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Fiorin A, López Pablo C, Lejeune M, Hamza Siraj A, Della Mea V. Enhancing AI Research for Breast Cancer: A Comprehensive Review of Tumor-Infiltrating Lymphocyte Datasets. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024:10.1007/s10278-024-01043-8. [PMID: 38806950 DOI: 10.1007/s10278-024-01043-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Revised: 01/19/2024] [Accepted: 02/07/2024] [Indexed: 05/30/2024]
Abstract
The field of immunology is fundamental to our understanding of the intricate dynamics of the tumor microenvironment. In particular, tumor-infiltrating lymphocyte (TIL) assessment emerges as essential aspect in breast cancer cases. To gain comprehensive insights, the quantification of TILs through computer-assisted pathology (CAP) tools has become a prominent approach, employing advanced artificial intelligence models based on deep learning techniques. The successful recognition of TILs requires the models to be trained, a process that demands access to annotated datasets. Unfortunately, this task is hampered not only by the scarcity of such datasets, but also by the time-consuming nature of the annotation phase required to create them. Our review endeavors to examine publicly accessible datasets pertaining to the TIL domain and thereby become a valuable resource for the TIL community. The overall aim of the present review is thus to make it easier to train and validate current and upcoming CAP tools for TIL assessment by inspecting and evaluating existing publicly available online datasets.
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Affiliation(s)
- Alessio Fiorin
- Oncological Pathology and Bioinformatics Research Group, Institut d'Investigació Sanitària Pere Virgili (IISPV), C/Esplanetes no 14, 43500, Tortosa, Spain.
- Department of Pathology, Hospital de Tortosa Verge de la Cinta (HTVC), Institut Català de la Salut (ICS), C/Esplanetes no 14, 43500, Tortosa, Spain.
- Department of Computer Engineering and Mathematics, Universitat Rovira i Virgili (URV), Tarragona, Spain.
| | - Carlos López Pablo
- Oncological Pathology and Bioinformatics Research Group, Institut d'Investigació Sanitària Pere Virgili (IISPV), C/Esplanetes no 14, 43500, Tortosa, Spain.
- Department of Pathology, Hospital de Tortosa Verge de la Cinta (HTVC), Institut Català de la Salut (ICS), C/Esplanetes no 14, 43500, Tortosa, Spain.
- Department of Computer Engineering and Mathematics, Universitat Rovira i Virgili (URV), Tarragona, Spain.
| | - Marylène Lejeune
- Oncological Pathology and Bioinformatics Research Group, Institut d'Investigació Sanitària Pere Virgili (IISPV), C/Esplanetes no 14, 43500, Tortosa, Spain
- Department of Pathology, Hospital de Tortosa Verge de la Cinta (HTVC), Institut Català de la Salut (ICS), C/Esplanetes no 14, 43500, Tortosa, Spain
- Department of Computer Engineering and Mathematics, Universitat Rovira i Virgili (URV), Tarragona, Spain
| | - Ameer Hamza Siraj
- Department of Mathematics, Computer Science and Physics, University of Udine, Udine, Italy
| | - Vincenzo Della Mea
- Department of Mathematics, Computer Science and Physics, University of Udine, Udine, Italy
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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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3
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Elmas H, Önal B, Steurer S, Hantzsch-Kuhn B, Claussen M, Mehdi E, Ince Ü, Rabe KF, Sauter G, Welker L. Rapid Remote Online Evaluation in Endoscopic Diagnostics: An Analysis of Biopsy-Proven Respiratory Cytopathology. Diagnostics (Basel) 2023; 13:3329. [PMID: 37958225 PMCID: PMC10647841 DOI: 10.3390/diagnostics13213329] [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: 09/21/2023] [Revised: 10/23/2023] [Accepted: 10/23/2023] [Indexed: 11/15/2023] Open
Abstract
BACKGROUND This prospective study assesses the use of rapid remote online cytological evaluation for diagnosing endoscopical achieved biopsies. It focuses on its effectiveness in identifying benign and malignant conditions using digital image processing. METHODS The study was conducted between April 2021 and September 2022 and involved analyses of 314 Rapid Remote Online Cytological Evaluations in total (154 imprint cytologies, 143 fine needle aspirations and 17 brush cytologies) performed on 239 patients at the LungenClinic Grosshansdorf. During on-site evaluation via telecytology, the time requirement was recorded and the findings were compared with the cyto-/histological and final diagnoses. RESULTS By means of rapid remote online evaluation, findings of 86 cytological benign, 190 malignant and 38 unclear diagnoses were recorded (Ø assessment time, 100 s; range, 11-370 s). In 27 of the 37 specimens with unclear diagnoses, the final findings were malignant tumours and only 6 were benign changes. The diagnosis of another 4 of these 37 findings remained unclear. Excluding these 37 specimens, rapid remote online evaluation achieved a sensitivity of 90.5% with a specificity of 98.5% and a correct classification rate of 92.4% with regard to the final diagnosis of all cases. As expected, an increase in the sensitivity rate for the cytological detection of malignant tumours (76.1% vs. 92.5%) was found especially in fine-needle aspirations. CONCLUSIONS Rapid remote online analysis allows the fast quantitative and qualitative evaluation of clinically obtained cytological specimens. With a correct classification rate of more than 93%, sampling deficiencies can be corrected promptly and diagnostic and therapeutic approaches can be derived.
