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Coudry RA, Assis EA, Frassetto FP, Jansen AM, da Silva LM, Parra-Medina R, Saieg M. Crossing the Andes: Challenges and opportunities for digital pathology in Latin America. J Pathol Inform 2024; 15:100369. [PMID: 38638195 PMCID: PMC11025004 DOI: 10.1016/j.jpi.2024.100369] [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: 10/18/2023] [Revised: 02/05/2024] [Accepted: 02/17/2024] [Indexed: 04/20/2024] Open
Abstract
The most widely accepted and used type of digital pathology (DP) is whole-slide imaging (WSI). The USFDA granted two WSI system approvals for primary diagnosis, the first in 2017. In Latin America, DP has the potential to reshape healthcare by enhancing diagnostic capabilities through artificial intelligence (AI) and standardizing pathology reports. Yet, we must tackle regulatory hurdles, training, resource availability, and unique challenges to the region. Collectively addressing these hurdles can enable the region to harness DP's advantages-enhancing disease diagnosis, medical research, and healthcare accessibility for its population. Americas Health Foundation assembled a panel of Latin American pathologists who are experts in DP to assess the hurdles to implementing it into pathologists' workflows in the region and provide recommendations for overcoming them. Some key steps recommended include creating a Latin American Society of Digital Pathology to provide continuing education, developing AI models trained on the Latin American population, establishing national regulatory frameworks for protecting the data, and standardizing formats for DP images to ensure that pathologists can collaborate and validate specimens across the various DP platforms.
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Affiliation(s)
| | | | | | | | | | - Rafael Parra-Medina
- National Cancer Institute (INC), Bogotá, Colombia
- Fundación Universitaria de Ciencias de la Salud (FUCS), Bogotá, Colombia
| | - Mauro Saieg
- Grupo Fleury, São Paulo, Brazil
- Santa Casa Medical School, São Paulo, SP, Brazil
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2
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Zerbe N, Schwen LO, Geißler C, Wiesemann K, Bisson T, Boor P, Carvalho R, Franz M, Jansen C, Kiehl TR, Lindequist B, Pohlan NC, Schmell S, Strohmenger K, Zakrzewski F, Plass M, Takla M, Küster T, Homeyer A, Hufnagl P. Joining forces for pathology diagnostics with AI assistance: The EMPAIA initiative. J Pathol Inform 2024; 15:100387. [PMID: 38984198 PMCID: PMC11231750 DOI: 10.1016/j.jpi.2024.100387] [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: 04/12/2024] [Accepted: 05/28/2024] [Indexed: 07/11/2024] Open
Abstract
Over the past decade, artificial intelligence (AI) methods in pathology have advanced substantially. However, integration into routine clinical practice has been slow due to numerous challenges, including technical and regulatory hurdles in translating research results into clinical diagnostic products and the lack of standardized interfaces. The open and vendor-neutral EMPAIA initiative addresses these challenges. Here, we provide an overview of EMPAIA's achievements and lessons learned. EMPAIA integrates various stakeholders of the pathology AI ecosystem, i.e., pathologists, computer scientists, and industry. In close collaboration, we developed technical interoperability standards, recommendations for AI testing and product development, and explainability methods. We implemented the modular and open-source EMPAIA Platform and successfully integrated 14 AI-based image analysis apps from eight different vendors, demonstrating how different apps can use a single standardized interface. We prioritized requirements and evaluated the use of AI in real clinical settings with 14 different pathology laboratories in Europe and Asia. In addition to technical developments, we created a forum for all stakeholders to share information and experiences on digital pathology and AI. Commercial, clinical, and academic stakeholders can now adopt EMPAIA's common open-source interfaces, providing a unique opportunity for large-scale standardization and streamlining of processes. Further efforts are needed to effectively and broadly establish AI assistance in routine laboratory use. To this end, a sustainable infrastructure, the non-profit association EMPAIA International, has been established to continue standardization and support broad implementation and advocacy for an AI-assisted digital pathology future.
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Affiliation(s)
- Norman Zerbe
- Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Pathology, Charitéplatz 1, 10117 Berlin, Germany
| | - Lars Ole Schwen
- Fraunhofer Institute for Digital Medicine MEVIS, Max-von-Laue-Straße 2, 28359 Bremen, Germany
| | - Christian Geißler
- Technische Universität Berlin, DAI-Labor, Ernst-Reuter-Platz 7, 10587 Berlin, Germany
| | | | - Tom Bisson
- Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Pathology, Charitéplatz 1, 10117 Berlin, Germany
| | - Peter Boor
- Institute of Pathology, University Hospital RWTH Aachen, Pauwelsstraße 30, 52074 Aachen, Germany
| | - Rita Carvalho
- Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Pathology, Charitéplatz 1, 10117 Berlin, Germany
| | - Michael Franz
- Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Pathology, Charitéplatz 1, 10117 Berlin, Germany
| | - Christoph Jansen
- Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Pathology, Charitéplatz 1, 10117 Berlin, Germany
| | - Tim-Rasmus Kiehl
- Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Pathology, Charitéplatz 1, 10117 Berlin, Germany
| | - Björn Lindequist
- Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Pathology, Charitéplatz 1, 10117 Berlin, Germany
| | - Nora Charlotte Pohlan
- Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Pathology, Charitéplatz 1, 10117 Berlin, Germany
| | - Sarah Schmell
- Institute of Pathology, Carl Gustav Carus University Hospital Dresden (UKD), TU Dresden (TUD), Fetscherstraße 74, 01307 Dresden, Germany
| | - Klaus Strohmenger
- Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Pathology, Charitéplatz 1, 10117 Berlin, Germany
| | - Falk Zakrzewski
- Institute of Pathology, Carl Gustav Carus University Hospital Dresden (UKD), TU Dresden (TUD), Fetscherstraße 74, 01307 Dresden, Germany
| | - Markus Plass
- Medical University of Graz, Diagnostic and Research Center for Molecular BioMedicine, Diagnostic & Research Institute of Pathology, Neue Stiftingtalstrasse 6, 8010 Graz, Austria
| | - Michael Takla
- Vitasystems GmbH, Gottlieb-Daimler-Straße 8, 68165 Mannheim, Germany
| | - Tobias Küster
- Technische Universität Berlin, DAI-Labor, Ernst-Reuter-Platz 7, 10587 Berlin, Germany
| | - André Homeyer
- Fraunhofer Institute for Digital Medicine MEVIS, Max-von-Laue-Straße 2, 28359 Bremen, Germany
| | - Peter Hufnagl
- Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Pathology, Charitéplatz 1, 10117 Berlin, Germany
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Hosseini MS, Bejnordi BE, Trinh VQH, Chan L, Hasan D, Li X, Yang S, Kim T, Zhang H, Wu T, Chinniah K, Maghsoudlou S, Zhang R, Zhu J, Khaki S, Buin A, Chaji F, Salehi A, Nguyen BN, Samaras D, Plataniotis KN. Computational pathology: A survey review and the way forward. J Pathol Inform 2024; 15:100357. [PMID: 38420608 PMCID: PMC10900832 DOI: 10.1016/j.jpi.2023.100357] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2023] [Revised: 12/21/2023] [Accepted: 12/23/2023] [Indexed: 03/02/2024] Open
Abstract
Computational Pathology (CPath) is an interdisciplinary science that augments developments of computational approaches to analyze and model medical histopathology images. The main objective for CPath is to develop infrastructure and workflows of digital diagnostics as an assistive CAD system for clinical pathology, facilitating transformational changes in the diagnosis and treatment of cancer that are mainly address by CPath tools. With evergrowing developments in deep learning and computer vision algorithms, and the ease of the data flow from digital pathology, currently CPath is witnessing a paradigm shift. Despite the sheer volume of engineering and scientific works being introduced for cancer image analysis, there is still a considerable gap of adopting and integrating these algorithms in clinical practice. This raises a significant question regarding the direction and trends that are undertaken in CPath. In this article we provide a comprehensive review of more than 800 papers to address the challenges faced in problem design all-the-way to the application and implementation viewpoints. We have catalogued each paper into a model-card by examining the key works and challenges faced to layout the current landscape in CPath. We hope this helps the community to locate relevant works and facilitate understanding of the field's future directions. In a nutshell, we oversee the CPath developments in cycle of stages which are required to be cohesively linked together to address the challenges associated with such multidisciplinary science. We overview this cycle from different perspectives of data-centric, model-centric, and application-centric problems. We finally sketch remaining challenges and provide directions for future technical developments and clinical integration of CPath. For updated information on this survey review paper and accessing to the original model cards repository, please refer to GitHub. Updated version of this draft can also be found from arXiv.
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Affiliation(s)
- Mahdi S. Hosseini
- Department of Computer Science and Software Engineering (CSSE), Concordia Univeristy, Montreal, QC H3H 2R9, Canada
| | | | - Vincent Quoc-Huy Trinh
- Institute for Research in Immunology and Cancer of the University of Montreal, Montreal, QC H3T 1J4, Canada
| | - Lyndon Chan
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Danial Hasan
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Xingwen Li
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Stephen Yang
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Taehyo Kim
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Haochen Zhang
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Theodore Wu
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Kajanan Chinniah
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Sina Maghsoudlou
- Department of Computer Science and Software Engineering (CSSE), Concordia Univeristy, Montreal, QC H3H 2R9, Canada
| | - Ryan Zhang
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Jiadai Zhu
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Samir Khaki
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Andrei Buin
- Huron Digitial Pathology, St. Jacobs, ON N0B 2N0, Canada
| | - Fatemeh Chaji
- Department of Computer Science and Software Engineering (CSSE), Concordia Univeristy, Montreal, QC H3H 2R9, Canada
| | - Ala Salehi
- Department of Electrical and Computer Engineering, University of New Brunswick, Fredericton, NB E3B 5A3, Canada
| | - Bich Ngoc Nguyen
- University of Montreal Hospital Center, Montreal, QC H2X 0C2, Canada
| | - Dimitris Samaras
- Department of Computer Science, Stony Brook University, Stony Brook, NY 11794, United States
| | - Konstantinos N. Plataniotis
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
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4
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Saha S, Vignarajan J, Flesch A, Jelinko P, Gorog P, Szep E, Toth C, Gombas P, Schvarcz T, Mihaly O, Kapin M, Zub A, Kuthi L, Tiszlavicz L, Glasz T, Frost S. An Artificial Intelligent System for Prostate Cancer Diagnosis in Whole Slide Images. J Med Syst 2024; 48:101. [PMID: 39466503 DOI: 10.1007/s10916-024-02118-3] [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: 02/19/2024] [Accepted: 10/08/2024] [Indexed: 10/30/2024]
Abstract
In recent years a significant demand to develop computer-assisted diagnostic tools to assess prostate cancer using whole slide images has been observed. In this study we develop and validate a machine learning system for cancer assessment, inclusive of detection of perineural invasion and measurement of cancer portion to meet clinical reporting needs. The system analyses the whole slide image in three consecutive stages: tissue detection, classification, and slide level analysis. The whole slide image is divided into smaller regions (patches). The tissue detection stage relies upon traditional machine learning to identify WSI patches containing tissue, which are then further assessed at the classification stage where deep learning algorithms are employed to detect and classify cancer tissue. At the slide level analysis stage, entire slide level information is generated by aggregating all the patch level information of the slide. A total of 2340 haematoxylin and eosin stained slides were used to train and validate the system. A medical team consisting of 11 board certified pathologists with prostatic pathology subspeciality competences working independently in 4 different medical centres performed the annotations. Pixel-level annotation based on an agreed set of 10 annotation terms, determined based on medical relevance and prevalence, was created by the team. The system achieved an accuracy of 99.53% in tissue detection, with sensitivity and specificity respectively of 99.78% and 99.12%. The system achieved an accuracy of 92.80% in classifying tissue terms, with sensitivity and specificity respectively 92.61% and 99.25%, when 5x magnification level was used. For 10x magnification, these values were respectively 91.04%, 90.49%, and 99.07%. For 20x magnification they were 84.71%, 83.95%, 90.13%.
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Affiliation(s)
- Sajib Saha
- Australian e-Health Research Centre, CSIRO, Kensington, Australia.
| | | | - Adam Flesch
- AI4Path (Prosperitree Pty Ltd), Roseville, Australia
| | | | - Petra Gorog
- Markusovszky University Teaching Hospital, Szombathely, Hungary
| | - Eniko Szep
- Markusovszky University Teaching Hospital, Szombathely, Hungary
| | - Csaba Toth
- Markusovszky University Teaching Hospital, Szombathely, Hungary
| | | | | | | | | | | | - Levente Kuthi
- Department of Pathology, Albert Szent-Györgyi Medical School, University of Szeged, Szeged, Hungary
| | - Laszlo Tiszlavicz
- Department of Pathology, Albert Szent-Györgyi Medical School, University of Szeged, Szeged, Hungary
| | - Tibor Glasz
- AI4Path (Prosperitree Pty Ltd), Roseville, Australia
| | - Shaun Frost
- Australian e-Health Research Centre, CSIRO, Kensington, Australia
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5
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Li F, Ma J, Wen T, Tian Z, Liang HN. HI-Net: A novel histopathologic image segmentation model for metastatic breast cancer via lightweight dataset construction. Heliyon 2024; 10:e38410. [PMID: 39421372 PMCID: PMC11483284 DOI: 10.1016/j.heliyon.2024.e38410] [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: 07/02/2023] [Revised: 09/22/2024] [Accepted: 09/24/2024] [Indexed: 10/19/2024] Open
Abstract
Since 2020, breast cancer has remained the most prevalent cancer worldwide and the World Health Organisation projects significant increases by 2040, with new cases expected to exceed 3 million annually (a 40% increase) and deaths to surpass 1 million (a 50% increase), highlighting the urgent need for advancements in detection and treatment. Current detection of metastasis is highly dependent on labour-intensive and error-prone pathological examination of large-scale biotissue. Given the high-resolution (100,000 × 100,000 gigapixels) but limited quantity of open-source pathological slide datasets, existing deep learning models face preprocessing challenges. This paper introduces HI-Net, a high-speed panoramic feature-extraction pyramid network for rapid and accurate detection of metastatic breast cancer, balancing panoramic segmentation and local attention. Additionally, a lightweight pathological slide dataset optimised for 512 x 512-pixel resolution, derived from downsampled and reassembled competitive datasets, accelerates training and reduces computational costs. HI-Net demonstrates superior performance on existing medical imaging competition datasets and our lightweight dataset, evidencing its effectiveness across datasets and potential for contributing to the generalisation of intelligent diagnostics.
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Affiliation(s)
- Fengze Li
- University of Liverpool, Liverpool, UK
- Xi'an Jiaotong-Liverpool University, Suzhou, China
| | - Jieming Ma
- Xi'an Jiaotong-Liverpool University, Suzhou, China
| | - Tianxi Wen
- Xi'an Jiaotong-Liverpool University, Suzhou, China
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6
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Leite KRM, Melo PADS. Artificial Intelligence in Uropathology. Diagnostics (Basel) 2024; 14:2279. [PMID: 39451602 PMCID: PMC11506825 DOI: 10.3390/diagnostics14202279] [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: 08/26/2024] [Revised: 09/25/2024] [Accepted: 10/10/2024] [Indexed: 10/26/2024] Open
Abstract
The global population is currently at unprecedented levels, with an estimated 7.8 billion people inhabiting the planet. We are witnessing a rise in cancer cases, attributed to improved control of cardiovascular diseases and a growing elderly population. While this has resulted in an increased workload for pathologists, it also presents an opportunity for advancement. The accurate classification of tumors and identification of prognostic and predictive factors demand specialized expertise and attention. Fortunately, the rapid progression of artificial intelligence (AI) offers new prospects in medicine, particularly in diagnostics such as image and surgical pathology. This article explores the transformative impact of AI in the field of uropathology, with a particular focus on its application in diagnosing, grading, and prognosticating various urological cancers. AI, especially deep learning algorithms, has shown significant potential in improving the accuracy and efficiency of pathology workflows. This comprehensive review is dedicated to providing an insightful overview of the primary data concerning the utilization of AI in diagnosing, predicting prognosis, and determining drug responses for tumors of the urinary tract. By embracing these advancements, we can look forward to improved outcomes and better patient care.
