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Brevet M, Li Z, Parwani A. Computational pathology in the identification of HER2-low breast cancer: Opportunities and challenges. J Pathol Inform 2024; 15:100343. [PMID: 38125925 PMCID: PMC10730362 DOI: 10.1016/j.jpi.2023.100343] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Revised: 09/18/2023] [Accepted: 11/01/2023] [Indexed: 12/23/2023] Open
Abstract
For the past 2 decades, pathologists have been accustomed to reporting the HER2 status of breast cancer as either positive or negative, based on HER2 IHC. Today, however, there is a clinical imperative to employ a 3-tier approach to interpreting HER2 IHC that can also identify tumours categorised as HER2-low. Meeting this need for a finer degree of discrimination may be challenging, and in this article, we consider the potential for the integration of computational approaches to support pathologists in achieving accurate and reproducible HER2 IHC scoring as well as outlining some of the practicalities involved.
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Affiliation(s)
| | - Zaibo Li
- Department of Pathology, The Ohio State University, Columbus, OH, USA
| | - Anil Parwani
- Department of Pathology, The Ohio State University, Columbus, OH, USA
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2
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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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3
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Evans H, Snead D. Understanding the errors made by artificial intelligence algorithms in histopathology in terms of patient impact. NPJ Digit Med 2024; 7:89. [PMID: 38600151 PMCID: PMC11006652 DOI: 10.1038/s41746-024-01093-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Accepted: 03/29/2024] [Indexed: 04/12/2024] Open
Abstract
An increasing number of artificial intelligence (AI) tools are moving towards the clinical realm in histopathology and across medicine. The introduction of such tools will bring several benefits to diagnostic specialities, namely increased diagnostic accuracy and efficiency, however, as no AI tool is infallible, their use will inevitably introduce novel errors. These errors made by AI tools are, most fundamentally, misclassifications made by a computational algorithm. Understanding of how these translate into clinical impact on patients is often lacking, meaning true reporting of AI tool safety is incomplete. In this Perspective we consider AI diagnostic tools in histopathology, which are predominantly assessed in terms of technical performance metrics such as sensitivity, specificity and area under the receiver operating characteristic curve. Although these metrics are essential and allow tool comparison, they alone give an incomplete picture of how an AI tool's errors could impact a patient's diagnosis, management and prognosis. We instead suggest assessing and reporting AI tool errors from a pathological and clinical stance, demonstrating how this is done in studies on human pathologist errors, and giving examples where available from pathology and radiology. Although this seems a significant task, we discuss ways to move towards this approach in terms of study design, guidelines and regulation. This Perspective seeks to initiate broader consideration of the assessment of AI tool errors in histopathology and across diagnostic specialities, in an attempt to keep patient safety at the forefront of AI tool development and facilitate safe clinical deployment.
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Affiliation(s)
- Harriet Evans
- Histopathology Department, University Hospitals Coventry and Warwickshire NHS Trust, Coventry, UK.
- Warwick Medical School, University of Warwick, Coventry, UK.
| | - David Snead
- Histopathology Department, University Hospitals Coventry and Warwickshire NHS Trust, Coventry, UK
- Department of Computer Science, University of Warwick, Coventry, UK
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4
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Ramani RS. Revolutionizing oral pathology and medicine: The artificial intelligence advantage. J Oral Pathol Med 2024; 53:233-235. [PMID: 38604744 DOI: 10.1111/jop.13534] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2024] [Accepted: 04/03/2024] [Indexed: 04/13/2024]
Affiliation(s)
- Rishi Sanjay Ramani
- Oral Medicine and Oral Cancer (OMOC) Group, Melbourne Dental School, University of Melbourne, Melbourne, Victoria, Australia
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5
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Naseri S, Shukla S, Hiwale KM, Jagtap MM, Gadkari P, Gupta K, Deshmukh M, Sagar S. From Pixels to Prognosis: A Narrative Review on Artificial Intelligence's Pioneering Role in Colorectal Carcinoma Histopathology. Cureus 2024; 16:e59171. [PMID: 38807833 PMCID: PMC11129955 DOI: 10.7759/cureus.59171] [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: 03/24/2024] [Accepted: 04/27/2024] [Indexed: 05/30/2024] Open
Abstract
Colorectal carcinoma, a prevalent and deadly malignancy, necessitates precise histopathological assessment for effective diagnosis and prognosis. Artificial intelligence (AI) emerges as a transformative force in this realm, offering innovative solutions to enhance traditional histopathological methods. This narrative review explores AI's pioneering role in colorectal carcinoma histopathology, encompassing its evolution, techniques, and advancements. AI algorithms, notably machine learning and deep learning, have revolutionized image analysis, facilitating accurate diagnosis and prognosis prediction. Furthermore, AI-driven histopathological analysis unveils potential biomarkers and therapeutic targets, heralding personalized treatment approaches. Despite its promise, challenges persist, including data quality, interpretability, and integration. Collaborative efforts among researchers, clinicians, and AI developers are imperative to surmount these hurdles and realize AI's full potential in colorectal carcinoma care. This review underscores AI's transformative impact and implications for future oncology research, clinical practice, and interdisciplinary collaboration.