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Affiliation(s)
- Hatice Elmas
- Section Cytopathology, Institute of Pathology, University Medical Center Hamburg-Eppendorf UKE, D-20246 Hamburg, Germany; (S.S.); (G.S.); (L.W.)
| | - Binnur Önal
- Acıbadem Healthcare Group, Pathology Department, Acıbadem University, 34752 Istanbul, Turkey; (B.Ö.); (Ü.I.)
| | - Stefan Steurer
- Section Cytopathology, Institute of Pathology, University Medical Center Hamburg-Eppendorf UKE, D-20246 Hamburg, Germany; (S.S.); (G.S.); (L.W.)
| | - Birgit Hantzsch-Kuhn
- LungenClinic Großhansdorf, D-22927 Großhansdorf, Germany; (B.H.-K.); (M.C.); (K.F.R.)
- Airway Research North (ARCN), Deutsches Zentrum für Lungenforschung (DZL), D-35037 Marburg, Germany
| | - Martin Claussen
- LungenClinic Großhansdorf, D-22927 Großhansdorf, Germany; (B.H.-K.); (M.C.); (K.F.R.)
- Airway Research North (ARCN), Deutsches Zentrum für Lungenforschung (DZL), D-35037 Marburg, Germany
| | - Elnur Mehdi
- Department of Nuclear Medicine, National Center of Oncology, 1012 Baku, Azerbaijan;
| | - Ümit Ince
- Acıbadem Healthcare Group, Pathology Department, Acıbadem University, 34752 Istanbul, Turkey; (B.Ö.); (Ü.I.)
| | - Klaus F. Rabe
- LungenClinic Großhansdorf, D-22927 Großhansdorf, Germany; (B.H.-K.); (M.C.); (K.F.R.)
- Airway Research North (ARCN), Deutsches Zentrum für Lungenforschung (DZL), D-35037 Marburg, Germany
| | - Guido Sauter
- Section Cytopathology, Institute of Pathology, University Medical Center Hamburg-Eppendorf UKE, D-20246 Hamburg, Germany; (S.S.); (G.S.); (L.W.)
| | - Lutz Welker
- Section Cytopathology, Institute of Pathology, University Medical Center Hamburg-Eppendorf UKE, D-20246 Hamburg, Germany; (S.S.); (G.S.); (L.W.)
- Airway Research North (ARCN), Deutsches Zentrum für Lungenforschung (DZL), D-35037 Marburg, Germany
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4
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Marini N, Otalora S, Wodzinski M, Tomassini S, Dragoni AF, Marchand-Maillet S, Morales JPD, Duran-Lopez L, Vatrano S, Müller H, Atzori M. Data-driven color augmentation for H&E stained images in computational pathology. J Pathol Inform 2023; 14:100183. [PMID: 36687531 PMCID: PMC9852546 DOI: 10.1016/j.jpi.2022.100183] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Revised: 11/28/2022] [Accepted: 12/28/2022] [Indexed: 01/05/2023] Open
Abstract
Computational pathology targets the automatic analysis of Whole Slide Images (WSI). WSIs are high-resolution digitized histopathology images, stained with chemical reagents to highlight specific tissue structures and scanned via whole slide scanners. The application of different parameters during WSI acquisition may lead to stain color heterogeneity, especially considering samples collected from several medical centers. Dealing with stain color heterogeneity often limits the robustness of methods developed to analyze WSIs, in particular Convolutional Neural Networks (CNN), the state-of-the-art algorithm for most computational pathology tasks. Stain color heterogeneity is still an unsolved problem, although several methods have been developed to alleviate it, such as Hue-Saturation-Contrast (HSC) color augmentation and stain augmentation methods. The goal of this paper is to present Data-Driven Color Augmentation (DDCA), a method to improve the efficiency of color augmentation methods by increasing the reliability of the samples used for training computational pathology models. During CNN training, a database including over 2 million H&E color variations collected from private and public datasets is used as a reference to discard augmented data with color distributions that do not correspond to realistic data. DDCA is applied to HSC color augmentation, stain augmentation and H&E-adversarial networks in colon and prostate cancer classification tasks. DDCA is then compared with 11 state-of-the-art baseline methods to handle color heterogeneity, showing that it can substantially improve classification performance on unseen data including heterogeneous color variations.
