovot, Israel
| | - Rami R Hagege
- Institute of Pathology, Tel-Aviv Sourasky Medical Center, 6 Weizmann Street, 6423906, Tel Aviv, Israel
| | - Dov Hershkovitz
- Institute of Pathology, Tel-Aviv Sourasky Medical Center, 6 Weizmann Street, 6423906, Tel Aviv, Israel
- Sackler Faculty of Medicine, Tel-Aviv University, Ramat Aviv 69978, Tel-Aviv, Israel
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Ataei A, Eggermont F, Verdonschot N, Lessmann N, Tanck E. The effect of deep learning-based lesion segmentation on failure load calculations of metastatic femurs using finite element analysis. Bone 2024; 179:116987. [PMID: 38061504 DOI: 10.1016/j.bone.2023.116987] [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: 07/21/2023] [Revised: 11/29/2023] [Accepted: 12/04/2023] [Indexed: 12/17/2023]
Abstract
Bone ranks as the third most frequent tissue affected by cancer metastases, following the lung and liver. Bone metastases are often painful and may result in pathological fracture, which is a major cause of morbidity and mortality in cancer patients. To quantify fracture risk, finite element (FE) analysis has shown to be a promising tool, but metastatic lesions are typically not specifically segmented and therefore their mechanical properties may not be represented adequately. Deep learning methods potentially provide the opportunity to automatically segment these lesions and change the mechanical properties more adequately. In this study, our primary focus was to gain insight into the performance of an automatic segmentation algorithm for femoral metastatic lesions using deep learning methods and the subsequent effects on FE outcomes. The aims were to determine the similarity between manual segmentation and automatic segmentation; the differences in predicted failure load between FE models with automatically segmented osteolytic and mixed lesions and the models with CT-based lesion values (the gold standard); and the effect on the BOne Strength (BOS) score (failure load adjusted for body weight) and subsequent fracture risk assessments. From two patient cohorts, a total number of 50 femurs with osteolytic and mixed metastatic lesions were included in this study. The femurs were segmented from CT images and transferred into FE meshes. The material behavior was implemented as non-linear isotropic. These FE models were considered as gold standard (Finite Element no Segmented Lesion: FE-no-SL), whereby the local calcium equivalent density of both femur and metastatic lesion was extracted from CT-values. Lesions in the femur were manually segmented by two biomechanical experts after which final lesion segmentation for each femur was obtained based on consensus of opinions between two observers. Subsequently, a self-configuring variant of the popular deep learning model U-Net known as nnU-Net was used to automatically segment metastatic lesions within the femur. For these models with segmented lesions (Finite Element with Segmented Lesion: FE-with-SL), the calcium equivalent density within the metastatic lesions was set to zero after being segmented by the neural network, simulating absence of load-bearing capacity of these lesions. The models (either with or without automatically segmented lesions) were loaded incrementally in axial direction until failure was simulated. Dice coefficient was used to evaluate the similarity of the manual and automatic segmentation. Mean calcium equivalent density values within the automatically segmented lesions were calculated. Failure loads and patterns were determined. Furthermore, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated for both groups by comparing the predictions to the occurrence or absence of actual fracture within the patient cohorts. The automatic segmentation algorithm performed in a none-robust manner. Dice coefficients describing the similarity between consented manual and automatic segmentations were relatively low (mean 0.45 ± standard deviation 0.33, median 0.54). Failure load difference between the FE-no-SL and FE-with-SL groups varied from 0 % to 48 % (mean 6.6 %). Correlation analysis of failure loads between the two groups showed a strong relationship (R2 > 0.9). From the 50 cases, four cases showed clear deviations for which models with automatic lesion segmentation (FE-with-SL) showed considerably lower failure loads. In the whole database including osteolytic and mixed lesions, sensitivity and NPV remained the same, but specificity and PPV decreased from 94 % to 83 %, and from 78 % to 54 % respectively from FE-no-SL to FE-with-SL. This study indicates that the nnU-Net yielded none-robust outcomes in femoral lesion segmentation and that other segmentation algorithms should be considered. However, the difference in failure pattern and failure load between FE models with automatically segmented osteolytic and mixed lesions were relatively small in most cases with a few exceptions. On the other hand, the accuracy of fracture risk assessment using the BOS score was lower compared to the FE-no-SL. In conclusion, this study showed that automatic lesion segmentation is a none-solved issue and therefore, quantifying lesion characteristics and the subsequent effect on the fracture risk using deep learning will remain challenging.
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Affiliation(s)
- Ali Ataei
- Orthopaedic Research Lab, Radboud university medical center, P.O. Box 9101, 6500, HB, Nijmegen, the Netherlands.
| | - Florieke Eggermont
- Orthopaedic Research Lab, Radboud university medical center, P.O. Box 9101, 6500, HB, Nijmegen, the Netherlands
| | - Nico Verdonschot
- Orthopaedic Research Lab, Radboud university medical center, P.O. Box 9101, 6500, HB, Nijmegen, the Netherlands; Laboratory for Biomechanical Engineering, University of Twente, Enschede, the Netherlands
| | - Nikolas Lessmann
- Diagnostic Image Analysis Group, Department of Medical Imaging, Radboud university medical center, Nijmegen, the Netherlands
| | - Esther Tanck
- Orthopaedic Research Lab, Radboud university medical center, P.O. Box 9101, 6500, HB, Nijmegen, the Netherlands
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Tozbikian G, Krishnamurthy S, Bui MM, Feldman M, Hicks DG, Jaffer S, Khoury T, Wei S, Wen H, Pohlmann P. Emerging Landscape of Targeted Therapy of Breast Cancers With Low Human Epidermal Growth Factor Receptor 2 Protein Expression. Arch Pathol Lab Med 2024; 148:242-255. [PMID: 37014972 DOI: 10.5858/arpa.2022-0335-ra] [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: 01/19/2023] [Indexed: 04/06/2023]
Abstract
CONTEXT.— Human epidermal growth factor receptor 2 (HER2) status in breast cancer is currently classified as negative or positive for selecting patients for anti-HER2 targeted therapy. The evolution of the HER2 status has included a new HER2-low category defined as an HER2 immunohistochemistry score of 1+ or 2+ without gene amplification. This new category opens the door to a targetable HER2-low breast cancer population for which new treatments may be effective. OBJECTIVE.— To review the current literature on the emerging category of breast cancers with low HER2 protein expression, including the clinical, histopathologic, and molecular features, and outline the clinical trials and best practice recommendations for identifying HER2-low-expressing breast cancers by immunohistochemistry. DATA SOURCES.— We conducted a literature review based on peer-reviewed original articles, review articles, regulatory communications, ongoing and past clinical trials identified through ClinicalTrials.gov, and the authors' practice experience. CONCLUSIONS.— The availability of new targeted therapy potentially effective for patients with breast cancers with low HER2 protein expression requires multidisciplinary recognition. In particular, pathologists need to recognize and identify this category to allow the optimal selection of patients for targeted therapy.