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Affiliation(s)
- Suhit Naseri
- Pathology, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education & Research, Wardha, IND
| | - Samarth Shukla
- Pathology, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education & Research, Wardha, IND
| | - K M Hiwale
- Pathology, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education & Research, Wardha, IND
| | - Miheer M Jagtap
- Pathology, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education & Research, Wardha, IND
| | - Pravin Gadkari
- Pathology, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education & Research, Wardha, IND
| | - Kartik Gupta
- Radiation Oncology, Delhi State Cancer Institute, Delhi, IND
| | - Mamta Deshmukh
- Pathology, Indian Institute of Medical Sciences and Research, Jalna, IND
| | - Shakti Sagar
- Pathology, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education & Research, Wardha, IND
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Neto PC, Montezuma D, Oliveira SP, Oliveira D, Fraga J, Monteiro A, Monteiro J, Ribeiro L, Gonçalves S, Reinhard S, Zlobec I, Pinto IM, Cardoso JS. An interpretable machine learning system for colorectal cancer diagnosis from pathology slides. NPJ Precis Oncol 2024; 8:56. [PMID: 38443695 PMCID: PMC10914836 DOI: 10.1038/s41698-024-00539-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Accepted: 02/08/2024] [Indexed: 03/07/2024] Open
Abstract
Considering the profound transformation affecting pathology practice, we aimed to develop a scalable artificial intelligence (AI) system to diagnose colorectal cancer from whole-slide images (WSI). For this, we propose a deep learning (DL) system that learns from weak labels, a sampling strategy that reduces the number of training samples by a factor of six without compromising performance, an approach to leverage a small subset of fully annotated samples, and a prototype with explainable predictions, active learning features and parallelisation. Noting some problems in the literature, this study is conducted with one of the largest WSI colorectal samples dataset with approximately 10,500 WSIs. Of these samples, 900 are testing samples. Furthermore, the robustness of the proposed method is assessed with two additional external datasets (TCGA and PAIP) and a dataset of samples collected directly from the proposed prototype. Our proposed method predicts, for the patch-based tiles, a class based on the severity of the dysplasia and uses that information to classify the whole slide. It is trained with an interpretable mixed-supervision scheme to leverage the domain knowledge introduced by pathologists through spatial annotations. The mixed-supervision scheme allowed for an intelligent sampling strategy effectively evaluated in several different scenarios without compromising the performance. On the internal dataset, the method shows an accuracy of 93.44% and a sensitivity between positive (low-grade and high-grade dysplasia) and non-neoplastic samples of 0.996. On the external test samples varied with TCGA being the most challenging dataset with an overall accuracy of 84.91% and a sensitivity of 0.996.
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Affiliation(s)
- Pedro C Neto
- Institute for Systems and Computer Engineering, Technology and Science (INESC TEC), R. Dr. Roberto Frias, Porto, 4200-465, Porto, Portugal.
- Faculty of Engineering, University of Porto (FEUP), R. Dr. Roberto Frias, Porto, 4200-465, Porto, Portugal.
| | - Diana Montezuma
- IMP Diagnostics, Praça do Bom Sucesso, 61, sala 808, Porto, 4150-146, Porto, Portugal.
- Cancer Biology and Epigenetics Group, Research Center of IPO Porto (CI-IPOP) / RISE@CI-IPOP (Health Research Network), Portuguese Oncology Institute of Porto (IPO Porto) / Porto Comprehensive Cancer Center (Porto.CCC), R. Dr. António Bernardino de Almeida 865, Porto, 4200-072, Porto, Portugal.
- Doctoral Programme in Medical Sciences, School of Medicine and Biomedical Sciences - University of Porto (ICBAS-UP), R. Jorge de Viterbo Ferreira 228, Porto, 4050-313, Porto, Portugal.
| | - Sara P Oliveira
- Institute for Systems and Computer Engineering, Technology and Science (INESC TEC), R. Dr. Roberto Frias, Porto, 4200-465, Porto, Portugal.
- Faculty of Engineering, University of Porto (FEUP), R. Dr. Roberto Frias, Porto, 4200-465, Porto, Portugal.
| | - Domingos Oliveira
- IMP Diagnostics, Praça do Bom Sucesso, 61, sala 808, Porto, 4150-146, Porto, Portugal
| | - João Fraga
- Department of Pathology, IPO-Porto, R. Dr. António Bernardino de Almeida 865, Porto, 4200-072, Porto, Portugal
| | - Ana Monteiro
- IMP Diagnostics, Praça do Bom Sucesso, 61, sala 808, Porto, 4150-146, Porto, Portugal
| | - João Monteiro
- IMP Diagnostics, Praça do Bom Sucesso, 61, sala 808, Porto, 4150-146, Porto, Portugal
| | - Liliana Ribeiro
- IMP Diagnostics, Praça do Bom Sucesso, 61, sala 808, Porto, 4150-146, Porto, Portugal
| | - Sofia Gonçalves
- IMP Diagnostics, Praça do Bom Sucesso, 61, sala 808, Porto, 4150-146, Porto, Portugal
| | - Stefan Reinhard
- Institute of Pathology, University of Bern, Uni Bern, Murtenstrasse 31, Bern, 3008, Bern, Switzerland
| | - Inti Zlobec
- Institute of Pathology, University of Bern, Uni Bern, Murtenstrasse 31, Bern, 3008, Bern, Switzerland
| | - Isabel M Pinto
- IMP Diagnostics, Praça do Bom Sucesso, 61, sala 808, Porto, 4150-146, Porto, Portugal
| | - Jaime S Cardoso
- Institute for Systems and Computer Engineering, Technology and Science (INESC TEC), R. Dr. Roberto Frias, Porto, 4200-465, Porto, Portugal
- Faculty of Engineering, University of Porto (FEUP), R. Dr. Roberto Frias, Porto, 4200-465, Porto, Portugal
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Rozario SY, Sarkar M, Farlie MK, Lazarus MD. Responding to the healthcare workforce shortage: A scoping review exploring anatomical pathologists' professional identities over time. ANATOMICAL SCIENCES EDUCATION 2024; 17:351-365. [PMID: 36748328 DOI: 10.1002/ase.2260] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 01/16/2023] [Accepted: 02/06/2023] [Indexed: 06/18/2023]
Abstract
Anatomical pathology (AP) is an anatomy-centric medical specialty devoted to tissue-based diagnosis of disease. The field faces a current and predicted workforce shortage, likely increasing diagnostic wait times and delaying patient access to urgent treatment. A lack of AP exposure is proposed to preclude recruitment to the field, as medical students are afforded only a limited understanding of who a pathologist is and what they do (their professional identity/PI and role). Anatomical sciences educators may be well placed to increase student understanding of anatomical pathologists' PI features, but until features of anatomical pathologists' PI are understood, recommendations for anatomy educators are premature. Thus, this scoping review asked: "What are the professional identity features of anatomical pathologists reported in the literature, and how have these changed over time?" A six-stage scoping review was performed. Medline and PubMed, Global Health, and Embase were used to identify relevant studies (n = 74). Team-based framework analysis identified that features of anatomical pathologists' professional identity encompass five overarching themes: professional practice, views about the role, training and education, personal implications, and technology. Technology was identified as an important theme of anatomical pathologists' PI, as it intersected with many other PI feature themes, including diagnosis and collaboration. This review found that pathologists may sometimes perceive professional competition with technology, such as artificial intelligence. These findings suggest unique opportunities for integrating AP-specific PI features into anatomy teaching, which may foster student interest in AP, and potentially increase recruitment into the field.
