1
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Hanna MG, Olson NH, Zarella M, Dash RC, Herrmann MD, Furtado LV, Stram MN, Raciti PM, Hassell L, Mays A, Pantanowitz L, Sirintrapun JS, Krishnamurthy S, Parwani A, Lujan G, Evans A, Glassy EF, Bui MM, Singh R, Souers RJ, de Baca ME, Seheult JN. Recommendations for Performance Evaluation of Machine Learning in Pathology: A Concept Paper From the College of American Pathologists. Arch Pathol Lab Med 2024; 148:e335-e361. [PMID: 38041522 DOI: 10.5858/arpa.2023-0042-cp] [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: 09/11/2023] [Indexed: 12/03/2023]
Abstract
CONTEXT.— Machine learning applications in the pathology clinical domain are emerging rapidly. As decision support systems continue to mature, laboratories will increasingly need guidance to evaluate their performance in clinical practice. Currently there are no formal guidelines to assist pathology laboratories in verification and/or validation of such systems. These recommendations are being proposed for the evaluation of machine learning systems in the clinical practice of pathology. OBJECTIVE.— To propose recommendations for performance evaluation of in vitro diagnostic tests on patient samples that incorporate machine learning as part of the preanalytical, analytical, or postanalytical phases of the laboratory workflow. Topics described include considerations for machine learning model evaluation including risk assessment, predeployment requirements, data sourcing and curation, verification and validation, change control management, human-computer interaction, practitioner training, and competency evaluation. DATA SOURCES.— An expert panel performed a review of the literature, Clinical and Laboratory Standards Institute guidance, and laboratory and government regulatory frameworks. CONCLUSIONS.— Review of the literature and existing documents enabled the development of proposed recommendations. This white paper pertains to performance evaluation of machine learning systems intended to be implemented for clinical patient testing. Further studies with real-world clinical data are encouraged to support these proposed recommendations. Performance evaluation of machine learning models is critical to verification and/or validation of in vitro diagnostic tests using machine learning intended for clinical practice.
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Affiliation(s)
- Matthew G Hanna
- From the Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York (Hanna, Sirintrapun)
| | - Niels H Olson
- The Defense Innovation Unit, Mountain View, California (Olson)
- The Department of Pathology, Uniformed Services University, Bethesda, Maryland (Olson)
| | - Mark Zarella
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota (Zarella, Seheult)
| | - Rajesh C Dash
- Department of Pathology, Duke University Health System, Durham, North Carolina (Dash)
| | - Markus D Herrmann
- Department of Pathology, Massachusetts General Hospital and Harvard Medical School, Boston (Herrmann)
| | - Larissa V Furtado
- Department of Pathology, St. Jude Children's Research Hospital, Memphis, Tennessee (Furtado)
| | - Michelle N Stram
- The Department of Forensic Medicine, New York University, and Office of Chief Medical Examiner, New York (Stram)
| | | | - Lewis Hassell
- Department of Pathology, Oklahoma University Health Sciences Center, Oklahoma City (Hassell)
| | - Alex Mays
- The MITRE Corporation, McLean, Virginia (Mays)
| | - Liron Pantanowitz
- Department of Pathology & Clinical Labs, University of Michigan, Ann Arbor (Pantanowitz)
| | - Joseph S Sirintrapun
- From the Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York (Hanna, Sirintrapun)
| | | | - Anil Parwani
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus (Parwani, Lujan)
| | - Giovanni Lujan
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus (Parwani, Lujan)
| | - Andrew Evans
- Laboratory Medicine, Mackenzie Health, Toronto, Ontario, Canada (Evans)
| | - Eric F Glassy
- Affiliated Pathologists Medical Group, Rancho Dominguez, California (Glassy)
| | - Marilyn M Bui
- Departments of Pathology and Machine Learning, Moffitt Cancer Center, Tampa, Florida (Bui)
| | - Rajendra Singh
- Department of Dermatopathology, Summit Health, Summit Woodland Park, New Jersey (Singh)
| | - Rhona J Souers
- Department of Biostatistics, College of American Pathologists, Northfield, Illinois (Souers)
| | | | - Jansen N Seheult
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota (Zarella, Seheult)
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2
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Schukow CP, Allen TC. Digital and Computational Pathology Are Pathologists' Physician Extenders. Arch Pathol Lab Med 2024; 148:866-870. [PMID: 38531382 DOI: 10.5858/arpa.2023-0537-ed] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/11/2024] [Indexed: 03/28/2024]
Affiliation(s)
- Casey P Schukow
- From the Department of Pathology, Corewell Health's Beaumont Hospital, Royal Oak, Michigan
| | - Timothy Craig Allen
- From the Department of Pathology, Corewell Health's Beaumont Hospital, Royal Oak, Michigan
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3
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Pantanowitz J, Manko CD, Pantanowitz L, Rashidi HH. Synthetic Data and Its Utility in Pathology and Laboratory Medicine. J Transl Med 2024; 104:102095. [PMID: 38925488 DOI: 10.1016/j.labinv.2024.102095] [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: 03/12/2024] [Revised: 06/03/2024] [Accepted: 06/12/2024] [Indexed: 06/28/2024] Open
Abstract
In our rapidly expanding landscape of artificial intelligence, synthetic data have become a topic of great promise and also some concern. This review aimed to provide pathologists and laboratory professionals with a primer on the role of synthetic data and how it may soon shape the landscape within our field. Using synthetic data presents many advantages but also introduces a milieu of new obstacles and limitations. This review aimed to provide pathologists and laboratory professionals with a primer on the general concept of synthetic data and its potential to transform our field. By leveraging synthetic data, we can help accelerate the development of various machine learning models and enhance our medical education and research/quality study needs. This review explored the methods for generating synthetic data, including rule-based, machine learning model-based and hybrid approaches, as they apply to applications within pathology and laboratory medicine. We also discussed the limitations and challenges associated with such synthetic data, including data quality, malicious use, and ethical bias/concerns and challenges. By understanding the potential benefits (ie, medical education, training artificial intelligence programs, and proficiency testing, etc) and limitations of this new data realm, we can not only harness its power to improve patient outcomes, advance research, and enhance the practice of pathology but also become readily aware of their intrinsic limitations.
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Affiliation(s)
- Joshua Pantanowitz
- Computational Pathology and AI Center of Excellence (CPACE), University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania; Department of Pathology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Christopher D Manko
- Guthrie Clinic Robert Packer Hospital; Geisinger Commonwealth School of Medicine, Guthrie, Pennsylvania
| | - Liron Pantanowitz
- Computational Pathology and AI Center of Excellence (CPACE), University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania; Department of Pathology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Hooman H Rashidi
- Computational Pathology and AI Center of Excellence (CPACE), University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania; Department of Pathology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania.
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4
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Chang J, Hatfield B. Advancements in computer vision and pathology: Unraveling the potential of artificial intelligence for precision diagnosis and beyond. Adv Cancer Res 2024; 161:431-478. [PMID: 39032956 DOI: 10.1016/bs.acr.2024.05.006] [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: 07/23/2024]
Abstract
The integration of computer vision into pathology through slide digitalization represents a transformative leap in the field's evolution. Traditional pathology methods, while reliable, are often time-consuming and susceptible to intra- and interobserver variability. In contrast, computer vision, empowered by artificial intelligence (AI) and machine learning (ML), promises revolutionary changes, offering consistent, reproducible, and objective results with ever-increasing speed and scalability. The applications of advanced algorithms and deep learning architectures like CNNs and U-Nets augment pathologists' diagnostic capabilities, opening new frontiers in automated image analysis. As these technologies mature and integrate into digital pathology workflows, they are poised to provide deeper insights into disease processes, quantify and standardize biomarkers, enhance patient outcomes, and automate routine tasks, reducing pathologists' workload. However, this transformative force calls for cross-disciplinary collaboration between pathologists, computer scientists, and industry innovators to drive research and development. While acknowledging its potential, this chapter addresses the limitations of AI in pathology, encompassing technical, practical, and ethical considerations during development and implementation.
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Affiliation(s)
- Justin Chang
- Virginia Commonwealth University Health System, Richmond, VA, United States
| | - Bryce Hatfield
- Virginia Commonwealth University Health System, Richmond, VA, United States.
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5
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McCaffrey C, Jahangir C, Murphy C, Burke C, Gallagher WM, Rahman A. Artificial intelligence in digital histopathology for predicting patient prognosis and treatment efficacy in breast cancer. Expert Rev Mol Diagn 2024; 24:363-377. [PMID: 38655907 DOI: 10.1080/14737159.2024.2346545] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Accepted: 04/19/2024] [Indexed: 04/26/2024]
Abstract
INTRODUCTION Histological images contain phenotypic information predictive of patient outcomes. Due to the heavy workload of pathologists, the time-consuming nature of quantitatively assessing histological features, and human eye limitations to recognize spatial patterns, manually extracting prognostic information in routine pathological workflows remains challenging. Digital pathology has facilitated the mining and quantification of these features utilizing whole-slide image (WSI) scanners and artificial intelligence (AI) algorithms. AI algorithms to identify image-based biomarkers from the tumor microenvironment (TME) have the potential to revolutionize the field of oncology, reducing delays between diagnosis and prognosis determination, allowing for rapid stratification of patients and prescription of optimal treatment regimes, thereby improving patient outcomes. AREAS COVERED In this review, the authors discuss how AI algorithms and digital pathology can predict breast cancer patient prognosis and treatment outcomes using image-based biomarkers, along with the challenges of adopting this technology in clinical settings. EXPERT OPINION The integration of AI and digital pathology presents significant potential for analyzing the TME and its diagnostic, prognostic, and predictive value in breast cancer patients. Widespread clinical adoption of AI faces ethical, regulatory, and technical challenges, although prospective trials may offer reassurance and promote uptake, ultimately improving patient outcomes by reducing diagnosis-to-prognosis delivery delays.
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Affiliation(s)
- Christine McCaffrey
- UCD School of Biomolecular and Biomedical Science, UCD Conway Institute, University College Dublin, Dublin, Ireland
| | - Chowdhury Jahangir
- UCD School of Biomolecular and Biomedical Science, UCD Conway Institute, University College Dublin, Dublin, Ireland
| | - Clodagh Murphy
- UCD School of Biomolecular and Biomedical Science, UCD Conway Institute, University College Dublin, Dublin, Ireland
| | - Caoimbhe Burke
- UCD School of Biomolecular and Biomedical Science, UCD Conway Institute, University College Dublin, Dublin, Ireland
| | - William M Gallagher
- UCD School of Biomolecular and Biomedical Science, UCD Conway Institute, University College Dublin, Dublin, Ireland
| | - Arman Rahman
- UCD School of Medicine, UCD Conway Institute, University College Dublin, Dublin, Ireland
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D’Abbronzo G, D’Antonio A, De Chiara A, Panico L, Sparano L, Diluvio A, Sica A, Svanera G, Franco R, Ronchi A. Development of an Artificial-Intelligence-Based Tool for Automated Assessment of Cellularity in Bone Marrow Biopsies in Ph-Negative Myeloproliferative Neoplasms. Cancers (Basel) 2024; 16:1687. [PMID: 38730640 PMCID: PMC11083301 DOI: 10.3390/cancers16091687] [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: 02/14/2024] [Revised: 04/22/2024] [Accepted: 04/24/2024] [Indexed: 05/13/2024] Open
Abstract
The cellularity assessment in bone marrow biopsies (BMBs) for the diagnosis of Philadelphia chromosome (Ph)-negative myeloproliferative neoplasms (MPNs) is a key diagnostic feature and is usually performed by the human eyes through an optical microscope with consequent inter-observer and intra-observer variability. Thus, the use of an automated tool may reduce variability, improving the uniformity of the evaluation. The aim of this work is to develop an accurate AI-based tool for the automated quantification of cellularity in BMB histology. A total of 55 BMB histological slides, diagnosed as Ph- MPN between January 2018 and June 2023 from the archives of the Pathology Unit of University "Luigi Vanvitelli" in Naples (Italy), were scanned on Ventana DP200 or Epredia P1000 and exported as whole-slide images (WSIs). Fifteen BMBs were randomly selected to obtain a training set of AI-based tools. An expert pathologist and a trained resident performed annotations of hematopoietic tissue and adipose tissue, and annotations were exported as .tiff images and .png labels with two colors (black for hematopoietic tissue and yellow for adipose tissue). Subsequently, we developed a semantic segmentation model for hematopoietic tissue and adipose tissue. The remaining 40 BMBs were used for model verification. The performance of our model was compared with an evaluation of the cellularity of five expert hematopathologists and three trainees; we obtained an optimal concordance between our model and the expert pathologists' evaluation, with poorer concordance for trainees. There were no significant differences in cellularity assessments between two different scanners.