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Affiliation(s)
- Katia Ramos Moreira Leite
- Laboratory of Medical Investigation, Urology Department, University of São Paulo Medical School, LIM55, Av Dr. Arnando 455, Sao Paulo 01246-903, SP, Brazil;
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7
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Huber AR, Whitney-Miller CL. Diagnostic yield of stains for infectious organisms in esophageal or gastroesophageal junction biopsies with esophagitis. J Histotechnol 2024:1-3. [PMID: 39397671 DOI: 10.1080/01478885.2024.2415154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2024] [Accepted: 10/05/2024] [Indexed: 10/15/2024]
Abstract
Stains frequently performed to exclude infectious etiologies in esophagitis include Grocott methenamine silver (GMS) and periodic acid-Schiff (PAS) as well as immunohistochemistry (IHC) assays for cytomegalovirus (CMV) and herpes simplex virus (HSV). The diagnostic yield of these tests, in this situation, has not been well studied. We retrospectively reviewed 261 esophageal biopsies, which had one or more of the above tests performed. The diagnostic yield for GMS and PAS was 8%, while CMV and HSV immunohistochemistry had a diagnostic yield of 1% and 0%, respectively. Our study suggests that routine use of ancillary labeling techniques in esophagitis biopsies may be of limited utility and have low diagnostic yield.
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Affiliation(s)
- Aaron R Huber
- Department of Pathology and Laboratory Medicine, University of Rochester School of Medicine and Dentistry, Rochester, NY, USA
| | - Christa L Whitney-Miller
- Department of Pathology and Laboratory Medicine, University of Rochester School of Medicine and Dentistry, Rochester, NY, USA
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8
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Yao J, Wei L, Hao P, Liu Z, Wang P. Application of artificial intelligence model in pathological staging and prognosis of clear cell renal cell carcinoma. Discov Oncol 2024; 15:545. [PMID: 39390246 PMCID: PMC11467134 DOI: 10.1007/s12672-024-01437-8] [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: 08/20/2024] [Accepted: 10/07/2024] [Indexed: 10/12/2024] Open
Abstract
This study aims to develop a deep learning (DL) model based on whole-slide images (WSIs) to predict the pathological stage of clear cell renal cell carcinoma (ccRCC). The histopathological images of 513 ccRCC patients were downloaded from The Cancer Genome Atlas (TCGA) database and randomly divided into training set and validation set according to the ratio of 8∶2. The CLAM algorithm was used to establish the DL model, and the stability of the model was evaluated in the external validation set. DL features were extracted from the model to construct a prognostic risk model, which was validated in an external dataset. The results showed that the DL model showed excellent prediction ability with an area under the curve (AUC) of 0.875 and an average accuracy score of 0.809, indicating that the model could reliably distinguish ccRCC patients at different stages from histopathological images. In addition, the prognostic risk model constructed by DL characteristics showed that the overall survival rate of patients in the high-risk group was significantly lower than that in the low-risk group (P = 0.003), and AUC values for predicting 1-, 3- and 5-year overall survival rates were 0.68, 0.69 and 0.69, respectively, indicating that the prediction model had high sensitivity and specificity. The results of the validation set are consistent with the above results. Therefore, DL model can accurately predict the pathological stage and prognosis of ccRCC patients, and provide certain reference value for clinical diagnosis.
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Affiliation(s)
- Jing Yao
- Department of Radiology, Tongji Hospital of Tongji University, Shanghai, 200065, China
- Institute of Medical Imaging Artificial Intelligence, Tongji University School of Medicine, Shanghai, 200065, China
| | - Lai Wei
- Department of Radiology, Tongji Hospital of Tongji University, Shanghai, 200065, China
- Institute of Medical Imaging Artificial Intelligence, Tongji University School of Medicine, Shanghai, 200065, China
| | - Peipei Hao
- Department of Radiology, Tongji Hospital of Tongji University, Shanghai, 200065, China
- Institute of Medical Imaging Artificial Intelligence, Tongji University School of Medicine, Shanghai, 200065, China
| | - Zhongliu Liu
- Department of Radiology, Tongji Hospital of Tongji University, Shanghai, 200065, China
- Institute of Medical Imaging Artificial Intelligence, Tongji University School of Medicine, Shanghai, 200065, China
| | - Peijun Wang
- Department of Radiology, Tongji Hospital of Tongji University, Shanghai, 200065, China.
- Institute of Medical Imaging Artificial Intelligence, Tongji University School of Medicine, Shanghai, 200065, China.
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9
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Schukow CP, Herndon C, Lopez-Nunez OF, Chen SD, Kahwash S. Exploring the Impact and Prospects of Social Media in Advancing Pediatric Pathology. Pediatr Dev Pathol 2024:10935266241284039. [PMID: 39324204 DOI: 10.1177/10935266241284039] [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] [Indexed: 09/27/2024]
Abstract
Social media has been recently highlighted as a unique and modern virtual force that allows for worldwide connection, collaboration, communication, and engagement between pathologists, trainees, and medical students. Much literature has been focused on the role of social media in recruitment and medical education practices of different pathology subspecialties, such as dermatopathology and hematopathology. However, current literature on pathology social media's status and potential future roles in promoting pediatric pathology is sparse. Herein, this review intends to narrow this knowledge gap by reviewing how social media has been utilized in different pediatric subspecialties, the current use of social media in pathology, and how the future of pediatric pathology social media use may look moving forward regarding education, research, and recruitment. Specific tips and related online resources are provided.
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Affiliation(s)
- Casey P Schukow
- Department of Pathology, Corewell Health William Beaumont University Hospital, Royal Oak, MI, USA
| | - Charles Herndon
- Department of Undergraduate Medical Education, Touro University Nevada College of Osteopathic Medicine, Henderson, NV, USA
| | - Oscar F Lopez-Nunez
- Department of Pathology and Laboratory Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Sonja D Chen
- Department of Pathology and Laboratory Medicine, Nationwide Children's Hospital, Columbus, OH, USA
| | - Samir Kahwash
- Department of Pathology and Laboratory Medicine, Nationwide Children's Hospital, Columbus, OH, USA
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10
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Reinecke D, Ruess D, Meissner AK, Fürtjes G, von Spreckelsen N, Ion-Margineanu A, Khalid F, Blau T, Stehle T, Al-Shugri A, Büttner R, Goldbrunner R, Ruge MI, Neuschmelting V. Streamlined Intraoperative Brain Tumor Classification and Molecular Subtyping in Stereotactic Biopsies Using Stimulated Raman Histology and Deep Learning. Clin Cancer Res 2024; 30:3824-3836. [PMID: 38976016 DOI: 10.1158/1078-0432.ccr-23-3842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2024] [Revised: 04/25/2024] [Accepted: 07/03/2024] [Indexed: 07/09/2024]
Abstract
PURPOSE Recent artificial intelligence algorithms aided intraoperative decision-making via stimulated Raman histology (SRH) during craniotomy. This study assesses deep learning algorithms for rapid intraoperative diagnosis from SRH images in small stereotactic-guided brain biopsies. It defines a minimum tissue sample size threshold to ensure diagnostic accuracy. EXPERIMENTAL DESIGN A prospective single-center study examined 121 SRH images from 84 patients with unclear intracranial lesions undergoing stereotactic brain biopsy. Unprocessed, label-free samples were imaged using a portable fiber laser Raman scattering microscope. Three deep learning models were tested to (i) identify tumorous/nontumorous tissue as qualitative biopsy control; (ii) subclassify into high-grade glioma (central nervous system World Health Organization grade 4), diffuse low-grade glioma (central nervous system World Health Organization grades 2-3), metastases, lymphoma, or gliosis; and (iii) molecularly subtype IDH and 1p/19q statuses of adult-type diffuse gliomas. Model predictions were evaluated against frozen section analysis and final neuropathologic diagnoses. RESULTS The first model identified tumorous/nontumorous tissue with 91.7% accuracy. Sample size on slides impacted accuracy in brain tumor subclassification (81.6%, κ = 0.72 frozen section; 73.9%, κ = 0.61 second model), with SRH images being smaller than hematoxylin and eosin images (4.1 ± 2.5 mm2 vs. 16.7 ± 8.2 mm2, P < 0.001). SRH images with more than 140 high-quality patches and a mean squeezed sample of 5.26 mm2 yielded 89.5% accuracy in subclassification and 93.9% in molecular subtyping of adult-type diffuse gliomas. CONCLUSIONS Artificial intelligence-based SRH image analysis is non-inferior to frozen section analysis in detecting and subclassifying brain tumors during small stereotactic-guided biopsies once a critical squeezed sample size is reached. Beyond frozen section analysis, it enables valid molecular glioma subtyping, allowing faster treatment decisions in the future; however, refinement is needed for long-term application.
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Affiliation(s)
- David Reinecke
- Department of Stereotactic and Functional Neurosurgery, Center for Neurosurgery, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
- Department of General Neurosurgery, Center for Neurosurgery, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Daniel Ruess
- Department of Stereotactic and Functional Neurosurgery, Center for Neurosurgery, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
- Centre for Integrated Oncology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Anna-Katharina Meissner
- Department of General Neurosurgery, Center for Neurosurgery, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Gina Fürtjes
- Department of General Neurosurgery, Center for Neurosurgery, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Niklas von Spreckelsen
- Department of General Neurosurgery, Center for Neurosurgery, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | | | | | - Tobias Blau
- Institute for Neuropathology, University of Essen, Essen, Germany
| | - Thomas Stehle
- Institute for Neuropathology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Abdulkader Al-Shugri
- Institute for Neuropathology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Reinhard Büttner
- Institute for Neuropathology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
- Institute of General Pathology and Pathological Anatomy, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Roland Goldbrunner
- Department of General Neurosurgery, Center for Neurosurgery, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
- Centre for Integrated Oncology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Maximilian I Ruge
- Department of Stereotactic and Functional Neurosurgery, Center for Neurosurgery, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
- Centre for Integrated Oncology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Volker Neuschmelting
- Department of General Neurosurgery, Center for Neurosurgery, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
- Centre for Integrated Oncology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
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11
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Rozario SY, Farlie MK, Sarkar M, Lazarus MD. The die-hards, negotiators and migrants: Portraits of doctors' career pathways through specialisation. MEDICAL EDUCATION 2024; 58:1071-1085. [PMID: 38468409 DOI: 10.1111/medu.15368] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Revised: 01/29/2024] [Accepted: 02/06/2024] [Indexed: 03/13/2024]
Abstract
INTRODUCTION Global workforce shortages in medical specialties strain healthcare systems, jeopardising patient outcomes. Enhancing recruitment strategies by supporting professional identity (PI) development may be one way to address this workforce gap-yet little research has explored this topic. The goal of the current study was to explore specialty-specific recruitment through considering PI. As proposed causes of workforce shortages in anatomical pathology (AP) bear similarities to many other specialties, this study uses the field of AP as a model for specialist PI development and asks: (1) why, how and when do doctors choose to pursue AP training and (2) what can be learned from this for recruitment to AP and other specialties? METHODS A qualitative research approach was undertaken using narrative inquiry. Interviews with junior doctors interested in AP, AP registrars and AP consultants from Australia and New Zealand were interpreted as stories via 're-storying'. Narrative synthesis of participants' collective stories identified chronological key events (i.e. 'turning points') in choosing AP. RESULTS Narrative synthesis resulted in identification of three portraits entering medical specialist training: (1) die-hards, deciding upon initial exposure; (2) negotiators, choosing after comparing specialties; and (3) migrants, seeking to move away from non-pathology specialties. The negotiators and migrants cemented their decision to pursue AP as a postgraduate doctor, whereas the die-hards made this decision during medical school. CONCLUSIONS Given the similarities in portrait traits between AP and other specialties across the literature, our results suggest ways to support specialty recruitment using PI development. We propose a medical specialist recruitment framework to support the PI development of doctors with die-hard, negotiator and migrant traits. Use of this framework could enhance current specialty-specific recruitment approaches, particularly in fields challenged by workforce shortages.
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Affiliation(s)
- Shemona Y Rozario
- Centre of Human Anatomy Education (CHAE), Department of Anatomy and Developmental Biology, Biomedical Discovery Institute, Faculty of Medicine, Nursing, and Health Sciences, Monash University, Melbourne, Victoria, Australia
| | - Melanie K Farlie
- Department of Physiotherapy, Faculty of Medicine, Nursing, and Health Sciences, Monash University, Melbourne, Victoria, Australia
- Monash Centre for Scholarship in Health Education (MCSHE), Faculty of Medicine, Nursing, and Health Sciences, Monash University, Melbourne, Victoria, Australia
| | - Mahbub Sarkar
- Monash Centre for Scholarship in Health Education (MCSHE), Faculty of Medicine, Nursing, and Health Sciences, Monash University, Melbourne, Victoria, Australia
- Monash Education Academy, Monash University, Melbourne, Victoria, Australia
| | - Michelle D Lazarus
- Centre of Human Anatomy Education (CHAE), Department of Anatomy and Developmental Biology, Biomedical Discovery Institute, Faculty of Medicine, Nursing, and Health Sciences, Monash University, Melbourne, Victoria, Australia
- Monash Centre for Scholarship in Health Education (MCSHE), Faculty of Medicine, Nursing, and Health Sciences, Monash University, Melbourne, Victoria, Australia
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12
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Anangwe N, Steimgrimson J, Cu-Uvin S. Evaluation of Pathology Resources for Cervical Cancer Detection Between 2018 & 2022: a Retrospective Study at Moi Teaching and Referral Hospital, Western Kenya. RESEARCH SQUARE 2024:rs.3.rs-4791370. [PMID: 39257969 PMCID: PMC11384803 DOI: 10.21203/rs.3.rs-4791370/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/12/2024]
Abstract
Background Cervical cancer cases are increasing in sub-Saharan Africa, particularly in Kenya, exacerbated by inadequate histopathology resources, posing a significant barrier to timely diagnosis and treatment. There has been little research on the availability and evolution of histopathology resources for diagnosing cervical cancer over the years. This retrospective study evaluated this evolution at Moi Teaching and Referral Hospital in Kenya between 2018 and 2022. Methods We used a mixed-methods approach. An in-depth interview was conducted with one of MTRH's pathology laboratory staff to assess the equipment, personnel, and quality control trends between 2018 and 2022. A thematic analysis was conducted in NVivo. We also retrospectively conducted a comprehensive inventory review of laboratory resources from 2018-2022 via purposive sampling. Microsoft Excel and Stata version 17 were utilized for descriptive statistical analysis. Turnaround time (TAT) was assessed against the UK's National Health Service Cervical Screening Program guidelines. Results The number of histopathology laboratory personnel at MTRH increased from 2018 to 2022, during which the facility included two pathologists, one records person, and one office administrator. Patient annual visits increased from approximately 350,000 in 2018 to approximately 500,000 in 2022. However, the histopathology personnel-to-population ratio decreased from 1.5 pathologists and 2.7 histo-technicians per 100,000 in 2018 to 1.4 pathologists and 1.8 histo-technicians per 100,000 in 2022. Despite this decrease, lab equipment, automatic tissue processors and embedding machines were added, and an average 14-day turnaround time was maintained for pathology reports. Conclusions Despite a decreased personnel-to-patient ratio, the addition of crucial histopathology equipment mirrors the operational commitment of the Moi Teaching and Referral Hospital. The 14-day TAT is commendable, contributes to operational effectiveness and significantly contributes to timely detection. The hospital's dedication to upgrading its infrastructure underscores a proactive approach to addressing growing healthcare demands and improving patient outcomes, even with limited human resources. The decline in the personnel-to-patient ratio underscores challenges in diagnosis, emphasizing the need to address workforce and infrastructure gaps to improve patient care within similar low-resource settings.