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Affiliation(s)
- Shemona Y Rozario
- Centre for Human Anatomy Education (CHAE), Department of Anatomy and Developmental Biology, Biomedical Discovery Institute, Faculty of Medicine, Nursing and Health Sciences, Monash University, Clayton, Victoria, Australia
| | - Mahbub Sarkar
- Monash Centre for Scholarship in Health Education (MCSHE), Faculty of Medicine, Nursing and Health Sciences, Monash University, Clayton, Victoria, Australia
| | - Melanie K Farlie
- Monash Centre for Scholarship in Health Education (MCSHE), Faculty of Medicine, Nursing and Health Sciences, Monash University, Clayton, Victoria, Australia
- Department of Physiotherapy, Faculty of Medicine Nursing and Health Sciences, Monash University, Clayton, Victoria, Australia
| | - Michelle D Lazarus
- Centre for Human Anatomy Education (CHAE), Department of Anatomy and Developmental Biology, Biomedical Discovery Institute, Faculty of Medicine, Nursing and Health Sciences, Monash University, Clayton, Victoria, Australia
- Monash Centre for Scholarship in Health Education (MCSHE), Faculty of Medicine, Nursing and Health Sciences, Monash University, Clayton, Victoria, Australia
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Stevenson E, Walsh C, Hibberd L. Can artificial intelligence replace biochemists? A study comparing interpretation of thyroid function test results by ChatGPT and Google Bard to practising biochemists. Ann Clin Biochem 2024; 61:143-149. [PMID: 37699796 DOI: 10.1177/00045632231203473] [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: 09/14/2023]
Abstract
BACKGROUND Public awareness of artificial intelligence (AI) is increasing and this novel technology is being used for a range of everyday tasks and more specialist clinical applications. On a background of increasing waits for GP appointments alongside patient access to laboratory test results through the NHS app, this study aimed to assess the accuracy and safety of two AI tools, ChatGPT and Google Bard, in providing interpretation of thyroid function test results as if posed by laboratory scientists or patients. METHODS Fifteen fictional cases were presented to a team of clinicians and clinical scientists to produce a consensus opinion. The cases were then presented to ChatGPT and Google Bard as though from healthcare providers and from patients. The responses were categorized as correct, partially correct or incorrect compared to consensus opinion and the advice assessed for safety to patients. RESULTS Of the 15 cases presented, ChatGPT and Google Bard correctly interpreted only 33.3% and 20.0% of cases, respectively. When queries were posed as a patient, 66.7% of ChatGPT responses were safe compared to 60.0% of Google Bard responses. Both AI tools were able to identify primary hypothyroidism and hyperthyroidism but failed to identify subclinical presentations, non-thyroidal illness or secondary hypothyroidism. CONCLUSIONS This study has demonstrated that AI tools do not currently have the capacity to generate consistently correct interpretation and safe advice to patients and should not be used as an alternative to a consultation with a qualified medical professional. Available AI in its current form cannot replace human clinical knowledge in this scenario.
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Affiliation(s)
- Emma Stevenson
- Clinical Biochemistry, Gloucestershire Hospitals NHS Foundation Trust, Cheltenham, UK
| | - Chelsey Walsh
- Clinical Biochemistry, Gloucestershire Hospitals NHS Foundation Trust, Cheltenham, UK
| | - Luke Hibberd
- Clinical Biochemistry, Gloucestershire Hospitals NHS Foundation Trust, Cheltenham, UK
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Ganjavi C, Eppler MB, Pekcan A, Biedermann B, Abreu A, Collins GS, Gill IS, Cacciamani GE. Publishers' and journals' instructions to authors on use of generative artificial intelligence in academic and scientific publishing: bibliometric analysis. BMJ 2024; 384:e077192. [PMID: 38296328 PMCID: PMC10828852 DOI: 10.1136/bmj-2023-077192] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 11/29/2023] [Indexed: 02/05/2024]
Abstract
OBJECTIVES To determine the extent and content of academic publishers' and scientific journals' guidance for authors on the use of generative artificial intelligence (GAI). DESIGN Cross sectional, bibliometric study. SETTING Websites of academic publishers and scientific journals, screened on 19-20 May 2023, with the search updated on 8-9 October 2023. PARTICIPANTS Top 100 largest academic publishers and top 100 highly ranked scientific journals, regardless of subject, language, or country of origin. Publishers were identified by the total number of journals in their portfolio, and journals were identified through the Scimago journal rank using the Hirsch index (H index) as an indicator of journal productivity and impact. MAIN OUTCOME MEASURES The primary outcomes were the content of GAI guidelines listed on the websites of the top 100 academic publishers and scientific journals, and the consistency of guidance between the publishers and their affiliated journals. RESULTS Among the top 100 largest publishers, 24% provided guidance on the use of GAI, of which 15 (63%) were among the top 25 publishers. Among the top 100 highly ranked journals, 87% provided guidance on GAI. Of the publishers and journals with guidelines, the inclusion of GAI as an author was prohibited in 96% and 98%, respectively. Only one journal (1%) explicitly prohibited the use of GAI in the generation of a manuscript, and two (8%) publishers and 19 (22%) journals indicated that their guidelines exclusively applied to the writing process. When disclosing the use of GAI, 75% of publishers and 43% of journals included specific disclosure criteria. Where to disclose the use of GAI varied, including in the methods or acknowledgments, in the cover letter, or in a new section. Variability was also found in how to access GAI guidelines shared between journals and publishers. GAI guidelines in 12 journals directly conflicted with those developed by the publishers. The guidelines developed by top medical journals were broadly similar to those of academic journals. CONCLUSIONS Guidelines by some top publishers and journals on the use of GAI by authors are lacking. Among those that provided guidelines, the allowable uses of GAI and how it should be disclosed varied substantially, with this heterogeneity persisting in some instances among affiliated publishers and journals. Lack of standardization places a burden on authors and could limit the effectiveness of the regulations. As GAI continues to grow in popularity, standardized guidelines to protect the integrity of scientific output are needed.