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Affiliation(s)
- Niccolò Marini
- Information Systems Institute, University of Applied Sciences Western Switzerland (HES-SO Valais), Sierre, Switzerland,Centre Universitaire d'Informatique, University of Geneva, Geneva, Switzerland,Corresponding author.
| | - Sebastian Otalora
- Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, Bern, Switzerland
| | - Marek Wodzinski
- Information Systems Institute, University of Applied Sciences Western Switzerland (HES-SO Valais), Sierre, Switzerland,Department of Measurement and Electronics, AGH University of Science and Technology, Krakow, Poland
| | - Selene Tomassini
- Department of Information Engineering, Engineering Faculty, Università Politecnica delle Marche, Ancona, Italy
| | - Aldo Franco Dragoni
- Department of Information Engineering, Engineering Faculty, Università Politecnica delle Marche, Ancona, Italy
| | | | - Juan Pedro Dominguez Morales
- Robotics and Technology of Computers Lab., ETSII-EPS, Universidad de Sevilla, Sevilla, Spain,SCORE Lab, I3US, Universidad de Sevilla, Spain
| | - Lourdes Duran-Lopez
- Robotics and Technology of Computers Lab., ETSII-EPS, Universidad de Sevilla, Sevilla, Spain,SCORE Lab, I3US, Universidad de Sevilla, Spain
| | - Simona Vatrano
- Pathology Unit, Gravina Hospital Caltagirone ASP, Catania, Italy
| | - Henning Müller
- Information Systems Institute, University of Applied Sciences Western Switzerland (HES-SO Valais), Sierre, Switzerland,Medical Faculty, University of Geneva, Geneva, Switzerland
| | - Manfredo Atzori
- Information Systems Institute, University of Applied Sciences Western Switzerland (HES-SO Valais), Sierre, Switzerland,Department of Neurosciences, University of Padua, Padua, Italy
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Yavuz A, Alpsoy A, Gedik EO, Celik MY, Bassorgun CI, Unal B, Elpek GO. Artificial intelligence applications in predicting the behavior of gastrointestinal cancers in pathology. Artif Intell Gastroenterol 2022; 3:142-162. [DOI: 10.35712/aig.v3.i5.142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/16/2022] [Revised: 11/25/2022] [Accepted: 12/14/2022] [Indexed: 12/28/2022] Open
Abstract
Recent research has provided a wealth of data supporting the application of artificial intelligence (AI)-based applications in routine pathology practice. Indeed, it is clear that these methods can significantly support an accurate and rapid diagnosis by eliminating errors, increasing reliability, and improving workflow. In addition, the effectiveness of AI in the pathological evaluation of prognostic parameters associated with behavior, course, and treatment in many types of tumors has also been noted. Regarding gastrointestinal system (GIS) cancers, the contribution of AI methods to pathological diagnosis has been investigated in many studies. On the other hand, studies focusing on AI applications in evaluating parameters to determine tumor behavior are relatively few. For this purpose, the potential of AI models has been studied over a broad spectrum, from tumor subtyping to the identification of new digital biomarkers. The capacity of AI to infer genetic alterations of cancer tissues from digital slides has been demonstrated. Although current data suggest the merit of AI-based approaches in assessing tumor behavior in GIS cancers, a wide range of challenges still need to be solved, from laboratory infrastructure to improving the robustness of algorithms, before incorporating AI applications into real-life GIS pathology practice. This review aims to present data from AI applications in evaluating pathological parameters related to the behavior of GIS cancer with an overview of the opportunities and challenges encountered in implementing AI in pathology.