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Affiliation(s)
- Gary Tozbikian
- From the Department of Pathology, The Ohio State University, Wexner Medical Center, Columbus (Tozbikian)
| | - Savitri Krishnamurthy
- the Department of Pathology (Krishnamurthy), The University of Texas MD Anderson Cancer Center, Houston
| | - Marilyn M Bui
- the Department of Pathology, Moffitt Cancer Center & Research Institute, Tampa, Florida (Bui)
| | - Michael Feldman
- the Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia (Feldman)
| | - David G Hicks
- the Department of Pathology, University of Rochester Medical Center, Rochester, New York (Hicks)
| | - Shabnam Jaffer
- the Department of Pathology, Mount Sinai Medical Center, New York, New York (Jaffer)
| | - Thaer Khoury
- the Department of Pathology, Roswell Park Comprehensive Cancer Center, Buffalo, New York (Khoury)
| | - Shi Wei
- the Department of Pathology, University of Kansas Medical Center; Kansas City (Wei)
| | - Hannah Wen
- the Department of Pathology, Memorial Sloan Kettering Cancer Center; New York, New York (Wen)
| | - Paula Pohlmann
- the Department of Breast Medical Oncology (Pohlmann), The University of Texas MD Anderson Cancer Center, Houston
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Wang B, Zou L, Chen J, Cao Y, Cai Z, Qiu Y, Mao L, Wang Z, Chen J, Gui L, Yang X. A Weakly Supervised Segmentation Network Embedding Cross-Scale Attention Guidance and Noise-Sensitive Constraint for Detecting Tertiary Lymphoid Structures of Pancreatic Tumors. IEEE J Biomed Health Inform 2024; 28:988-999. [PMID: 38064334 DOI: 10.1109/jbhi.2023.3340686] [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: 02/06/2024]
Abstract
The presence of tertiary lymphoid structures (TLSs) on pancreatic pathological images is an important prognostic indicator of pancreatic tumors. Therefore, TLSs detection on pancreatic pathological images plays a crucial role in diagnosis and treatment for patients with pancreatic tumors. However, fully supervised detection algorithms based on deep learning usually require a large number of manual annotations, which is time-consuming and labor-intensive. In this paper, we aim to detect the TLSs in a manner of few-shot learning by proposing a weakly supervised segmentation network. We firstly obtain the lymphocyte density maps by combining a pretrained model for nuclei segmentation and a domain adversarial network for lymphocyte nuclei recognition. Then, we establish a cross-scale attention guidance mechanism by jointly learning the coarse-scale features from the original histopathology images and fine-scale features from our designed lymphocyte density attention. A noise-sensitive constraint is introduced by an embedding signed distance function loss in the training procedure to reduce tiny prediction errors. Experimental results on two collected datasets demonstrate that our proposed method significantly outperforms the state-of-the-art segmentation-based algorithms in terms of TLSs detection accuracy. Additionally, we apply our method to study the congruent relationship between the density of TLSs and peripancreatic vascular invasion and obtain some clinically statistical results.
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Fisher TB, Saini G, Rekha TS, Krishnamurthy J, Bhattarai S, Callagy G, Webber M, Janssen EAM, Kong J, Aneja R. Digital image analysis and machine learning-assisted prediction of neoadjuvant chemotherapy response in triple-negative breast cancer. Breast Cancer Res 2024; 26:12. [PMID: 38238771 PMCID: PMC10797728 DOI: 10.1186/s13058-023-01752-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Accepted: 12/11/2023] [Indexed: 01/22/2024] Open
Abstract
BACKGROUND Pathological complete response (pCR) is associated with favorable prognosis in patients with triple-negative breast cancer (TNBC). However, only 30-40% of TNBC patients treated with neoadjuvant chemotherapy (NAC) show pCR, while the remaining 60-70% show residual disease (RD). The role of the tumor microenvironment in NAC response in patients with TNBC remains unclear. In this study, we developed a machine learning-based two-step pipeline to distinguish between various histological components in hematoxylin and eosin (H&E)-stained whole slide images (WSIs) of TNBC tissue biopsies and to identify histological features that can predict NAC response. METHODS H&E-stained WSIs of treatment-naïve biopsies from 85 patients (51 with pCR and 34 with RD) of the model development cohort and 79 patients (41 with pCR and 38 with RD) of the validation cohort were separated through a stratified eightfold cross-validation strategy for the first step and leave-one-out cross-validation strategy for the second step. A tile-level histology label prediction pipeline and four machine-learning classifiers were used to analyze 468,043 tiles of WSIs. The best-trained classifier used 55 texture features from each tile to produce a probability profile during testing. The predicted histology classes were used to generate a histology classification map of the spatial distributions of different tissue regions. A patient-level NAC response prediction pipeline was trained with features derived from paired histology classification maps. The top graph-based features capturing the relevant spatial information across the different histological classes were provided to the radial basis function kernel support vector machine (rbfSVM) classifier for NAC treatment response prediction. RESULTS The tile-level prediction pipeline achieved 86.72% accuracy for histology class classification, while the patient-level pipeline achieved 83.53% NAC response (pCR vs. RD) prediction accuracy of the model development cohort. The model was validated with an independent cohort with tile histology validation accuracy of 83.59% and NAC prediction accuracy of 81.01%. The histological class pairs with the strongest NAC response predictive ability were tumor and tumor tumor-infiltrating lymphocytes for pCR and microvessel density and polyploid giant cancer cells for RD. CONCLUSION Our machine learning pipeline can robustly identify clinically relevant histological classes that predict NAC response in TNBC patients and may help guide patient selection for NAC treatment.