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Affiliation(s)
- Conner Ganjavi
- Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- USC Institute of Urology and Catherine and Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Artificial Intelligence Center at USC Urology, USC Institute of Urology, University of Southern California, Los Angeles, CA, USA
| | - Michael B Eppler
- Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- USC Institute of Urology and Catherine and Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Artificial Intelligence Center at USC Urology, USC Institute of Urology, University of Southern California, Los Angeles, CA, USA
| | - Asli Pekcan
- Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- USC Institute of Urology and Catherine and Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Artificial Intelligence Center at USC Urology, USC Institute of Urology, University of Southern California, Los Angeles, CA, USA
| | - Brett Biedermann
- Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- USC Institute of Urology and Catherine and Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Artificial Intelligence Center at USC Urology, USC Institute of Urology, University of Southern California, Los Angeles, CA, USA
| | - Andre Abreu
- Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- USC Institute of Urology and Catherine and Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Artificial Intelligence Center at USC Urology, USC Institute of Urology, University of Southern California, Los Angeles, CA, USA
| | - Gary S Collins
- UK EQUATOR Centre, Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Inderbir S Gill
- Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- USC Institute of Urology and Catherine and Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Artificial Intelligence Center at USC Urology, USC Institute of Urology, University of Southern California, Los Angeles, CA, USA
| | - Giovanni E Cacciamani
- Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- USC Institute of Urology and Catherine and Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Artificial Intelligence Center at USC Urology, USC Institute of Urology, University of Southern California, Los Angeles, CA, USA
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Arvisais-Anhalt S, Gonias SL, Murray SG. Establishing priorities for implementation of large language models in pathology and laboratory medicine. Acad Pathol 2024; 11:100101. [PMID: 38292297 PMCID: PMC10825232 DOI: 10.1016/j.acpath.2023.100101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Revised: 10/02/2023] [Accepted: 10/29/2023] [Indexed: 02/01/2024] Open
Abstract
Artificial intelligence and machine learning have numerous applications in pathology and laboratory medicine. The release of ChatGPT prompted speculation regarding the potentially transformative role of large-language models (LLMs) in academic pathology, laboratory medicine, and pathology education. Because of the potential to improve LLMs over the upcoming years, pathology and laboratory medicine clinicians are encouraged to embrace this technology, identify pathways by which LLMs may support our missions in education, clinical practice, and research, participate in the refinement of AI modalities, and design user-friendly interfaces that integrate these tools into our most important workflows. Challenges regarding the use of LLMs, which have already received considerable attention in a general sense, are also reviewed herein within the context of the pathology field and are important to consider as LLM applications are identified and operationalized.
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Affiliation(s)
- Simone Arvisais-Anhalt
- Department of Laboratory Medicine, University of California San Francisco, San Francisco, CA, USA
| | - Steven L. Gonias
- Department of Pathology, University of California San Diego, La Jolla, CA, USA
| | - Sara G. Murray
- Department of Medicine, University of California San Francisco, San Francisco, CA, USA
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11
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Evans H, Snead D. Why do errors arise in artificial intelligence diagnostic tools in histopathology and how can we minimize them? Histopathology 2024; 84:279-287. [PMID: 37921030 DOI: 10.1111/his.15071] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Revised: 09/22/2023] [Accepted: 09/27/2023] [Indexed: 11/04/2023]
Abstract
Artificial intelligence (AI)-based diagnostic tools can offer numerous benefits to the field of histopathology, including improved diagnostic accuracy, efficiency and productivity. As a result, such tools are likely to have an increasing role in routine practice. However, all AI tools are prone to errors, and these AI-associated errors have been identified as a major risk in the introduction of AI into healthcare. The errors made by AI tools are different, in terms of both cause and nature, to the errors made by human pathologists. As highlighted by the National Institute for Health and Care Excellence, it is imperative that practising pathologists understand the potential limitations of AI tools, including the errors made. Pathologists are in a unique position to be gatekeepers of AI tool use, maximizing patient benefit while minimizing harm. Furthermore, their pathological knowledge is essential to understanding when, and why, errors have occurred and so to developing safer future algorithms. This paper summarises the literature on errors made by AI diagnostic tools in histopathology. These include erroneous errors, data concerns (data bias, hidden stratification, data imbalances, distributional shift, and lack of generalisability), reinforcement of outdated practices, unsafe failure mode, automation bias, and insensitivity to impact. Methods to reduce errors in both tool design and clinical use are discussed, and the practical roles for pathologists in error minimisation are highlighted. This aims to inform and empower pathologists to move safely through this seismic change in practice and help ensure that novel AI tools are adopted safely.
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Affiliation(s)
- Harriet Evans
- Histopathology Department, University Hospitals Coventry and Warwickshire NHS Trust, Coventry, UK
- Warwick Medical School, University of Warwick, Coventry, UK
| | - David Snead
- Histopathology Department, University Hospitals Coventry and Warwickshire NHS Trust, Coventry, UK
- Warwick Medical School, University of Warwick, Coventry, UK
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12
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S Alshuhri M, Al-Musawi SG, Al-Alwany AA, Uinarni H, Rasulova I, Rodrigues P, Alkhafaji AT, Alshanberi AM, Alawadi AH, Abbas AH. Artificial intelligence in cancer diagnosis: Opportunities and challenges. Pathol Res Pract 2024; 253:154996. [PMID: 38118214 DOI: 10.1016/j.prp.2023.154996] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Revised: 11/20/2023] [Accepted: 11/27/2023] [Indexed: 12/22/2023]
Abstract
Since cancer is one of the world's top causes of death, early diagnosis is critical to improving patient outcomes. Artificial intelligence (AI) has become a viable technique for cancer diagnosis by using machine learning algorithms to examine large volumes of data for accurate and efficient diagnosis. AI has the potential to alter the way cancer is detected fundamentally. Still, it has several disadvantages, such as requiring a large amount of data, technological limitations, and ethical concerns. This overview looks at the possibilities and restrictions of AI in cancer detection, as well as current applications and possible future developments. We can better understand how to use AI to improve patient outcomes and reduce cancer mortality rates by looking at its potential for cancer detection.