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13
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Chen S, Wang X, Zhang J, Jiang L, Gao F, Xiang J, Yang S, Yang W, Zheng J, Han X. Deep learning-based diagnosis and survival prediction of patients with renal cell carcinoma from primary whole slide images. Pathology 2024:S0031-3025(24)00185-5. [PMID: 39168777 DOI: 10.1016/j.pathol.2024.05.012] [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: 10/12/2023] [Revised: 05/06/2024] [Accepted: 05/20/2024] [Indexed: 08/23/2024]
Abstract
There is an urgent clinical demand to explore novel diagnostic and prognostic biomarkers for renal cell carcinoma (RCC). We proposed deep learning-based artificial intelligence strategies. The study included 1752 whole slide images from multiple centres. Based on the pixel-level of RCC segmentation, the diagnosis diagnostic model achieved an area under the receiver operating characteristic curve (AUC) of 0.977 (95% CI 0.969-0.984) in the external validation cohort. In addition, our diagnostic model exhibited excellent performance in the differential diagnosis of RCC from renal oncocytoma, which achieved an AUC of 0.951 (0.922-0.972). The graderisk for the recognition of high-grade tumour achieved AUCs of 0.840 (0.805-0.871) in the Cancer Genome Atlas (TCGA) cohort, 0.857 (0.813-0.894) in the Shanghai General Hospital (General) cohort, and 0.894 (0.842-0.933) in the Clinical Proteomic Tumor Analysis Consortium (CPTAC) cohort, for the recognition of high-grade tumour. The OSrisk for predicting 5-year survival status achieved an AUC of 0.784 (0.746-0.819) in the TCGA cohort, which was further verified in the independent general cohort and the CPTAC cohort, with AUCs of 0.774 (0.723-0.820) and 0.702 (0.632-0.765), respectively. Moreover, the competing-risk nomogram (CRN) showed its potential to be a prognostic indicator, with a hazard ratio (HR) of 5.664 (3.893-8.239, p<0.0001), outperforming other traditional clinical prognostic indicators. Kaplan-Meier survival analysis further illustrated that our CRN could significantly distinguish patients with high survival risk. Deep learning-based artificial intelligence could be a useful tool for clinicians to diagnose and predict the prognosis of RCC patients, thus improving the process of individualised treatment.
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Affiliation(s)
- Siteng Chen
- Department of Urology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiyue Wang
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA
| | | | - Liren Jiang
- Department of Pathology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Feng Gao
- Department of Pathology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | | | | | | | - Junhua Zheng
- Department of Urology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
| | - Xiao Han
- Tencent AI Lab, Shenzhen, China.
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14
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Schukow CP, Allen TC. Digital and Computational Pathology Are Pathologists' Physician Extenders. Arch Pathol Lab Med 2024; 148:866-870. [PMID: 38531382 DOI: 10.5858/arpa.2023-0537-ed] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/11/2024] [Indexed: 03/28/2024]
Affiliation(s)
- Casey P Schukow
- From the Department of Pathology, Corewell Health's Beaumont Hospital, Royal Oak, Michigan
| | - Timothy Craig Allen
- From the Department of Pathology, Corewell Health's Beaumont Hospital, Royal Oak, Michigan
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15
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Wang Z, Peng H, Wan J, Song A. Identification of histopathological classification and establishment of prognostic indicators of gastric adenocarcinoma based on deep learning algorithm. Med Mol Morphol 2024:10.1007/s00795-024-00399-8. [PMID: 39088070 DOI: 10.1007/s00795-024-00399-8] [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: 03/11/2024] [Accepted: 07/15/2024] [Indexed: 08/02/2024]
Abstract
The aim of this study is to establish a deep learning (DL) model to predict the pathological type of gastric adenocarcinoma cancer based on whole-slide images(WSIs). We downloaded 356 histopathological images of gastric adenocarcinoma (STAD) patients from The Cancer Genome Atlas database and randomly divided them into the training set, validation set and test set (8:1:1). Additionally, 80 H&E-stained WSIs of STAD were collected for external validation. The CLAM tool was used to cut the WSIs and further construct the model by DL algorithm, achieving an accuracy of over 90% in identifying and predicting histopathological subtypes. External validation results demonstrated the model had a certain generalization ability. Moreover, DL features were extracted from the model to further investigate the differences in immune infiltration and patient prognosis between the two subtypes. The DL model can accurately predict the pathological classification of STAD patients, and provide certain reference value for clinical diagnosis. The nomogram combining DL-signature, gene-signature and clinical features can be used as a prognostic classifier for clinical decision-making and treatment.
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Affiliation(s)
- Zhihui Wang
- Department of Ultrasound Imaging, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430101, Hubei, China
| | - Hui Peng
- Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430101, Hubei, China
| | - Jie Wan
- Department of Pathology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430101, Hubei, China
| | - Anping Song
- Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430101, Hubei, China.
- Department of Oncology, Tongji Hospital Sino-French New City Branch, Caidian District, No.288 Xintian Avenue, Wuhan, 430101, Hubei, China.
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16
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Kildal W, Cyll K, Kalsnes J, Islam R, Julbø FM, Pradhan M, Ersvær E, Shepherd N, Vlatkovic L, Tekpli X, Garred Ø, Kristensen GB, Askautrud HA, Hveem TS, Danielsen HE. Deep learning for automated scoring of immunohistochemically stained tumour tissue sections - Validation across tumour types based on patient outcomes. Heliyon 2024; 10:e32529. [PMID: 39040241 PMCID: PMC11261074 DOI: 10.1016/j.heliyon.2024.e32529] [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: 05/26/2024] [Accepted: 06/05/2024] [Indexed: 07/24/2024] Open
Abstract
We aimed to develop deep learning (DL) models to detect protein expression in immunohistochemically (IHC) stained tissue-sections, and to compare their accuracy and performance with manually scored clinically relevant proteins in common cancer types. Five cancer patient cohorts (colon, two prostate, breast, and endometrial) were included. We developed separate DL models for scoring IHC-stained tissue-sections with nuclear, cytoplasmic, and membranous staining patterns. For training, we used images with annotations of cells with positive and negative staining from the colon cohort stained for Ki-67 and PMS2 (nuclear model), the prostate cohort 1 stained for PTEN (cytoplasmic model) and β-catenin (membranous model). The nuclear DL model was validated for MSH6 in the colon, MSH6 and PMS2 in the endometrium, Ki-67 and CyclinB1 in prostate, and oestrogen and progesterone receptors in the breast cancer cohorts. The cytoplasmic DL model was validated for PTEN and Mapre2, and the membranous DL model for CD44 and Flotillin1, all in prostate cohorts. When comparing the results of manual and DL scores in the validation sets, using manual scores as the ground truth, we observed an average correct classification rate of 91.5 % (76.9-98.5 %) for the nuclear model, 85.6 % (73.3-96.6 %) for the cytoplasmic model, and 78.4 % (75.5-84.3 %) for the membranous model. In survival analyses, manual and DL scores showed similar prognostic impact, with similar hazard ratios and p-values for all DL models. Our findings demonstrate that DL models offer a promising alternative to manual IHC scoring, providing efficiency and reproducibility across various data sources and markers.
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Affiliation(s)
- Wanja Kildal
- Institute for Cancer Genetics and Informatics, Oslo University Hospital, NO-0424, Oslo, Norway
| | - Karolina Cyll
- Institute for Cancer Genetics and Informatics, Oslo University Hospital, NO-0424, Oslo, Norway
| | - Joakim Kalsnes
- Institute for Cancer Genetics and Informatics, Oslo University Hospital, NO-0424, Oslo, Norway
| | - Rakibul Islam
- Institute for Cancer Genetics and Informatics, Oslo University Hospital, NO-0424, Oslo, Norway
| | - Frida M. Julbø
- Institute for Cancer Genetics and Informatics, Oslo University Hospital, NO-0424, Oslo, Norway
| | - Manohar Pradhan
- Institute for Cancer Genetics and Informatics, Oslo University Hospital, NO-0424, Oslo, Norway
| | - Elin Ersvær
- Institute for Cancer Genetics and Informatics, Oslo University Hospital, NO-0424, Oslo, Norway
| | - Neil Shepherd
- Gloucestershire Cellular Pathology Laboratory, Gloucester, GL53 7AN, UK
| | - Ljiljana Vlatkovic
- Institute for Cancer Genetics and Informatics, Oslo University Hospital, NO-0424, Oslo, Norway
| | - OSBREAC
- Institute for Cancer Genetics and Informatics, Oslo University Hospital, NO-0424, Oslo, Norway
- Gloucestershire Cellular Pathology Laboratory, Gloucester, GL53 7AN, UK
- Department of Medical Genetics, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo and Oslo University Hospital, NO-0450, Oslo, Norway
- Department of Pathology, Oslo University Hospital, NO-0424, Oslo, Norway
- Nuffield Division of Clinical Laboratory Sciences, University of Oxford, Oxford, OX3 9DU, UK
| | - Xavier Tekpli
- Department of Medical Genetics, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo and Oslo University Hospital, NO-0450, Oslo, Norway
| | - Øystein Garred
- Department of Pathology, Oslo University Hospital, NO-0424, Oslo, Norway
| | - Gunnar B. Kristensen
- Institute for Cancer Genetics and Informatics, Oslo University Hospital, NO-0424, Oslo, Norway
| | - Hanne A. Askautrud
- Institute for Cancer Genetics and Informatics, Oslo University Hospital, NO-0424, Oslo, Norway
| | - Tarjei S. Hveem
- Institute for Cancer Genetics and Informatics, Oslo University Hospital, NO-0424, Oslo, Norway
| | - Håvard E. Danielsen
- Institute for Cancer Genetics and Informatics, Oslo University Hospital, NO-0424, Oslo, Norway
- Nuffield Division of Clinical Laboratory Sciences, University of Oxford, Oxford, OX3 9DU, UK
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17
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Youssef A, Rosenwald A, Rosenfeldt MT. TelePi: an affordable telepathology microscope camera system anyone can build and use. Virchows Arch 2024; 485:115-122. [PMID: 37935902 PMCID: PMC11271423 DOI: 10.1007/s00428-023-03685-5] [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: 08/10/2023] [Revised: 10/20/2023] [Accepted: 10/24/2023] [Indexed: 11/09/2023]
Abstract
Telepathology facilitates histological diagnoses through sharing expertise between pathologists. However, the associated costs are high and frequently prohibitive, especially in low-resource settings, where telepathology would paradoxically be of paramount importance due to a paucity of pathologists.We have constructed a telepathology system (TelePi) with a budget of < €120 using the small, single-board computer Raspberry Pi Zero and its High-Quality Camera Module in conjunction with a standard microscope and open-source software. The system requires no maintenance costs or service contracts, has a small footprint, can be moved and shared across several microscopes, and is independent from other computer operating systems. TelePi uses a responsive and high-resolution web-based live stream which allows remote consultation between two or more locations. TelePi can serve as a telepathology system for remote diagnostics of frozen sections. Additionally, it can be used as a standard microscope camera for teaching of medical students and for basic research. The quality of the TelePi system compared favorable to a commercially available telepathology system that exceed its cost by more than 125-fold. Additionally, still images are of publication quality equal to that of a whole slide scanner that costs 800 times more.In summary, TelePi is an affordable, versatile, and inexpensive camera system that potentially enables telepathology in low-resource settings without sacrificing image quality.
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Affiliation(s)
- Almoatazbellah Youssef
- Institute of Pathology and Comprehensive Cancer Centre Mainfranken, Julius Maximilian University of Würzburg, Josef-Schneider-Str. 2, 97080, Würzburg, Germany.
| | - Andreas Rosenwald
- Institute of Pathology and Comprehensive Cancer Centre Mainfranken, Julius Maximilian University of Würzburg, Josef-Schneider-Str. 2, 97080, Würzburg, Germany
| | - Mathias Tillmann Rosenfeldt
- Institute of Pathology and Comprehensive Cancer Centre Mainfranken, Julius Maximilian University of Würzburg, Josef-Schneider-Str. 2, 97080, Würzburg, Germany
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18
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Retamero JA, Gulturk E, Bozkurt A, Liu S, Gorgan M, Moral L, Horton M, Parke A, Malfroid K, Sue J, Rothrock B, Oakley G, DeMuth G, Millar E, Fuchs TJ, Klimstra DS. Artificial Intelligence Helps Pathologists Increase Diagnostic Accuracy and Efficiency in the Detection of Breast Cancer Lymph Node Metastases. Am J Surg Pathol 2024; 48:846-854. [PMID: 38809272 PMCID: PMC11191045 DOI: 10.1097/pas.0000000000002248] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/30/2024]
Abstract
The detection of lymph node metastases is essential for breast cancer staging, although it is a tedious and time-consuming task where the sensitivity of pathologists is suboptimal. Artificial intelligence (AI) can help pathologists detect lymph node metastases, which could help alleviate workload issues. We studied how pathologists' performance varied when aided by AI. An AI algorithm was trained using more than 32 000 breast sentinel lymph node whole slide images (WSIs) matched with their corresponding pathology reports from more than 8000 patients. The algorithm highlighted areas suspicious of harboring metastasis. Three pathologists were asked to review a dataset comprising 167 breast sentinel lymph node WSIs, of which 69 harbored cancer metastases of different sizes, enriched for challenging cases. Ninety-eight slides were benign. The pathologists read the dataset twice, both digitally, with and without AI assistance, randomized for slide and reading orders to reduce bias, separated by a 3-week washout period. Their slide-level diagnosis was recorded, and they were timed during their reads. The average reading time per slide was 129 seconds during the unassisted phase versus 58 seconds during the AI-assisted phase, resulting in an overall efficiency gain of 55% ( P <0.001). These efficiency gains are applied to both benign and malignant WSIs. Two of the 3 reading pathologists experienced significant sensitivity improvements, from 74.5% to 93.5% ( P ≤0.006). This study highlights that AI can help pathologists shorten their reading times by more than half and also improve their metastasis detection rate.
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Affiliation(s)
| | | | | | - Sandy Liu
- New England Pathology Associates, Springfield, MA
| | - Maria Gorgan
- New England Pathology Associates, Springfield, MA
| | - Luis Moral
- New England Pathology Associates, Springfield, MA
| | | | | | | | - Jill Sue
- Paige.AI. 11 Times Square, New York, NY
| | | | | | | | - Ewan Millar
- Paige.AI. 11 Times Square, New York, NY
- Department of Anatomical Pathology, NSW Health Pathology, St George Hospital, Sydney, NSW, Australia
| | - Thomas J. Fuchs
- Paige.AI. 11 Times Square, New York, NY
- Windreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY
- Hasso Platner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY
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Ramos J, Aung PP. International Medical Graduates and the Shortage of US Pathologists: Challenges and Opportunities. Arch Pathol Lab Med 2024; 148:735-738. [PMID: 37787415 DOI: 10.5858/arpa.2023-0290-ep] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/28/2023] [Indexed: 10/04/2023]
Abstract
CONTEXT.— Physician shortages are affecting many communities across the United States and all medical specialties, including pathology. International medical graduates (IMGs) make up a significant proportion of US physicians and graduate medical education (GME) trainees, including pathologists. However, noncitizen IMGs continue to face great challenges in entering the US health care workforce. OBJECTIVE.— To show recent and historical data on noncitizen IMGs in pathology GME training and current limitations on them remaining in the US health care workforce. DATA SOURCES.— Compared with applicants who do not need a visa, applicants who need a visa to train in the United States have a greatly reduced chance of matching to a residency program. After completion of residency and fellowship, noncitizen IMGs with J-1 visas face the 2-year home country residence requirement unless they obtain a waiver. H-1B visas facilitate the transition to independent practice but have limited availability. Job announcements for pathologists often do not indicate whether J-1 and H-1B visa holders are considered, which makes the job search process difficult for noncitizen IMGs. CONCLUSIONS.— Academic and nonacademic institutions with departments of pathology should increase awareness of the pathologist shortage in the United States and the rules and regulations that limit hiring of non-US IMGs. Such institutions should also actively educate policymakers to promote durable solutions to these issues. One potential solution to these shortages may be to make it easier for noncitizen IMGs to access GME and join and remain in the US physician workforce.