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Affiliation(s)
- Timothy B Fisher
- Department of Biology, Georgia State University, Atlanta, GA, 30302, USA
| | - Geetanjali Saini
- School of Health Professions, University of Alabama at Birmingham, Birmingham, AL, 35294, USA
| | - T S Rekha
- JSSAHER (JSS Academy of Higher Education and Research) Medical College, Mysuru, Karnataka, India
| | - Jayashree Krishnamurthy
- JSSAHER (JSS Academy of Higher Education and Research) Medical College, Mysuru, Karnataka, India
| | - Shristi Bhattarai
- School of Health Professions, University of Alabama at Birmingham, Birmingham, AL, 35294, USA
| | - Grace Callagy
- Discipline of Pathology, University of Galway, Galway, Ireland
| | - Mark Webber
- Discipline of Pathology, University of Galway, Galway, Ireland
| | - Emiel A M Janssen
- Department of Pathology, Stavanger University Hospital, Stavanger, Norway
- Department of Chemistry, Bioscience and Environmental Engineering, University of Stavanger, Stavanger, Norway
| | - Jun Kong
- Department of Mathematics and Statistics, Georgia State University, Atlanta, GA, 30303, USA.
| | - Ritu Aneja
- Department of Biology, Georgia State University, Atlanta, GA, 30302, USA.
- School of Health Professions, University of Alabama at Birmingham, Birmingham, AL, 35294, USA.
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Dy A, Nguyen NNJ, Meyer J, Dawe M, Shi W, Androutsos D, Fyles A, Liu FF, Done S, Khademi A. AI improves accuracy, agreement and efficiency of pathologists for Ki67 assessments in breast cancer. Sci Rep 2024; 14:1283. [PMID: 38218973 PMCID: PMC10787826 DOI: 10.1038/s41598-024-51723-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Accepted: 01/09/2024] [Indexed: 01/15/2024] Open
Abstract
The Ki-67 proliferation index (PI) guides treatment decisions in breast cancer but suffers from poor inter-rater reproducibility. Although AI tools have been designed for Ki-67 assessment, their impact on pathologists' work remains understudied. 90 international pathologists were recruited to assess the Ki-67 PI of ten breast cancer tissue microarrays with and without AI. Accuracy, agreement, and turnaround time with and without AI were compared. Pathologists' perspectives on AI were collected. Using AI led to a significant decrease in PI error (2.1% with AI vs. 5.9% without AI, p < 0.001), better inter-rater agreement (ICC: 0.70 vs. 0.92; Krippendorff's α: 0.63 vs. 0.89; Fleiss' Kappa: 0.40 vs. 0.86), and an 11.9% overall median reduction in turnaround time. Most pathologists (84%) found the AI reliable. For Ki-67 assessments, 76% of respondents believed AI enhances accuracy, 82% said it improves consistency, and 83% trust it will improve efficiency. This study highlights AI's potential to standardize Ki-67 scoring, especially between 5 and 30% PI-a range with low PI agreement. This could pave the way for a universally accepted PI score to guide treatment decisions, emphasizing the promising role of AI integration into pathologist workflows.
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Affiliation(s)
- Amanda Dy
- Electrical, Computer, and Biomedical Engineering, Toronto Metropolitan University, Toronto, ON, Canada.
| | | | - Julien Meyer
- School of Health Services Management, Toronto Metropolitan University, Toronto, ON, Canada
| | - Melanie Dawe
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
| | - Wei Shi
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
| | - Dimitri Androutsos
- Electrical, Computer, and Biomedical Engineering, Toronto Metropolitan University, Toronto, ON, Canada
| | - Anthony Fyles
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
| | - Fei-Fei Liu
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
| | - Susan Done
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
| | - April Khademi
- Electrical, Computer, and Biomedical Engineering, Toronto Metropolitan University, Toronto, ON, Canada
- Keenan Research Center for Biomedical Science, St. Michael's Hospital, Unity Health Network, Toronto, ON, Canada
- Institute for Biomedical Engineering, Science Tech (iBEST), A Partnership Between St. Michael's Hospital and Toronto Metropolitan University, Toronto, ON, Canada
- Vector Institute for Artificial Intelligence, Toronto, ON, Canada
- Department of Medical Imaging, University of Toronto, Toronto, ON, Canada
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Hubbard-Perez M, Luchian A, Milford C, Ressel L. Use of deep learning for the classification of hyperplastic lymph node and common subtypes of canine lymphomas: a preliminary study. Front Vet Sci 2024; 10:1309877. [PMID: 38283371 PMCID: PMC10811236 DOI: 10.3389/fvets.2023.1309877] [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: 10/08/2023] [Accepted: 12/04/2023] [Indexed: 01/30/2024] Open
Abstract
Artificial Intelligence has observed significant growth in its ability to classify different types of tumors in humans due to advancements in digital pathology technology. Among these tumors, lymphomas are quite common in dogs, despite studies on the application of AI in domestic species are scarce. This research aims to employ deep learning (DL) through convolutional neural networks (CNNs) to distinguish between normal lymph nodes and 3 WHO common subtypes of canine lymphomas. To train and validate the CNN, 1,530 high-resolution microscopic images derived from whole slide scans (WSIs) were used, including those of background areas, hyperplastic lymph nodes (n = 4), and three different lymphoma subtypes: diffuse large B cell lymphoma (DLBCL; n = 5), lymphoblastic (LBL; n = 5), and marginal zone lymphoma (MZL; n = 3). The CNN was able to correctly identify 456 images of the possible 457 test sets, achieving a maximum accuracy of 99.34%. The results of this study have demonstrated the feasibility of using deep learning to differentiate between hyperplastic lymph nodes and lymphomas, as well as to classify common WHO subtypes. Further research is required to explore the implications of these findings and validate the ability of the network to classify a broader range of lymphomas.