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Affiliation(s)
- Giuseppe D’Abbronzo
- Department of Mental and Physical Health and Preventive Medicine, Università degli Studi della Campania “Luigi Vanvitelli”, 80138 Naples, Italy; (G.D.); (A.D.); (A.R.)
| | | | - Annarosaria De Chiara
- Histopathology of lymphomas and Sarcoma SSD, Istituto Nazionale dei Tumori I.R.C.C.S. Fondazione “Pascale”, 80131 Naples, Italy;
| | - Luigi Panico
- Pathology Unit, Hospital “Monaldi”, 80131 Naples, Italy;
| | | | - Anna Diluvio
- Department of Mental and Physical Health and Preventive Medicine, Università degli Studi della Campania “Luigi Vanvitelli”, 80138 Naples, Italy; (G.D.); (A.D.); (A.R.)
| | - Antonello Sica
- Haematology and Oncology Unit, Vanvitelli Hospital, 80131 Naples, Italy;
| | - Gino Svanera
- Haematology Unit, ASL Na2 North, 80014 Giugliano, Italy;
| | - Renato Franco
- Department of Mental and Physical Health and Preventive Medicine, Università degli Studi della Campania “Luigi Vanvitelli”, 80138 Naples, Italy; (G.D.); (A.D.); (A.R.)
- Pathology Unit, Vanvitelli Hospital, 80138 Naples, Italy
| | - Andrea Ronchi
- Department of Mental and Physical Health and Preventive Medicine, Università degli Studi della Campania “Luigi Vanvitelli”, 80138 Naples, Italy; (G.D.); (A.D.); (A.R.)
- Pathology Unit, Vanvitelli Hospital, 80138 Naples, Italy
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7
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Katayama A, Aoki Y, Watanabe Y, Horiguchi J, Rakha EA, Oyama T. Current status and prospects of artificial intelligence in breast cancer pathology: convolutional neural networks to prospective Vision Transformers. Int J Clin Oncol 2024:10.1007/s10147-024-02513-3. [PMID: 38619651 DOI: 10.1007/s10147-024-02513-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Accepted: 03/12/2024] [Indexed: 04/16/2024]
Abstract
Breast cancer is the most prevalent cancer among women, and its diagnosis requires the accurate identification and classification of histological features for effective patient management. Artificial intelligence, particularly through deep learning, represents the next frontier in cancer diagnosis and management. Notably, the use of convolutional neural networks and emerging Vision Transformers (ViT) has been reported to automate pathologists' tasks, including tumor detection and classification, in addition to improving the efficiency of pathology services. Deep learning applications have also been extended to the prediction of protein expression, molecular subtype, mutation status, therapeutic efficacy, and outcome prediction directly from hematoxylin and eosin-stained slides, bypassing the need for immunohistochemistry or genetic testing. This review explores the current status and prospects of deep learning in breast cancer diagnosis with a focus on whole-slide image analysis. Artificial intelligence applications are increasingly applied to many tasks in breast pathology ranging from disease diagnosis to outcome prediction, thus serving as valuable tools for assisting pathologists and supporting breast cancer management.
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Affiliation(s)
- Ayaka Katayama
- Diagnostic Pathology, Gunma University Graduate School of Medicine, 3-39-22 Showamachi, Maebashi, Gunma, 371-8511, Japan.
| | - Yuki Aoki
- Center for Mathematics and Data Science, Gunma University, Maebashi, Japan
| | - Yukako Watanabe
- Clinical Training Center, Gunma University Hospital, Maebashi, Japan
| | - Jun Horiguchi
- Department of Breast Surgery, International University of Health and Welfare, Narita, Japan
| | - Emad A Rakha
- Department of Histopathology School of Medicine, University of Nottingham, University Park, Nottingham, UK
- Department of Pathology, Hamad Medical Corporation, Doha, Qatar
| | - Tetsunari Oyama
- Diagnostic Pathology, Gunma University Graduate School of Medicine, 3-39-22 Showamachi, Maebashi, Gunma, 371-8511, Japan
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8
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Jongsma KR, Sand M, Milota M. Why we should not mistake accuracy of medical AI for efficiency. NPJ Digit Med 2024; 7:57. [PMID: 38438477 PMCID: PMC10912629 DOI: 10.1038/s41746-024-01047-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Accepted: 02/16/2024] [Indexed: 03/06/2024] Open
Affiliation(s)
- Karin Rolanda Jongsma
- Bioethics & Health Humanities, Julius Center, University Medical Center Utrecht, Utrecht University, PO Box 85500, 3508 CA, Utrecht, The Netherlands.
| | - Martin Sand
- TU Delft, Department of Values, Technology and Innovation, Faculty of Technology, Policy and Management, Jaffalaan 5, 2628 BX, Delft, The Netherlands
| | - Megan Milota
- Bioethics & Health Humanities, Julius Center, University Medical Center Utrecht, Utrecht University, PO Box 85500, 3508 CA, Utrecht, The Netherlands
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9
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Magalhães G, Calisto R, Freire C, Silva R, Montezuma D, Canberk S, Schmitt F. Invisible for a few but essential for many: the role of Histotechnologists in the establishment of digital pathology. J Histotechnol 2024; 47:39-52. [PMID: 37869882 DOI: 10.1080/01478885.2023.2268297] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Accepted: 10/03/2023] [Indexed: 10/24/2023]
Abstract
Digital pathology (DP) is indisputably the future for histopathology laboratories. The process of digital implementation requires deep workflow reorganisation which involves an interdisciplinary team. This transformation may have the greatest impact on the Histotechnologist (HTL) profession. Our review of the literature has clearly revealed that the role of HTLs in the establishment of DP is being unnoticed and guidance is limited. This article aims to bring HTLs from behind-the-scenes into the spotlight. Our objective is to provide them guidance and practical recommendations to successfully contribute to the implementation of a new digital workflow. Furthermore, it also intends to contribute for improvement of study programs, ensuring the role of HTL in DP is addressed as part of graduate and post-graduate education. In our review, we report on the differences encountered between workflow schemes and the limitations observed in this process. The authors propose a digital workflow to achieve its limitless potential, focusing on the HTL's role. This article explores the novel responsibilities of HTLs during specimen gross dissection, embedding, microtomy, staining, digital scanning, and whole slide image quality control. Furthermore, we highlight the benefits and challenges that DP implementation might bring the HTLs career. HTLs have an important role in the digital workflow: the responsibility of achieving the perfect glass slide.
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Affiliation(s)
- Gisela Magalhães
- Histopathology Department, Portsmouth Hospital University NHS Trust, Portsmouth, UK
- Department of Pathological Anatomy, School of Health Polytechnic of Porto (ESS|P.PORTO), Porto, Portugal
| | - Rita Calisto
- Department of Pathological Anatomy, School of Health Polytechnic of Porto (ESS|P.PORTO), Porto, Portugal
- Department of Pathological Anatomy, Hospital do Divino Espírito Santo, Ponta Delgada, Portugal
| | - Catarina Freire
- Department of Pathological Anatomy, School of Health Polytechnic of Porto (ESS|P.PORTO), Porto, Portugal
- Department of Pathological Anatomy, Hospital do Divino Espírito Santo, Ponta Delgada, Portugal
| | - Regina Silva
- Department of Pathological Anatomy, School of Health Polytechnic of Porto (ESS|P.PORTO), Porto, Portugal
- Centro de Investigação em Saúde e Ambiente, ESS,P.PORTO, Porto, Portugal
| | - Diana Montezuma
- Research & Development Unit, IMP Diagnostics, Porto, Portugal
- School of Medicine and Biomedical Sciences, University of Porto (ICBAS-UP), Porto, Portugal
| | - Sule Canberk
- Institute for Research and Innovation in Health (i3S), University of Porto, Porto, Portugal
- Cancer Signalling & Metabolism, Institute of Molecular Pathology and Immunology of the University of Porto (Ipatimup), Porto, Portugal
- Faculty of Medicine of the University of Porto (FMUP), Porto, Portugal
| | - Fernando Schmitt
- Department of Pathology, Faculty of Medicine of University of Porto, Porto, Portugal
- CINTESIS@RISE, Health Research Network, Alameda Prof. Hernâni Monteiro, Portugal
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10
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Ibrahim A, Jahanifar M, Wahab N, Toss MS, Makhlouf S, Atallah N, Lashen AG, Katayama A, Graham S, Bilal M, Bhalerao A, Ahmed Raza SE, Snead D, Minhas F, Rajpoot N, Rakha E. Artificial Intelligence-Based Mitosis Scoring in Breast Cancer: Clinical Application. Mod Pathol 2024; 37:100416. [PMID: 38154653 DOI: 10.1016/j.modpat.2023.100416] [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: 06/16/2023] [Revised: 10/27/2023] [Accepted: 12/14/2023] [Indexed: 12/30/2023]
Abstract
In recent years, artificial intelligence (AI) has demonstrated exceptional performance in mitosis identification and quantification. However, the implementation of AI in clinical practice needs to be evaluated against the existing methods. This study is aimed at assessing the optimal method of using AI-based mitotic figure scoring in breast cancer (BC). We utilized whole slide images from a large cohort of BC with extended follow-up comprising a discovery (n = 1715) and a validation (n = 859) set (Nottingham cohort). The Cancer Genome Atlas of breast invasive carcinoma (TCGA-BRCA) cohort (n = 757) was used as an external test set. Employing automated mitosis detection, the mitotic count was assessed using 3 different methods, the mitotic count per tumor area (MCT; calculated by dividing the number of mitotic figures by the total tumor area), the mitotic index (MI; defined as the average number of mitotic figures per 1000 malignant cells), and the mitotic activity index (MAI; defined as the number of mitotic figures in 3 mm2 area within the mitotic hotspot). These automated metrics were evaluated and compared based on their correlation with the well-established visual scoring method of the Nottingham grading system and Ki67 score, clinicopathologic parameters, and patient outcomes. AI-based mitotic scores derived from the 3 methods (MCT, MI, and MAI) were significantly correlated with the clinicopathologic characteristics and patient survival (P < .001). However, the mitotic counts and the derived cutoffs varied significantly between the 3 methods. Only MAI and MCT were positively correlated with the gold standard visual scoring method used in Nottingham grading system (r = 0.8 and r = 0.7, respectively) and Ki67 scores (r = 0.69 and r = 0.55, respectively), and MAI was the only independent predictor of survival (P < .05) in multivariate Cox regression analysis. For clinical applications, the optimum method of scoring mitosis using AI needs to be considered. MAI can provide reliable and reproducible results and can accurately quantify mitotic figures in BC.
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Affiliation(s)
- Asmaa Ibrahim
- Academic Unit for Translational Medical Sciences, School of Medicine, University of Nottingham, Nottingham, United Kingdom; Department of Pathology, Faculty of Medicine, Suez Canal University, Egypt
| | - Mostafa Jahanifar
- Tissue Image Analytics Centre, University of Warwick, United Kingdom
| | - Noorul Wahab
- Tissue Image Analytics Centre, University of Warwick, United Kingdom
| | - Michael S Toss
- Academic Unit for Translational Medical Sciences, School of Medicine, University of Nottingham, Nottingham, United Kingdom; Histopathology Department, Royal Hallamshire Hospital, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, United Kingdom
| | - Shorouk Makhlouf
- Academic Unit for Translational Medical Sciences, School of Medicine, University of Nottingham, Nottingham, United Kingdom
| | - Nehal Atallah
- Academic Unit for Translational Medical Sciences, School of Medicine, University of Nottingham, Nottingham, United Kingdom
| | - Ayat G Lashen
- Academic Unit for Translational Medical Sciences, School of Medicine, University of Nottingham, Nottingham, United Kingdom
| | - Ayaka Katayama
- Academic Unit for Translational Medical Sciences, School of Medicine, University of Nottingham, Nottingham, United Kingdom
| | - Simon Graham
- Tissue Image Analytics Centre, University of Warwick, United Kingdom
| | - Mohsin Bilal
- Tissue Image Analytics Centre, University of Warwick, United Kingdom
| | - Abhir Bhalerao
- Tissue Image Analytics Centre, University of Warwick, United Kingdom
| | - Shan E Ahmed Raza
- Tissue Image Analytics Centre, University of Warwick, United Kingdom
| | - David Snead
- Cellular Pathology, University Hospitals Coventry and Warwickshire NHS Trust, United Kingdom
| | - Fayyaz Minhas
- Tissue Image Analytics Centre, University of Warwick, United Kingdom
| | - Nasir Rajpoot
- Tissue Image Analytics Centre, University of Warwick, United Kingdom.
| | - Emad Rakha
- Academic Unit for Translational Medical Sciences, School of Medicine, University of Nottingham, Nottingham, United Kingdom; Nottingham University Hospitals NHS Trust, Nottingham, United Kingdom; Pathology Department, Hamad Medical Corporation, Doha, Qatar.