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Du X, Hao S, Olsson H, Kartasalo K, Mulliqi N, Rai B, Menges D, Heintz E, Egevad L, Eklund M, Clements M. Effectiveness and Cost-effectiveness of Artificial Intelligence-assisted Pathology for Prostate Cancer Diagnosis in Sweden: A Microsimulation Study. Eur Urol Oncol 2024:S2588-9311(24)00133-0. [PMID: 38789385 DOI: 10.1016/j.euo.2024.05.004] [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: 03/05/2024] [Revised: 04/23/2024] [Accepted: 05/13/2024] [Indexed: 05/26/2024]
Abstract
BACKGROUND AND OBJECTIVE Image-based artificial intelligence (AI) methods have shown high accuracy in prostate cancer (PCa) detection. Their impact on patient outcomes and cost effectiveness in comparison to human pathologists remains unknown. Our aim was to evaluate the effectiveness and cost-effectiveness of AI-assisted pathology for PCa diagnosis in Sweden. METHODS We modeled quadrennial prostate-specific antigen (PSA) screening for men between the ages of 50 and 74 yr over a lifetime horizon using a health care perspective. Men with PSA ≥3 ng/ml were referred for standard biopsy (SBx), for which cores were either examined via AI followed by a pathologist for AI-labeled positive cores, or a pathologist alone. The AI performance characteristics were estimated using an internal STHLM3 validation data set. Outcome measures included the number of tests, PCa incidence and mortality, overdiagnosis, quality-adjusted life years (QALYs), and the potential reduction in pathologist-evaluated biopsy cores if AI were used. Cost-effectiveness was assessed using the incremental cost-effectiveness ratio. KEY FINDINGS AND LIMITATIONS In comparison to a pathologist alone, the AI-assisted workflow increased the number of PSA tests, SBx procedures, and PCa deaths by ≤0.03%, and slightly reduced PCa incidence and overdiagnosis. AI would reduce the proportion of biopsy cores evaluated by a pathologist by 80%. At a cost of €10 per case, the AI-assisted workflow would cost less and result in <0.001% lower QALYs in comparison to a pathologist alone. The results were sensitive to the AI cost. CONCLUSIONS AND CLINICAL IMPLICATIONS According to our model, AI-assisted pathology would significantly decrease the workload of pathologists, would not affect patient quality of life, and would yield cost savings in Sweden when compared to a human pathologist alone. PATIENT SUMMARY We compared outcomes for prostate cancer patients and relevant costs for two methods of assessing prostate biopsies in Sweden: (1) artificial intelligence (AI) technology and review of positive biopsies by a human pathologist; and (2) a human pathologist alone for all biopsies. We found that addition of AI would reduce the pathology workload and save money, and would not affect patient outcomes when compared to a human pathologist alone. The results suggest that adding AI to prostate pathology in Sweden would save costs.
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Affiliation(s)
- Xiaoyang Du
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
| | - Shuang Hao
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Henrik Olsson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Kimmo Kartasalo
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Nita Mulliqi
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Balram Rai
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Dominik Menges
- Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland
| | - Emelie Heintz
- Department of Learning, Informatics, Management and Ethics, Karolinska Institutet, Stockholm, Sweden; Centre for Health Economics, Informatics and Health Services Research, Stockholm Health Care Services, Stockholm, Sweden
| | - Lars Egevad
- Department of Oncology and Pathology, Karolinska Institutet, Stockholm, Sweden
| | - Martin Eklund
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Mark Clements
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
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Wang R, Ekem L, Gallagher J, Factor RE, Hall A, Ramanujam N. A color-based tumor segmentation method for clinical ex vivo breast tissue assessment utilizing a multi-contrast brightfield imaging strategy. JOURNAL OF BIOPHOTONICS 2024; 17:e202300241. [PMID: 38348582 PMCID: PMC11065618 DOI: 10.1002/jbio.202300241] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 01/20/2024] [Accepted: 01/21/2024] [Indexed: 03/21/2024]
Abstract
We demonstrate an automated two-step tumor segmentation method leveraging color information from brightfield images of fresh core needle biopsies of breast tissue. Three different color spaces (HSV, CIELAB, YCbCr) were explored for the segmentation task. By leveraging white-light and green-light images, we identified two different types of color transformations that could separate adipose from benign and tumor or cancerous tissue. We leveraged these two distinct color transformation methods in a two-step process where adipose tissue segmentation was followed by benign tissue segmentation thereby isolating the malignant region of the biopsy. Our tumor segmentation algorithm and imaging probe could highlight suspicious regions on unprocessed biopsy tissue to guide selection of areas most similar to malignant tissues for tissue pathology whether it be formalin fixed or frozen sections, expedite tissue selection for molecular testing, detect positive tumor margins, or serve an alternative to tissue pathology, in countries where these services are lacking.
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Affiliation(s)
- Roujia Wang
- Department of Biomedical Engineering, Duke University, 27710 Durham, NC, USA
| | - Lillian Ekem
- Department of Biomedical Engineering, Duke University, 27710 Durham, NC, USA
| | - Jennifer Gallagher
- Department of Surgery, Duke University School of Medicine, 27710 Durham, NC, USA
| | | | - Allison Hall
- Department of Pathology, Duke University, 27710 Durham, NC, USA
| | - Nimmi Ramanujam
- Department of Biomedical Engineering, Duke University, 27710 Durham, NC, USA
- Department of Pharmacology and Cancer Biology, Duke University Medical Center, 27710 Durham, NC, USA
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22
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Schukow CP, Allen TC. Remote Pathology Practice: The Time for Remote Diagnostic Pathology in This Digital Era is Now. Arch Pathol Lab Med 2024; 148:508-514. [PMID: 38133942 DOI: 10.5858/arpa.2023-0385-ed] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/04/2023] [Indexed: 12/24/2023]
Affiliation(s)
- Casey P Schukow
- From the Department of Pathology, Corewell Health's Beaumont Hospital, Royal Oak, Michigan
| | - Timothy Craig Allen
- From the Department of Pathology, Corewell Health's Beaumont Hospital, Royal Oak, Michigan
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23
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Imran M, Islam Tiwana M, Mohsan MM, Alghamdi NS, Akram MU. Transformer-based framework for multi-class segmentation of skin cancer from histopathology images. Front Med (Lausanne) 2024; 11:1380405. [PMID: 38741771 PMCID: PMC11089103 DOI: 10.3389/fmed.2024.1380405] [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: 02/01/2024] [Accepted: 04/01/2024] [Indexed: 05/16/2024] Open
Abstract
Introduction Non-melanoma skin cancer comprising Basal cell carcinoma (BCC), Squamous cell carcinoma (SCC), and Intraepidermal carcinoma (IEC) has the highest incidence rate among skin cancers. Intelligent decision support systems may address the issue of the limited number of subject experts and help in mitigating the parity of health services between urban centers and remote areas. Method In this research, we propose a transformer-based model for the segmentation of histopathology images not only into inflammation and cancers such as BCC, SCC, and IEC but also to identify skin tissues and boundaries that are important in decision-making. Accurate segmentation of these tissue types will eventually lead to accurate detection and classification of non-melanoma skin cancer. The segmentation according to tissue types and their visual representation before classification enhances the trust of pathologists and doctors being relatable to how most pathologists approach this problem. The visualization of the confidence of the model in its prediction through uncertainty maps is also what distinguishes this study from most deep learning methods. Results The evaluation of proposed system is carried out using publicly available dataset. The application of our proposed segmentation system demonstrated good performance with an F1 score of 0.908, mean intersection over union (mIoU) of 0.653, and average accuracy of 83.1%, advocating that the system can be used as a decision support system successfully and has the potential of subsequently maturing into a fully automated system. Discussion This study is an attempt to automate the segmentation of the most occurring non-melanoma skin cancer using a transformer-based deep learning technique applied to histopathology skin images. Highly accurate segmentation and visual representation of histopathology images according to tissue types by the proposed system implies that the system can be used for skin-related routine pathology tasks including cancer and other anomaly detection, their classification, and measurement of surgical margins in the case of cancer cases.
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Affiliation(s)
- Muhammad Imran
- Department of Mechatronics Engineering, National University of Sciences and Technology, Islamabad, Pakistan
| | - Mohsin Islam Tiwana
- Department of Mechatronics Engineering, National University of Sciences and Technology, Islamabad, Pakistan
| | - Mashood Mohammad Mohsan
- Department of Computer and Software Engineering, National University of Sciences and Technology, Islamabad, Pakistan
| | - Norah Saleh Alghamdi
- Department of Computer Sciences, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia
| | - Muhammad Usman Akram
- Department of Computer and Software Engineering, National University of Sciences and Technology, Islamabad, Pakistan
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24
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McCoy CA, Coleman HG, McShane CM, McCluggage WG, Wylie J, Quinn D, McMenamin ÚC. Factors associated with interobserver variation amongst pathologists in the diagnosis of endometrial hyperplasia: A systematic review. PLoS One 2024; 19:e0302252. [PMID: 38683770 PMCID: PMC11057740 DOI: 10.1371/journal.pone.0302252] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Accepted: 03/30/2024] [Indexed: 05/02/2024] Open
Abstract
OBJECTIVE Reproducible diagnoses of endometrial hyperplasia (EH) remains challenging and has potential implications for patient management. This systematic review aimed to identify pathologist-specific factors associated with interobserver variation in the diagnosis and reporting of EH. METHODS Three electronic databases, namely MEDLINE, Embase and Web of Science, were searched from 1st January 2000 to 25th March 2023, using relevant key words and subject headings. Eligible studies reported on pathologist-specific factors or working practices influencing interobserver variation in the diagnosis of EH, using either the World Health Organisation (WHO) 2014 or 2020 classification or the endometrioid intraepithelial neoplasia (EIN) classification system. Quality assessment was undertaken using the QUADAS-2 tool, and findings were narratively synthesised. RESULTS Eight studies were identified. Interobserver variation was shown to be significant even amongst specialist gynaecological pathologists in most studies. Few studies investigated pathologist-specific characteristics, but pathologists were shown to have different diagnostic styles, with some more likely to under-diagnose and others likely to over-diagnose EH. Some novel working practices were identified, such as grading the "degree" of nuclear atypia and the incorporation of objective methods of diagnosis such as semi-automated quantitative image analysis/deep learning models. CONCLUSIONS This review highlighted the impact of pathologist-specific factors and working practices in the accurate diagnosis of EH, although few studies have been conducted. Further research is warranted in the development of more objective criteria that could improve reproducibility in EH diagnostic reporting, as well as determining the applicability of novel methods such as grading the degree of nuclear atypia in clinical settings.
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Affiliation(s)
- Chloe A. McCoy
- Centre for Public Health, Queen’s University Belfast, Belfast, Northern Ireland, United Kingdom
| | - Helen G. Coleman
- Centre for Public Health, Queen’s University Belfast, Belfast, Northern Ireland, United Kingdom
| | - Charlene M. McShane
- Centre for Public Health, Queen’s University Belfast, Belfast, Northern Ireland, United Kingdom
| | - W. Glenn McCluggage
- Department of Pathology, Belfast Health and Social Care Trust, Belfast, Northern Ireland, United Kingdom
| | - James Wylie
- Department of Obstetrics and Gynaecology, Antrim Area Hospital, Northern Health and Social Care Trust, Antrim, Northern Ireland, United Kingdom
| | - Declan Quinn
- Department of Obstetrics and Gynaecology, Antrim Area Hospital, Northern Health and Social Care Trust, Antrim, Northern Ireland, United Kingdom
| | - Úna C. McMenamin
- Centre for Public Health, Queen’s University Belfast, Belfast, Northern Ireland, United Kingdom
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James ND, Tannock I, N'Dow J, Feng F, Gillessen S, Ali SA, Trujillo B, Al-Lazikani B, Attard G, Bray F, Compérat E, Eeles R, Fatiregun O, Grist E, Halabi S, Haran Á, Herchenhorn D, Hofman MS, Jalloh M, Loeb S, MacNair A, Mahal B, Mendes L, Moghul M, Moore C, Morgans A, Morris M, Murphy D, Murthy V, Nguyen PL, Padhani A, Parker C, Rush H, Sculpher M, Soule H, Sydes MR, Tilki D, Tunariu N, Villanti P, Xie LP. The Lancet Commission on prostate cancer: planning for the surge in cases. Lancet 2024; 403:1683-1722. [PMID: 38583453 DOI: 10.1016/s0140-6736(24)00651-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Revised: 09/28/2023] [Accepted: 03/27/2024] [Indexed: 04/09/2024]
Affiliation(s)
- Nicholas D James
- Institute of Cancer Research, London, UK; The Royal Marsden NHS Foundation Trust, London, UK.
| | - Ian Tannock
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
| | | | - Felix Feng
- University of California, San Francisco, USA
| | - Silke Gillessen
- Oncology Institute of Southern Switzerland, Bellinzona, Switzerland
| | - Syed Adnan Ali
- University of Manchester, Manchester, UK; The Christie Hospital, Manchester, UK
| | | | | | | | - Freddie Bray
- International Agency for Research on Cancer, Lyon, France
| | - Eva Compérat
- Tenon Hospital, Sorbonne University, Paris; AKH Medical University, Vienna, Austria
| | - Ros Eeles
- Institute of Cancer Research, London, UK; The Royal Marsden NHS Foundation Trust, London, UK
| | | | | | | | - Áine Haran
- The Royal United Hospitals Bath NHS Foundation Trust, Bath, UK
| | | | | | | | - Stacy Loeb
- New York University, New York, NY, USA; Manhattan Veterans Affairs, New York, NY, USA
| | | | | | | | - Masood Moghul
- Institute of Cancer Research, London, UK; The Royal Marsden NHS Foundation Trust, London, UK
| | | | | | - Michael Morris
- Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Declan Murphy
- Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | | | | | | | | | | | | | - Howard Soule
- Prostate Cancer Foundation, Santa Monica, CA, USA
| | | | - Derya Tilki
- Martini-Klinik Prostate Cancer Center and Department of Urology, University Hospital Hamburg-Eppendorf, Hamburg, Germany; Department of Urology, Koc University Hospital, Istanbul, Türkiye
| | - Nina Tunariu
- Institute of Cancer Research, London, UK; The Royal Marsden NHS Foundation Trust, London, UK
| | | | - Li-Ping Xie
- First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
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Black-Schaffer WS, Gross DJ, Nouri Z, DeLisle A, Dill M, Park JY, Crawford JM, Cohen MB, Johnson RL, Karcher DS, Wheeler TM, Robboy SJ. Re-evaluation of the methodology for estimating the US specialty physician workforce. HEALTH AFFAIRS SCHOLAR 2024; 2:qxae033. [PMID: 38756177 PMCID: PMC11034525 DOI: 10.1093/haschl/qxae033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Revised: 02/20/2024] [Accepted: 03/14/2024] [Indexed: 05/18/2024]
Abstract
Increasing pursuit of subspecialized training has quietly revolutionized physician training, but the potential impact on physician workforce estimates has not previously been recognized. The Physicians Specialty Data Reports of the Association of American Medical Colleges, derived from specialty designations in the American Medical Association (AMA) Physician Professional Data (PPD), are the reference source for US physician workforce estimates; by 2020, the report for pathologists was an undercount of 39% when compared with the PPD. Most of the difference was due to the omission of pathology subspecialty designations. The rest resulted from reliance on only the first of the AMA PPD's 2 specialty data fields. Placement of specialty designation in these 2 fields is sensitive to sequence of training and is thus affected by multiple or intercalated (between years of residency training) fellowships. Both these phenomena have become progressively more common and are not unique to pathology. Our findings demonstrate the need to update definitions and methodology underlying estimates of the US physician workforce for pathology and suggest a like need in other specialties affected by similar trends.
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Affiliation(s)
- W Stephen Black-Schaffer
- Department of Pathology, Massachusetts General Hospital, and Harvard Medical School, Boston, MA 02114, United States
| | - David J Gross
- Policy Roundtable, College of American Pathologists, Washington, DC 20001, United States
| | - Zakia Nouri
- Workforce Studies, Association of American Medical Colleges, Washington, DC 20001, United States
| | - Aidan DeLisle
- Policy Roundtable, College of American Pathologists, Washington, DC 20001, United States
| | - Michael Dill
- Workforce Studies, Association of American Medical Colleges, Washington, DC 20001, United States
| | - Jason Y Park
- Department of Pathology, University of Texas Southwestern Medical Center, Dallas, TX 75390, United States
| | - James M Crawford
- Department of Pathology and Laboratory Medicine, Northwell Health, New Hyde Park, NY 11040, United States
| | - Michael B Cohen
- Department of Pathology, Wake Forest University, Winston-Salem, NC 27109, United States
| | - Rebecca L Johnson
- American Board of Pathology (retired), Tampa, FL 33609, United States
| | - Donald S Karcher
- Department of Pathology, The George Washington University School of Medicine and Health Sciences, Washington, DC 20052, United States
| | - Thomas M Wheeler
- Department of Pathology and Immunology, Baylor College of Medicine, Houston, TX 77030, United States
| | - Stanley J Robboy
- Duke Pathology, Duke University School of Medicine, Durham, NC 27710, United States
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Seth I, Lim B, Joseph K, Gracias D, Xie Y, Ross RJ, Rozen WM. Use of artificial intelligence in breast surgery: a narrative review. Gland Surg 2024; 13:395-411. [PMID: 38601286 PMCID: PMC11002485 DOI: 10.21037/gs-23-414] [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: 10/09/2023] [Accepted: 02/21/2024] [Indexed: 04/12/2024]
Abstract
Background and Objective We have witnessed tremendous advances in artificial intelligence (AI) technologies. Breast surgery, a subspecialty of general surgery, has notably benefited from AI technologies. This review aims to evaluate how AI has been integrated into breast surgery practices, to assess its effectiveness in improving surgical outcomes and operational efficiency, and to identify potential areas for future research and application. Methods Two authors independently conducted a comprehensive search of PubMed, Google Scholar, EMBASE, and Cochrane CENTRAL databases from January 1, 1950, to September 4, 2023, employing keywords pertinent to AI in conjunction with breast surgery or cancer. The search focused on English language publications, where relevance was determined through meticulous screening of titles, abstracts, and full-texts, followed by an additional review of references within these articles. The review covered a range of studies illustrating the applications of AI in breast surgery encompassing lesion diagnosis to postoperative follow-up. Publications focusing specifically on breast reconstruction were excluded. Key Content and Findings AI models have preoperative, intraoperative, and postoperative applications in the field of breast surgery. Using breast imaging scans and patient data, AI models have been designed to predict the risk of breast cancer and determine the need for breast cancer surgery. In addition, using breast imaging scans and histopathological slides, models were used for detecting, classifying, segmenting, grading, and staging breast tumors. Preoperative applications included patient education and the display of expected aesthetic outcomes. Models were also designed to provide intraoperative assistance for precise tumor resection and margin status assessment. As well, AI was used to predict postoperative complications, survival, and cancer recurrence. Conclusions Extra research is required to move AI models from the experimental stage to actual implementation in healthcare. With the rapid evolution of AI, further applications are expected in the coming years including direct performance of breast surgery. Breast surgeons should be updated with the advances in AI applications in breast surgery to provide the best care for their patients.