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Affiliation(s)
| | | | | | - Lorenzo Ressel
- DiMoLab, Institute of Infection Veterinary and Ecological Sciences, Department of Veterinary Anatomy Physiology and Pathology, University of Liverpool, Liverpool, United Kingdom
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Fang Y, Chen X, Cao C. Cancer immunotherapy efficacy and machine learning. Expert Rev Anticancer Ther 2024; 24:21-28. [PMID: 38288663 DOI: 10.1080/14737140.2024.2311684] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Accepted: 01/25/2024] [Indexed: 02/03/2024]
Abstract
INTRODUCTION Immunotherapy is one of the major breakthroughs in the treatment of cancer, and it has become a powerful clinical strategy, however, not all patients respond to immune checkpoint blockade and other immunotherapy strategies. Applying machine learning (ML) techniques to predict the efficacy of cancer immunotherapy is useful for clinical decision-making. AREAS COVERED Applying ML including deep learning (DL) in radiomics, pathomics, tumor microenvironment (TME) and immune-related genes analysis to predict immunotherapy efficacy. The studies in this review were searched from PubMed and ClinicalTrials.gov (January 2023). EXPERT OPINION An increasing number of studies indicate that ML has been applied to various aspects of oncology research, with the potential to provide more effective individualized immunotherapy strategies and enhance treatment decisions. With advances in ML technology, more efficient methods of predicting the efficacy of immunotherapy may become available in the future.
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Affiliation(s)
- Yuting Fang
- Department of Radiation Oncology, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences; Key Laboratory of Head & Neck Cancer Translational Research of Zhejiang Province, Hangzhou, China
- Postgraduate Training Base Alliance of Wenzhou Medical University (Zhejiang Cancer Hospital), Hangzhou, Zhejiang, China
| | - Xiaozhong Chen
- Department of Radiation Oncology, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences; Key Laboratory of Head & Neck Cancer Translational Research of Zhejiang Province, Hangzhou, China
| | - Caineng Cao
- Department of Radiation Oncology, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences; Key Laboratory of Head & Neck Cancer Translational Research of Zhejiang Province, Hangzhou, China
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Guitton T, Allaume P, Rabilloud N, Rioux-Leclercq N, Henno S, Turlin B, Galibert-Anne MD, Lièvre A, Lespagnol A, Pécot T, Kammerer-Jacquet SF. Artificial Intelligence in Predicting Microsatellite Instability and KRAS, BRAF Mutations from Whole-Slide Images in Colorectal Cancer: A Systematic Review. Diagnostics (Basel) 2023; 14:99. [PMID: 38201408 PMCID: PMC10795725 DOI: 10.3390/diagnostics14010099] [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: 11/22/2023] [Revised: 12/22/2023] [Accepted: 12/24/2023] [Indexed: 01/12/2024] Open
Abstract
Mismatch repair deficiency (d-MMR)/microsatellite instability (MSI), KRAS, and BRAF mutational status are crucial for treating advanced colorectal cancer patients. Traditional methods like immunohistochemistry or polymerase chain reaction (PCR) can be challenged by artificial intelligence (AI) based on whole slide images (WSI) to predict tumor status. In this systematic review, we evaluated the role of AI in predicting MSI status, KRAS, and BRAF mutations in colorectal cancer. Studies published in PubMed up to June 2023 were included (n = 17), and we reported the risk of bias and the performance for each study. Some studies were impacted by the reduced number of slides included in the data set and the lack of external validation cohorts. Deep learning models for the d-MMR/MSI status showed a good performance in training cohorts (mean AUC = 0.89, [0.74-0.97]) but slightly less than expected in the validation cohort when available (mean AUC = 0.82, [0.63-0.98]). Contrary to the MSI status, the prediction of KRAS and BRAF mutations was less explored with a less robust methodology. The performance was lower, with a maximum of 0.77 in the training cohort, 0.58 in the validation cohort for KRAS, and 0.82 AUC in the training cohort for BRAF.
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Affiliation(s)
- Theo Guitton
- Department of Pathology CHU de Rennes, Rennes 1 University, Pontchaillou Hospital, 2 Rue Henri Le Guilloux, CEDEX 09, 35033 Rennes, France; (P.A.); (N.R.-L.); (S.-F.K.-J.)
| | - Pierre Allaume
- Department of Pathology CHU de Rennes, Rennes 1 University, Pontchaillou Hospital, 2 Rue Henri Le Guilloux, CEDEX 09, 35033 Rennes, France; (P.A.); (N.R.-L.); (S.-F.K.-J.)
| | - Noémie Rabilloud
- Impact TEAM, Laboratoire Traitement du Signal et de l’Image (LTSI) INSERM, Rennes 1 University, Pontchaillou Hospital, CEDEX 09, 35033 Rennes, France
| | - Nathalie Rioux-Leclercq
- Department of Pathology CHU de Rennes, Rennes 1 University, Pontchaillou Hospital, 2 Rue Henri Le Guilloux, CEDEX 09, 35033 Rennes, France; (P.A.); (N.R.-L.); (S.-F.K.-J.)
| | - Sébastien Henno
- Department of Pathology CHU de Rennes, Rennes 1 University, Pontchaillou Hospital, 2 Rue Henri Le Guilloux, CEDEX 09, 35033 Rennes, France; (P.A.); (N.R.-L.); (S.-F.K.-J.)
| | - Bruno Turlin
- Department of Pathology CHU de Rennes, Rennes 1 University, Pontchaillou Hospital, 2 Rue Henri Le Guilloux, CEDEX 09, 35033 Rennes, France; (P.A.); (N.R.-L.); (S.-F.K.-J.)
| | - Marie-Dominique Galibert-Anne
- Department of Molecular Genetics and Medical Genomics CHU de Rennes, Rennes 1 University, Pontchaillou Hospital, 2 Rue Henri Le Guilloux, CEDEX 09, 35033 Rennes, France; (M.-D.G.-A.); (A.L.)
| | - Astrid Lièvre
- Department of Gastro-Entrology CHU de Rennes, Rennes 1 University, Pontchaillou Hospital, 2 Rue Henri Le Guilloux, CEDEX 09, 35033 Rennes, France;
| | - Alexandra Lespagnol
- Department of Molecular Genetics and Medical Genomics CHU de Rennes, Rennes 1 University, Pontchaillou Hospital, 2 Rue Henri Le Guilloux, CEDEX 09, 35033 Rennes, France; (M.-D.G.-A.); (A.L.)
| | - Thierry Pécot
- Facility for Artificial Intelligence and Image Analysis (FAIIA), Biosit UAR 3480 CNRS-US18 INSERM, Rennes University, 2 Avenue du Professeur Léon Bernard, 35042 Rennes, France
| | - Solène-Florence Kammerer-Jacquet
- Department of Pathology CHU de Rennes, Rennes 1 University, Pontchaillou Hospital, 2 Rue Henri Le Guilloux, CEDEX 09, 35033 Rennes, France; (P.A.); (N.R.-L.); (S.-F.K.-J.)