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Affiliation(s)
- Aysen Yavuz
- Department of Pathology, Akdeniz University Medical School, Antalya 07070, Turkey
| | - Anil Alpsoy
- Department of Pathology, Akdeniz University Medical School, Antalya 07070, Turkey
| | - Elif Ocak Gedik
- Department of Pathology, Akdeniz University Medical School, Antalya 07070, Turkey
| | | | | | - Betul Unal
- Department of Pathology, Akdeniz University Medical School, Antalya 07070, Turkey
| | - Gulsum Ozlem Elpek
- Department of Pathology, Akdeniz University Medical School, Antalya 07070, Turkey
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6
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Chiriboga L, Callis GM, Wang Y, Chlipala E. Guide for collecting and reporting metadata on protocol variables and parameters from slide-based histotechnology assays to enhance reproducibility. J Histotechnol 2022; 45:132-147. [DOI: 10.1080/01478885.2022.2134022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Affiliation(s)
- Luis Chiriboga
- Department of Pathology, NYU Grossman School of Medicine, New York, NY, USA
- NYULH Center for Biospecimen Research and Development, New York, NY, USA
| | | | - Yongfu Wang
- Stowers Institute for Medical Research, Kansas, MO, USA
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7
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Faust K, Lee MK, Dent A, Fiala C, Portante A, Rabindranath M, Alsafwani N, Gao A, Djuric U, Diamandis P. Integrating morphologic and molecular histopathological features through whole slide image registration and deep learning. Neurooncol Adv 2022; 4:vdac001. [PMID: 35156037 PMCID: PMC8826810 DOI: 10.1093/noajnl/vdac001] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Abstract
Background
Modern molecular pathology workflows in neuro-oncology heavily rely on the integration of morphologic and immunohistochemical patterns for analysis, classification, and prognostication. However, despite the recent emergence of digital pathology platforms and artificial intelligence-driven computational image analysis tools, automating the integration of histomorphologic information found across these multiple studies is challenged by large files sizes of whole slide images (WSIs) and shifts/rotations in tissue sections introduced during slide preparation.
Methods
To address this, we develop a workflow that couples different computer vision tools including scale-invariant feature transform (SIFT) and deep learning to efficiently align and integrate histopathological information found across multiple independent studies. We highlight the utility and automation potential of this workflow in the molecular subclassification and discovery of previously unappreciated spatial patterns in diffuse gliomas.
Results
First, we show how a SIFT-driven computer vision workflow was effective at automated WSI alignment in a cohort of 107 randomly selected surgical neuropathology cases (97/107 (91%) showing appropriate matches, AUC = 0.96). This alignment allows our AI-driven diagnostic workflow to not only differentiate different brain tumor types, but also integrate and carry out molecular subclassification of diffuse gliomas using relevant immunohistochemical biomarkers (IDH1-R132H, ATRX). To highlight the discovery potential of this workflow, we also examined spatial distributions of tumors showing heterogenous expression of the proliferation marker MIB1 and Olig2. This analysis helped uncovered an interesting and unappreciated association of Olig2 positive and proliferative areas in some gliomas (r = 0.62).
Conclusion
This efficient neuropathologist-inspired workflow provides a generalizable approach to help automate a variety of advanced immunohistochemically compatible diagnostic and discovery exercises in surgical neuropathology and neuro-oncology.