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Affiliation(s)
- Ishith Seth
- Department of Plastic Surgery, Peninsula Health, Melbourne, Victoria, Australia
- Central Clinical School at Monash University, The Alfred Centre, Melbourne, Victoria, Australia
| | - Bryan Lim
- Department of Plastic Surgery, Peninsula Health, Melbourne, Victoria, Australia
- Central Clinical School at Monash University, The Alfred Centre, Melbourne, Victoria, Australia
| | - Konrad Joseph
- Department of Surgery, Port Macquarie Base Hospital, New South Wales, Australia
| | - Dylan Gracias
- Department of Surgery, Townsville Hospital, Queensland, Australia
| | - Yi Xie
- Department of Plastic Surgery, Peninsula Health, Melbourne, Victoria, Australia
| | - Richard J. Ross
- Department of Plastic Surgery, Peninsula Health, Melbourne, Victoria, Australia
- Central Clinical School at Monash University, The Alfred Centre, Melbourne, Victoria, Australia
| | - Warren M. Rozen
- Department of Plastic Surgery, Peninsula Health, Melbourne, Victoria, Australia
- Central Clinical School at Monash University, The Alfred Centre, Melbourne, Victoria, Australia
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28
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Bou-Nassif R, Reiner AS, Pease M, Bale T, Cohen MA, Rosenblum M, Tabar V. Development and prospective validation of an artificial intelligence-based smartphone app for rapid intraoperative pituitary adenoma identification. COMMUNICATIONS MEDICINE 2024; 4:45. [PMID: 38480833 PMCID: PMC10937994 DOI: 10.1038/s43856-024-00469-z] [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: 05/03/2023] [Accepted: 02/28/2024] [Indexed: 03/17/2024] Open
Abstract
BACKGROUND Intraoperative pathology consultation plays a crucial role in tumor surgery. The ability to accurately and rapidly distinguish tumor from normal tissue can greatly impact intraoperative surgical oncology management. However, this is dependent on the availability of a specialized pathologist for a reliable diagnosis. We developed and prospectively validated an artificial intelligence-based smartphone app capable of differentiating between pituitary adenoma and normal pituitary gland using stimulated Raman histology, almost instantly. METHODS The study consisted of three parts. After data collection (part 1) and development of a deep learning-based smartphone app (part 2), we conducted a prospective study that included 40 consecutive patients with 194 samples to evaluate the app in real-time in a surgical setting (part 3). The smartphone app's sensitivity, specificity, positive predictive value, and negative predictive value were evaluated by comparing the diagnosis rendered by the app to the ground-truth diagnosis set by a neuropathologist. RESULTS The app exhibits a sensitivity of 96.1% (95% CI: 89.9-99.0%), specificity of 92.7% (95% CI: 74-99.3%), positive predictive value of 98% (95% CI: 92.2-99.8%), and negative predictive value of 86.4% (95% CI: 66.2-96.8%). An external validation of the smartphone app on 40 different adenoma tumors and a total of 191 scanned SRH specimens from a public database shows a sensitivity of 93.7% (95% CI: 89.3-96.7%). CONCLUSIONS The app can be readily expanded and repurposed to work on different types of tumors and optical images. Rapid recognition of normal versus tumor tissue during surgery may contribute to improved intraoperative surgical management and oncologic outcomes. In addition to the accelerated pathological assessments during surgery, this platform can be of great benefit in community hospitals and developing countries, where immediate access to a specialized pathologist during surgery is limited.
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Affiliation(s)
- Rabih Bou-Nassif
- Department of Neurosurgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Multidisciplinary Pituitary and Skull Base Tumor Center, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Anne S Reiner
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Matthew Pease
- Department of Neurosurgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Multidisciplinary Pituitary and Skull Base Tumor Center, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Tejus Bale
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Marc A Cohen
- Department of Neurosurgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Multidisciplinary Pituitary and Skull Base Tumor Center, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Department of Surgery, Head and Neck Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Marc Rosenblum
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Viviane Tabar
- Department of Neurosurgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
- Multidisciplinary Pituitary and Skull Base Tumor Center, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
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Rydzewski NR, Shi Y, Li C, Chrostek MR, Bakhtiar H, Helzer KT, Bootsma ML, Berg TJ, Harari PM, Floberg JM, Blitzer GC, Kosoff D, Taylor AK, Sharifi MN, Yu M, Lang JM, Patel KR, Citrin DE, Sundling KE, Zhao SG. A platform-independent AI tumor lineage and site (ATLAS) classifier. Commun Biol 2024; 7:314. [PMID: 38480799 PMCID: PMC10937974 DOI: 10.1038/s42003-024-05981-5] [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: 08/29/2023] [Accepted: 02/27/2024] [Indexed: 03/17/2024] Open
Abstract
Histopathologic diagnosis and classification of cancer plays a critical role in guiding treatment. Advances in next-generation sequencing have ushered in new complementary molecular frameworks. However, existing approaches do not independently assess both site-of-origin (e.g. prostate) and lineage (e.g. adenocarcinoma) and have minimal validation in metastatic disease, where classification is more difficult. Utilizing gradient-boosted machine learning, we developed ATLAS, a pair of separate AI Tumor Lineage and Site-of-origin models from RNA expression data on 8249 tumor samples. We assessed performance independently in 10,376 total tumor samples, including 1490 metastatic samples, achieving an accuracy of 91.4% for cancer site-of-origin and 97.1% for cancer lineage. High confidence predictions (encompassing the majority of cases) were accurate 98-99% of the time in both localized and remarkably even in metastatic samples. We also identified emergent properties of our lineage scores for tumor types on which the model was never trained (zero-shot learning). Adenocarcinoma/sarcoma lineage scores differentiated epithelioid from biphasic/sarcomatoid mesothelioma. Also, predicted lineage de-differentiation identified neuroendocrine/small cell tumors and was associated with poor outcomes across tumor types. Our platform-independent single-sample approach can be easily translated to existing RNA-seq platforms. ATLAS can complement and guide traditional histopathologic assessment in challenging situations and tumors of unknown primary.
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Affiliation(s)
- Nicholas R Rydzewski
- Radiation Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
- Department of Human Oncology, University of Wisconsin, Madison, WI, USA
| | - Yue Shi
- Department of Human Oncology, University of Wisconsin, Madison, WI, USA
| | - Chenxuan Li
- Department of Human Oncology, University of Wisconsin, Madison, WI, USA
| | | | - Hamza Bakhtiar
- Department of Human Oncology, University of Wisconsin, Madison, WI, USA
| | - Kyle T Helzer
- Department of Human Oncology, University of Wisconsin, Madison, WI, USA
| | - Matthew L Bootsma
- Department of Human Oncology, University of Wisconsin, Madison, WI, USA
| | - Tracy J Berg
- Department of Human Oncology, University of Wisconsin, Madison, WI, USA
| | - Paul M Harari
- Department of Human Oncology, University of Wisconsin, Madison, WI, USA
- Carbone Cancer Center, University of Wisconsin, Madison, WI, USA
| | - John M Floberg
- Department of Human Oncology, University of Wisconsin, Madison, WI, USA
- Carbone Cancer Center, University of Wisconsin, Madison, WI, USA
| | - Grace C Blitzer
- Department of Human Oncology, University of Wisconsin, Madison, WI, USA
- Carbone Cancer Center, University of Wisconsin, Madison, WI, USA
| | - David Kosoff
- Carbone Cancer Center, University of Wisconsin, Madison, WI, USA
- Department of Medicine, University of Wisconsin, Madison, WI, USA
| | - Amy K Taylor
- Carbone Cancer Center, University of Wisconsin, Madison, WI, USA
- Department of Medicine, University of Wisconsin, Madison, WI, USA
| | - Marina N Sharifi
- Carbone Cancer Center, University of Wisconsin, Madison, WI, USA
- Department of Medicine, University of Wisconsin, Madison, WI, USA
| | - Menggang Yu
- Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, WI, USA
| | - Joshua M Lang
- Carbone Cancer Center, University of Wisconsin, Madison, WI, USA
- Department of Medicine, University of Wisconsin, Madison, WI, USA
| | - Krishnan R Patel
- Radiation Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Deborah E Citrin
- Radiation Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Kaitlin E Sundling
- Department of Pathology and Laboratory Medicine, University of Wisconsin, Madison, WI, USA
- Wisconsin State Laboratory of Hygiene, University of Wisconsin, Madison, WI, USA
| | - Shuang G Zhao
- Department of Human Oncology, University of Wisconsin, Madison, WI, USA.
- Carbone Cancer Center, University of Wisconsin, Madison, WI, USA.
- William S. Middleton Veterans Hospital, Madison, WI, USA.
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Iwuajoku V, Haas A, Ekici K, Khan MZ, Stögbauer F, Steiger K, Mogler C, Schüffler PJ. [Digital transformation of a routine histopathology lab : Dos and don'ts!]. PATHOLOGIE (HEIDELBERG, GERMANY) 2024; 45:98-105. [PMID: 38189845 PMCID: PMC10902067 DOI: 10.1007/s00292-023-01291-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 11/15/2023] [Indexed: 01/09/2024]
Abstract
The implementation of digital histopathology in the laboratory marks a crucial milestone in the overall digital transformation of pathology. This shift offers a range of new possibilities, including access to extensive datasets for AI-assisted analyses, the flexibility of remote work and home office arrangements for specialists, and the expedited and simplified sharing of images and data for research, conferences, and tumor boards. However, the transition to a fully digital workflow involves significant technological and personnel-related efforts. It necessitates careful and adaptable change management to minimize disruptions, particularly in the personnel domain, and to prevent the loss of valuable potential from employees who may be resistant to change. This article consolidates our institute's experiences, highlighting technical and personnel-related challenges encountered during the transition to digital pathology. It also presents a comprehensive overview of potential difficulties at various interfaces when converting routine operations to a digital workflow.
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Affiliation(s)
- Viola Iwuajoku
- Institut für Pathologie, TUM School of Medicine and Health, Technische Universität München, Trogerstraße 18, 81675, München, Deutschland
| | - Anette Haas
- Institut für Pathologie, TUM School of Medicine and Health, Technische Universität München, Trogerstraße 18, 81675, München, Deutschland
| | - Kübra Ekici
- Institut für Pathologie, TUM School of Medicine and Health, Technische Universität München, Trogerstraße 18, 81675, München, Deutschland
| | - Mohammad Zaid Khan
- Institut für Pathologie, TUM School of Medicine and Health, Technische Universität München, Trogerstraße 18, 81675, München, Deutschland
| | - Fabian Stögbauer
- Institut für Pathologie, TUM School of Medicine and Health, Technische Universität München, Trogerstraße 18, 81675, München, Deutschland
| | - Katja Steiger
- Institut für Pathologie, TUM School of Medicine and Health, Technische Universität München, Trogerstraße 18, 81675, München, Deutschland
| | - Carolin Mogler
- Institut für Pathologie, TUM School of Medicine and Health, Technische Universität München, Trogerstraße 18, 81675, München, Deutschland
| | - Peter J Schüffler
- Institut für Pathologie, TUM School of Medicine and Health, Technische Universität München, Trogerstraße 18, 81675, München, Deutschland.
- TUM School of Computational Information and Technology, Technische Universität München, München, Deutschland.
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Rozario SY, Sarkar M, Farlie MK, Lazarus MD. Responding to the healthcare workforce shortage: A scoping review exploring anatomical pathologists' professional identities over time. ANATOMICAL SCIENCES EDUCATION 2024; 17:351-365. [PMID: 36748328 DOI: 10.1002/ase.2260] [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: 06/06/2022] [Revised: 01/16/2023] [Accepted: 02/06/2023] [Indexed: 06/18/2023]
Abstract
Anatomical pathology (AP) is an anatomy-centric medical specialty devoted to tissue-based diagnosis of disease. The field faces a current and predicted workforce shortage, likely increasing diagnostic wait times and delaying patient access to urgent treatment. A lack of AP exposure is proposed to preclude recruitment to the field, as medical students are afforded only a limited understanding of who a pathologist is and what they do (their professional identity/PI and role). Anatomical sciences educators may be well placed to increase student understanding of anatomical pathologists' PI features, but until features of anatomical pathologists' PI are understood, recommendations for anatomy educators are premature. Thus, this scoping review asked: "What are the professional identity features of anatomical pathologists reported in the literature, and how have these changed over time?" A six-stage scoping review was performed. Medline and PubMed, Global Health, and Embase were used to identify relevant studies (n = 74). Team-based framework analysis identified that features of anatomical pathologists' professional identity encompass five overarching themes: professional practice, views about the role, training and education, personal implications, and technology. Technology was identified as an important theme of anatomical pathologists' PI, as it intersected with many other PI feature themes, including diagnosis and collaboration. This review found that pathologists may sometimes perceive professional competition with technology, such as artificial intelligence. These findings suggest unique opportunities for integrating AP-specific PI features into anatomy teaching, which may foster student interest in AP, and potentially increase recruitment into the field.
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Affiliation(s)
- Shemona Y Rozario
- Centre for Human Anatomy Education (CHAE), Department of Anatomy and Developmental Biology, Biomedical Discovery Institute, Faculty of Medicine, Nursing and Health Sciences, Monash University, Clayton, Victoria, Australia
| | - Mahbub Sarkar
- Monash Centre for Scholarship in Health Education (MCSHE), Faculty of Medicine, Nursing and Health Sciences, Monash University, Clayton, Victoria, Australia
| | - Melanie K Farlie
- Monash Centre for Scholarship in Health Education (MCSHE), Faculty of Medicine, Nursing and Health Sciences, Monash University, Clayton, Victoria, Australia
- Department of Physiotherapy, Faculty of Medicine Nursing and Health Sciences, Monash University, Clayton, Victoria, Australia
| | - Michelle D Lazarus
- Centre for Human Anatomy Education (CHAE), Department of Anatomy and Developmental Biology, Biomedical Discovery Institute, Faculty of Medicine, Nursing and Health Sciences, Monash University, Clayton, Victoria, Australia
- Monash Centre for Scholarship in Health Education (MCSHE), Faculty of Medicine, Nursing and Health Sciences, Monash University, Clayton, Victoria, Australia
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Busby D, Grauer R, Pandav K, Khosla A, Jain P, Menon M, Haines GK, Cordon-Cardo C, Gorin MA, Tewari AK. Applications of artificial intelligence in prostate cancer histopathology. Urol Oncol 2024; 42:37-47. [PMID: 36639335 DOI: 10.1016/j.urolonc.2022.12.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Revised: 11/27/2022] [Accepted: 12/03/2022] [Indexed: 01/12/2023]
Abstract
The diagnosis of prostate cancer (PCa) depends on the evaluation of core needle biopsies by trained pathologists. Artificial intelligence (AI) derived models have been created to address the challenges posed by pathologists' increasing workload, workforce shortages, and variability in histopathology assessment. These models with histopathological parameters integrated into sophisticated neural networks demonstrate remarkable ability to identify, grade, and predict outcomes for PCa. Though the fully autonomous diagnosis of PCa remains elusive, recently published data suggests that AI has begun to serve as an initial screening tool, an assistant in the form of a real-time interactive interface during histological analysis, and as a second read system to detect false negative diagnoses. Our article aims to describe recent advances and future opportunities for AI in PCa histopathology.