- Impact TEAM, Laboratoire Traitement du Signal et de l’Image (LTSI) INSERM, Rennes 1 University, Pontchaillou Hospital, CEDEX 09, 35033 Rennes, France
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McGenity C, Randell R, Bellamy C, Burt A, Cratchley A, Goldin R, Hubscher SG, Neil DAH, Quaglia A, Tiniakos D, Wyatt J, Treanor D. Survey of liver pathologists to assess attitudes towards digital pathology and artificial intelligence. J Clin Pathol 2023; 77:27-33. [PMID: 36599660 PMCID: PMC10804041 DOI: 10.1136/jcp-2022-208614] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Accepted: 11/24/2022] [Indexed: 01/05/2023]
Abstract
AIMS A survey of members of the UK Liver Pathology Group (UKLPG) was conducted, comprising consultant histopathologists from across the UK who report liver specimens and participate in the UK National Liver Pathology External Quality Assurance scheme. The aim of this study was to understand attitudes and priorities of liver pathologists towards digital pathology and artificial intelligence (AI). METHODS The survey was distributed to all full consultant members of the UKLPG via email. This comprised 50 questions, with 48 multiple choice questions and 2 free-text questions at the end, covering a range of topics and concepts pertaining to the use of digital pathology and AI in liver disease. RESULTS Forty-two consultant histopathologists completed the survey, representing 36% of fully registered members of the UKLPG (42/116). Questions examining digital pathology showed respondents agreed with the utility of digital pathology for primary diagnosis 83% (34/41), second opinions 90% (37/41), research 85% (35/41) and training and education 95% (39/41). Fatty liver diseases were an area of demand for AI tools with 80% in agreement (33/41), followed by neoplastic liver diseases with 59% in agreement (24/41). Participants were concerned about AI development without pathologist involvement 73% (30/41), however, 63% (26/41) disagreed when asked whether AI would replace pathologists. CONCLUSIONS This study outlines current interest, priorities for research and concerns around digital pathology and AI for liver pathologists. The majority of UK liver pathologists are in favour of the application of digital pathology and AI in clinical practice, research and education.
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Affiliation(s)
- Clare McGenity
- Pathology and Data Analytics, University of Leeds, Leeds, UK
- Department of Histopathology, Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | - Rebecca Randell
- Faculty of Health Sciences, University of Bradford, Bradford, UK
- Wolfson Centre for Applied Health Research, Bradford, UK
| | | | - Alastair Burt
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Alyn Cratchley
- Department of Histopathology, Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | - Robert Goldin
- Division of Digestive Diseases, Imperial College London, London, UK
| | - Stefan G Hubscher
- Institute of Immunology and Immunotherapy, University of Birmingham, Birmingham, UK
| | - Desley A H Neil
- Institute of Immunology and Immunotherapy, University of Birmingham, Birmingham, UK
- Department of Cellular Pathology, Queen Elizabeth Hospital Birmingham, Birmingham, UK
| | - Alberto Quaglia
- Department of Cellular Pathology, Royal Free Hospital, London, UK
| | - Dina Tiniakos
- Translational and Clinical Research Institute, Newcastle University, Newcastle, UK
- Department of Pathology, National and Kapodistrian University of Athens, Athens, Greece
| | - Judy Wyatt
- Department of Histopathology, Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | - Darren Treanor
- Pathology and Data Analytics, University of Leeds, Leeds, UK
- Department of Histopathology, Leeds Teaching Hospitals NHS Trust, Leeds, UK
- Department of Clinical Pathology and Department of Clinical and Experimental Medicine, Linköping University, Linköping, Sweden
- Centre for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
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Rinck D, Dittmer M, Tinker D, Smith K, Heinecke G. National resident survey in dermatopathology: The role of slide scanners in resident learning. J Cutan Pathol 2023; 50:1078-1082. [PMID: 37749824 PMCID: PMC10843035 DOI: 10.1111/cup.14538] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Revised: 08/07/2023] [Accepted: 09/13/2023] [Indexed: 09/27/2023]
Abstract
BACKGROUND Dermatology residents gain exposure to dermatopathology through a variety of educational modalities. While virtual pathology applications have risen dramatically, resident utilization of digital libraries, slide scanner availability, and comfort with virtual slides are not well-known. This study aims to assess the current landscape of educational resources used by dermatology residents. METHODS A 17-question survey was sent to dermatology residents through a national email database. The survey was a self-assessment of their experience in dermatopathology education and the use of departmental slide scanners. RESULTS The use of digital dermatopathology is high among trainees, despite only half of respondents reporting slide scanner access. Residents report using virtual images more often in non-clinical dermatopathology didactics and independent studies compared to clinical dermatopathology rotations. Public slide set use was common, while professional society and departmental slide sets may be underutilized. Over half of respondents report being extremely or very comfortable navigating interactive scanned slides. CONCLUSIONS Survey data suggests digital slides are currently predominantly used in non-clinical dermatopathology rotations and independent studies. Incorporation of slide scanners into departments may benefit resident education through the development of high-quality, curated departmental slide sets.