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Affiliation(s)
- Mohammed S Alshuhri
- Radiology and Medical Imaging Department, College of Applied Medical Sciences, Prince Sattam bin Abdulaziz University, Kharj, Saudi Arabia
| | | | | | - Herlina Uinarni
- Department of Anatomy, School of Medicine and Health Sciences Atma Jaya Catholic University of Indonesia, Indonesia; Radiology department of Pantai Indah Kapuk Hospital Jakarta, Jakarta, Indonesia.
| | - Irodakhon Rasulova
- School of Humanities, Natural & Social Sciences, New Uzbekistan University, 54 Mustaqillik Ave., Tashkent 100007, Uzbekistan; Department of Public Health, Samarkand State Medical University, Amir Temur Street 18, Samarkand, Uzbekistan
| | - Paul Rodrigues
- Department of Computer Engineering, College of Computer Science, King Khalid University, Al-Faraa, Abha, Asir, Kingdom of Saudi Arabia
| | | | - Asim Muhammed Alshanberi
- Department of Community Medicine & Pilgrim Healthcare, Umm Alqura University, Makkah 24382, Saudi Arabia; General Medicine Practice Program, Batterjee Medical College, Jeddah 21442, Saudi Arabia
| | - Ahmed Hussien Alawadi
- College of Technical Engineering, the Islamic University, Najaf, Iraq; College of Technical Engineering, the Islamic University of Al Diwaniyah, Iraq; College of Technical Engineering, the Islamic University of Babylon, Iraq
| | - Ali Hashim Abbas
- College of Technical Engineering, Imam Ja'afar Al-Sadiq University, Al-Muthanna 66002, Iraq
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13
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Malik S, Zaheer S. ChatGPT as an aid for pathological diagnosis of cancer. Pathol Res Pract 2024; 253:154989. [PMID: 38056135 DOI: 10.1016/j.prp.2023.154989] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Revised: 11/26/2023] [Accepted: 11/27/2023] [Indexed: 12/08/2023]
Abstract
Diagnostic workup of cancer patients is highly reliant on the science of pathology using cytopathology, histopathology, and other ancillary techniques like immunohistochemistry and molecular cytogenetics. Data processing and learning by means of artificial intelligence (AI) has become a spearhead for the advancement of medicine, with pathology and laboratory medicine being no exceptions. ChatGPT, an artificial intelligence (AI)-based chatbot, that was recently launched by OpenAI, is currently a talk of the town, and its role in cancer diagnosis is also being explored meticulously. Pathology workflow by integration of digital slides, implementation of advanced algorithms, and computer-aided diagnostic techniques extend the frontiers of the pathologist's view beyond a microscopic slide and enables effective integration, assimilation, and utilization of knowledge that is beyond human limits and boundaries. Despite of it's numerous advantages in the pathological diagnosis of cancer, it comes with several challenges like integration of digital slides with input language parameters, problems of bias, and legal issues which have to be addressed and worked up soon so that we as a pathologists diagnosing malignancies are on the same band wagon and don't miss the train.
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Affiliation(s)
- Shaivy Malik
- Department of Pathology, Vardhman Mahavir Medical College and Safdarjung Hospital, New Delhi, India
| | - Sufian Zaheer
- Department of Pathology, Vardhman Mahavir Medical College and Safdarjung Hospital, New Delhi, India.
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14
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Gruver AM, Lu H, Zhao X, Fulford AD, Soper MD, Ballard D, Hanson JC, Schade AE, Hsi ED, Gottlieb K, Credille KM. Pathologist-trained machine learning classifiers developed to quantitate celiac disease features differentiate endoscopic biopsies according to modified marsh score and dietary intervention response. Diagn Pathol 2023; 18:122. [PMID: 37951937 PMCID: PMC10638821 DOI: 10.1186/s13000-023-01412-x] [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: 08/23/2023] [Accepted: 11/02/2023] [Indexed: 11/14/2023] Open
Abstract
BACKGROUND Histologic evaluation of the mucosal changes associated with celiac disease is important for establishing an accurate diagnosis and monitoring the impact of investigational therapies. While the Marsh-Oberhuber classification has been used to categorize the histologic findings into discrete stages (i.e., Type 0-3c), significant variability has been documented between observers using this ordinal scoring system. Therefore, we evaluated whether pathologist-trained machine learning classifiers can be developed to objectively quantitate the pathological changes of villus blunting, intraepithelial lymphocytosis, and crypt hyperplasia in small intestine endoscopic biopsies. METHODS A convolutional neural network (CNN) was trained and combined with a secondary algorithm to quantitate intraepithelial lymphocytes (IEL) with 5 classes on CD3 immunohistochemistry whole slide images (WSI) and used to correlate feature outputs with ground truth modified Marsh scores in a total of 116 small intestine biopsies. RESULTS Across all samples, median %CD3 counts (positive cells/enterocytes) from villous epithelium (VE) increased with higher Marsh scores (Type 0%CD3 VE = 13.4; Type 1-3%CD3 VE = 41.9, p < 0.0001). Indicators of villus blunting and crypt hyperplasia were also observed (Type 0-2 villous epithelium/lamina propria area ratio = 0.81; Type 3a-3c villous epithelium/lamina propria area ratio = 0.29, p < 0.0001), and Type 0-1 crypt/villous epithelial area ratio = 0.59; Type 2-3 crypt/villous epithelial area ratio = 1.64, p < 0.0001). Using these individual features, a combined feature machine learning score (MLS) was created to evaluate a set of 28 matched pre- and post-intervention biopsies captured before and after dietary gluten restriction. The disposition of the continuous MLS paired biopsy result aligned with the Marsh score in 96.4% (27/28) of the cohort. CONCLUSIONS Machine learning classifiers can be developed to objectively quantify histologic features and capture additional data not achievable with manual scoring. Such approaches should be further investigated to improve biopsy evaluation, especially for clinical trials.