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Affiliation(s)
- Dallin Busby
- Department of Urology, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Ralph Grauer
- Department of Urology, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Krunal Pandav
- Department of Urology, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Akshita Khosla
- Department of Internal Medicine, Crozer Chester Medical Center, Philadelphia, PA
| | | | - Mani Menon
- Department of Urology, Icahn School of Medicine at Mount Sinai, New York, NY
| | - G Kenneth Haines
- Department of Urology, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Carlos Cordon-Cardo
- Department of Pathology, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Michael A Gorin
- Department of Urology, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Ashutosh K Tewari
- Department of Urology, Icahn School of Medicine at Mount Sinai, New York, NY.
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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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Kim M, Quiñones Robles WR, Ko YS, Wong B, Lee S, Yi MY. A predicted-loss based active learning approach for robust cancer pathology image analysis in the workplace. BMC Med Imaging 2024; 24:5. [PMID: 38166690 PMCID: PMC10763414 DOI: 10.1186/s12880-023-01170-8] [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: 07/27/2023] [Accepted: 12/01/2023] [Indexed: 01/05/2024] Open
Abstract
BACKGROUND Convolutional neural network-based image processing research is actively being conducted for pathology image analysis. As a convolutional neural network model requires a large amount of image data for training, active learning (AL) has been developed to produce efficient learning with a small amount of training data. However, existing studies have not specifically considered the characteristics of pathological data collected from the workplace. For various reasons, noisy patches can be selected instead of clean patches during AL, thereby reducing its efficiency. This study proposes an effective AL method for cancer pathology that works robustly on noisy datasets. METHODS Our proposed method to develop a robust AL approach for noisy histopathology datasets consists of the following three steps: 1) training a loss prediction module, 2) collecting predicted loss values, and 3) sampling data for labeling. This proposed method calculates the amount of information in unlabeled data as predicted loss values and removes noisy data based on predicted loss values to reduce the rate at which noisy data are selected from the unlabeled dataset. We identified a suitable threshold for optimizing the efficiency of AL through sensitivity analysis. RESULTS We compared the results obtained with the identified threshold with those of existing representative AL methods. In the final iteration, the proposed method achieved a performance of 91.7% on the noisy dataset and 92.4% on the clean dataset, resulting in a performance reduction of less than 1%. Concomitantly, the noise selection ratio averaged only 2.93% on each iteration. CONCLUSIONS The proposed AL method showed robust performance on datasets containing noisy data by avoiding data selection in predictive loss intervals where noisy data are likely to be distributed. The proposed method contributes to medical image analysis by screening data and producing a robust and effective classification model tailored for cancer pathology image processing in the workplace.
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Grants
- G01180115 Seegene Medical Foundation, South Korea, under the project "Research on Developing a Next Generation Medical Diagnosis System Using Deep Learning"
- G01180115 Seegene Medical Foundation, South Korea, under the project "Research on Developing a Next Generation Medical Diagnosis System Using Deep Learning"
- G01180115 Seegene Medical Foundation, South Korea, under the project "Research on Developing a Next Generation Medical Diagnosis System Using Deep Learning"
- G01180115 Seegene Medical Foundation, South Korea, under the project "Research on Developing a Next Generation Medical Diagnosis System Using Deep Learning"
- G01180115 Seegene Medical Foundation, South Korea, under the project "Research on Developing a Next Generation Medical Diagnosis System Using Deep Learning"
- G01180115 Seegene Medical Foundation, South Korea, under the project "Research on Developing a Next Generation Medical Diagnosis System Using Deep Learning"
- Seegene Medical Foundation, South Korea, under the project “Research on Developing a Next Generation Medical Diagnosis System Using Deep Learning”
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Affiliation(s)
- Mujin Kim
- Graduate School of Data Science, Department of Industrial and Systems Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea
| | - Willmer Rafell Quiñones Robles
- Graduate School of Data Science, Department of Industrial and Systems Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea
| | - Young Sin Ko
- Pathology Center, Seegene Medical Foundation, Seoul, South Korea
| | - Bryan Wong
- Graduate School of Data Science, Department of Industrial and Systems Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea
| | - Sol Lee
- Graduate School of Data Science, Department of Industrial and Systems Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea
| | - Mun Yong Yi
- Graduate School of Data Science, Department of Industrial and Systems Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea.
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Khoshpouri P, Khalili N, Khalili N, Sherbaf FG, Glastonbury CM, Yousem DM. Visa Opportunities for International Medical Graduates Applying for U.S. Academic Radiology Department Faculty Positions: A National Survey. AJR Am J Roentgenol 2024; 222:e2330008. [PMID: 37910038 DOI: 10.2214/ajr.23.30008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2023]
Abstract
BACKGROUND. International medical graduates (IMGs) are a source of physicians who could help alleviate radiologist workforce shortages in the United States. However, IMGs may face barriers in obtaining appropriate visas (e.g., H-1B or O-1 visas) to allow faculty employment. OBJECTIVE. The purpose of this study was to assess the policies and experiences of U.S. academic radiology departments in offering visas to IMGs applying for faculty positions. METHODS. A web-based survey on policies and experiences in offering visas to IMG faculty candidates was distributed to chairs of U.S. radiology departments with a diagnostic radiology training program recognized by the National Resident Matching Program. Individual survey questions were optional. The initial survey and subsequent reminders were sent from October 7, 2022, through November 7, 2022. RESULTS. The survey response rate was 81% (143/177). A total of 24% (28/115), 38% (44/115), 17% (20/115), and 20% (23/115) of departments offered H-1B visas to IMG faculty frequently, sometimes, rarely, and never, respectively; 3% (3/113), 27% (31/113), 22% (25/113), and 48% (54/113) of departments offered O-1 visas frequently, sometimes, rarely, and never, respectively. However, 41% (46/113) and 5% (6/113) of departments had default policies of offering H-1B and O-1 visas for IMG faculty candidates, respectively. The most common reasons given for why departments did not offer visas included, for both H-1B and O-1 visas, the time-consuming process, lack of reliability of candidates' starting time, and the expense of the visa application; for O-1 visas, the reasons given also included lack of expertise. A total of 15% (16/108) of departments set their own visa policies, 75% (81/108) followed institutional policies, and 10% (11/108) followed policies set by other entities (e.g., state government). CONCLUSION. Although to at least some extent most U.S. academic radiology departments offer H-1B and O-1 visas for IMGs seeking faculty positions, use of such visas typically is not the departments' default policy. A variety of barriers contributed to visas not being offered. The departments' visa policies were primarily determined at the institutional level. CLINICAL IMPACT. The identified barriers faced by U.S. academic radiology departments in offering visas to IMG faculty candidates impact the role of IMGs in helping to address radiologist workforce shortages.
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Affiliation(s)
- Parisa Khoshpouri
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins Medical Institution, Baltimore, MD
| | | | - Neda Khalili
- Children's Hospital of Philadelphia, Philadelphia, PA
| | - Farzaneh G Sherbaf
- Department of Radiology, University of California San Diego School of Medicine, La Jolla, CA
| | - Christine M Glastonbury
- Department of Radiology & Biomedical Imaging, University of California San Francisco, 505 Parnassus Ave, San Francisco, CA 94117
| | - David M Yousem
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins Medical Institution, Baltimore, MD
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36
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Liang H, Li Z, Lin W, Xie Y, Zhang S, Li Z, Luo H, Li T, Han S. Enhancing Gastrointestinal Stromal Tumor (GIST) Diagnosis: An Improved YOLOv8 Deep Learning Approach for Precise Mitotic Detection. IEEE ACCESS 2024; 12:116829-116840. [DOI: 10.1109/access.2024.3446613] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/14/2024]
Affiliation(s)
- Haoxin Liang
- Department of General Surgery, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Zhichun Li
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, SAR, China
| | - Weijie Lin
- The Second Clinical College, Southern Medical University, Guangzhou, Guangdong, China
| | - Yuheng Xie
- The Second Clinical College, Southern Medical University, Guangzhou, Guangdong, China
| | - Shuo Zhang
- School of Instrument Science and Engineering, Southeast University, Nanjing, Jiangsu, China
| | - Zhou Li
- Department of General Surgery, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Hongyu Luo
- Department of General Surgery, The Sixth People’s Hospital of Huizhou City, Huizhou, China
| | - Tian Li
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, SAR, China
| | - Shuai Han
- Department of General Surgery, Zhujiang Hospital, Southern Medical University, Guangzhou, China
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Rohr JM, Ginnebaugh K, Tuthill M, Pimentel J, Markin R. Real-Time Telepathology Is Substantially Equivalent to In-Person Intraoperative Frozen Section Diagnosis. Arch Pathol Lab Med 2024; 148:68-73. [PMID: 36920004 DOI: 10.5858/arpa.2022-0261-oa] [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: 01/03/2023] [Indexed: 03/16/2023]
Abstract
CONTEXT.— Intraoperative diagnosis by frozen section is a mainstay of surgical pathology practice, providing immediate feedback to the surgical team. Despite good accuracy with modern methods, access to intraoperative surgical pathology with an appropriate turnaround time (TAT) has been a limiting factor for small or remote surgical centers, with negative impacts on cost and patient care. Telepathology offers immediate expert anatomic pathology consultation to sites without an in-house or subspecialized pathologist. OBJECTIVE.— To assess the utility of live telepathology in frozen section practice. DESIGN.— Frozen section diagnoses by telemicroscopy from 2 tertiary care centers with a combined 3 satellite hospitals were queried for anatomic site, TAT per block, pathologist, and concordance with paraffin diagnosis. TAT and concordance were compared to glass diagnoses in the same period. RESULTS.— For 748 intraoperative diagnoses by telemicroscopy, 694 had TATs with a mean of 18 minutes 56 seconds ± 8 minutes 45 seconds, which was slower than on glass (14 minutes 25 seconds ± 7 minutes 8 seconds, P < .001). Twenty-two (2.89% of available) were discordant, which was not significantly different from the on-glass rate (P = .44) or categorical distribution (P = .31). Two cases (0.27%) had technical failures. CONCLUSIONS.— Although in-person diagnoses were statistically faster, the great majority of telemicroscopic diagnoses were returned in less than 20 minutes. This remained true through numerous pathologists, pathology assistants and/or technicians, different hospitals, and during a combined 6 years. The concentration of discordant diagnoses among relatively few pathologists suggests individual comfort with telepathology and/or frozen section diagnosis. In rare cases, technical issues prevented telemicroscopic diagnosis. Overall, this justifies continued use and expansion of telemicroscopic services in primary intraoperative diagnoses.
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Affiliation(s)
- Joseph M Rohr
- From the Department of Pathology and Microbiology, University of Nebraska Medical Center, Omaha (Rohr, Markin)
| | - Kevin Ginnebaugh
- the Department of Pathology and Laboratory Medicine, Henry Ford Health System, Detroit, Michigan (Ginnebaugh, Tuthill, Pimentel)
| | - Mark Tuthill
- the Department of Pathology and Laboratory Medicine, Henry Ford Health System, Detroit, Michigan (Ginnebaugh, Tuthill, Pimentel)
| | - Jason Pimentel
- the Department of Pathology and Laboratory Medicine, Henry Ford Health System, Detroit, Michigan (Ginnebaugh, Tuthill, Pimentel)
| | - Rodney Markin
- From the Department of Pathology and Microbiology, University of Nebraska Medical Center, Omaha (Rohr, Markin)
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Sato J, Matsumoto T, Nakao R, Tanaka H, Nagahara H, Niioka H, Takamatsu T. Deep UV-excited fluorescence microscopy installed with CycleGAN-assisted image translation enhances precise detection of lymph node metastasis towards rapid intraoperative diagnosis. Sci Rep 2023; 13:21363. [PMID: 38049475 PMCID: PMC10696085 DOI: 10.1038/s41598-023-48319-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Accepted: 11/24/2023] [Indexed: 12/06/2023] Open
Abstract
Rapid and precise intraoperative diagnosing systems are required for improving surgical outcomes and patient prognosis. Because of the poor quality and time-intensive process of the prevalent frozen section procedure, various intraoperative diagnostic imaging systems have been explored. Microscopy with ultraviolet surface excitation (MUSE) is an inexpensive, maintenance-free, and rapid imaging technique that yields images like thin-sectioned samples without sectioning. However, pathologists find it nearly impossible to assign diagnostic labels to MUSE images of unfixed specimens; thus, AI for intraoperative diagnosis cannot be trained in a supervised learning manner. In this study, we propose a deep-learning pipeline model for lymph node metastasis detection, in which CycleGAN translate MUSE images of unfixed lymph nodes to formalin-fixed paraffin-embedded (FFPE) sample, and diagnostic prediction is performed using deep convolutional neural network trained on FFPE sample images. Our pipeline yielded an average accuracy of 84.6% when using each of the three deep convolutional neural networks, which is a 18.3% increase over the classification-only model without CycleGAN. The modality translation to FFPE sample images using CycleGAN can be applied to various intraoperative diagnostic imaging systems and eliminate the difficulty for pathologists in labeling new modality images in clinical sites. We anticipate our pipeline to be a starting point for accurate rapid intraoperative diagnostic systems for new imaging modalities, leading to healthcare quality improvement.
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Affiliation(s)
- Junya Sato
- Graduate School of Information Science and Technology, Osaka University, 1-5, Yamadaoka, Suita, Osaka, 565-0871, Japan
- Department of Radiology, Osaka University Graduate School of Medicine, 2-2, Yamadaoka, Suita, Osaka, 565-0871, Japan
- Department of Artificial Intelligence Diagnostic Radiology, Osaka University Graduate School of Medicine, 2-2, Yamadaoka, Suita, Osaka, 565-0871, Japan
| | - Tatsuya Matsumoto
- Department of Pathology and Cell Regulation, Kyoto Prefectural University of Medicine, 465 Kajiicho, Kawaramachi-Hirokoji, Kamigyo-ku, Kyoto, 602-8566, Japan
| | - Ryuta Nakao
- Department of Pathology and Cell Regulation, Kyoto Prefectural University of Medicine, 465 Kajiicho, Kawaramachi-Hirokoji, Kamigyo-ku, Kyoto, 602-8566, Japan
| | - Hideo Tanaka
- Department of Pathology and Cell Regulation, Kyoto Prefectural University of Medicine, 465 Kajiicho, Kawaramachi-Hirokoji, Kamigyo-ku, Kyoto, 602-8566, Japan
| | - Hajime Nagahara
- Graduate School of Information Science and Technology, Osaka University, 1-5, Yamadaoka, Suita, Osaka, 565-0871, Japan
- Institute for Datability Science, Osaka University, 2-8 Yamadaoka, Suita, 565-0871, Japan
| | - Hirohiko Niioka
- Graduate School of Information Science and Technology, Osaka University, 1-5, Yamadaoka, Suita, Osaka, 565-0871, Japan.
- Institute for Datability Science, Osaka University, 2-8 Yamadaoka, Suita, 565-0871, Japan.
| | - Tetsuro Takamatsu
- Department of Pathology and Cell Regulation, Kyoto Prefectural University of Medicine, 465 Kajiicho, Kawaramachi-Hirokoji, Kamigyo-ku, Kyoto, 602-8566, Japan.
- Department of Medical Photonics, Kyoto Prefectural University of Medicine, 465 Kajiicho, Kawaramachi-Hirokoji, Kamigyo-ku, Kyoto, 602-8566, Japan.