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Affiliation(s)
- Kevin Faust
- Department of Computer Science, University of Toronto, 40 St. George Street, Toronto, ON M5S 2E4, Canada
- Laboratory Medicine Program, Department of Pathology, University Health Network, 200 Elizabeth Street, Toronto, ON M5G 2C4, Canada
| | - Michael K Lee
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON M5S 1A8, Canada
| | - Anglin Dent
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON M5S 1A8, Canada
| | - Clare Fiala
- Laboratory Medicine Program, Department of Pathology, University Health Network, 200 Elizabeth Street, Toronto, ON M5G 2C4, Canada
| | - Alessia Portante
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON M5S 1A8, Canada
| | - Madhu Rabindranath
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON M5S 1A8, Canada
| | - Noor Alsafwani
- Laboratory Medicine Program, Department of Pathology, University Health Network, 200 Elizabeth Street, Toronto, ON M5G 2C4, Canada
- Department of Pathology, College of Medicine, Imam Abdulrahman Bin Faisal University, P.O. Box.2208, Dammam, 31441, Saudi Arabia
| | - Andrew Gao
- Laboratory Medicine Program, Department of Pathology, University Health Network, 200 Elizabeth Street, Toronto, ON M5G 2C4, Canada
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON M5S 1A8, Canada
| | - Ugljesa Djuric
- Princess Margaret Cancer Centre, 101 College Street, Toronto, ON M5G 1L7, Canada
| | - Phedias Diamandis
- Laboratory Medicine Program, Department of Pathology, University Health Network, 200 Elizabeth Street, Toronto, ON M5G 2C4, Canada
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON M5S 1A8, Canada
- Princess Margaret Cancer Centre, 101 College Street, Toronto, ON M5G 1L7, Canada
- Department of Medical Biophysics, University of Toronto, 101 College St, Toronto, ON M5G 1L7, Canada
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Alpsoy A, Yavuz A, Elpek GO. Artificial intelligence in pathological evaluation of gastrointestinal cancers. Artif Intell Gastroenterol 2021; 2:141-156. [DOI: 10.35712/aig.v2.i6.141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Revised: 12/19/2021] [Accepted: 12/27/2021] [Indexed: 02/06/2023] Open
Abstract
The integration of artificial intelligence (AI) has shown promising benefits in many fields of diagnostic histopathology, including for gastrointestinal cancers (GCs), such as tumor identification, classification, and prognosis prediction. In parallel, recent evidence suggests that AI may help reduce the workload in gastrointestinal pathology by automatically detecting tumor tissues and evaluating prognostic parameters. In addition, AI seems to be an attractive tool for biomarker/genetic alteration prediction in GC, as it can contain a massive amount of information from visual data that is complex and partially understandable by pathologists. From this point of view, it is suggested that advances in AI could lead to revolutionary changes in many fields of pathology. Unfortunately, these findings do not exclude the possibility that there are still many hurdles to overcome before AI applications can be safely and effectively applied in actual pathology practice. These include a broad spectrum of challenges from needs identification to cost-effectiveness. Therefore, unlike other disciplines of medicine, no histopathology-based AI application, including in GC, has ever been approved either by a regulatory authority or approved for public reimbursement. The purpose of this review is to present data related to the applications of AI in pathology practice in GC and present the challenges that need to be overcome for their implementation.
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Affiliation(s)
- Anil Alpsoy
- Department of Pathology, Akdeniz University Medical School, Antalya 07070, Turkey
| | - Aysen Yavuz
- Department of Pathology, Akdeniz University Medical School, Antalya 07070, Turkey
| | - Gulsum Ozlem Elpek
- Department of Pathology, Akdeniz University Medical School, Antalya 07070, Turkey
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9
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Patel A, Balis UGJ, Cheng J, Li Z, Lujan G, McClintock DS, Pantanowitz L, Parwani A. Contemporary Whole Slide Imaging Devices and Their Applications within the Modern Pathology Department: A Selected Hardware Review. J Pathol Inform 2021; 12:50. [PMID: 35070479 PMCID: PMC8721869 DOI: 10.4103/jpi.jpi_66_21] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Accepted: 09/22/2021] [Indexed: 12/21/2022] Open
Abstract
Digital pathology (DP) has disrupted the practice of traditional pathology, including applications in education, research, and clinical practice. Contemporary whole slide imaging (WSI) devices include technological advances that help address some of the challenges facing modern pathology, such as increasing workloads with fewer subspecialized pathologists, expanding integrated delivery networks with global reach, and greater customization when working up cases for precision medicine. This review focuses on integral hardware components of 43 market available and soon-to-be released digital WSI devices utilized throughout the world. Components such as objective lens type and magnification, scanning camera, illumination, and slide capacity were evaluated with respect to scan time, throughput, accuracy of scanning, and image quality. This analysis of assorted modern WSI devices offers essential, valuable information for successfully selecting and implementing a digital WSI solution for any given pathology practice.