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Affiliation(s)
- Aaron M Gruver
- Clinical Diagnostics Laboratory, Eli Lilly and Company, Lilly Corporate Center, Indianapolis, IN, 46285, USA
| | - Haiyan Lu
- Wake Forest University School of Medicine, Winston-Salem, NC, 27157, USA
| | - Xiaoxian Zhao
- Wake Forest University School of Medicine, Winston-Salem, NC, 27157, USA
| | - Angie D Fulford
- Clinical Diagnostics Laboratory, Eli Lilly and Company, Lilly Corporate Center, Indianapolis, IN, 46285, USA
| | - Michael D Soper
- Clinical Diagnostics Laboratory, Eli Lilly and Company, Lilly Corporate Center, Indianapolis, IN, 46285, USA
| | - Darryl Ballard
- Clinical Diagnostics Laboratory, Eli Lilly and Company, Lilly Corporate Center, Indianapolis, IN, 46285, USA
| | - Jeffrey C Hanson
- Research Informatics, Eli Lilly and Company, Indianapolis, IN, 46285, USA
| | - Andrew E Schade
- Clinical Diagnostics Laboratory, Eli Lilly and Company, Lilly Corporate Center, Indianapolis, IN, 46285, USA
| | - Eric D Hsi
- Wake Forest University School of Medicine, Winston-Salem, NC, 27157, USA
| | - Klaus Gottlieb
- Immunology, Eli Lilly and Company, Indianapolis, IN, 46285, USA
| | - Kelly M Credille
- Clinical Diagnostics Laboratory, Eli Lilly and Company, Lilly Corporate Center, Indianapolis, IN, 46285, USA.
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15
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Schwen LO, Kiehl TR, Carvalho R, Zerbe N, Homeyer A. Digitization of Pathology Labs: A Review of Lessons Learned. J Transl Med 2023; 103:100244. [PMID: 37657651 DOI: 10.1016/j.labinv.2023.100244] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Revised: 07/18/2023] [Accepted: 08/23/2023] [Indexed: 09/03/2023] Open
Abstract
Pathology laboratories are increasingly using digital workflows. This has the potential of increasing laboratory efficiency, but the digitization process also involves major challenges. Several reports have been published describing the individual experiences of specific laboratories with the digitization process. However, a comprehensive overview of the lessons learned is still lacking. We provide an overview of the lessons learned for different aspects of the digitization process, including digital case management, digital slide reading, and computer-aided slide reading. We also cover metrics used for monitoring performance and pitfalls and corresponding values observed in practice. The overview is intended to help pathologists, information technology decision makers, and administrators to benefit from the experiences of others and to implement the digitization process in an optimal way to make their own laboratory future-proof.
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Affiliation(s)
- Lars Ole Schwen
- Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany.
| | - Tim-Rasmus Kiehl
- Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Institute of Pathology, Berlin, Germany
| | - Rita Carvalho
- Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Institute of Pathology, Berlin, Germany
| | - Norman Zerbe
- Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Institute of Pathology, Berlin, Germany
| | - André Homeyer
- Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany
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16
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Shafi S, Parwani AV. Artificial intelligence in diagnostic pathology. Diagn Pathol 2023; 18:109. [PMID: 37784122 PMCID: PMC10546747 DOI: 10.1186/s13000-023-01375-z] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Accepted: 07/21/2023] [Indexed: 10/04/2023] Open
Abstract
Digital pathology (DP) is being increasingly employed in cancer diagnostics, providing additional tools for faster, higher-quality, accurate diagnosis. The practice of diagnostic pathology has gone through a staggering transformation wherein new tools such as digital imaging, advanced artificial intelligence (AI) algorithms, and computer-aided diagnostic techniques are being used for assisting, augmenting and empowering the computational histopathology and AI-enabled diagnostics. This is paving the way for advancement in precision medicine in cancer. Automated whole slide imaging (WSI) scanners are now rendering diagnostic quality, high-resolution images of entire glass slides and combining these images with innovative digital pathology tools is making it possible to integrate imaging into all aspects of pathology reporting including anatomical, clinical, and molecular pathology. The recent approvals of WSI scanners for primary diagnosis by the FDA as well as the approval of prostate AI algorithm has paved the way for starting to incorporate this exciting technology for use in primary diagnosis. AI tools can provide a unique platform for innovations and advances in anatomical and clinical pathology workflows. In this review, we describe the milestones and landmark trials in the use of AI in clinical pathology with emphasis on future directions.
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Affiliation(s)
- Saba Shafi
- Department of Pathology, The Ohio State University Wexner Medical Center, E409 Doan Hall, 410 West 10th Ave, Columbus, OH, 43210, USA
| | - Anil V Parwani
- Department of Pathology, The Ohio State University Wexner Medical Center, E409 Doan Hall, 410 West 10th Ave, Columbus, OH, 43210, USA.
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17
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Browning L, Winter L, Cooper RA, Ghosh A, Dytor T, Colling R, Fryer E, Rittscher J, Verrill C. Impact of the transition to digital pathology in a clinical setting on histopathologists in training: experiences and perceived challenges within a UK training region. J Clin Pathol 2023; 76:712-718. [PMID: 35906044 PMCID: PMC10511979 DOI: 10.1136/jcp-2022-208416] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Accepted: 07/08/2022] [Indexed: 11/03/2022]
Abstract
AIMS With increasing utility of digital pathology (DP), it is important to consider the experiences of histopathologists in training, particularly in view of the varied access to DP across a training region and the consequent need to remain competent in reporting on glass slides (GS), which is also relevant for the Fellowship of the Royal College of Pathologists part 2 examination. Understanding the impact of DP on training is limited but could aid development of guidance to support the transition. We sought to investigate the perceptions of histopathologists in training around the introduction of DP for clinical diagnosis within a training region, and the potential training benefits and challenges. METHODS An anonymous online survey was circulated to 24 histopathologists in training within a UK training region, including a hospital which has been fully digitised since summer 2020. RESULTS 19 of 24 histopathologists in training responded (79%). The results indicate that DP offers many benefits to training, including ease of access to cases to enhance individual learning and teaching in general. Utilisation of DP for diagnosis appears variable; almost half of the (10 of 19) respondents with DP experience using it only for ancillary purposes such as measurements, reporting varying levels of confidence in using DP clinically. For those yet to undergo the transition, there was a perceived anxiety regarding digital reporting despite experience with DP in other contexts. CONCLUSIONS The survey evidences the need for provision of training and support for histopathologists in training during the transition to DP, and for consideration of their need to maintain competence and confidence with GS reporting.