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11
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Soliman A, Li Z, Parwani AV. Artificial intelligence's impact on breast cancer pathology: a literature review. Diagn Pathol 2024; 19:38. [PMID: 38388367 PMCID: PMC10882736 DOI: 10.1186/s13000-024-01453-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Accepted: 01/26/2024] [Indexed: 02/24/2024] Open
Abstract
This review discusses the profound impact of artificial intelligence (AI) on breast cancer (BC) diagnosis and management within the field of pathology. It examines the various applications of AI across diverse aspects of BC pathology, highlighting key findings from multiple studies. Integrating AI into routine pathology practice stands to improve diagnostic accuracy, thereby contributing to reducing avoidable errors. Additionally, AI has excelled in identifying invasive breast tumors and lymph node metastasis through its capacity to process large whole-slide images adeptly. Adaptive sampling techniques and powerful convolutional neural networks mark these achievements. The evaluation of hormonal status, which is imperative for BC treatment choices, has also been enhanced by AI quantitative analysis, aiding interobserver concordance and reliability. Breast cancer grading and mitotic count evaluation also benefit from AI intervention. AI-based frameworks effectively classify breast carcinomas, even for moderately graded cases that traditional methods struggle with. Moreover, AI-assisted mitotic figures quantification surpasses manual counting in precision and sensitivity, fostering improved prognosis. The assessment of tumor-infiltrating lymphocytes in triple-negative breast cancer using AI yields insights into patient survival prognosis. Furthermore, AI-powered predictions of neoadjuvant chemotherapy response demonstrate potential for streamlining treatment strategies. Addressing limitations, such as preanalytical variables, annotation demands, and differentiation challenges, is pivotal for realizing AI's full potential in BC pathology. Despite the existing hurdles, AI's multifaceted contributions to BC pathology hold great promise, providing enhanced accuracy, efficiency, and standardization. Continued research and innovation are crucial for overcoming obstacles and fully harnessing AI's transformative capabilities in breast cancer diagnosis and assessment.
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Affiliation(s)
- Amr Soliman
- Department of Pathology, Ohio State University, Columbus, OH, USA
| | - Zaibo Li
- Department of Pathology, Ohio State University, Columbus, OH, USA
| | - Anil V Parwani
- Department of Pathology, Ohio State University, Columbus, OH, USA.
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12
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Shen J, Choi YL, Lee T, Kim H, Chae YK, Dulken BW, Bogdan S, Huang M, Fisher GA, Park S, Lee SH, Hwang JE, Chung JH, Kim L, Song H, Pereira S, Shin S, Lim Y, Ahn CH, Kim S, Oum C, Kim S, Park G, Song S, Jung W, Kim S, Bang YJ, Mok TSK, Ali SM, Ock CY. Inflamed immune phenotype predicts favorable clinical outcomes of immune checkpoint inhibitor therapy across multiple cancer types. J Immunother Cancer 2024; 12:e008339. [PMID: 38355279 PMCID: PMC10868175 DOI: 10.1136/jitc-2023-008339] [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] [Accepted: 01/27/2024] [Indexed: 02/16/2024] Open
Abstract
BACKGROUND The inflamed immune phenotype (IIP), defined by enrichment of tumor-infiltrating lymphocytes (TILs) within intratumoral areas, is a promising tumor-agnostic biomarker of response to immune checkpoint inhibitor (ICI) therapy. However, it is challenging to define the IIP in an objective and reproducible manner during manual histopathologic examination. Here, we investigate artificial intelligence (AI)-based immune phenotypes capable of predicting ICI clinical outcomes in multiple solid tumor types. METHODS Lunit SCOPE IO is a deep learning model which determines the immune phenotype of the tumor microenvironment based on TIL analysis. We evaluated the correlation between the IIP and ICI treatment outcomes in terms of objective response rates (ORR), progression-free survival (PFS), and overall survival (OS) in a cohort of 1,806 ICI-treated patients representing over 27 solid tumor types retrospectively collected from multiple institutions. RESULTS We observed an overall IIP prevalence of 35.2% and significantly more favorable ORRs (26.3% vs 15.8%), PFS (median 5.3 vs 3.1 months, HR 0.68, 95% CI 0.61 to 0.76), and OS (median 25.3 vs 13.6 months, HR 0.66, 95% CI 0.57 to 0.75) after ICI therapy in IIP compared with non-IIP patients, respectively (p<0.001 for all comparisons). On subgroup analysis, the IIP was generally prognostic of favorable PFS across major patient subgroups, with the exception of the microsatellite unstable/mismatch repair deficient subgroup. CONCLUSION The AI-based IIP may represent a practical, affordable, clinically actionable, and tumor-agnostic biomarker prognostic of ICI therapy response across diverse tumor types.
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Affiliation(s)
- Jeanne Shen
- Department of Pathology, Stanford University School of Medicine, Stanford, California, USA
- Center for Artificial Intelligence in Medicine & Imaging, Stanford University, Stanford, California, USA
| | - Yoon-La Choi
- Department of Pathology and Translational Genomics, Sungkyunkwan University School of Medicine, Suwon, Korea (the Republic of)
- Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Korea (the Republic of)
| | - Taebum Lee
- Department of Pathology, Chonnam National University Medical School, Gwangju, Korea (the Republic of)
| | - Hyojin Kim
- Department of Pathology, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Seongnam, Korea (the Republic of)
| | - Young Kwang Chae
- Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | - Ben W Dulken
- Department of Pathology, Stanford University School of Medicine, Stanford, California, USA
| | - Stephanie Bogdan
- Center for Artificial Intelligence in Medicine & Imaging, Stanford University, Stanford, California, USA
| | - Maggie Huang
- UCLA Health, University of California, Los Angeles, Los Angeles, California, USA
| | - George A Fisher
- Department of Medicine, Stanford University School of Medicine, Stanford, California, USA
| | - Sehhoon Park
- Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea (the Republic of)
| | - Se-Hoon Lee
- Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea (the Republic of)
| | - Jun-Eul Hwang
- Department of Internal Medicine, Chonnam National University Medical School, Gwangju, Korea (the Republic of)
| | - Jin-Haeng Chung
- Department of Pathology, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Seongnam, Korea (the Republic of)
| | - Leeseul Kim
- AMITA Health Saint Francis Hospital Evanston, Evanston, Illinois, USA
| | - Heon Song
- Lunit, Seoul, Korea (the Republic of)
| | | | | | | | | | | | | | | | | | | | | | - Seokhwi Kim
- Department of Pathology, Ajou University School of Medicine, Suwon, Korea (the Republic of)
| | - Yung-Jue Bang
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea (the Republic of)
| | - Tony S K Mok
- Department of Clinical Oncology, The Chinese University of Hong Kong, New Territories, Hong Kong
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13
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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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14
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Wagner SJ, Matek C, Shetab Boushehri S, Boxberg M, Lamm L, Sadafi A, Winter DJE, Marr C, Peng T. Built to Last? Reproducibility and Reusability of Deep Learning Algorithms in Computational Pathology. Mod Pathol 2024; 37:100350. [PMID: 37827448 DOI: 10.1016/j.modpat.2023.100350] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Revised: 10/02/2023] [Accepted: 10/03/2023] [Indexed: 10/14/2023]
Abstract
Recent progress in computational pathology has been driven by deep learning. While code and data availability are essential to reproduce findings from preceding publications, ensuring a deep learning model's reusability is more challenging. For that, the codebase should be well-documented and easy to integrate into existing workflows and models should be robust toward noise and generalizable toward data from different sources. Strikingly, only a few computational pathology algorithms have been reused by other researchers so far, let alone employed in a clinical setting. To assess the current state of reproducibility and reusability of computational pathology algorithms, we evaluated peer-reviewed articles available in PubMed, published between January 2019 and March 2021, in 5 use cases: stain normalization; tissue type segmentation; evaluation of cell-level features; genetic alteration prediction; and inference of grading, staging, and prognostic information. We compiled criteria for data and code availability and statistical result analysis and assessed them in 160 publications. We found that only one-quarter (41 of 160 publications) made code publicly available. Among these 41 studies, three-quarters (30 of 41) analyzed their results statistically, half of them (20 of 41) released their trained model weights, and approximately a third (16 of 41) used an independent cohort for evaluation. Our review is intended for both pathologists interested in deep learning and researchers applying algorithms to computational pathology challenges. We provide a detailed overview of publications with published code in the field, list reusable data handling tools, and provide criteria for reproducibility and reusability.
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Affiliation(s)
- Sophia J Wagner
- Helmholtz AI, Helmholtz Munich-German Research Center for Environmental Health, Neuherberg, Germany; School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
| | - Christian Matek
- Institute of AI for Health, Helmholtz Munich-German Research Center for Environmental Health, Neuherberg, Germany; Institute of Pathology, University Hospital Erlangen, Erlangen, Germany
| | - Sayedali Shetab Boushehri
- School of Computation, Information and Technology, Technical University of Munich, Garching, Germany; Institute of AI for Health, Helmholtz Munich-German Research Center for Environmental Health, Neuherberg, Germany; Data & Analytics (D&A), Roche Pharma Research and Early Development (pRED), Roche Innovation Center Munich, Germany
| | - Melanie Boxberg
- Institute of Pathology, Technical University Munich, Munich, Germany; Institute of Pathology Munich-North, Munich, Germany
| | - Lorenz Lamm
- Helmholtz AI, Helmholtz Munich-German Research Center for Environmental Health, Neuherberg, Germany; Helmholtz Pioneer Campus, Helmholtz Munich-German Research Center for Environmental Health, Neuherberg, Germany
| | - Ario Sadafi
- School of Computation, Information and Technology, Technical University of Munich, Garching, Germany; Institute of AI for Health, Helmholtz Munich-German Research Center for Environmental Health, Neuherberg, Germany
| | - Dominik J E Winter
- Institute of AI for Health, Helmholtz Munich-German Research Center for Environmental Health, Neuherberg, Germany; School of Life Sciences, Technical University of Munich, Weihenstephan, Germany
| | - Carsten Marr
- Institute of AI for Health, Helmholtz Munich-German Research Center for Environmental Health, Neuherberg, Germany.
| | - Tingying Peng
- Helmholtz AI, Helmholtz Munich-German Research Center for Environmental Health, Neuherberg, Germany.
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15
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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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16
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Fahoum I, Naamneh R, Silberberg K, Hagege R, Hershkovitz D. Detection of Muscularis propria Invasion in Urothelial Carcinoma Using Artificial Intelligence. Technol Cancer Res Treat 2024; 23:15330338241257479. [PMID: 38803309 PMCID: PMC11135091 DOI: 10.1177/15330338241257479] [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: 02/13/2024] [Revised: 04/06/2024] [Accepted: 05/06/2024] [Indexed: 05/29/2024] Open
Abstract
Background & Objective: Assessment of muscularis propria invasion is a crucial step in the management of urothelial carcinoma since it necessitates aggressive treatment. The diagnosis of muscle invasion is a challenging process for pathologists. Artificial intelligence is developing rapidly and being implemented in various fields of pathology. The purpose of this study was to develop an algorithm for the detection of muscularis propria invasion in urothelial carcinoma. Methods: The Training cohort consisted of 925 images from 50 specimens of urothelial carcinoma. Ninety-seven images from 10 new specimens were used as a validation cohort. Clinical validation used 127 whole specimens with a total of 617 slides. The algorithm determined areas where tumor and muscularis propria events were in nearest proximity, and presented these areas to the pathologist. Results: Analytical evaluation showed a sensitivity of 72% for muscularis propria and 65% for tumor, and a specificity of 46% and 77% for muscularis propria and tumor detection, respectively. The incorporation of the spatial proximity factor between muscularis propria and tumor in the clinical validation significantly improved the detection of muscularis propria invasion, as the algorithm managed to identify all except for one case with muscle invasive bladder cancer in the clinical validation cohort. The case missed by the algorithm was nested urothelial carcinoma, a rare subtype with unusual morphologic features. The pathologist managed to identify muscle invasion based on the images provided by the algorithm in a short time, with an average of approximately 5 s. Conclusion: The algorithm we developed may greatly aid in accurate identification of muscularis propria invasion by imitating the thought process of the pathologist.
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Affiliation(s)
- Ibrahim Fahoum
- Institute of Pathology, Tel-Aviv Sourasky Medical Center, Tel-Aviv, Israel
| | - Rabab Naamneh
- Institute of Pathology, Rabin Medical Center, Petah-Tikva, Israel
| | - Keren Silberberg
- Institute of Pathology, Tel-Aviv Sourasky Medical Center, Tel-Aviv, Israel
| | - Rami Hagege
- Institute of Pathology, Tel-Aviv Sourasky Medical Center, Tel-Aviv, Israel
| | - Dov Hershkovitz
- Institute of Pathology, Tel-Aviv Sourasky Medical Center, Tel-Aviv, Israel
- Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel
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17
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Lim Y, Choi S, Oh HJ, Kim C, Song S, Kim S, Song H, Park S, Kim JW, Kim JW, Kim JH, Kang M, Kang SB, Kim DW, Oh HK, Lee HS, Lee KW. Artificial intelligence-powered spatial analysis of tumor-infiltrating lymphocytes for prediction of prognosis in resected colon cancer. NPJ Precis Oncol 2023; 7:124. [PMID: 37985785 PMCID: PMC10662481 DOI: 10.1038/s41698-023-00470-0] [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: 05/08/2023] [Accepted: 10/24/2023] [Indexed: 11/22/2023] Open
Abstract
Tumor-infiltrating lymphocytes (TIL) have been suggested as an important prognostic marker in colorectal cancer, but assessment usually requires additional tissue processing and interpretational efforts. The aim of this study is to assess the clinical significance of artificial intelligence (AI)-powered spatial TIL analysis using only a hematoxylin and eosin (H&E)-stained whole-slide image (WSI) for the prediction of prognosis in stage II-III colon cancer treated with surgery and adjuvant therapy. In this retrospective study, we used Lunit SCOPE IO, an AI-powered H&E WSI analyzer, to assess intratumoral TIL (iTIL) and tumor-related stromal TIL (sTIL) densities from WSIs of 289 patients. The patients with confirmed recurrences had significantly lower sTIL densities (mean sTIL density 630.2/mm2 in cases with confirmed recurrence vs. 1021.3/mm2 in no recurrence, p < 0.001). Additionally, significantly higher recurrence rates were observed in patients having sTIL or iTIL in the lower quartile groups. Risk groups defined as high-risk (both iTIL and sTIL in the lowest quartile groups), low-risk (sTIL higher than the median), or intermediate-risk (not high- or low-risk) were predictive of recurrence and were independently associated with clinical outcomes after adjusting for other clinical factors. AI-powered TIL analysis can provide prognostic information in stage II/III colon cancer in a practical manner.