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Erion Barner LA, Gao G, Reddi DM, Lan L, Burke W, Mahmood F, Grady WM, Liu JTC. Artificial Intelligence-Triaged 3-Dimensional Pathology to Improve Detection of Esophageal Neoplasia While Reducing Pathologist Workloads. Mod Pathol 2023; 36:100322. [PMID: 37657711 DOI: 10.1016/j.modpat.2023.100322] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2023] [Revised: 07/25/2023] [Accepted: 08/25/2023] [Indexed: 09/03/2023]
Abstract
Early detection of esophageal neoplasia via evaluation of endoscopic surveillance biopsies is the key to maximizing survival for patients with Barrett's esophagus, but it is hampered by the sampling limitations of conventional slide-based histopathology. Comprehensive evaluation of whole biopsies with 3-dimensional (3D) pathology may improve early detection of malignancies, but large 3D pathology data sets are tedious for pathologists to analyze. Here, we present a deep learning-based method to automatically identify the most critical 2-dimensional (2D) image sections within 3D pathology data sets for pathologists to review. Our method first generates a 3D heatmap of neoplastic risk for each biopsy, then classifies all 2D image sections within the 3D data set in order of neoplastic risk. In a clinical validation study, we diagnose esophageal biopsies with artificial intelligence-triaged 3D pathology (3 images per biopsy) vs standard slide-based histopathology (16 images per biopsy) and show that our method improves detection sensitivity while reducing pathologist workloads.
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Affiliation(s)
| | - Gan Gao
- Department of Mechanical Engineering, University of Washington, Seattle, Washington
| | - Deepti M Reddi
- Department of Laboratory Medicine & Pathology, University of Washington School of Medicine, Seattle, Washington
| | - Lydia Lan
- Department of Mechanical Engineering, University of Washington, Seattle, Washington; Department of Biology, University of Washington, Seattle, Washington
| | - Wynn Burke
- Department of Laboratory Medicine & Pathology, University of Washington School of Medicine, Seattle, Washington; Department of Medicine (Gastroenterology Division), University of Washington School of Medicine, Seattle, Washington
| | - Faisal Mahmood
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts; Cancer Program, Broad Institute of Harvard and MIT, Cambridge, Massachusetts; Harvard Data Science Initiative, Harvard University, Cambridge, Massachusetts
| | - William M Grady
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Jonathan T C Liu
- Department of Mechanical Engineering, University of Washington, Seattle, Washington; Department of Laboratory Medicine & Pathology, University of Washington School of Medicine, Seattle, Washington; Department of Bioengineering, University of Washington, Seattle, Washington.
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40
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Kataria T, Rajamani S, Ayubi AB, Bronner M, Jedrzkiewicz J, Knudsen BS, Elhabian SY. Automating Ground Truth Annotations for Gland Segmentation Through Immunohistochemistry. Mod Pathol 2023; 36:100331. [PMID: 37716506 DOI: 10.1016/j.modpat.2023.100331] [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: 03/21/2023] [Revised: 08/14/2023] [Accepted: 09/08/2023] [Indexed: 09/18/2023]
Abstract
Microscopic evaluation of glands in the colon is of utmost importance in the diagnosis of inflammatory bowel disease and cancer. When properly trained, deep learning pipelines can provide a systematic, reproducible, and quantitative assessment of disease-related changes in glandular tissue architecture. The training and testing of deep learning models require large amounts of manual annotations, which are difficult, time-consuming, and expensive to obtain. Here, we propose a method for automated generation of ground truth in digital hematoxylin and eosin (H&E)-stained slides using immunohistochemistry (IHC) labels. The image processing pipeline generates annotations of glands in H&E histopathology images from colon biopsy specimens by transfer of gland masks from KRT8/18, CDX2, or EPCAM IHC. The IHC gland outlines are transferred to coregistered H&E images for training of deep learning models. We compared the performance of the deep learning models to that of manual annotations using an internal held-out set of biopsy specimens as well as 2 public data sets. Our results show that EPCAM IHC provides gland outlines that closely match manual gland annotations (Dice = 0.89) and are resilient to damage by inflammation. In addition, we propose a simple data sampling technique that allows models trained on data from several sources to be adapted to a new data source using just a few newly annotated samples. The best performing models achieved average Dice scores of 0.902 and 0.89 on Gland Segmentation and Colorectal Adenocarcinoma Gland colon cancer public data sets, respectively, when trained with only 10% of annotated cases from either public cohort. Altogether, the performances of our models indicate that automated annotations using cell type-specific IHC markers can safely replace manual annotations. Automated IHC labels from single-institution cohorts can be combined with small numbers of hand-annotated cases from multi-institutional cohorts to train models that generalize well to diverse data sources.
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Affiliation(s)
- Tushar Kataria
- Kahlert School of Computing, University of Utah, Salt Lake City, Utah; Kahlert School of Computing, Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, Utah
| | - Saradha Rajamani
- Kahlert School of Computing, University of Utah, Salt Lake City, Utah; Kahlert School of Computing, Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, Utah
| | - Abdul Bari Ayubi
- Department of Pathology, University of Utah, Salt Lake City, Utah
| | - Mary Bronner
- Department of Pathology, University of Utah, Salt Lake City, Utah; Department of Pathology, ARUP Laboratories, Salt Lake City, Utah
| | - Jolanta Jedrzkiewicz
- Department of Pathology, University of Utah, Salt Lake City, Utah; Department of Pathology, ARUP Laboratories, Salt Lake City, Utah
| | - Beatrice S Knudsen
- Kahlert School of Computing, Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, Utah; Department of Pathology, University of Utah, Salt Lake City, Utah.
| | - Shireen Y Elhabian
- Kahlert School of Computing, University of Utah, Salt Lake City, Utah; Kahlert School of Computing, Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, Utah.
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41
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Lee J, Han C, Kim K, Park GH, Kwak JT. CaMeL-Net: Centroid-aware metric learning for efficient multi-class cancer classification in pathology images. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 241:107749. [PMID: 37579551 DOI: 10.1016/j.cmpb.2023.107749] [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: 05/09/2023] [Revised: 07/25/2023] [Accepted: 08/05/2023] [Indexed: 08/16/2023]
Abstract
BACKGROUND AND OBJECTIVE Cancer grading in pathology image analysis is a major task due to its importance in patient care, treatment, and management. The recent developments in artificial neural networks for computational pathology have demonstrated great potential to improve the accuracy and quality of cancer diagnosis. These improvements are generally ascribable to the advance in the architecture of the networks, often leading to increase in the computation and resources. In this work, we propose an efficient convolutional neural network that is designed to conduct multi-class cancer classification in an accurate and robust manner via metric learning. METHODS We propose a centroid-aware metric learning network for an improved cancer grading in pathology images. The proposed network utilizes centroids of different classes within the feature embedding space to optimize the relative distances between pathology images, which manifest the innate similarities/dissimilarities between them. For improved optimization, we introduce a new loss function and a training strategy that are tailored to the proposed network and metric learning. RESULTS We evaluated the proposed approach on multiple datasets of colorectal and gastric cancers. For the colorectal cancer, two different datasets were employed that were collected from different acquisition settings. the proposed method achieved an accuracy, F1-score, quadratic weighted kappa of 88.7%, 0.849, and 0.946 for the first dataset and 83.3%, 0.764, and 0.907 for the second dataset, respectively. For the gastric cancer, the proposed method obtained an accuracy of 85.9%, F1-score of 0.793, and quadratic weighted kappa of 0.939. We also found that the proposed method outperforms other competing models and is computationally efficient. CONCLUSIONS The experimental results demonstrate that the prediction results by the proposed network are both accurate and reliable. The proposed network not only outperformed other related methods in cancer classification but also achieved superior computational efficiency during training and inference. The future study will entail further development of the proposed method and the application of the method to other problems and domains.
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Affiliation(s)
- Jaeung Lee
- School of Electrical Engineering, Korea University, Seoul, Republic of Korea
| | - Chiwon Han
- Department of Computer Science and Engineering, Sejong University, Seoul, Republic of Korea
| | - Kyungeun Kim
- Department of Pathology, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Gi-Ho Park
- Department of Computer Science and Engineering, Sejong University, Seoul, Republic of Korea
| | - Jin Tae Kwak
- School of Electrical Engineering, Korea University, Seoul, Republic of Korea.
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42
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Hanna MG, Brogi E. Future Practices of Breast Pathology Using Digital and Computational Pathology. Adv Anat Pathol 2023; 30:421-433. [PMID: 37737690 DOI: 10.1097/pap.0000000000000414] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/23/2023]
Abstract
Pathology clinical practice has evolved by adopting technological advancements initially regarded as potentially disruptive, such as electron microscopy, immunohistochemistry, and genomic sequencing. Breast pathology has a critical role as a medical domain, where the patient's pathology diagnosis has significant implications for prognostication and treatment of diseases. The advent of digital and computational pathology has brought about significant advancements in the field, offering new possibilities for enhancing diagnostic accuracy and improving patient care. Digital slide scanning enables to conversion of glass slides into high-fidelity digital images, supporting the review of cases in a digital workflow. Digitization offers the capability to render specimen diagnoses, digital archival of patient specimens, collaboration, and telepathology. Integration of image analysis and machine learning-based systems layered atop the high-resolution digital images offers novel workflows to assist breast pathologists in their clinical, educational, and research endeavors. Decision support tools may improve the detection and classification of breast lesions and the quantification of immunohistochemical studies. Computational biomarkers may help to contribute to patient management or outcomes. Furthermore, using digital and computational pathology may increase standardization and quality assurance, especially in areas with high interobserver variability. This review explores the current landscape and possible future applications of digital and computational techniques in the field of breast pathology.
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Affiliation(s)
- Matthew G Hanna
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY
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Yang Y, Sun K, Gao Y, Wang K, Yu G. Preparing Data for Artificial Intelligence in Pathology with Clinical-Grade Performance. Diagnostics (Basel) 2023; 13:3115. [PMID: 37835858 PMCID: PMC10572440 DOI: 10.3390/diagnostics13193115] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Revised: 09/27/2023] [Accepted: 09/28/2023] [Indexed: 10/15/2023] Open
Abstract
The pathology is decisive for disease diagnosis but relies heavily on experienced pathologists. In recent years, there has been growing interest in the use of artificial intelligence in pathology (AIP) to enhance diagnostic accuracy and efficiency. However, the impressive performance of deep learning-based AIP in laboratory settings often proves challenging to replicate in clinical practice. As the data preparation is important for AIP, the paper has reviewed AIP-related studies in the PubMed database published from January 2017 to February 2022, and 118 studies were included. An in-depth analysis of data preparation methods is conducted, encompassing the acquisition of pathological tissue slides, data cleaning, screening, and subsequent digitization. Expert review, image annotation, dataset division for model training and validation are also discussed. Furthermore, we delve into the reasons behind the challenges in reproducing the high performance of AIP in clinical settings and present effective strategies to enhance AIP's clinical performance. The robustness of AIP depends on a randomized collection of representative disease slides, incorporating rigorous quality control and screening, correction of digital discrepancies, reasonable annotation, and sufficient data volume. Digital pathology is fundamental in clinical-grade AIP, and the techniques of data standardization and weakly supervised learning methods based on whole slide image (WSI) are effective ways to overcome obstacles of performance reproduction. The key to performance reproducibility lies in having representative data, an adequate amount of labeling, and ensuring consistency across multiple centers. Digital pathology for clinical diagnosis, data standardization and the technique of WSI-based weakly supervised learning will hopefully build clinical-grade AIP.
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Affiliation(s)
- Yuanqing Yang
- Department of Biomedical Engineering, School of Basic Medical Sciences, Central South University, Changsha 410013, China; (Y.Y.); (K.S.)
- Department of Biomedical Engineering, School of Medical, Tsinghua University, Beijing 100084, China
| | - Kai Sun
- Department of Biomedical Engineering, School of Basic Medical Sciences, Central South University, Changsha 410013, China; (Y.Y.); (K.S.)
- Furong Laboratory, Changsha 410013, China
| | - Yanhua Gao
- Department of Ultrasound, Shaanxi Provincial People’s Hospital, Xi’an 710068, China;
| | - Kuansong Wang
- Department of Pathology, School of Basic Medical Sciences, Central South University, Changsha 410013, China;
- Department of Pathology, Xiangya Hospital, Central South University, Changsha 410013, China
| | - Gang Yu
- Department of Biomedical Engineering, School of Basic Medical Sciences, Central South University, Changsha 410013, China; (Y.Y.); (K.S.)
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44
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Ahn JS, Shin S, Yang SA, Park EK, Kim KH, Cho SI, Ock CY, Kim S. Artificial Intelligence in Breast Cancer Diagnosis and Personalized Medicine. J Breast Cancer 2023; 26:405-435. [PMID: 37926067 PMCID: PMC10625863 DOI: 10.4048/jbc.2023.26.e45] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 09/25/2023] [Accepted: 10/06/2023] [Indexed: 11/07/2023] Open
Abstract
Breast cancer is a significant cause of cancer-related mortality in women worldwide. Early and precise diagnosis is crucial, and clinical outcomes can be markedly enhanced. The rise of artificial intelligence (AI) has ushered in a new era, notably in image analysis, paving the way for major advancements in breast cancer diagnosis and individualized treatment regimens. In the diagnostic workflow for patients with breast cancer, the role of AI encompasses screening, diagnosis, staging, biomarker evaluation, prognostication, and therapeutic response prediction. Although its potential is immense, its complete integration into clinical practice is challenging. Particularly, these challenges include the imperatives for extensive clinical validation, model generalizability, navigating the "black-box" conundrum, and pragmatic considerations of embedding AI into everyday clinical environments. In this review, we comprehensively explored the diverse applications of AI in breast cancer care, underlining its transformative promise and existing impediments. In radiology, we specifically address AI in mammography, tomosynthesis, risk prediction models, and supplementary imaging methods, including magnetic resonance imaging and ultrasound. In pathology, our focus is on AI applications for pathologic diagnosis, evaluation of biomarkers, and predictions related to genetic alterations, treatment response, and prognosis in the context of breast cancer diagnosis and treatment. Our discussion underscores the transformative potential of AI in breast cancer management and emphasizes the importance of focused research to realize the full spectrum of benefits of AI in patient care.
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Affiliation(s)
| | | | | | | | | | | | | | - Seokhwi Kim
- Department of Pathology, Ajou University School of Medicine, Suwon, Korea
- Department of Biomedical Sciences, Ajou University Graduate School of Medicine, Suwon, Korea.
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45
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Neary-Zajiczek L, Beresna L, Razavi B, Pawar V, Shaw M, Stoyanov D. Minimum resolution requirements of digital pathology images for accurate classification. Med Image Anal 2023; 89:102891. [PMID: 37536022 DOI: 10.1016/j.media.2023.102891] [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: 04/19/2022] [Revised: 05/22/2023] [Accepted: 07/06/2023] [Indexed: 08/05/2023]
Abstract
Digitization of pathology has been proposed as an essential mitigation strategy for the severe staffing crisis facing most pathology departments. Despite its benefits, several barriers have prevented widespread adoption of digital workflows, including cost and pathologist reluctance due to subjective image quality concerns. In this work, we quantitatively determine the minimum image quality requirements for binary classification of histopathology images of breast tissue in terms of spatial and sampling resolution. We train an ensemble of deep learning classifier models on publicly available datasets to obtain a baseline accuracy and computationally degrade these images according to our derived theoretical model to identify the minimum resolution necessary for acceptable diagnostic accuracy. Our results show that images can be degraded significantly below the resolution of most commercial whole-slide imaging systems while maintaining reasonable accuracy, demonstrating that macroscopic features are sufficient for binary classification of stained breast tissue. A rapid low-cost imaging system capable of identifying healthy tissue not requiring human assessment could serve as a triage system for reducing caseloads and alleviating the significant strain on the current workforce.