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Affiliation(s)
- Danielle Rinck
- Department of Pathology, Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
| | - Martin Dittmer
- Department of Pathology, University of Washington, Seattle, Washington, USA
| | - Daniel Tinker
- Department of Dermatology, Saint Louis University School of Medicine, Saint Louis, Missouri, USA
| | - Kristin Smith
- Department of Dermatology, Saint Louis University School of Medicine, Saint Louis, Missouri, USA
| | - Gillian Heinecke
- Department of Dermatology, Saint Louis University School of Medicine, Saint Louis, Missouri, USA
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Xing F, Yang X, Cornish TC, Ghosh D. Learning with limited target data to detect cells in cross-modality images. Med Image Anal 2023; 90:102969. [PMID: 37802010 DOI: 10.1016/j.media.2023.102969] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 08/16/2023] [Accepted: 09/11/2023] [Indexed: 10/08/2023]
Abstract
Deep neural networks have achieved excellent cell or nucleus quantification performance in microscopy images, but they often suffer from performance degradation when applied to cross-modality imaging data. Unsupervised domain adaptation (UDA) based on generative adversarial networks (GANs) has recently improved the performance of cross-modality medical image quantification. However, current GAN-based UDA methods typically require abundant target data for model training, which is often very expensive or even impossible to obtain for real applications. In this paper, we study a more realistic yet challenging UDA situation, where (unlabeled) target training data is limited and previous work seldom delves into cell identification. We first enhance a dual GAN with task-specific modeling, which provides additional supervision signals to assist with generator learning. We explore both single-directional and bidirectional task-augmented GANs for domain adaptation. Then, we further improve the GAN by introducing a differentiable, stochastic data augmentation module to explicitly reduce discriminator overfitting. We examine source-, target-, and dual-domain data augmentation for GAN enhancement, as well as joint task and data augmentation in a unified GAN-based UDA framework. We evaluate the framework for cell detection on multiple public and in-house microscopy image datasets, which are acquired with different imaging modalities, staining protocols and/or tissue preparations. The experiments demonstrate that our method significantly boosts performance when compared with the reference baseline, and it is superior to or on par with fully supervised models that are trained with real target annotations. In addition, our method outperforms recent state-of-the-art UDA approaches by a large margin on different datasets.
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Affiliation(s)
- Fuyong Xing
- Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, 13001 E 17th Pl, Aurora, CO 80045, USA.
| | - Xinyi Yang
- Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, 13001 E 17th Pl, Aurora, CO 80045, USA
| | - Toby C Cornish
- Department of Pathology, University of Colorado Anschutz Medical Campus, 13001 E 17th Pl, Aurora, CO 80045, USA
| | - Debashis Ghosh
- Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, 13001 E 17th Pl, Aurora, CO 80045, USA
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Lami K, Ota N, Yamaoka S, Bychkov A, Matsumoto K, Uegami W, Munkhdelger J, Seki K, Sukhbaatar O, Attanoos R, Berezowska S, Brcic L, Cavazza A, English JC, Fabro AT, Ishida K, Kashima Y, Kitamura Y, Larsen BT, Marchevsky AM, Miyazaki T, Morimoto S, Ozasa M, Roden AC, Schneider F, Smith ML, Tabata K, Takano AM, Tanaka T, Tsuchiya T, Nagayasu T, Sakanashi H, Fukuoka J. Standardized Classification of Lung Adenocarcinoma Subtypes and Improvement of Grading Assessment Through Deep Learning. THE AMERICAN JOURNAL OF PATHOLOGY 2023; 193:2066-2079. [PMID: 37544502 DOI: 10.1016/j.ajpath.2023.07.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/18/2023] [Revised: 06/04/2023] [Accepted: 07/12/2023] [Indexed: 08/08/2023]
Abstract
The histopathologic distinction of lung adenocarcinoma (LADC) subtypes is subject to high interobserver variability, which can compromise the optimal assessment of patient prognosis. Therefore, this study developed convolutional neural networks capable of distinguishing LADC subtypes and predicting disease-specific survival, according to the recently established LADC tumor grades. Consensus LADC histopathologic images were obtained from 17 expert pulmonary pathologists and one pathologist in training. Two deep learning models (AI-1 and AI-2) were trained to predict eight different LADC classes. Furthermore, the trained models were tested on an independent cohort of 133 patients. The models achieved high precision, recall, and F1 scores exceeding 0.90 for most of the LADC classes. Clear stratification of the three LADC grades was reached in predicting the disease-specific survival by the two models, with both Kaplan-Meier curves showing significance (P = 0.0017 and 0.0003). Moreover, both trained models showed high stability in the segmentation of each pair of predicted grades with low variation in the hazard ratio across 200 bootstrapped samples. These findings indicate that the trained convolutional neural networks improve the diagnostic accuracy of the pathologist and refine LADC grade assessment. Thus, the trained models are promising tools that may assist in the routine evaluation of LADC subtypes and grades in clinical practice.
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Affiliation(s)
- Kris Lami
- Department of Pathology Informatics, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki, Japan
| | - Noriaki Ota
- Systems Research & Development Center, Technology Bureau, NS Solutions Corp., Yokohama, Japan
| | - Shinsuke Yamaoka
- Systems Research & Development Center, Technology Bureau, NS Solutions Corp., Yokohama, Japan
| | - Andrey Bychkov
- Department of Pathology, Kameda Medical Center, Kamogawa, Japan
| | - Keitaro Matsumoto
- Department of Surgical Oncology, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki, Japan
| | - Wataru Uegami
- Department of Pathology, Kameda Medical Center, Kamogawa, Japan
| | | | - Kurumi Seki
- Department of Pathology, Kameda Medical Center, Kamogawa, Japan
| | | | - Richard Attanoos
- Department of Cellular Pathology, Cardiff University, Cardiff, United Kingdom
| | - Sabina Berezowska
- Department of Laboratory Medicine and Pathology, Institute of Pathology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Luka Brcic
- Diagnostic and Research Institute of Pathology, Medical University of Graz, Graz, Austria
| | - Alberto Cavazza
- Unit of Pathologic Anatomy, Azienda USL/IRCCS di Reggio Emilia, Reggio Emilia, Italy
| | - John C English
- Department of Pathology, Vancouver General Hospital, Vancouver, British Columbia, Canada
| | - Alexandre Todorovic Fabro
- Department of Pathology and Legal Medicine, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, Brazil
| | - Kaori Ishida
- Department of Pathology, Kansai Medical University, Hirakata City, Japan
| | - Yukio Kashima
- Department of Pathology, Hyogo Prefectural Awaji Medical Center, Sumoto City, Japan
| | - Yuka Kitamura
- Department of Pathology Informatics, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki, Japan; N Lab Co. Ltd., Nagasaki, Japan
| | - Brandon T Larsen
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Scottsdale, Arizona
| | | | - Takuro Miyazaki
- Department of Surgical Oncology, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki, Japan
| | - Shimpei Morimoto
- Innovation Platform & Office for Precision Medicine, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki, Japan
| | - Mutsumi Ozasa
- Department of Pathology Informatics, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki, Japan
| | - Anja C Roden
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota
| | - Frank Schneider
- Department of Pathology and Laboratory Medicine, Emory University, Atlanta, Georgia
| | - Maxwell L Smith
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Scottsdale, Arizona
| | - Kazuhiro Tabata
- Department of Pathology, Kagoshima University Graduate School of Medical and Dental Sciences, Kagoshima, Japan
| | - Angela M Takano
- Department of Anatomical Pathology, Singapore General Hospital, Singapore
| | - Tomonori Tanaka
- Department of Diagnostic Pathology, Kobe University Hospital, Kobe, Japan
| | - Tomoshi Tsuchiya
- Department of Surgical Oncology, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki, Japan
| | - Takeshi Nagayasu
- Department of Surgical Oncology, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki, Japan
| | - Hidenori Sakanashi
- Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology, Tsukuba, Japan
| | - Junya Fukuoka
- Department of Pathology Informatics, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki, Japan; Department of Pathology, Kameda Medical Center, Kamogawa, Japan.