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Affiliation(s)
- Lisa Browning
- NIHR Oxford Biomedical Research Centre, Oxford, UK
- Cellular Pathology, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Lucinda Winter
- Cellular Pathology, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | | | - Abhisek Ghosh
- Cellular Pathology, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
- Nuffield Department of Clinical Laboratory Sciences, University of Oxford, Oxford, UK
| | - Thomas Dytor
- Cellular Pathology, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Richard Colling
- Cellular Pathology, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
| | - Eve Fryer
- Cellular Pathology, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Jens Rittscher
- NIHR Oxford Biomedical Research Centre, Oxford, UK
- Department of Engineering Science, University of Oxford, Oxford, UK
- Big Data Institute, University of Oxford, Oxford, UK
| | - Clare Verrill
- NIHR Oxford Biomedical Research Centre, Oxford, UK
- Cellular Pathology, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
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18
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Hoque MZ, Keskinarkaus A, Nyberg P, Xu H, Seppänen T. Invasion depth estimation of carcinoma cells using adaptive stain normalization to improve epidermis segmentation accuracy. Comput Med Imaging Graph 2023; 108:102276. [PMID: 37611486 DOI: 10.1016/j.compmedimag.2023.102276] [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: 07/25/2023] [Accepted: 07/26/2023] [Indexed: 08/25/2023]
Abstract
Submucosal invasion depth is a significant prognostic factor when assessing lymph node metastasis and cancer itself to plan proper treatment for the patient. Conventionally, oncologists measure the invasion depth by hand which is a laborious, subjective, and time-consuming process. The manual pathological examination by measuring accurate carcinoma cell invasion with considerable inter-observer and intra-observer variations is still challenging. The increasing use of medical imaging and artificial intelligence reveals a significant role in clinical medicine and pathology. In this paper, we propose an approach to study invasive behavior and measure the invasion depth of carcinoma from stained histopathology images. Specifically, our model includes adaptive stain normalization, color decomposition, and morphological reconstruction with adaptive thresholding to separate the epithelium with blue ratio image. Our method splits the image into multiple non-overlapping meaningful segments and successfully finds the homogeneous segments to measure accurate invasion depth. The invasion depths are measured from the inner epithelium edge to outermost pixels of the deepest part of particles in image. We conduct our experiments on skin melanoma tissue samples as well as on organotypic invasion model utilizing myoma tissue and oral squamous cell carcinoma. The performance is experimentally compared to three closely related reference methods and our method provides a superior result in measuring invasion depth. This computational technique will be beneficial for the segmentation of epithelium and other particles for the development of novel computer-aided diagnostic tools in biobank applications.
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Affiliation(s)
- Md Ziaul Hoque
- Center for Machine Vision and Signal Analysis, Faculty of Information Technology and Electrical Engineering, University of Oulu, Finland; Division of Nephrology and Intelligent Critical Care, Department of Medicine, University of Florida, Gainesville, USA.
| | - Anja Keskinarkaus
- Center for Machine Vision and Signal Analysis, Faculty of Information Technology and Electrical Engineering, University of Oulu, Finland
| | - Pia Nyberg
- Biobank Borealis of Northern Finland, Oulu University Hospital, Finland; Translational Medicine Research Unit, Medical Research Center Oulu, Faculty of Medicine, University of Oulu, Finland
| | - Hongming Xu
- Department of Electrical and Computer Engineering, University of Alberta, Canada; School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, China
| | - Tapio Seppänen
- Center for Machine Vision and Signal Analysis, Faculty of Information Technology and Electrical Engineering, University of Oulu, Finland
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19
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Liu Q, Jiang N, Hao Y, Hao C, Wang W, Bian T, Wang X, Li H, zhang Y, Kang Y, Xie F, Li Y, Jiang X, Feng Y, Mao Z, Wang Q, Gao Q, Zhang W, Cui B, Dong T. Identification of lymph node metastasis in pre-operation cervical cancer patients by weakly supervised deep learning from histopathological whole-slide biopsy images. Cancer Med 2023; 12:17952-17966. [PMID: 37559500 PMCID: PMC10523985 DOI: 10.1002/cam4.6437] [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: 04/29/2023] [Revised: 07/28/2023] [Accepted: 07/31/2023] [Indexed: 08/11/2023] Open
Abstract
BACKGROUND Lymph node metastasis (LNM) significantly impacts the prognosis of individuals diagnosed with cervical cancer, as it is closely linked to disease recurrence and mortality, thereby impacting therapeutic schedule choices for patients. However, accurately predicting LNM prior to treatment remains challenging. Consequently, this study seeks to utilize digital pathological features extracted from histopathological slides of primary cervical cancer patients to preoperatively predict the presence of LNM. METHODS A deep learning (DL) model was trained using the Vision transformer (ViT) and recurrent neural network (RNN) frameworks to predict LNM. This prediction was based on the analysis of 554 histopathological whole-slide images (WSIs) obtained from Qilu Hospital of Shandong University. To validate the model's performance, an external test was conducted using 336 WSIs from four other hospitals. Additionally, the efficiency of the DL model was evaluated using 190 cervical biopsies WSIs in a prospective set. RESULTS In the internal test set, our DL model achieved an area under the curve (AUC) of 0.919, with sensitivity and specificity values of 0.923 and 0.905, respectively, and an accuracy (ACC) of 0.909. The performance of the DL model remained strong in the external test set. In the prospective cohort, the AUC was 0.91, and the ACC was 0.895. Additionally, the DL model exhibited higher accuracy compared to imaging examination in the evaluation of LNM. By utilizing the transformer visualization method, we generated a heatmap that illustrates the local pathological features in primary lesions relevant to LNM. CONCLUSION DL-based image analysis has demonstrated efficiency in predicting LNM in early operable cervical cancer through the utilization of biopsies WSI. This approach has the potential to enhance therapeutic decision-making for patients diagnosed with cervical cancer.