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Affiliation(s)
| | - Songji Choi
- Department of Internal Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Republic of Korea
| | - Hyeon Jeong Oh
- Department of Pathology, Seoul National University Bundang Hospital, Seongnam, Republic of Korea.
| | - Chanyoung Kim
- Department of Pathology, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
| | | | | | | | | | - Ji-Won Kim
- Department of Internal Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Republic of Korea
| | - Jin Won Kim
- Department of Internal Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Republic of Korea
| | - Jee Hyun Kim
- Department of Internal Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Republic of Korea
| | - Minsu Kang
- Department of Internal Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Republic of Korea
| | - Sung-Bum Kang
- Department of Surgery, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Korea
| | - Duck-Woo Kim
- Department of Surgery, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Korea
| | - Heung-Kwon Oh
- Department of Surgery, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Korea
| | - Hye Seung Lee
- Department of Pathology, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Keun-Wook Lee
- Department of Internal Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Republic of Korea.
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18
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Waqas A, Bui MM, Glassy EF, El Naqa I, Borkowski P, Borkowski AA, Rasool G. Revolutionizing Digital Pathology With the Power of Generative Artificial Intelligence and Foundation Models. J Transl Med 2023; 103:100255. [PMID: 37757969 DOI: 10.1016/j.labinv.2023.100255] [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: 04/14/2023] [Revised: 09/06/2023] [Accepted: 09/19/2023] [Indexed: 09/29/2023] Open
Abstract
Digital pathology has transformed the traditional pathology practice of analyzing tissue under a microscope into a computer vision workflow. Whole-slide imaging allows pathologists to view and analyze microscopic images on a computer monitor, enabling computational pathology. By leveraging artificial intelligence (AI) and machine learning (ML), computational pathology has emerged as a promising field in recent years. Recently, task-specific AI/ML (eg, convolutional neural networks) has risen to the forefront, achieving above-human performance in many image-processing and computer vision tasks. The performance of task-specific AI/ML models depends on the availability of many annotated training datasets, which presents a rate-limiting factor for AI/ML development in pathology. Task-specific AI/ML models cannot benefit from multimodal data and lack generalization, eg, the AI models often struggle to generalize to new datasets or unseen variations in image acquisition, staining techniques, or tissue types. The 2020s are witnessing the rise of foundation models and generative AI. A foundation model is a large AI model trained using sizable data, which is later adapted (or fine-tuned) to perform different tasks using a modest amount of task-specific annotated data. These AI models provide in-context learning, can self-correct mistakes, and promptly adjust to user feedback. In this review, we provide a brief overview of recent advances in computational pathology enabled by task-specific AI, their challenges and limitations, and then introduce various foundation models. We propose to create a pathology-specific generative AI based on multimodal foundation models and present its potentially transformative role in digital pathology. We describe different use cases, delineating how it could serve as an expert companion of pathologists and help them efficiently and objectively perform routine laboratory tasks, including quantifying image analysis, generating pathology reports, diagnosis, and prognosis. We also outline the potential role that foundation models and generative AI can play in standardizing the pathology laboratory workflow, education, and training.
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Affiliation(s)
- Asim Waqas
- Department of Machine Learning, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida; Department of Electrical Engineering, University of South Florida, Tampa, Florida.
| | - Marilyn M Bui
- Department of Machine Learning, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida; Department of Pathology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida; University of South Florida, Morsani College of Medicine, Tampa, Florida
| | - Eric F Glassy
- Affiliated Pathologists Medical Group, Inc., Rancho Dominguez, California
| | - Issam El Naqa
- Department of Machine Learning, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida
| | - Piotr Borkowski
- Quest Diagnostics/Ameripath, Tampa, Florida; Center of Excellence for Digital and AI-Empowered Pathology, Quest Diagnostics, Tampa, Florida
| | - Andrew A Borkowski
- University of South Florida, Morsani College of Medicine, Tampa, Florida; James A. Haley Veterans' Hospital, Tampa, Florida; National Artificial Intelligence Institute, Washington, District of Columbia
| | - Ghulam Rasool
- Department of Machine Learning, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida; Department of Electrical Engineering, University of South Florida, Tampa, Florida; University of South Florida, Morsani College of Medicine, Tampa, Florida; Department of Neuro-Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida
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19
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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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20
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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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21
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Cooper M, Ji Z, Krishnan RG. Machine learning in computational histopathology: Challenges and opportunities. Genes Chromosomes Cancer 2023; 62:540-556. [PMID: 37314068 DOI: 10.1002/gcc.23177] [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: 01/31/2023] [Revised: 05/18/2023] [Accepted: 05/20/2023] [Indexed: 06/15/2023] Open
Abstract
Digital histopathological images, high-resolution images of stained tissue samples, are a vital tool for clinicians to diagnose and stage cancers. The visual analysis of patient state based on these images are an important part of oncology workflow. Although pathology workflows have historically been conducted in laboratories under a microscope, the increasing digitization of histopathological images has led to their analysis on computers in the clinic. The last decade has seen the emergence of machine learning, and deep learning in particular, a powerful set of tools for the analysis of histopathological images. Machine learning models trained on large datasets of digitized histopathology slides have resulted in automated models for prediction and stratification of patient risk. In this review, we provide context for the rise of such models in computational histopathology, highlight the clinical tasks they have found success in automating, discuss the various machine learning techniques that have been applied to this domain, and underscore open problems and opportunities.
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Affiliation(s)
- Michael Cooper
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
- University Health Network, Toronto, Ontario, Canada
- Vector Institute, Toronto, Ontario, Canada
| | - Zongliang Ji
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
- Vector Institute, Toronto, Ontario, Canada
| | - Rahul G Krishnan
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
- Vector Institute, Toronto, Ontario, Canada
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada
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22
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Fu Y, Karanian M, Perret R, Camara A, Le Loarer F, Jean-Denis M, Hostein I, Michot A, Ducimetiere F, Giraud A, Courreges JB, Courtet K, Laizet Y, Bendjebbar E, Du Terrail JO, Schmauch B, Maussion C, Blay JY, Italiano A, Coindre JM. Deep learning predicts patients outcome and mutations from digitized histology slides in gastrointestinal stromal tumor. NPJ Precis Oncol 2023; 7:71. [PMID: 37488222 PMCID: PMC10366108 DOI: 10.1038/s41698-023-00421-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Accepted: 06/29/2023] [Indexed: 07/26/2023] Open
Abstract
Risk assessment of gastrointestinal stromal tumor (GIST) according to the AFIP/Miettinen classification and mutational profiling are major tools for patient management. However, the AFIP/Miettinen classification depends heavily on mitotic counts, which is laborious and sometimes inconsistent between pathologists. It has also been shown to be imperfect in stratifying patients. Molecular testing is costly and time-consuming, therefore, not systematically performed in all countries. New methods to improve risk and molecular predictions are hence crucial to improve the tailoring of adjuvant therapy. We have built deep learning (DL) models on digitized HES-stained whole slide images (WSI) to predict patients' outcome and mutations. Models were trained with a cohort of 1233 GIST and validated on an independent cohort of 286 GIST. DL models yielded comparable results to the Miettinen classification for relapse-free-survival prediction in localized GIST without adjuvant Imatinib (C-index=0.83 in cross-validation and 0.72 for independent testing). DL splitted Miettinen intermediate risk GIST into high/low-risk groups (p value = 0.002 in the training set and p value = 0.29 in the testing set). DL models achieved an area under the receiver operating characteristic curve (AUC) of 0.81, 0.91, and 0.71 for predicting mutations in KIT, PDGFRA and wild type, respectively, in cross-validation and 0.76, 0.90, and 0.55 in independent testing. Notably, PDGFRA exon18 D842V mutation, which is resistant to Imatinib, was predicted with an AUC of 0.87 and 0.90 in cross-validation and independent testing, respectively. Additionally, novel histological criteria predictive of patients' outcome and mutations were identified by reviewing the tiles selected by the models. As a proof of concept, our study showed the possibility of implementing DL with digitized WSI and may represent a reproducible way to improve tailoring therapy and precision medicine for patients with GIST.
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Affiliation(s)
- Yu Fu
- Owkin, Inc., New York, NY, USA
| | - Marie Karanian
- Cancer Research Center of Lyon, Centre Léon Bérard, Lyon, France
| | - Raul Perret
- Department of Biopathology, Institut Bergonié, Bordeaux, France
| | | | - François Le Loarer
- Department of Biopathology, Institut Bergonié, Bordeaux, France
- Faculty of Medicine, University of Bordeaux, Bordeaux, France
| | | | | | - Audrey Michot
- Department of Surgical Oncology, Institut Bergonié, Bordeaux, France
| | | | - Antoine Giraud
- Clinical Research and Clinical Epidemiology Unit, Institut Bergonié, Bordeaux, France
| | | | - Kevin Courtet
- Department of Biopathology, Institut Bergonié, Bordeaux, France
| | - Yech'an Laizet
- Department of Biopathology, Institut Bergonié, Bordeaux, France
| | | | | | | | | | - Jean-Yves Blay
- Cancer Research Center of Lyon, Centre Léon Bérard, Lyon, France
| | - Antoine Italiano
- Faculty of Medicine, University of Bordeaux, Bordeaux, France
- Department of Medicine, Institut Bergonié, Bordeaux, France
| | - Jean-Michel Coindre
- Department of Biopathology, Institut Bergonié, Bordeaux, France
- Faculty of Medicine, University of Bordeaux, Bordeaux, France
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23
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Choi S, Kim S. Artificial Intelligence in the Pathology of Gastric Cancer. J Gastric Cancer 2023; 23:410-427. [PMID: 37553129 PMCID: PMC10412971 DOI: 10.5230/jgc.2023.23.e25] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Revised: 07/09/2023] [Accepted: 07/14/2023] [Indexed: 08/10/2023] Open
Abstract
Recent advances in artificial intelligence (AI) have provided novel tools for rapid and precise pathologic diagnosis. The introduction of digital pathology has enabled the acquisition of scanned slide images that are essential for the application of AI. The application of AI for improved pathologic diagnosis includes the error-free detection of potentially negligible lesions, such as a minute focus of metastatic tumor cells in lymph nodes, the accurate diagnosis of potentially controversial histologic findings, such as very well-differentiated carcinomas mimicking normal epithelial tissues, and the pathological subtyping of the cancers. Additionally, the utilization of AI algorithms enables the precise decision of the score of immunohistochemical markers for targeted therapies, such as human epidermal growth factor receptor 2 and programmed death-ligand 1. Studies have revealed that AI assistance can reduce the discordance of interpretation between pathologists and more accurately predict clinical outcomes. Several approaches have been employed to develop novel biomarkers from histologic images using AI. Moreover, AI-assisted analysis of the cancer microenvironment showed that the distribution of tumor-infiltrating lymphocytes was related to the response to the immune checkpoint inhibitor therapy, emphasizing its value as a biomarker. As numerous studies have demonstrated the significance of AI-assisted interpretation and biomarker development, the AI-based approach will advance diagnostic pathology.
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Affiliation(s)
- Sangjoon Choi
- Department of Pathology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - 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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24
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van Bergeijk SA, Stathonikos N, ter Hoeve ND, Lafarge MW, Nguyen TQ, van Diest PJ, Veta M. Deep learning supported mitoses counting on whole slide images: A pilot study for validating breast cancer grading in the clinical workflow. J Pathol Inform 2023; 14:100316. [PMID: 37273455 PMCID: PMC10238836 DOI: 10.1016/j.jpi.2023.100316] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Revised: 04/13/2023] [Accepted: 04/28/2023] [Indexed: 06/06/2023] Open
Abstract
Introduction Breast cancer (BC) prognosis is largely influenced by histopathological grade, assessed according to the Nottingham modification of Bloom-Richardson (BR). Mitotic count (MC) is a component of histopathological grading but is prone to subjectivity. This study investigated whether mitoses counting in BC using digital whole slide images (WSI) compares better to light microscopy (LM) when assisted by artificial intelligence (AI), and to which extent differences in digital MC (AI assisted or not) result in BR grade variations. Methods Fifty BC patients with paired core biopsies and resections were randomly selected. Component scores for BR grade were extracted from pathology reports. MC was assessed using LM, WSI, and AI. Different modalities (LM-MC, WSI-MC, and AI-MC) were analyzed for correlation with scatterplots and linear regression, and for agreement in final BR with Cohen's κ. Results MC modalities strongly correlated in both biopsies and resections: LM-MC and WSI-MC (R2 0.85 and 0.83, respectively), LM-MC and AI-MC (R2 0.85 and 0.95), and WSI-MC and AI-MC (R2 0.77 and 0.83). Agreement in BR between modalities was high in both biopsies and resections: LM-MC and WSI-MC (κ 0.93 and 0.83, respectively), LM-MC and AI-MC (κ 0.89 and 0.83), and WSI-MC and AI-MC (κ 0.96 and 0.73). Conclusion This first validation study shows that WSI-MC may compare better to LM-MC when using AI. Agreement between BR grade based on the different mitoses counting modalities was high. These results suggest that mitoses counting on WSI can well be done, and validate the presented AI algorithm for pathologist supervised use in daily practice. Further research is required to advance our knowledge of AI-MC, but it appears at least non-inferior to LM-MC.