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Aliar K, Waterhouse HR, Vyas F, Krebs N, Zhang B, Poulton E, Chan N, Gonzalez R, Jang GH, Bronsert P, Fischer SE, Gallinger S, Grünwald BT, Khokha R. Hourglass, a rapid analysis framework for heterogeneous bioimaging data, identifies sex disparity in IL-6/STAT3-associated immune phenotypes in pancreatic cancer. J Pathol 2023; 261:413-426. [PMID: 37768107 DOI: 10.1002/path.6199] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 07/06/2023] [Accepted: 08/11/2023] [Indexed: 09/29/2023]
Abstract
Integration and mining of bioimaging data remains a challenge and lags behind the rapidly expanding digital pathology field. We introduce Hourglass, an open-access analytical framework that streamlines biology-driven visualization, interrogation, and statistical assessment of multiparametric datasets. Cognizant of tissue and clinical heterogeneity, Hourglass systematically organizes observations across spatial and global levels and within patient subgroups. Applied to an extensive bioimaging dataset, Hourglass promptly consolidated a breadth of known interleukin-6 (IL-6) functions via its downstream effector STAT3 and uncovered a so-far unknown sexual dimorphism in the IL-6/STAT3-linked intratumoral T-cell response in human pancreatic cancer. As an R package and cross-platform application, Hourglass facilitates knowledge extraction from multi-layered bioimaging datasets for users with or without computational proficiency and provides unique and widely accessible analytical means to harness insights hidden within heterogeneous tissues at the sample and patient level. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.
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Affiliation(s)
- Kazeera Aliar
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
| | - Henry R Waterhouse
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
| | - Foram Vyas
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
| | - Niklas Krebs
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
- Department of Gastroenterology, Gastrointestinal Oncology and Endocrinology, University Medical Center Göttingen, Göttingen, Germany
| | - Bowen Zhang
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
| | - Emily Poulton
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
| | - Nathan Chan
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
| | - Ricardo Gonzalez
- Department of Laboratory Medicine and Pathology, Division of Computational Pathology and Artificial Intelligence, Mayo Clinic, Rochester, MN, USA
| | - Gun Ho Jang
- PanCuRx Translational Research Initiative, Ontario Institute for Cancer Research, Toronto, ON, Canada
| | - Peter Bronsert
- Institute for Surgical Pathology, Medical Center - University of Freiburg, Freiburg, Germany
| | - Sandra E Fischer
- Department of Laboratory Medicine and Pathobiology, University of Toronto, University Health Network, Toronto, ON, Canada
- Division of Anatomic Pathology, Laboratory Medicine Program, University Health Network, Toronto, ON, Canada
| | - Steven Gallinger
- PanCuRx Translational Research Initiative, Ontario Institute for Cancer Research, Toronto, ON, Canada
- Department of Surgery, University of Toronto, Toronto, ON, Canada
- Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, ON, Canada
- Hepatobiliary/Pancreatic Surgical Oncology Program, University Health Network, Toronto, ON, Canada
| | - Barbara T Grünwald
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
| | - Rama Khokha
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
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Duci M, Magoni A, Santoro L, Dei Tos AP, Gamba P, Uccheddu F, Fascetti-Leon F. Enhancing diagnosis of Hirschsprung's disease using deep learning from histological sections of post pull-through specimens: preliminary results. Pediatr Surg Int 2023; 40:12. [PMID: 38019366 PMCID: PMC10687181 DOI: 10.1007/s00383-023-05590-z] [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] [Accepted: 11/01/2023] [Indexed: 11/30/2023]
Abstract
PURPOSE Accurate histological diagnosis in Hirschsprung disease (HD) is challenging, due to its complexity and potential for errors. In this study, we present an artificial intelligence (AI)-based method designed to identify ganglionic cells and hypertrophic nerves in HD histology. METHODS Formalin-fixed samples were used and an expert pathologist and a surgeon annotated these slides on a web-based platform, identifying ganglionic cells and nerves. Images were partitioned into square sections, augmented through data manipulation techniques and used to develop two distinct U-net models: one for detecting ganglionic cells and normal nerves; the other to recognise hypertrophic nerves. RESULTS The study included 108 annotated samples, resulting in 19,600 images after data augmentation and manually segmentation. Subsequently, 17,655 slides without target elements were excluded. The algorithm was trained using 1945 slides (930 for model 1 and 1015 for model 2) with 1556 slides used for training the supervised network and 389 for validation. The accuracy of model 1 was found to be 92.32%, while model 2 achieved an accuracy of 91.5%. CONCLUSION The AI-based U-net technique demonstrates robustness in detecting ganglion cells and nerves in HD. The deep learning approach has the potential to standardise and streamline HD diagnosis, benefiting patients and aiding in training of pathologists.