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Affiliation(s)
- Lydia Neary-Zajiczek
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, Charles Bell House, 43-45 Foley Street, Fitzrovia, London, W1W 7TS, United Kingdom; Department of Computer Science, University College London, Gower Street, London, WC1E 6BT, United Kingdom.
| | - Linas Beresna
- Department of Computer Science, University College London, Gower Street, London, WC1E 6BT, United Kingdom
| | - Benjamin Razavi
- University College London Medical School, 74 Huntley Street, London, WC1E 6BT, United Kingdom
| | - Vijay Pawar
- Department of Computer Science, University College London, Gower Street, London, WC1E 6BT, United Kingdom
| | - Michael Shaw
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, Charles Bell House, 43-45 Foley Street, Fitzrovia, London, W1W 7TS, United Kingdom; Department of Computer Science, University College London, Gower Street, London, WC1E 6BT, United Kingdom; National Physical Laboratory, Hampton Road, Teddington, TW11 0LW, United Kingdom
| | - Danail Stoyanov
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, Charles Bell House, 43-45 Foley Street, Fitzrovia, London, W1W 7TS, United Kingdom; Department of Computer Science, University College London, Gower Street, London, WC1E 6BT, United Kingdom
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46
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Movahed-Ezazi M, Nasir-Moin M, Fang C, Pizzillo I, Galbraith K, Drexler S, Krasnozhen-Ratush OA, Shroff S, Zagzag D, William C, Orringer D, Snuderl M. Clinical Validation of Stimulated Raman Histology for Rapid Intraoperative Diagnosis of Central Nervous System Tumors. Mod Pathol 2023; 36:100219. [PMID: 37201685 PMCID: PMC10527246 DOI: 10.1016/j.modpat.2023.100219] [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: 01/31/2023] [Revised: 03/31/2023] [Accepted: 05/03/2023] [Indexed: 05/20/2023]
Abstract
Stimulated Raman histology (SRH) is an ex vivo optical imaging method that enables microscopic examination of fresh tissue intraoperatively. The conventional intraoperative method uses frozen section analysis, which is labor and time intensive, introduces artifacts that limit diagnostic accuracy, and consumes tissue. SRH imaging allows rapid microscopic imaging of fresh tissue, avoids tissue loss, and enables remote telepathology review. This improves access to expert neuropathology consultation in both low- and high-resource practices. We clinically validated SRH by performing a blinded, retrospective two-arm telepathology study to clinically validate SRH for telepathology at our institution. Using surgical specimens from 47 subjects, we generated a data set composed of 47 SRH images and 47 matched whole slide images (WSIs) of formalin-fixed, paraffin-embedded tissue stained with hematoxylin and eosin, with associated intraoperative clinicoradiologic information and structured diagnostic questions. We compared diagnostic concordance between WSI and SRH-rendered diagnoses. Also, we compared the 1-year median turnaround time (TAT) of intraoperative conventional neuropathology frozen sections with prospectively rendered SRH-telepathology TAT. All SRH images were of sufficient quality for diagnostic review. A review of SRH images showed high accuracy in distinguishing glial from nonglial tumors (96.5% SRH vs 98% WSIs) and predicting final diagnosis (85.9% SRH vs 93.1% WSIs). SRH-based diagnosis and WSI-permanent section diagnosis had high concordance (κ = 0.76). The median TAT for prospectively SRH-rendered diagnosis was 3.7 minutes, approximately 10-fold shorter than the median frozen section TAT (31 minutes). The SRH-imaging procedure did not affect ancillary studies. SRH generates diagnostic virtual histologic images with accuracy comparable to conventional hematoxylin and eosin-based methods in a rapid manner. Our study represents the largest and most rigorous clinical validation of SRH to date. It supports the feasibility of implementing SRH as a rapid method for intraoperative diagnosis complementary to conventional pathology laboratory methods.
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Affiliation(s)
- Misha Movahed-Ezazi
- Department of Pathology and Laboratory Medicine, NYU Langone, New York, New York
| | | | - Camila Fang
- Department of Pathology and Laboratory Medicine, NYU Langone, New York, New York
| | - Isabella Pizzillo
- Department of Pathology and Laboratory Medicine, NYU Langone, New York, New York
| | - Kristyn Galbraith
- Department of Pathology and Laboratory Medicine, NYU Langone, New York, New York
| | - Steven Drexler
- Department of Pathology and Laboratory Medicine, NYU, Mineola, New York
| | | | - Seema Shroff
- Department of Pathology and Laboratory Medicine, AdventHealth Orlando, Orlando, Florida
| | - David Zagzag
- Department of Pathology and Laboratory Medicine, NYU Langone, New York, New York; Department of Neurosurgery, NYU Langone, New York, New York
| | - Christopher William
- Department of Pathology and Laboratory Medicine, NYU Langone, New York, New York
| | | | - Matija Snuderl
- Department of Pathology and Laboratory Medicine, NYU Langone, New York, New York.
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Verghese G, Lennerz JK, Ruta D, Ng W, Thavaraj S, Siziopikou KP, Naidoo T, Rane S, Salgado R, Pinder SE, Grigoriadis A. Computational pathology in cancer diagnosis, prognosis, and prediction - present day and prospects. J Pathol 2023; 260:551-563. [PMID: 37580849 PMCID: PMC10785705 DOI: 10.1002/path.6163] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 06/12/2023] [Accepted: 06/17/2023] [Indexed: 08/16/2023]
Abstract
Computational pathology refers to applying deep learning techniques and algorithms to analyse and interpret histopathology images. Advances in artificial intelligence (AI) have led to an explosion in innovation in computational pathology, ranging from the prospect of automation of routine diagnostic tasks to the discovery of new prognostic and predictive biomarkers from tissue morphology. Despite the promising potential of computational pathology, its integration in clinical settings has been limited by a range of obstacles including operational, technical, regulatory, ethical, financial, and cultural challenges. Here, we focus on the pathologists' perspective of computational pathology: we map its current translational research landscape, evaluate its clinical utility, and address the more common challenges slowing clinical adoption and implementation. We conclude by describing contemporary approaches to drive forward these techniques. © 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)
- Gregory Verghese
- School of Cancer & Pharmaceutical Sciences, Faculty of Life Sciences and MedicineKing's College LondonLondonUK
- The Breast Cancer Now Research Unit, School of Cancer and Pharmaceutical Sciences, Faculty of Life Sciences and MedicineKing's College LondonLondonUK
| | - Jochen K Lennerz
- Center for Integrated Diagnostics, Department of PathologyMassachusetts General Hospital/Harvard Medical SchoolBostonMAUSA
| | - Danny Ruta
- Guy's CancerGuy's and St Thomas’ NHS Foundation TrustLondonUK
| | - Wen Ng
- Department of Cellular PathologyGuy's and St Thomas NHS Foundation TrustLondonUK
| | - Selvam Thavaraj
- Head & Neck PathologyGuy's and St Thomas NHS Foundation TrustLondonUK
- Centre for Clinical, Oral & Translational Science, Faculty of Dentistry, Oral & Craniofacial SciencesKing's College LondonLondonUK
| | - Kalliopi P Siziopikou
- Department of Pathology, Section of Breast PathologyNorthwestern University Feinberg School of MedicineChicagoILUSA
| | - Threnesan Naidoo
- Department of Laboratory Medicine and Pathology, Walter Sisulu University, Mthatha, Eastern CapeSouth Africa and Africa Health Research InstituteDurbanSouth Africa
| | - Swapnil Rane
- Department of PathologyTata Memorial Centre – ACTRECHBNINavi MumbaiIndia
- Computational Pathology, AI & Imaging LaboratoryTata Memorial Centre – ACTREC, HBNINavi MumbaiIndia
| | - Roberto Salgado
- Department of PathologyGZA–ZNA ZiekenhuizenAntwerpBelgium
- Division of ResearchPeter MacCallum Cancer CentreMelbourneVictoriaAustralia
| | - Sarah E Pinder
- School of Cancer & Pharmaceutical Sciences, Faculty of Life Sciences and MedicineKing's College LondonLondonUK
- Department of Cellular PathologyGuy's and St Thomas NHS Foundation TrustLondonUK
| | - Anita Grigoriadis
- School of Cancer & Pharmaceutical Sciences, Faculty of Life Sciences and MedicineKing's College LondonLondonUK
- The Breast Cancer Now Research Unit, School of Cancer and Pharmaceutical Sciences, Faculty of Life Sciences and MedicineKing's College LondonLondonUK
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48
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Bousis D, Verras GI, Bouchagier K, Antzoulas A, Panagiotopoulos I, Katinioti A, Kehagias D, Kaplanis C, Kotis K, Anagnostopoulos CN, Mulita F. The role of deep learning in diagnosing colorectal cancer. PRZEGLAD GASTROENTEROLOGICZNY 2023; 18:266-273. [PMID: 37937113 PMCID: PMC10626379 DOI: 10.5114/pg.2023.129494] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 01/15/2023] [Accepted: 02/24/2023] [Indexed: 11/09/2023]
Abstract
Colon cancer is a major public health issue, affecting a growing number of individuals worldwide. Proper and early diagnosis of colon cancer is the necessary first step toward effective treatment and/or prevention of future disease relapse. Artificial intelligence and its subtypes, deep learning in particular, tend nowadays to have an expanding role in all fields of medicine, and diagnosing colon cancer is no exception. This report aims to summarize the entire application spectrum of deep learning in all diagnostic tests regarding colon cancer, from endoscopy and histologic examination to medical imaging and screening serologic tests.
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Affiliation(s)
- Dimitrios Bousis
- Department of Internal Medicine, General University Hospital of Patras, Patras, Greece
| | | | | | - Andreas Antzoulas
- Department of Surgery, General University Hospital of Patras, Patras, Greece
| | | | | | - Dimitrios Kehagias
- Department of Surgery, General University Hospital of Patras, Patras, Greece
| | | | - Konstantinos Kotis
- Intelligent Systems Lab, Department of Cultural Technology and Communication, University of the Aegean, Mytilene, Greece
| | | | - Francesk Mulita
- Department of Surgery, General University Hospital of Patras, Patras, Greece
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49
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Liu Y, Zhao S, Wu Z, Liang H, Chen X, Huang C, Lu H, Yuan M, Xue X, Luo C, Liu C, Gao J. Virtual biopsy using CT radiomics for evaluation of disagreement in pathology between endoscopic biopsy and postoperative specimens in patients with gastric cancer: a dual-energy CT generalizability study. Insights Imaging 2023; 14:118. [PMID: 37405591 DOI: 10.1186/s13244-023-01459-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Accepted: 06/03/2023] [Indexed: 07/06/2023] Open
Abstract
PURPOSE To develop a noninvasive radiomics-based nomogram for identification of disagreement in pathology between endoscopic biopsy and postoperative specimens in gastric cancer (GC). MATERIALS AND METHODS This observational study recruited 181 GC patients who underwent pre-treatment computed tomography (CT) and divided them into a training set (n = 112, single-energy CT, SECT), a test set (n = 29, single-energy CT, SECT) and a validation cohort (n = 40, dual-energy CT, DECT). Radiomics signatures (RS) based on five machine learning algorithms were constructed from the venous-phase CT images. AUC and DeLong test were used to evaluate and compare the performance of the RS. We assessed the dual-energy generalization ability of the best RS. An individualized nomogram combined the best RS and clinical variables was developed, and its discrimination, calibration, and clinical usefulness were determined. RESULTS RS obtained with support vector machine (SVM) showed promising predictive capability with AUC of 0.91 and 0.83 in the training and test sets, respectively. The AUC of the best RS in the DECT validation cohort (AUC, 0.71) was significantly lower than that of the training set (Delong test, p = 0.035). The clinical-radiomic nomogram accurately predicted pathologic disagreement in the training and test sets, fitting well in the calibration curves. Decision curve analysis confirmed the clinical usefulness of the nomogram. CONCLUSION CT-based radiomics nomogram showed potential as a clinical aid for predicting pathologic disagreement status between biopsy samples and resected specimens in GC. When practicability and stability are considered, the SECT-based radiomics model is not recommended for DECT generalization. CRITICAL RELEVANCE STATEMENT Radiomics can identify disagreement in pathology between endoscopic biopsy and postoperative specimen.
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Affiliation(s)
- Yiyang Liu
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China
- Henan Key Laboratory of Imaging Diagnosis and Treatment for Digestive System Tumor, Zhengzhou, 450052, China
| | - Shuai Zhao
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China
- Henan Key Laboratory of Imaging Diagnosis and Treatment for Digestive System Tumor, Zhengzhou, 450052, China
| | - Zixin Wu
- Department of Urology Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China
| | - Hejun Liang
- Department of Gastroenterology, Xuanwu Hospital Capital Medical University, Beijing, 100053, China
| | - Xingzhi Chen
- Department of Research Collaboration, R&D Center, Beijing Deepwise and League of PHD Technology Co., Ltd, Beijing, 100080, China
| | - Chencui Huang
- Department of Research Collaboration, R&D Center, Beijing Deepwise and League of PHD Technology Co., Ltd, Beijing, 100080, China
| | - Hao Lu
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China
- Henan Key Laboratory of Imaging Diagnosis and Treatment for Digestive System Tumor, Zhengzhou, 450052, China
| | - Mengchen Yuan
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China
- Henan Key Laboratory of Imaging Diagnosis and Treatment for Digestive System Tumor, Zhengzhou, 450052, China
| | - Xiaonan Xue
- Department of Gastroenterology, The Third Affiliated Hospital of Xinxiang Medical University, Xinxiang, 453003, China
| | - Chenglong Luo
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China
- Henan Key Laboratory of Imaging Diagnosis and Treatment for Digestive System Tumor, Zhengzhou, 450052, China
| | - Chenchen Liu
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China
- Henan Key Laboratory of Imaging Diagnosis and Treatment for Digestive System Tumor, Zhengzhou, 450052, China
| | - Jianbo Gao
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China.
- Henan Key Laboratory of Imaging Diagnosis and Treatment for Digestive System Tumor, Zhengzhou, 450052, China.
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50
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Kriegsmann M, Kriegsmann K, Steinbuss G, Zgorzelski C, Albrecht T, Heinrich S, Farkas S, Roth W, Dang H, Hausen A, Gaida MM. Implementation of deep learning in liver pathology optimizes diagnosis of benign lesions and adenocarcinoma metastasis. Clin Transl Med 2023; 13:e1299. [PMID: 37415390 PMCID: PMC10326372 DOI: 10.1002/ctm2.1299] [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: 05/22/2023] [Accepted: 05/28/2023] [Indexed: 07/08/2023] Open
Abstract
INTRODUCTION Differentiation of histologically similar structures in the liver, including anatomical structures, benign bile duct lesions, or common types of liver metastases, can be challenging with conventional histological tissue sections alone. Accurate histopathological classification is paramount for the diagnosis and adequate treatment of the disease. Deep learning algorithms have been proposed for objective and consistent assessment of digital histopathological images. MATERIALS AND METHODS In the present study, we trained and evaluated deep learning algorithms based on the EfficientNetV2 and ResNetRS architectures to discriminate between different histopathological classes. For the required dataset, specialized surgical pathologists annotated seven different histological classes, including different non-neoplastic anatomical structures, benign bile duct lesions, and liver metastases from colorectal and pancreatic adenocarcinoma in a large patient cohort. Annotation resulted in a total of 204.159 image patches, followed by discrimination analysis using our deep learning models. Model performance was evaluated on validation and test data using confusion matrices. RESULTS Evaluation of the test set based on tiles and cases revealed overall highly satisfactory prediction capability of our algorithm for the different histological classes, resulting in a tile accuracy of 89% (38 413/43 059) and case accuracy of 94% (198/211). Importantly, the separation of metastasis versus benign lesions was certainly confident on case level, confirming the classification model performed with high diagnostic accuracy. Moreover, the whole curated raw data set is made publically available. CONCLUSIONS Deep learning is a promising approach in surgical liver pathology supporting decision making in personalized medicine.
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Affiliation(s)
- Mark Kriegsmann
- Institute of PathologyHeidelberg UniversityHeidelbergGermany
- Pathology WiesbadenWiesbadenGermany
| | - Katharina Kriegsmann
- Department of HematologyOncology and RheumatologyHeidelberg UniversityHeidelbergGermany
- Laborarztpraxis Rhein‐Main MVZ GbRFrankfurt am MainFrankfurtGermany
| | - Georg Steinbuss
- Department of HematologyOncology and RheumatologyHeidelberg UniversityHeidelbergGermany
| | | | - Thomas Albrecht
- Institute of PathologyHeidelberg UniversityHeidelbergGermany
| | - Stefan Heinrich
- Department of SurgeryJGU‐MainzUniversity Medical Center MainzMainzGermany
| | - Stefan Farkas
- Department of SurgerySt. Josefs‐ HospitalWiesbadenGermany
| | - Wilfried Roth
- Institute of PathologyJGU‐MainzUniversity Medical Center MainzMainzGermany
| | - Hien Dang
- Department of SurgeryDepartment of Surgical ResearchThomas Jefferson UniversityPhiladelphiaPennsylvaniaUSA
| | - Anne Hausen
- Institute of PathologyJGU‐MainzUniversity Medical Center MainzMainzGermany
| | - Matthias M. Gaida
- Institute of PathologyJGU‐MainzUniversity Medical Center MainzMainzGermany
- TRONJGU‐MainzTranslational Oncology at the University Medical CenterMainzGermany
- Research Center for ImmunotherapyJGU‐MainzUniversity Medical Center MainzMainzGermany
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