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Affiliation(s)
- Stijn A. van Bergeijk
- Department of Pathology, University Medical Center Utrecht, Postal Box 85500, 3508 GA Utrecht, The Netherlands
| | - Nikolas Stathonikos
- Department of Pathology, University Medical Center Utrecht, Postal Box 85500, 3508 GA Utrecht, The Netherlands
| | - Natalie D. ter Hoeve
- Department of Pathology, University Medical Center Utrecht, Postal Box 85500, 3508 GA Utrecht, The Netherlands
| | - Maxime W. Lafarge
- Medical Image Analysis Group (IMAG/e), Eindhoven University of Technology, Eindhoven, The Netherlands
- Computational and Translational Pathology Group, Department of Pathology and Molecular Pathology, University Hospital and University of Zürich, Schmelzbergstrasse 12, 8091 Zurich, Switzerland
| | - Tri Q. Nguyen
- Department of Pathology, University Medical Center Utrecht, Postal Box 85500, 3508 GA Utrecht, The Netherlands
| | - Paul J. van Diest
- Department of Pathology, University Medical Center Utrecht, Postal Box 85500, 3508 GA Utrecht, The Netherlands
| | - Mitko Veta
- Medical Image Analysis Group (IMAG/e), Eindhoven University of Technology, Eindhoven, The Netherlands
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25
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Balaji P, Muniasamy V, Bilfaqih SM, Muniasamy A, Tharanidharan S, Mani D, Alsid LEG. Chimp Optimization Algorithm Influenced Type-2 Intuitionistic Fuzzy C-Means Clustering-Based Breast Cancer Detection System. Cancers (Basel) 2023; 15:cancers15041131. [PMID: 36831474 PMCID: PMC9953815 DOI: 10.3390/cancers15041131] [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: 12/15/2022] [Revised: 01/30/2023] [Accepted: 02/07/2023] [Indexed: 02/12/2023] Open
Abstract
In recent years, breast cancer detection is an important area of concentration towards curative image dispensation and exploration. Detection of a disease at an early stage is an important factor in taking it to the next level of treatment. Accuracy plays an important role in the detection of disease. COA-T2FCM (Chimp Optimization Algorithm Based Type-2 Intuitionistic Fuzzy C-Means Clustering) is constructed for detection of such malignancy with the highest accuracy in this paper. The proposed detection process is designed with the combination of type-2 intuitionistic fuzzy c-means clustering in addition to oppositional function. In the type-2 intuitionistic fuzzy c-means clustering, the efficient cluster center can be preferred using the chimp optimization algorithm. Initially, the objective function of the type-2 intuitionistic fuzzy c-means clustering is considered. The chimp optimization algorithm is utilized to optimize the cluster center and fuzzifier in the clustering method. The projected technique is implemented, and in addition, performance metrics such as specificity, sensitivity, accuracy, Jaccard Similarity Index (JSI), and Dice Similarity Coefficient (DSC) are assessed. The projected technique is compared with the conventional technique such as fuzzy c means clustering and k mean clustering methods. The resulting method was also compared with existing methods to ensure the accuracy in the proposed method. The proposed algorithm is tested for its effectiveness on the mammogram images of the three different datasets collected from the Mini-Mammographic Image Analysis Society (Mini-MIAS), the Digital Database for Screening Mammography (DDSM), and Inbreast. The accuracy and Jaccard index score are generally used to measure the similarity between the proposed output and the actual cancer affected regions from the image considered. On an average the proposed method achieved an accuracy of 97.29% and JSI of 95.
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Affiliation(s)
- Prasanalakshmi Balaji
- College of Computer Science, King Khalid University, Abha 61421, Saudi Arabia
- Correspondence:
| | - Vasanthi Muniasamy
- Applied Science College, Mahala Campus, King Khalid University, Abha 61421, Saudi Arabia
| | | | | | - Sridevi Tharanidharan
- Applied Science College, Mahala Campus, King Khalid University, Abha 61421, Saudi Arabia
| | - Devi Mani
- College of Science and Arts, Sarat Abidah Campus, King Khalid University, Abha 61421, Saudi Arabia
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26
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Annotating for Artificial Intelligence Applications in Digital Pathology: A Practical Guide for Pathologists and Researchers. Mod Pathol 2023; 36:100086. [PMID: 36788085 DOI: 10.1016/j.modpat.2022.100086] [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: 10/26/2022] [Revised: 11/24/2022] [Accepted: 12/14/2022] [Indexed: 01/13/2023]
Abstract
Training machine learning models for artificial intelligence (AI) applications in pathology often requires extensive annotation by human experts, but there is little guidance on the subject. In this work, we aimed to describe our experience and provide a simple, useful, and practical guide addressing annotation strategies for AI development in computational pathology. Annotation methodology will vary significantly depending on the specific study's objectives, but common difficulties will be present across different settings. We summarize key aspects and issue guiding principles regarding team interaction, ground-truth quality assessment, different annotation types, and available software and hardware options and address common difficulties while annotating. This guide was specifically designed for pathology annotation, intending to help pathologists, other researchers, and AI developers with this process.
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27
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Next Generation Digital Pathology: Emerging Trends and Measurement Challenges for Molecular Pathology. JOURNAL OF MOLECULAR PATHOLOGY 2022. [DOI: 10.3390/jmp3030014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Digital pathology is revolutionising the analysis of histological features and is becoming more and more widespread in both the clinic and research. Molecular pathology extends the tissue morphology information provided by conventional histopathology by providing spatially resolved molecular information to complement the structural information provided by histopathology. The multidimensional nature of the molecular data poses significant challenge for data processing, mining, and analysis. One of the key challenges faced by new and existing pathology practitioners is how to choose the most suitable molecular pathology technique for a given diagnosis. By providing a comparison of different methods, this narrative review aims to introduce the field of molecular pathology, providing a high-level overview of many different methods. Since each pixel of an image contains a wealth of molecular information, data processing in molecular pathology is more complex. The key data processing steps and variables, and their effect on the data, are also discussed.
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28
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Papparella S, Crescio MI, Baldassarre V, Brunetti B, Burrai GP, Cocumelli C, Grieco V, Iussich S, Maniscalco L, Mariotti F, Millanta F, Paciello O, Rasotto R, Romanucci M, Sfacteria A, Zappulli V. Reproducibility and Feasibility of Classification and National Guidelines for Histological Diagnosis of Canine Mammary Gland Tumours: A Multi-Institutional Ring Study. Vet Sci 2022; 9:357. [PMID: 35878374 PMCID: PMC9325225 DOI: 10.3390/vetsci9070357] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Revised: 07/04/2022] [Accepted: 07/06/2022] [Indexed: 11/30/2022] Open
Abstract
Histological diagnosis of Canine Mammary Tumours (CMTs) provides the basis for proper treatment and follow-up. Nowadays, its accuracy is poorly understood and variable interpretation of histological criteria leads to a lack of standardisation and impossibility to compare studies. This study aimed to quantify the reproducibility of histological diagnosis and grading in CMTs. A blinded ring test on 36 CMTs was performed by 15 veterinary pathologists with different levels of education, after discussion of critical points on the Davis-Thompson Foundation Classification and providing consensus guidelines. Kappa statistics were used to compare the interobserver variability. The overall concordance rate of diagnostic interpretations of WP on identification of hyperplasia-dysplasia/benign/malignant lesions showed a substantial agreement (average k ranging from 0.66 to 0.82, with a k-combined of 0.76). Instead, outcomes on ICD-O-3.2 morphological code /diagnosis of histotype had only a moderate agreement (average k ranging from 0.44 and 0.64, with a k-combined of 0.54). The results demonstrated that standardised classification and consensus guidelines can produce moderate to substantial agreement; however, further efforts are needed to increase this agreement in distinguishing benign versus malignant lesions and in histological grading.
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Affiliation(s)
- Serenella Papparella
- Department of Veterinary Medicine and Animal Production, Unit of Pathology, University of Naples Federico II, 80138 Naples, Italy; (S.P.); (V.B.); (O.P.)
| | - Maria Ines Crescio
- National Reference Center for the Veterinary and Comparative Oncology (CEROVEC), Experimental Zooprophylactic Institute of Piedmont, Liguria and Valle d’Aosta, 10154 Turin, Italy;
| | - Valeria Baldassarre
- Department of Veterinary Medicine and Animal Production, Unit of Pathology, University of Naples Federico II, 80138 Naples, Italy; (S.P.); (V.B.); (O.P.)
| | - Barbara Brunetti
- Department of Veterinary Medical Sciences, University of Bologna, 40126 Bologna, Italy;
| | - Giovanni P. Burrai
- Department of Veterinary Medicine, University of Sassari, 07100 Sassari, Italy;
- Mediterranean Center for Disease Control (MCDC), University of Sassari, 07100 Sassari, Italy
| | - Cristiano Cocumelli
- Experimental Zooprophylactic Institute of Lazio and Toscana M. Aleandri, 00178 Rome, Italy;
| | - Valeria Grieco
- Department of Veterinary Medicine, University of Milan, 26900 Lodi, Italy;
| | - Selina Iussich
- Department of Veterinary Science, University of Turin, 10095 Turin, Italy; (S.I.); (L.M.)
| | - Lorella Maniscalco
- Department of Veterinary Science, University of Turin, 10095 Turin, Italy; (S.I.); (L.M.)
| | - Francesca Mariotti
- School of Bioscience and Veterinary Medicine, University of Camerino, 62032 Camerino, Italy;
| | - Francesca Millanta
- Department of Veterinary Sciences, University of Pisa, 56124 Pisa, Italy;
| | - Orlando Paciello
- Department of Veterinary Medicine and Animal Production, Unit of Pathology, University of Naples Federico II, 80138 Naples, Italy; (S.P.); (V.B.); (O.P.)
| | - Roberta Rasotto
- Independent Researcher, Via Messer Ottonello 1, 37127 Verona, Italy;
| | | | | | - Valentina Zappulli
- Department of Comparative Biomedicine and Food Science, University of Padua, 35020 Padua, Italy
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29
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Viswanathan VS, Toro P, Corredor G, Mukhopadhyay S, Madabhushi A. The state of the art for artificial intelligence in lung digital pathology. J Pathol 2022; 257:413-429. [PMID: 35579955 PMCID: PMC9254900 DOI: 10.1002/path.5966] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Revised: 04/26/2022] [Accepted: 05/15/2022] [Indexed: 12/03/2022]
Abstract
Lung diseases carry a significant burden of morbidity and mortality worldwide. The advent of digital pathology (DP) and an increase in computational power have led to the development of artificial intelligence (AI)-based tools that can assist pathologists and pulmonologists in improving clinical workflow and patient management. While previous works have explored the advances in computational approaches for breast, prostate, and head and neck cancers, there has been a growing interest in applying these technologies to lung diseases as well. The application of AI tools on radiology images for better characterization of indeterminate lung nodules, fibrotic lung disease, and lung cancer risk stratification has been well documented. In this article, we discuss methodologies used to build AI tools in lung DP, describing the various hand-crafted and deep learning-based unsupervised feature approaches. Next, we review AI tools across a wide spectrum of lung diseases including cancer, tuberculosis, idiopathic pulmonary fibrosis, and COVID-19. We discuss the utility of novel imaging biomarkers for different types of clinical problems including quantification of biomarkers like PD-L1, lung disease diagnosis, risk stratification, and prediction of response to treatments such as immune checkpoint inhibitors. We also look briefly at some emerging applications of AI tools in lung DP such as multimodal data analysis, 3D pathology, and transplant rejection. Lastly, we discuss the future of DP-based AI tools, describing the challenges with regulatory approval, developing reimbursement models, planning clinical deployment, and addressing AI biases. © 2022 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)
| | - Paula Toro
- Department of PathologyCleveland ClinicClevelandOHUSA
| | - Germán Corredor
- Department of Biomedical EngineeringCase Western Reserve UniversityClevelandOHUSA
- Louis Stokes Cleveland VA Medical CenterClevelandOHUSA
| | | | - Anant Madabhushi
- Department of Biomedical EngineeringCase Western Reserve UniversityClevelandOHUSA
- Louis Stokes Cleveland VA Medical CenterClevelandOHUSA
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30
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Ibrahim A, Lashen AG, Katayama A, Mihai R, Ball G, Toss MS, Rakha EA. Defining the area of mitoses counting in invasive breast cancer using whole slide image. Mod Pathol 2022; 35:739-748. [PMID: 34897279 PMCID: PMC9174050 DOI: 10.1038/s41379-021-00981-w] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Revised: 11/19/2021] [Accepted: 11/19/2021] [Indexed: 01/02/2023]
Abstract
Although counting mitoses is part of breast cancer grading, concordance studies showed low agreement. Refining the criteria for mitotic counting can improve concordance, particularly when using whole slide images (WSIs). This study aims to refine the methodology for optimal mitoses counting on WSI. Digital images of 595 hematoxylin and eosin stained sections were evaluated. Several morphological criteria were investigated and applied to define mitotic hotspots. Reproducibility, representativeness, time, and association with outcome were the criteria used to evaluate the best area size for mitoses counting. Three approaches for scoring mitoses on WSIs (single and multiple annotated rectangles and multiple digital high-power (×40) screen fields (HPSFs)) were evaluated. The relative increase in tumor cell density was the most significant and easiest parameter for identifying hotspots. Counting mitoses in 3 mm2 area was the most representative regarding saturation and concordance levels. Counting in area <2 mm2 resulted in a significant reduction in mitotic count (P = 0.02), whereas counting in area ≥4 mm2 was time-consuming and did not add a significant rise in overall mitotic count (P = 0.08). Using multiple HPSF, following calibration, provided the most reliable, timesaving, and practical method for mitoses counting on WSI. This study provides evidence-based methodology for defining the area and methodology of visual mitoses counting using WSI. Visual mitoses scoring on WSI can be performed reliably by adjusting the number of monitor screens.