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Affiliation(s)
- Miriam Duci
- Division of Pediatric Surgery, Department of Women's and Children's Health, University of Padova, Via Giustiniani 2, 35128, Padova, Italy
- Pediatric Surgery Unit, Division of Women's and Children's Health, Padova University Hospital, Padova, Italy
| | - Alessia Magoni
- Department of Industrial Engineering, Padova University, Padova, Italy
| | - Luisa Santoro
- Surgical Pathology and Cytopathology Unit, Department of Medicine, Padova University, Padova, Italy
| | - Angelo Paolo Dei Tos
- Surgical Pathology and Cytopathology Unit, Department of Medicine, Padova University, Padova, Italy
| | - Piergiorgio Gamba
- Division of Pediatric Surgery, Department of Women's and Children's Health, University of Padova, Via Giustiniani 2, 35128, Padova, Italy
- Pediatric Surgery Unit, Division of Women's and Children's Health, Padova University Hospital, Padova, Italy
| | | | - Francesco Fascetti-Leon
- Division of Pediatric Surgery, Department of Women's and Children's Health, University of Padova, Via Giustiniani 2, 35128, Padova, Italy.
- Pediatric Surgery Unit, Division of Women's and Children's Health, Padova University Hospital, Padova, Italy.
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Chen B, Jin J, Liu H, Yang Z, Zhu H, Wang Y, Lin J, Wang S, Chen S. Trends and hotspots in research on medical images with deep learning: a bibliometric analysis from 2013 to 2023. Front Artif Intell 2023; 6:1289669. [PMID: 38028662 PMCID: PMC10665961 DOI: 10.3389/frai.2023.1289669] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Accepted: 10/27/2023] [Indexed: 12/01/2023] Open
Abstract
Background With the rapid development of the internet, the improvement of computer capabilities, and the continuous advancement of algorithms, deep learning has developed rapidly in recent years and has been widely applied in many fields. Previous studies have shown that deep learning has an excellent performance in image processing, and deep learning-based medical image processing may help solve the difficulties faced by traditional medical image processing. This technology has attracted the attention of many scholars in the fields of computer science and medicine. This study mainly summarizes the knowledge structure of deep learning-based medical image processing research through bibliometric analysis and explores the research hotspots and possible development trends in this field. Methods Retrieve the Web of Science Core Collection database using the search terms "deep learning," "medical image processing," and their synonyms. Use CiteSpace for visual analysis of authors, institutions, countries, keywords, co-cited references, co-cited authors, and co-cited journals. Results The analysis was conducted on 562 highly cited papers retrieved from the database. The trend chart of the annual publication volume shows an upward trend. Pheng-Ann Heng, Hao Chen, and Klaus Hermann Maier-Hein are among the active authors in this field. Chinese Academy of Sciences has the highest number of publications, while the institution with the highest centrality is Stanford University. The United States has the highest number of publications, followed by China. The most frequent keyword is "Deep Learning," and the highest centrality keyword is "Algorithm." The most cited author is Kaiming He, and the author with the highest centrality is Yoshua Bengio. Conclusion The application of deep learning in medical image processing is becoming increasingly common, and there are many active authors, institutions, and countries in this field. Current research in medical image processing mainly focuses on deep learning, convolutional neural networks, classification, diagnosis, segmentation, image, algorithm, and artificial intelligence. The research focus and trends are gradually shifting toward more complex and systematic directions, and deep learning technology will continue to play an important role.
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Affiliation(s)
- Borui Chen
- First School of Clinical Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou, China
| | - Jing Jin
- College of Rehabilitation Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou, China
| | - Haichao Liu
- College of Rehabilitation Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou, China
| | - Zhengyu Yang
- College of Rehabilitation Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou, China
| | - Haoming Zhu
- College of Rehabilitation Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou, China
| | - Yu Wang
- First School of Clinical Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou, China
| | - Jianping Lin
- The School of Health, Fujian Medical University, Fuzhou, China
| | - Shizhong Wang
- The School of Health, Fujian Medical University, Fuzhou, China
| | - Shaoqing Chen
- College of Rehabilitation Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou, China
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50
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Kim J, Choi W, Yoo D, Kim M, Cho H, Sung HJ, Choi G, Uh J, Kim J, Go H, Choi KH. Solution-free and simplified H&E staining using a hydrogel-based stamping technology. Front Bioeng Biotechnol 2023; 11:1292785. [PMID: 38026905 PMCID: PMC10665566 DOI: 10.3389/fbioe.2023.1292785] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Accepted: 10/30/2023] [Indexed: 12/01/2023] Open
Abstract
Hematoxylin and eosin (H&E) staining has been widely used as a fundamental and essential tool for diagnosing diseases and understanding biological phenomena by observing cellular arrangements and tissue morphological changes. However, conventional staining methods commonly involve solution-based, complex, multistep processes that are susceptible to user-handling errors. Moreover, inconsistent staining results owing to staining artifacts pose real challenges for accurate diagnosis. This study introduces a solution-free H&E staining method based on agarose hydrogel patches that is expected to represent a valuable tool to overcome the limitations of the solution-based approach. Using two agarose gel-based hydrogel patches containing hematoxylin and eosin dyes, H&E staining can be performed through serial stamping processes, minimizing color variation from handling errors. This method allows easy adjustments of the staining color by controlling the stamping time, effectively addressing variations in staining results caused by various artifacts, such as tissue processing and thickness. Moreover, the solution-free approach eliminates the need for water, making it applicable even in environmentally limited middle- and low-income countries, while still achieving a staining quality equivalent to that of the conventional method. In summary, this hydrogel-based H&E staining method can be used by researchers and medical professionals in resource-limited settings as a powerful tool to diagnose and understand biological phenomena.
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Affiliation(s)
- Jinho Kim
- Noul Co., Ltd., Yongin-si, Republic of Korea
| | | | - Dahyeon Yoo
- Noul Co., Ltd., Yongin-si, Republic of Korea
| | - Mijin Kim
- Noul Co., Ltd., Yongin-si, Republic of Korea
| | - Haeyon Cho
- Department of Pathology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Hyun-Jung Sung
- Department of Pathology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Gyuheon Choi
- Department of Pathology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Jisu Uh
- Department of Pathology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Jinseong Kim
- Department of Pathology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Heounjeong Go
- Department of Pathology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
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