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Affiliation(s)
- Ankush Patel
- Department of Pathology, The Ohio State University, Columbus, Ohio, USA
| | | | - Jerome Cheng
- Department of Pathology, University of Michigan, Ann Arbor, Michigan, USA
| | - Zaibo Li
- Department of Pathology, The Ohio State University, Columbus, Ohio, USA
| | - Giovanni Lujan
- Department of Pathology, The Ohio State University, Columbus, Ohio, USA
| | | | - Liron Pantanowitz
- Department of Pathology, University of Michigan, Ann Arbor, Michigan, USA
| | - Anil Parwani
- Department of Pathology, The Ohio State University, Columbus, Ohio, USA
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10
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Atypia in breast pathology: what pathologists need to know. Pathology 2021; 54:20-31. [PMID: 34872753 DOI: 10.1016/j.pathol.2021.09.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Revised: 09/07/2021] [Accepted: 09/14/2021] [Indexed: 10/19/2022]
Abstract
Despite the importance of atypia in diagnosing and classifying breast lesions, the definition of atypia varies depending on the context, with a lack of consistent and objective criteria for assessment. Atypia in breast pathology may be cytonuclear and/or architectural with different applications and implications. Cytonuclear atypia is used to assist the distinction of various intraductal epithelial proliferative lesions including usual ductal hyperplasia (UDH) versus atypical ductal hyperplasia (ADH) or ductal carcinoma in situ (DCIS), and to grade DCIS. In invasive carcinoma, nuclear atypia (i.e., nuclear pleomorphism) is a component of the histological grading system. Stromal cell cytonuclear atypia is one of the key features used to distinguish fibroadenoma from phyllodes tumour (PT) and to classify PT as benign, borderline or malignant. Similarly, cytonuclear atypia is used in the evaluation of myoepithelial cell alterations in the breast. Architectural atypia is used to differentiate flat epithelial atypia (FEA) from ADH or DCIS. In addition to the inherent subjectivity in the interpretation of atypia, which presents as a morphological continuum reflecting a biological spectrum, the lack of standardisation in defining atypia augments diagnostic discordance in breast pathology, with potential implications for patient management. Evidence to date suggests that the traditional criteria used to assess atypia may require modification in the era of digital pathology primary diagnosis. This review aims to provide a comprehensive review of atypia in breast pathology with reference to inconsistencies, challenges and limitations.
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11
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Recent advances in the standardization of fluorescence microscopy for quantitative image analysis. Biophys Rev 2021; 14:33-39. [DOI: 10.1007/s12551-021-00871-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Accepted: 10/22/2021] [Indexed: 12/19/2022] Open
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12
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Noh KW, Buettner R, Klein S. Shifting Gears in Precision Oncology-Challenges and Opportunities of Integrative Data Analysis. Biomolecules 2021; 11:biom11091310. [PMID: 34572523 PMCID: PMC8465238 DOI: 10.3390/biom11091310] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Revised: 08/26/2021] [Accepted: 09/01/2021] [Indexed: 02/07/2023] Open
Abstract
For decades, research relating to modification of host immunity towards antitumor response activation has been ongoing, with the breakthrough discovery of immune-checkpoint blockers. Several biomarkers with potential predictive value have been reported in recent studies for these novel therapies. However, with the plethora of therapeutic options existing for a given cancer entity, modern oncology is now being confronted with multifactorial interpretation to devise “the best therapy” for the individual patient. Into the bargain come the multiverse guidelines for established and emerging diagnostic biomarkers, as well as the complex interplay between cancer cells and tumor microenvironment, provoking immense challenges in the therapy decision-making process. Through this review, we present various molecular diagnostic modalities and techniques, such as genomics, immunohistochemistry and quantitative image analysis, which have the potential of becoming powerful tools in the development of an optimal treatment regime when analogized with patient characteristics. We will summarize the underlying complexities of these methods and shed light upon the necessary considerations and requirements for data integration. It is our hope to provide compelling evidence to emphasize on the need for inclusion of integrative data analysis in modern cancer therapy, and thereupon paving a path towards precision medicine and better patient outcomes.