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Affiliation(s)
- Asmaa Ibrahim
- Division of Cancer and Stem Cells, School of Medicine, University of Nottingham Biodiscovery Institute, University Park, Nottingham, UK
- Department of Pathology, Faculty of Medicine, Suez Canal University, Ismailia, Egypt
| | - Ayat G Lashen
- Division of Cancer and Stem Cells, School of Medicine, University of Nottingham Biodiscovery Institute, University Park, Nottingham, UK
- Department of Pathology, Faculty of Medicine, Menoufia University, Shebin El Kom, Egypt
| | - Ayaka Katayama
- Division of Cancer and Stem Cells, School of Medicine, University of Nottingham Biodiscovery Institute, University Park, Nottingham, UK
- Diagnostic Pathology, Gunma University Graduate School of Medicine, Maebashi, Japan
| | - Raluca Mihai
- Department of Pathology, Queen Elizabeth University Hospital, 1345 Govan Rd, Glasgow, G51 4TF, UK
| | - Graham Ball
- John van Geest Cancer Research Centre, School of Science and Technology, Nottingham Trent University, Nottingham, UK
| | - Michael S Toss
- Division of Cancer and Stem Cells, School of Medicine, University of Nottingham Biodiscovery Institute, University Park, Nottingham, UK
| | - Emad A Rakha
- Division of Cancer and Stem Cells, School of Medicine, University of Nottingham Biodiscovery Institute, University Park, Nottingham, UK.
- Department of Pathology, Faculty of Medicine, Menoufia University, Shebin El Kom, Egypt.
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31
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Heinemann F, Lempp C, Colbatzky F, Deschl U, Nolte T. Quantification of Hepatocellular Mitoses in a Toxicological Study in Rats Using a Convolutional Neural Network. Toxicol Pathol 2022; 50:344-352. [PMID: 35321595 DOI: 10.1177/01926233221083500] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Convolutional neural networks (CNNs) have been recognized as valuable tools for rapid quantitative analysis of morphological changes in toxicologic histopathology. We have assessed the performance of CNN-based (Halo-AI) mitotic figure detection in hepatocytes in comparison with detection by pathologists. In addition, we compared with Ki-67 and 5-bromodesoxyuridin (BrdU) immunohistochemistry labeling indices (LIs) obtained by image analysis. Tissues were from an exploratory toxicity study with a glycogen synthase kinase-3 (GSK-3) inhibitor. Our investigations revealed that (1) the CNN achieved similarly accurate but faster results than pathologists, (2) results of mitotic figure detection were comparable to Ki-67 and BrdU LIs, and (3) data from different methods were only moderately correlated. The latter is likely related to differences in the cell cycle component captured by each method. This highlights the importance of considering the differences of the available methods upon selection. Also, the pharmacology of our test item acting as a GSK-3 inhibitor potentially reduced the correlation. We conclude that hepatocyte cell proliferation assessment by CNNs can have several advantages when compared with the current gold standard: it relieves the pathologist of tedious routine tasks and contributes to standardization of results; the CNN algorithm can be shared and iteratively improved; it can be performed on routine histological slides; it does not require an additional animal experiment and in this way can contribute to animal welfare according to the 3R principles.
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Affiliation(s)
- Fabian Heinemann
- Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach an der Riß, Germany
| | - Charlotte Lempp
- Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach an der Riß, Germany
| | - Florian Colbatzky
- Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach an der Riß, Germany
| | - Ulrich Deschl
- Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach an der Riß, Germany
| | - Thomas Nolte
- Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach an der Riß, Germany
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32
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Park S, Ock CY, Kim H, Pereira S, Park S, Ma M, Choi S, Kim S, Shin S, Aum BJ, Paeng K, Yoo D, Cha H, Park S, Suh KJ, Jung HA, Kim SH, Kim YJ, Sun JM, Chung JH, Ahn JS, Ahn MJ, Lee JS, Park K, Song SY, Bang YJ, Choi YL, Mok TS, Lee SH. Artificial Intelligence-Powered Spatial Analysis of Tumor-Infiltrating Lymphocytes as Complementary Biomarker for Immune Checkpoint Inhibition in Non-Small-Cell Lung Cancer. J Clin Oncol 2022; 40:1916-1928. [PMID: 35271299 PMCID: PMC9177249 DOI: 10.1200/jco.21.02010] [Citation(s) in RCA: 96] [Impact Index Per Article: 48.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
Abstract
PURPOSE Biomarkers on the basis of tumor-infiltrating lymphocytes (TIL) are potentially valuable in predicting the effectiveness of immune checkpoint inhibitors (ICI). However, clinical application remains challenging because of methodologic limitations and laborious process involved in spatial analysis of TIL distribution in whole-slide images (WSI). METHODS We have developed an artificial intelligence (AI)–powered WSI analyzer of TIL in the tumor microenvironment that can define three immune phenotypes (IPs): inflamed, immune-excluded, and immune-desert. These IPs were correlated with tumor response to ICI and survival in two independent cohorts of patients with advanced non–small-cell lung cancer (NSCLC). RESULTS Inflamed IP correlated with enrichment in local immune cytolytic activity, higher response rate, and prolonged progression-free survival compared with patients with immune-excluded or immune-desert phenotypes. At the WSI level, there was significant positive correlation between tumor proportion score (TPS) as determined by the AI model and control TPS analyzed by pathologists (P < .001). Overall, 44.0% of tumors were inflamed, 37.1% were immune-excluded, and 18.9% were immune-desert. Incidence of inflamed IP in patients with programmed death ligand-1 TPS at < 1%, 1%-49%, and ≥ 50% was 31.7%, 42.5%, and 56.8%, respectively. Median progression-free survival and overall survival were, respectively, 4.1 months and 24.8 months with inflamed IP, 2.2 months and 14.0 months with immune-excluded IP, and 2.4 months and 10.6 months with immune-desert IP. CONCLUSION The AI-powered spatial analysis of TIL correlated with tumor response and progression-free survival of ICI in advanced NSCLC. This is potentially a supplementary biomarker to TPS as determined by a pathologist.
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Affiliation(s)
- Sehhoon Park
- Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Republic of Korea
| | | | - Hyojin Kim
- Department of Pathology, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
| | | | | | | | - Sangjoon Choi
- Department of Pathology and Translational Genomics, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Seokhwi Kim
- Department of Pathology, Ajou University School of Medicine, Suwon, Republic of Korea
| | | | | | | | | | - Hongui Cha
- Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Republic of Korea
| | - Sunyoung Park
- Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Republic of Korea
| | - Koung Jin Suh
- Division of Hematology-Oncology, Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
| | - Hyun Ae Jung
- Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Republic of Korea
| | - Se Hyun Kim
- Division of Hematology-Oncology, Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
| | - Yu Jung Kim
- Division of Hematology-Oncology, Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
| | - Jong-Mu Sun
- Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Republic of Korea
| | - Jin-Haeng Chung
- Department of Pathology, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
| | - Jin Seok Ahn
- Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Republic of Korea
| | - Myung-Ju Ahn
- Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Republic of Korea
| | - Jong Seok Lee
- Division of Hematology-Oncology, Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
| | - Keunchil Park
- Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Republic of Korea
| | - Sang Yong Song
- Department of Pathology and Translational Genomics, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Yung-Jue Bang
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Yoon-La Choi
- Department of Pathology and Translational Genomics, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Tony S Mok
- State Key Laboratory of Translational Oncology, Department of Clinical Oncology, Chinese University of Hong Kong, Hong Kong, China
| | - Se-Hoon Lee
- Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Republic of Korea.,Department of Health Sciences and Technology, Samsung Advanced Institute of Health Sciences and Technology, Sungkyunkwan University, Seoul, Republic of Korea
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Hanna MG, Ardon O, Reuter VE, Sirintrapun SJ, England C, Klimstra DS, Hameed MR. Integrating digital pathology into clinical practice. Mod Pathol 2022; 35:152-164. [PMID: 34599281 DOI: 10.1038/s41379-021-00929-0] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Revised: 09/03/2021] [Accepted: 09/12/2021] [Indexed: 11/09/2022]
Abstract
The field of anatomic pathology has been evolving in the last few decades and the advancements have been largely fostered by innovative technology. Immunohistochemistry enabled a paradigm shift in discovery and diagnostic evaluation, followed by booming genomic advancements which allowed for submicroscopic pathologic characterization, and now the field of digital pathology coupled with machine learning and big data acquisition is paving the way to revolutionize the pathology medical domain. Whole slide imaging (WSI) is a disruptive technology where glass slides are digitized to produce on-screen whole slide images. Specifically, in the past decade, there have been significant advances in digital pathology systems that have allowed this technology to promote integration into clinical practice. Whole slide images (WSI), or digital slides, can be viewed and navigated comparable to glass slides on a microscope, as digital files. Whole slide imaging has increased in adoption among pathologists, pathology departments, and scientists for clinical, educational, and research initiatives. Integration of digital pathology systems requires a coordinated effort with numerous stakeholders, not only within the pathology department, but across the entire enterprise. Each pathology department has distinct needs, use cases and blueprints, however the framework components and variables for successful clinical integration can be generalized across any organization seeking to undergo a digital transformation at any scale. This article will review those components and considerations for integrating digital pathology systems into clinical practice.
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Affiliation(s)
- Matthew G Hanna
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
| | - Orly Ardon
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Victor E Reuter
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | | | - Christine England
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - David S Klimstra
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Meera R Hameed
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
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34
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Drogt J, Milota M, Vos S, Bredenoord A, Jongsma K. Integrating artificial intelligence in pathology: a qualitative interview study of users' experiences and expectations. Mod Pathol 2022; 35:1540-1550. [PMID: 35927490 PMCID: PMC9596368 DOI: 10.1038/s41379-022-01123-6] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Revised: 05/24/2022] [Accepted: 05/31/2022] [Indexed: 11/24/2022]
Abstract
Recent progress in the development of artificial intelligence (AI) has sparked enthusiasm for its potential use in pathology. As pathology labs are currently starting to shift their focus towards AI implementation, a better understanding how AI tools can be optimally aligned with the medical and social context of pathology daily practice is urgently needed. Strikingly, studies often fail to mention the ways in which AI tools should be integrated in the decision-making processes of pathologists, nor do they address how this can be achieved in an ethically sound way. Moreover, the perspectives of pathologists and other professionals within pathology concerning the integration of AI within pathology remains an underreported topic. This article aims to fill this gap in the literature and presents the first in-depth interview study in which professionals' perspectives on the possibilities, conditions and prerequisites of AI integration in pathology are explicated. The results of this study have led to the formulation of three concrete recommendations to support AI integration, namely: (1) foster a pragmatic attitude toward AI development, (2) provide task-sensitive information and training to health care professionals working in pathology departments and (3) take time to reflect upon users' changing roles and responsibilities.
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Affiliation(s)
- Jojanneke Drogt
- Department of Medical Humanities, University Medical Center, Utrecht, The Netherlands.
| | - Megan Milota
- grid.7692.a0000000090126352Department of Medical Humanities, University Medical Center, Utrecht, The Netherlands
| | - Shoko Vos
- grid.10417.330000 0004 0444 9382Department of Pathology, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Annelien Bredenoord
- grid.7692.a0000000090126352Department of Medical Humanities, University Medical Center, Utrecht, The Netherlands
| | - Karin Jongsma
- grid.7692.a0000000090126352Department of Medical Humanities, University Medical Center, Utrecht, The Netherlands
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35
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Garberis I, Andre F, Lacroix-Triki M. L’intelligence artificielle pourrait-elle intervenir dans l’aide au diagnostic des cancers du sein ? – L’exemple de HER2. Bull Cancer 2022; 108:11S35-11S45. [PMID: 34969514 DOI: 10.1016/s0007-4551(21)00635-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
HER2 is an important prognostic and predictive biomarker in breast cancer. Its detection makes it possible to define which patients will benefit from a targeted treatment. While assessment of HER2 status by immunohistochemistry in positive vs negative categories is well implemented and reproducible, the introduction of a new "HER2-low" category could raise some concerns about its scoring and reproducibility. We herein described the current HER2 testing methods and the application of innovative machine learning techniques to improve these determinations, as well as the main challenges and opportunities related to the implementation of digital pathology in the up-and-coming AI era.
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Affiliation(s)
- Ingrid Garberis
- Inserm UMR 981, Gustave Roussy Cancer Campus, Villejuif, France; Université Paris-Saclay, 94270 Le Kremlin-Bicêtre, France.
| | - Fabrice Andre
- Inserm UMR 981, Gustave Roussy Cancer Campus, Villejuif, France; Université Paris-Saclay, 94270 Le Kremlin-Bicêtre, France; Département d'oncologie médicale, Gustave-Roussy, Villejuif, France
| | - Magali Lacroix-Triki
- Inserm UMR 981, Gustave Roussy Cancer Campus, Villejuif, France; Département d'anatomie et cytologie pathologiques, Gustave-Roussy, Villejuif, France
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Bertram CA, Aubreville M, Donovan TA, Bartel A, Wilm F, Marzahl C, Assenmacher CA, Becker K, Bennett M, Corner S, Cossic B, Denk D, Dettwiler M, Gonzalez BG, Gurtner C, Haverkamp AK, Heier A, Lehmbecker A, Merz S, Noland EL, Plog S, Schmidt A, Sebastian F, Sledge DG, Smedley RC, Tecilla M, Thaiwong T, Fuchs-Baumgartinger A, Meuten DJ, Breininger K, Kiupel M, Maier A, Klopfleisch R. Computer-assisted mitotic count using a deep learning–based algorithm improves interobserver reproducibility and accuracy. Vet Pathol 2021; 59:211-226. [PMID: 34965805 PMCID: PMC8928234 DOI: 10.1177/03009858211067478] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
The mitotic count (MC) is an important histological parameter for prognostication of malignant neoplasms. However, it has inter- and intraobserver discrepancies due to difficulties in selecting the region of interest (MC-ROI) and in identifying or classifying mitotic figures (MFs). Recent progress in the field of artificial intelligence has allowed the development of high-performance algorithms that may improve standardization of the MC. As algorithmic predictions are not flawless, computer-assisted review by pathologists may ensure reliability. In the present study, we compared partial (MC-ROI preselection) and full (additional visualization of MF candidates and display of algorithmic confidence values) computer-assisted MC analysis to the routine (unaided) MC analysis by 23 pathologists for whole-slide images of 50 canine cutaneous mast cell tumors (ccMCTs). Algorithmic predictions aimed to assist pathologists in detecting mitotic hotspot locations, reducing omission of MFs, and improving classification against imposters. The interobserver consistency for the MC significantly increased with computer assistance (interobserver correlation coefficient, ICC = 0.92) compared to the unaided approach (ICC = 0.70). Classification into prognostic stratifications had a higher accuracy with computer assistance. The algorithmically preselected hotspot MC-ROIs had a consistently higher MCs than the manually selected MC-ROIs. Compared to a ground truth (developed with immunohistochemistry for phosphohistone H3), pathologist performance in detecting individual MF was augmented when using computer assistance (F1-score of 0.68 increased to 0.79) with a reduction in false negatives by 38%. The results of this study demonstrate that computer assistance may lead to more reproducible and accurate MCs in ccMCTs.
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Affiliation(s)
- Christof A. Bertram
- University of Veterinary Medicine, Vienna, Austria
- Freie Universität Berlin, Berlin, Germany
| | | | | | | | - Frauke Wilm
- Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Christian Marzahl
- Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | | | | | | | | | | | | | | | | | | | | | | | | | - Sophie Merz
- IDEXX Vet Med Labor GmbH, Kornwestheim, Germany
| | | | | | | | | | | | | | - Marco Tecilla
- Roche Pharmaceutical Research and Early Development (pRED), Basel, Switzerland
| | | | | | | | | | | | - Andreas Maier
- Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
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Yousif M, van Diest PJ, Laurinavicius A, Rimm D, van der Laak J, Madabhushi A, Schnitt S, Pantanowitz L. Artificial intelligence applied to breast pathology. Virchows Arch 2021; 480:191-209. [PMID: 34791536 DOI: 10.1007/s00428-021-03213-3] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Revised: 09/12/2021] [Accepted: 09/27/2021] [Indexed: 12/12/2022]
Abstract
The convergence of digital pathology and computer vision is increasingly enabling computers to perform tasks performed by humans. As a result, artificial intelligence (AI) is having an astoundingly positive effect on the field of pathology, including breast pathology. Research using machine learning and the development of algorithms that learn patterns from labeled digital data based on "deep learning" neural networks and feature-engineered approaches to analyze histology images have recently provided promising results. Thus far, image analysis and more complex AI-based tools have demonstrated excellent success performing tasks such as the quantification of breast biomarkers and Ki67, mitosis detection, lymph node metastasis recognition, tissue segmentation for diagnosing breast carcinoma, prognostication, computational assessment of tumor-infiltrating lymphocytes, and prediction of molecular expression as well as treatment response and benefit of therapy from routine H&E images. This review critically examines the literature regarding these applications of AI in the area of breast pathology.
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Affiliation(s)
- Mustafa Yousif
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA.
- Department of Pathology, Vanderbilt University Medical Center, Nashville, TN, USA.
| | - Paul J van Diest
- Department of Pathology, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Arvydas Laurinavicius
- Department of Pathology, Pharmacology and Forensic Medicine, Faculty of Medicine, Vilnius University, and National Center of Pathology, Affiliate of Vilnius University Hospital Santaros Klinikos, Vilnius, Lithuania
| | - David Rimm
- Department of Pathology, Yale University School of Medicine, New Haven, CT, USA
| | - Jeroen van der Laak
- Department of Pathology, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Anant Madabhushi
- Department of Biomedical Engineering, Case Western Reserve University, and Louis Stokes Cleveland Veterans Administration Medical Center, Cleveland, OH, USA
- Louis Stokes Cleveland Veterans Administration Medical Center, Cleveland, OH, USA
| | - Stuart Schnitt
- Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Breast Oncology Program, Dana-Farber/Brigham and Women's Cancer Center, Boston, MA, USA
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Christensen AW, Goldberg AF. Mitotic count of fine needle aspiration material of gastrointestinal stromal tumours of the stomach underestimates actual mitotic count. Cytopathology 2021; 33:100-106. [PMID: 34390589 DOI: 10.1111/cyt.13050] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Revised: 07/26/2021] [Accepted: 08/12/2021] [Indexed: 12/12/2022]
Abstract
INTRODUCTION A mitotic count is required for histological grading in resections of gastrointestinal stromal tumours (GISTs). However, no consensus on the utility of mitotic count in fine needle aspiration (FNA) GIST material currently exists. This study examines the relationship between mitotic counts of FNAs and subsequent resections of GISTs of the stomach. MATERIALS AND METHODS We identified 39 cases of GISTs of the stomach diagnosed via FNA at our institution between January 1, 2014, and December 31, 2019, with subsequent resection. We noted if rapid on-site evaluation (ROSE) was performed. Cell block (CB) material from FNAs was analysed for total area, percentage containing neoplastic cells, and number of mitoses. We compared the mitotic counts in CBs and subsequent resections with t tests. RESULTS ROSE was performed in 82% of cases and called adequate every time. Mean values for total CB area, neoplastic material percentage, and area of neoplastic cells were 54.7 mm2 (range 1-986), 45% (range 10%-90%), and 19.2 mm2 , respectively; 27 cases (69%) had greater than 50 high powered fields of GIST material in the CB. Mean numbers of mitoses per 5 mm2 were 0.38 (range 0-11) for CBs versus 5.92 (range 0-70) for resections (P < 0.05). CONCLUSION At our institution, ROSE adequacy of spindle cell lesions focuses on diagnosing GIST rather than obtaining adequate material for histological grading. Mitotic figures were statistically lower in FNA CB material than subsequent resections, and using mitotic counts from CB material may underestimate the histological grade of GISTs of the stomach.
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Affiliation(s)
- Aaron W Christensen
- Department of Pathology, Anatomy, and Cell Biology, Thomas Jefferson University, Philadelphia, PA, USA
| | - Allison F Goldberg
- Department of Pathology, Anatomy, and Cell Biology, Thomas Jefferson University, Philadelphia, PA, USA
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Roszkowiak L, Korzynska A, Siemion K, Zak J, Pijanowska D, Bosch R, Lejeune M, Lopez C. System for quantitative evaluation of DAB&H-stained breast cancer biopsy digital images (CHISEL). Sci Rep 2021; 11:9291. [PMID: 33927266 PMCID: PMC8085130 DOI: 10.1038/s41598-021-88611-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2020] [Accepted: 04/14/2021] [Indexed: 02/02/2023] Open
Abstract
This study presents CHISEL (Computer-assisted Histopathological Image Segmentation and EvaLuation), an end-to-end system capable of quantitative evaluation of benign and malignant (breast cancer) digitized tissue samples with immunohistochemical nuclear staining of various intensity and diverse compactness. It stands out with the proposed seamless segmentation based on regions of interest cropping as well as the explicit step of nuclei cluster splitting followed by a boundary refinement. The system utilizes machine learning and recursive local processing to eliminate distorted (inaccurate) outlines. The method was validated using two labeled datasets which proved the relevance of the achieved results. The evaluation was based on the IISPV dataset of tissue from biopsy of breast cancer patients, with markers of T cells, along with Warwick Beta Cell Dataset of DAB&H-stained tissue from postmortem diabetes patients. Based on the comparison of the ground truth with the results of the detected and classified objects, we conclude that the proposed method can achieve better or similar results as the state-of-the-art methods. This system deals with the complex problem of nuclei quantification in digitalized images of immunohistochemically stained tissue sections, achieving best results for DAB&H-stained breast cancer tissue samples. Our method has been prepared with user-friendly graphical interface and was optimized to fully utilize the available computing power, while being accessible to users with fewer resources than needed by deep learning techniques.
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Affiliation(s)
- Lukasz Roszkowiak
- Nalecz Institute of Biocybernetics and Biomedical Engineering Polish Academy of Sciences, Ks. Trojdena 4 st., 02-109, Warsaw, Poland.
| | - Anna Korzynska
- Nalecz Institute of Biocybernetics and Biomedical Engineering Polish Academy of Sciences, Ks. Trojdena 4 st., 02-109, Warsaw, Poland
| | - Krzysztof Siemion
- Nalecz Institute of Biocybernetics and Biomedical Engineering Polish Academy of Sciences, Ks. Trojdena 4 st., 02-109, Warsaw, Poland
- Medical Pathomorphology Department, Medical University of Bialystok, Białystok, Poland
| | - Jakub Zak
- Nalecz Institute of Biocybernetics and Biomedical Engineering Polish Academy of Sciences, Ks. Trojdena 4 st., 02-109, Warsaw, Poland
| | - Dorota Pijanowska
- Nalecz Institute of Biocybernetics and Biomedical Engineering Polish Academy of Sciences, Ks. Trojdena 4 st., 02-109, Warsaw, Poland
| | - Ramon Bosch
- Pathology Department, Hospital de Tortosa Verge de la Cinta, Institut d'Investigacio Sanitaria Pere Virgili (IISPV), URV, Tortosa, Spain
| | - Marylene Lejeune
- Molecular Biology and Research Section, Hospital de Tortosa Verge de la Cinta, Institut d'Investigacio Sanitaria Pere Virgili (IISPV), URV, Tortosa, Spain
| | - Carlos Lopez
- Molecular Biology and Research Section, Hospital de Tortosa Verge de la Cinta, Institut d'Investigacio Sanitaria Pere Virgili (IISPV), URV, Tortosa, Spain
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Histologic grading of breast carcinoma: a multi-institution study of interobserver variation using virtual microscopy. Mod Pathol 2021; 34:701-709. [PMID: 33077923 PMCID: PMC7987728 DOI: 10.1038/s41379-020-00698-2] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2020] [Revised: 09/23/2020] [Accepted: 09/24/2020] [Indexed: 12/14/2022]
Abstract
Breast carcinoma grading is an important prognostic feature recently incorporated into the AJCC Cancer Staging Manual. There is increased interest in applying virtual microscopy (VM) using digital whole slide imaging (WSI) more broadly. Little is known regarding concordance in grading using VM and how such variability might affect AJCC prognostic staging (PS). We evaluated interobserver variability amongst a multi-institutional group of breast pathologists using digital WSI and how discrepancies in grading would affect PS. A digitally scanned slide from 143 invasive carcinomas was independently reviewed by 6 pathologists and assigned grades based on established criteria for tubule formation (TF), nuclear pleomorphism (NP), and mitotic count (MC). Statistical analysis was performed. Interobserver agreement for grade was moderate (κ = 0.497). Agreement was fair (κ = 0.375), moderate (κ = 0.491), and good (κ = 0.705) for grades 2, 3, and 1, respectively. Observer pair concordance ranged from fair to good (κ = 0.354-0.684) Perfect agreement was observed in 43 cases (30%). Interobserver agreement for the individual components was best for TF (κ = 0.503) and worst for MC (κ = 0.281). Seventeen of 86 (19.8%) discrepant cases would have resulted in changes in PS and discrepancies most frequently resulted in a PS change from IA to IB (n = 9). For two of these nine cases, Oncotype DX results would have led to a PS of 1A regardless of grade. Using VM, a multi-institutional cohort of pathologists showed moderate concordance for breast cancer grading, similar to studies using light microscopy. Agreement was the best at the extremes of grade and for evaluation of TF. Whether the higher variability noted for MC is a consequence of VM grading warrants further investigation. Discordance in grading infrequently leads to clinically meaningful changes in the prognostic stage.