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Affiliation(s)
- Ka-Won Noh
- Institute for Pathology, Faculty of Medicine and University Hospital Cologne, University of Cologne, 50937 Cologne, Germany; (K.-W.N.); (R.B.)
| | - Reinhard Buettner
- Institute for Pathology, Faculty of Medicine and University Hospital Cologne, University of Cologne, 50937 Cologne, Germany; (K.-W.N.); (R.B.)
| | - Sebastian Klein
- Gerhard-Domagk-Institute of Pathology, University Hospital Münster, 48149 Münster, Germany
- Correspondence: ; Tel.: +49-251-83-57670
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Yoshida H, Kiyuna T. Requirements for implementation of artificial intelligence in the practice of gastrointestinal pathology. World J Gastroenterol 2021; 27:2818-2833. [PMID: 34135556 PMCID: PMC8173389 DOI: 10.3748/wjg.v27.i21.2818] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Revised: 03/16/2021] [Accepted: 04/28/2021] [Indexed: 02/06/2023] Open
Abstract
Tremendous advances in artificial intelligence (AI) in medical image analysis have been achieved in recent years. The integration of AI is expected to cause a revolution in various areas of medicine, including gastrointestinal (GI) pathology. Currently, deep learning algorithms have shown promising benefits in areas of diagnostic histopathology, such as tumor identification, classification, prognosis prediction, and biomarker/genetic alteration prediction. While AI cannot substitute pathologists, carefully constructed AI applications may increase workforce productivity and diagnostic accuracy in pathology practice. Regardless of these promising advances, unlike the areas of radiology or cardiology imaging, no histopathology-based AI application has been approved by a regulatory authority or for public reimbursement. Thus, implying that there are still some obstacles to be overcome before AI applications can be safely and effectively implemented in real-life pathology practice. The challenges have been identified at different stages of the development process, such as needs identification, data curation, model development, validation, regulation, modification of daily workflow, and cost-effectiveness balance. The aim of this review is to present challenges in the process of AI development, validation, and regulation that should be overcome for its implementation in real-life GI pathology practice.
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Affiliation(s)
- Hiroshi Yoshida
- Department of Diagnostic Pathology, National Cancer Center Hospital, Tokyo 104-0045, Japan
| | - Tomoharu Kiyuna
- Digital Healthcare Business Development Office, NEC Corporation, Tokyo 108-8556, Japan
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Wilbur DC, Smith ML, Cornell LD, Andryushkin A, Pettus JR. Automated identification of glomeruli and synchronised review of special stains in renal biopsies by machine learning and slide registration: a cross-institutional study. Histopathology 2021; 79:499-508. [PMID: 33813779 DOI: 10.1111/his.14376] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Revised: 03/11/2021] [Accepted: 03/29/2021] [Indexed: 11/30/2022]
Abstract
AIMS Machine learning in digital pathology can improve efficiency and accuracy via prescreening with automated feature identification. Studies using uniform histological material have shown promise. Generalised application requires validation on slides from multiple institutions. We used machine learning to identify glomeruli on renal biopsies and compared performance between single and multiple institutions. METHODS AND RESULTS Randomly selected, adequately sampled renal core biopsy cases (71) consisting of four stains each (haematoxylin and eosin, trichrome, silver, periodic acid Schiff) from three institutions were digitised at ×40. Glomeruli were manually annotated by three renal pathologists using a digital tool. Cases were divided into training/validation (n = 52) and evaluation (n = 19) cohorts. An algorithm was trained to develop three convolutional neural network (CNN) models which tested case cohorts intra- and inter-institutionally. Raw CNN search data from each of the four slides per case were merged into composite regions of interest containing putative glomeruli. The sensitivity and modified specificity of glomerulus detection (versus annotated truth) were calculated for each model/cohort. Intra-institutional (3) sensitivity ranged from 90 to 93%, with modified specificity from 86 to 98%. Interinstitutional (1) sensitivity was 77%, with modified specificity 97%. Combined intra- and inter-institutional (1) sensitivity was 86%, with modified specificity 92%. CONCLUSIONS Feature detection sensitivity degrades when training and test material originate from different sites. Training using a combined set of digital slides from three institutions improves performance. Differing histology methods probably account for algorithm performance contrasts. Our data highlight the need for diverse training sets for the development of generalisable machine learning histology algorithms.
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Affiliation(s)
| | - Maxwell L Smith
- Department of Laboratory Medicine and Pathology, Mayo Clinic - Scottsdale, Phoenix, AZ, USA
| | - Lynn D Cornell
- Department of Laboratory Medicine and Pathology, Mayo Clinic - Rochester, Rochester, MN, USA
| | | | - Jason R Pettus
- Department of Pathology and Laboratory Medicine, Dartmouth-Hitchcock Medical Center, Lebanon, MH, USA
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