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Dong Z, Meng X, Yang W, Zhang J, Sun P, Zhang H, Fang X, Wang DA, Fan C. Progress of gelatin-based microspheres (GMSs) as delivery vehicles of drug and cell. MATERIALS SCIENCE & ENGINEERING. C, MATERIALS FOR BIOLOGICAL APPLICATIONS 2021; 122:111949. [PMID: 33641932 DOI: 10.1016/j.msec.2021.111949] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/28/2020] [Revised: 01/30/2021] [Accepted: 02/02/2021] [Indexed: 12/13/2022]
Abstract
Gelatin has various attractive features as biomedical materials, for instance, biocompatibility, low immunogenicity, biodegradability, and ease of manipulation. In recent years, various gelatin-based microspheres (GMSs) have been fabricated with innovative technologies to serve as sustained delivery vehicles of drugs and genetic materials as well as beneficial bacteria. Moreover, GMSs have exhibited promising potentials to act as both cell carriers and 3D scaffold components in tissue engineering and regenerative medicine, which not only exhibit excellent injectability but also could be integrated into a macroscale construct with the laden cells. Herein, we aim to thoroughly summarize the recent progress in the preparations and biomedical applications of GMSs and then to point out the research direction in future. First, various methods for the fabrication of GMSs will be described. Second, the recent use of GMSs in tumor embolization and in the delivery of cells, drugs, and genetic material as well as bacteria will be presented. Finally, several key factors that may enhance the improvement of GMSs were suggested as delivery vehicles.
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Affiliation(s)
- Zuoxiang Dong
- Institute for Translational Medicine, The Affiliated Hospital of Qingdao University, Qingdao Medical College, Qingdao University, Qingdao 266000, Shandong, China; Department of Neurosurgery, The Affiliated Hospital of Qingdao University, Qingdao 266000, Shandong, China
| | - Xinyue Meng
- Institute for Translational Medicine, The Affiliated Hospital of Qingdao University, Qingdao Medical College, Qingdao University, Qingdao 266000, Shandong, China
| | - Wei Yang
- Institute for Translational Medicine, The Affiliated Hospital of Qingdao University, Qingdao Medical College, Qingdao University, Qingdao 266000, Shandong, China
| | - Jinfeng Zhang
- Department of Surgery, Songshan Hospital of Qingdao University, Qingdao 266021, Shandong, China
| | - Peng Sun
- Department of Neurosurgery, The Affiliated Hospital of Qingdao University, Qingdao 266000, Shandong, China
| | - Huawei Zhang
- Department of Neurosurgery, The Affiliated Hospital of Qingdao University, Qingdao 266000, Shandong, China
| | - Xing Fang
- Department of Pharmacology and Toxicology, University of Mississippi Medical Center, Jackson, MS 39216, USA
| | - Dong-An Wang
- Department of Biomedical Engineering, City University of Hong Kong, Kowloon, Hong Kong Special Administrative Region; Shenzhen Research Institute, City University of Hong Kong, Shenzhen Hi-tech Industrial Park, Shenzhen, Guangdong 518057, China; Karolinska Institute Ming Wai Lau Centre for Reparative Medicine, HKSTP, Sha Tin, Hong Kong Special Administrative Region.
| | - Changjiang Fan
- Institute for Translational Medicine, The Affiliated Hospital of Qingdao University, Qingdao Medical College, Qingdao University, Qingdao 266000, Shandong, China.
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Marazzi F, Tagliaferri L, Masiello V, Moschella F, Colloca GF, Corvari B, Sanchez AM, Capocchiano ND, Pastorino R, Iacomini C, Lenkowicz J, Masciocchi C, Patarnello S, Franceschini G, Gambacorta MA, Masetti R, Valentini V. GENERATOR Breast DataMart-The Novel Breast Cancer Data Discovery System for Research and Monitoring: Preliminary Results and Future Perspectives. J Pers Med 2021; 11:jpm11020065. [PMID: 33498985 PMCID: PMC7911086 DOI: 10.3390/jpm11020065] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2020] [Revised: 01/18/2021] [Accepted: 01/20/2021] [Indexed: 02/07/2023] Open
Abstract
Background: Artificial Intelligence (AI) is increasingly used for process management in daily life. In the medical field AI is becoming part of computerized systems to manage information and encourage the generation of evidence. Here we present the development of the application of AI to IT systems present in the hospital, for the creation of a DataMart for the management of clinical and research processes in the field of breast cancer. Materials and methods: A multidisciplinary team of radiation oncologists, epidemiologists, medical oncologists, breast surgeons, data scientists, and data management experts worked together to identify relevant data and sources located inside the hospital system. Combinations of open-source data science packages and industry solutions were used to design the target framework. To validate the DataMart directly on real-life cases, the working team defined tumoral pathology and clinical purposes of proof of concepts (PoCs). Results: Data were classified into “Not organized, not ‘ontologized’ data”, “Organized, not ‘ontologized’ data”, and “Organized and ‘ontologized’ data”. Archives of real-world data (RWD) identified were platform based on ontology, hospital data warehouse, PDF documents, and electronic reports. Data extraction was performed by direct connection with structured data or text-mining technology. Two PoCs were performed, by which waiting time interval for radiotherapy and performance index of breast unit were tested and resulted available. Conclusions: GENERATOR Breast DataMart was created for supporting breast cancer pathways of care. An AI-based process automatically extracts data from different sources and uses them for generating trend studies and clinical evidence. Further studies and more proof of concepts are needed to exploit all the potentials of this system.
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Affiliation(s)
- Fabio Marazzi
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, UOC di Radioterapia Oncologica, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, 00186 Rome, Italy; (F.M.); (L.T.); (G.F.C.); (B.C.); (M.A.G.); (V.V.)
| | - Luca Tagliaferri
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, UOC di Radioterapia Oncologica, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, 00186 Rome, Italy; (F.M.); (L.T.); (G.F.C.); (B.C.); (M.A.G.); (V.V.)
| | - Valeria Masiello
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, UOC di Radioterapia Oncologica, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, 00186 Rome, Italy; (F.M.); (L.T.); (G.F.C.); (B.C.); (M.A.G.); (V.V.)
- Correspondence:
| | - Francesca Moschella
- Dipartimento di Scienze della Salute della Donna e del Bambino e di Sanità Pubblica, UOC di Chirurgia Senologica, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, 00186 Roma, Italy; (F.M.); (A.M.S.); (G.F.); (R.M.)
| | - Giuseppe Ferdinando Colloca
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, UOC di Radioterapia Oncologica, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, 00186 Rome, Italy; (F.M.); (L.T.); (G.F.C.); (B.C.); (M.A.G.); (V.V.)
| | - Barbara Corvari
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, UOC di Radioterapia Oncologica, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, 00186 Rome, Italy; (F.M.); (L.T.); (G.F.C.); (B.C.); (M.A.G.); (V.V.)
| | - Alejandro Martin Sanchez
- Dipartimento di Scienze della Salute della Donna e del Bambino e di Sanità Pubblica, UOC di Chirurgia Senologica, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, 00186 Roma, Italy; (F.M.); (A.M.S.); (G.F.); (R.M.)
| | - Nikola Dino Capocchiano
- Istituto di Radiologia, Università Cattolica del Sacro Cuore, 00186 Rome, Italy; (N.D.C.); (J.L.)
| | - Roberta Pastorino
- Fondazione Policlinico Universitario “A. Gemelli” IRCCS, 00186 Roma, Italy; (R.P.); (C.I.); (C.M.); (S.P.)
| | - Chiara Iacomini
- Fondazione Policlinico Universitario “A. Gemelli” IRCCS, 00186 Roma, Italy; (R.P.); (C.I.); (C.M.); (S.P.)
| | - Jacopo Lenkowicz
- Istituto di Radiologia, Università Cattolica del Sacro Cuore, 00186 Rome, Italy; (N.D.C.); (J.L.)
| | - Carlotta Masciocchi
- Fondazione Policlinico Universitario “A. Gemelli” IRCCS, 00186 Roma, Italy; (R.P.); (C.I.); (C.M.); (S.P.)
| | - Stefano Patarnello
- Fondazione Policlinico Universitario “A. Gemelli” IRCCS, 00186 Roma, Italy; (R.P.); (C.I.); (C.M.); (S.P.)
| | - Gianluca Franceschini
- Dipartimento di Scienze della Salute della Donna e del Bambino e di Sanità Pubblica, UOC di Chirurgia Senologica, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, 00186 Roma, Italy; (F.M.); (A.M.S.); (G.F.); (R.M.)
- Istituto di Semeiotica Chirurgica, Università Cattolica del Sacro Cuore, 00186 Rome, Italy
| | - Maria Antonietta Gambacorta
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, UOC di Radioterapia Oncologica, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, 00186 Rome, Italy; (F.M.); (L.T.); (G.F.C.); (B.C.); (M.A.G.); (V.V.)
- Istituto di Radiologia, Università Cattolica del Sacro Cuore, 00186 Rome, Italy; (N.D.C.); (J.L.)
| | - Riccardo Masetti
- Dipartimento di Scienze della Salute della Donna e del Bambino e di Sanità Pubblica, UOC di Chirurgia Senologica, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, 00186 Roma, Italy; (F.M.); (A.M.S.); (G.F.); (R.M.)
- Istituto di Semeiotica Chirurgica, Università Cattolica del Sacro Cuore, 00186 Rome, Italy
| | - Vincenzo Valentini
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, UOC di Radioterapia Oncologica, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, 00186 Rome, Italy; (F.M.); (L.T.); (G.F.C.); (B.C.); (M.A.G.); (V.V.)
- Istituto di Radiologia, Università Cattolica del Sacro Cuore, 00186 Rome, Italy; (N.D.C.); (J.L.)
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Jackson BR, Ye Y, Crawford JM, Becich MJ, Roy S, Botkin JR, de Baca ME, Pantanowitz L. The Ethics of Artificial Intelligence in Pathology and Laboratory Medicine: Principles and Practice. Acad Pathol 2021; 8:2374289521990784. [PMID: 33644301 PMCID: PMC7894680 DOI: 10.1177/2374289521990784] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Revised: 11/24/2020] [Accepted: 12/28/2020] [Indexed: 12/24/2022] Open
Abstract
Growing numbers of artificial intelligence applications are being developed and applied to pathology and laboratory medicine. These technologies introduce risks and benefits that must be assessed and managed through the lens of ethics. This article describes how long-standing principles of medical and scientific ethics can be applied to artificial intelligence using examples from pathology and laboratory medicine.
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Affiliation(s)
- Brian R. Jackson
- Department of Pathology, University of Utah School of Medicine, Salt Lake City, UT, USA
- ARUP Laboratories, Salt Lake City, UT, USA
| | - Ye Ye
- Department of Biomedical Informatics, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - James M. Crawford
- Department of Pathology and Laboratory Medicine, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
| | - Michael J. Becich
- Department of Biomedical Informatics, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Somak Roy
- Division of Pathology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA
| | - Jeffrey R. Botkin
- Department of Pediatrics, University of Utah School of Medicine, Salt Lake City, UT, USA
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The histopathological diagnosis of atypical meningioma: glass slide versus whole slide imaging for grading assessment. Virchows Arch 2020; 478:747-756. [PMID: 33305338 PMCID: PMC7990834 DOI: 10.1007/s00428-020-02988-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Revised: 11/14/2020] [Accepted: 12/03/2020] [Indexed: 12/23/2022]
Abstract
Limited studies on whole slide imaging (WSI) in surgical neuropathology reported a perceived limitation in the recognition of mitoses. This study analyzed and compared the inter- and intra-observer concordance for atypical meningioma, using glass slides and WSI. Two neuropathologists and two residents assessed the histopathological features of 35 meningiomas-originally diagnosed as atypical-in a representative glass slide and corresponding WSI. For each histological parameter and final diagnosis, we calculated the inter- and intra-observer concordance in the two viewing modes and the predictive accuracy on recurrence. The concordance rates for atypical meningioma on glass slides and on WSI were 54% and 60% among four observers and 63% and 74% between two neuropathologists. The inter-observer agreement was higher using WSI than with glass slides for all parameters, with the exception of high mitotic index. For all histological features, we found median intra-observer concordance of ≥ 79% and similar predictive accuracy for recurrence between the two viewing modes. The higher concordance for atypical meningioma using WSI than with glass slides and the similar predictive accuracy for recurrence in the two modalities suggest that atypical meningioma may be safely diagnosed using WSI.
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