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Affiliation(s)
- Qingqing Liu
- Cheeloo College of MedicineShandong UniversityJinan CityChina
| | - Nan Jiang
- Cheeloo College of MedicineShandong UniversityJinan CityChina
| | - Yiping Hao
- Cheeloo College of MedicineShandong UniversityJinan CityChina
| | - Chunyan Hao
- Department of Pathology, School of Basic Medical Science, Cheeloo College of MedicineShandong UniversityJinan CityChina
- Department of PathologyQilu Hospital of Shandong UniversityJinan CityChina
| | - Wei Wang
- Department of PathologyAffiliated Hospital of Jining Medical UniversityJining CityChina
| | - Tingting Bian
- Department of Medical ImagingAffiliated Hospital of Jining Medical UniversityJining CityChina
| | - Xiaohong Wang
- Department of Obstetrics and GynecologyJinan People's HospitalJinan CityChina
| | - Hua Li
- Department of Obstetrics and GynecologyTai'an City Central HospitalTai'an CityChina
| | - Yan zhang
- Department of Obstetrics and GynecologyWeifang People's HospitalWeifang CityChina
| | - Yanjun Kang
- Department of Obstetrics and GynecologyWomen and Children's Hospital, Qingdao UniversityQingdao CityChina
| | - Fengxiang Xie
- Department of PathologyKingMed DiagnosticsJinan CityChina
| | - Yawen Li
- Department of PathologyQilu Hospital of Shandong UniversityJinan CityChina
| | - XuJi Jiang
- Cheeloo College of MedicineShandong UniversityJinan CityChina
| | - Yuan Feng
- Cheeloo College of MedicineShandong UniversityJinan CityChina
| | - Zhonghao Mao
- Cheeloo College of MedicineShandong UniversityJinan CityChina
| | - Qi Wang
- Department of Obstetrics and Gynecology, Shandong Provincial Qianfoshan HospitalShandong UniversityJinan CityChina
| | - Qun Gao
- Department of Obstetrics and GynecologyThe Affiliated Hospital of Qingdao UniversityQingdao CityChina
| | - Wenjing Zhang
- Department of Obstetrics and GynecologyQilu Hospital of Shandong UniversityJinan CityChina
| | - Baoxia Cui
- Department of Obstetrics and GynecologyQilu Hospital of Shandong UniversityJinan CityChina
| | - Taotao Dong
- Department of Obstetrics and GynecologyQilu Hospital of Shandong UniversityJinan CityChina
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20
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Kiran N, Sapna F, Kiran F, Kumar D, Raja F, Shiwlani S, Paladini A, Sonam F, Bendari A, Perkash RS, Anjali F, Varrassi G. Digital Pathology: Transforming Diagnosis in the Digital Age. Cureus 2023; 15:e44620. [PMID: 37799211 PMCID: PMC10547926 DOI: 10.7759/cureus.44620] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Accepted: 09/03/2023] [Indexed: 10/07/2023] Open
Abstract
In the context of rapid technological advancements, the narrative review titled "Digital Pathology: Transforming Diagnosis in the Digital Age" explores the significant impact of digital pathology in reshaping diagnostic approaches. This review delves into the various effects of the field, including remote consultations and artificial intelligence (AI)-assisted analysis, revealing the ongoing transformation taking place. The investigation explores the process of digitizing traditional glass slides, which aims to improve accessibility and facilitate sharing. Additionally, it addresses the complexities associated with data security and standardization challenges. Incorporating AI enhances pathologists' diagnostic capabilities and accelerates analytical procedures. Furthermore, the review highlights the growing importance of collaborative networks facilitating global knowledge sharing. It also emphasizes the significant impact of this technology on medical education and patient care. This narrative review aims to provide an overview of digital pathology's transformative and innovative potential, highlighting its disruptive nature in reshaping diagnostic practices.
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Affiliation(s)
- Nfn Kiran
- Pathology and Laboratory Medicine, Staten Island University Hospital, New York, USA
| | - Fnu Sapna
- Pathology and Laboratory Medicine, Albert Einstein College of Medicine, New York, USA
| | - Fnu Kiran
- Pathology and Laboratory Medicine, University of Missouri School of Medicine, Columbia, USA
| | - Deepak Kumar
- Pathology and Laboratory Medicine, University of Missouri, Columbia, USA
| | - Fnu Raja
- Pathology and Laboratory Medicine, MetroHealth Medical Center, Cleveland, USA
| | - Sheena Shiwlani
- Pathology and Laboratory Medicine, Isra University, Karachi, PAK
- Pathology, Mount Sinai Hospital, New York, USA
| | - Antonella Paladini
- Clinical Medicine, Public Health and Life Science (MESVA), University of L'Aquila, L'Aquila, ITA
| | - Fnu Sonam
- Pathology and Laboratory Medicine, Liaquat University of Medical and Health Sciences, Sukkur, PAK
- Medicine, Mustafai Trust Central Hospital, Sukkur, PAK
| | - Ahmed Bendari
- Pathology and Laboratory Medicine, Lenox Hill Hospital, New York, USA
| | | | - Fnu Anjali
- Internal Medicine, Sakhi Baba General Hospital, Sukkur, PAK
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21
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Corbin CK, Maclay R, Acharya A, Mony S, Punnathanam S, Thapa R, Kotecha N, Shah NH, Chen JH. DEPLOYR: a technical framework for deploying custom real-time machine learning models into the electronic medical record. J Am Med Inform Assoc 2023; 30:1532-1542. [PMID: 37369008 PMCID: PMC10436147 DOI: 10.1093/jamia/ocad114] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Revised: 05/16/2023] [Accepted: 06/13/2023] [Indexed: 06/29/2023] Open
Abstract
OBJECTIVE Heatlhcare institutions are establishing frameworks to govern and promote the implementation of accurate, actionable, and reliable machine learning models that integrate with clinical workflow. Such governance frameworks require an accompanying technical framework to deploy models in a resource efficient, safe and high-quality manner. Here we present DEPLOYR, a technical framework for enabling real-time deployment and monitoring of researcher-created models into a widely used electronic medical record system. MATERIALS AND METHODS We discuss core functionality and design decisions, including mechanisms to trigger inference based on actions within electronic medical record software, modules that collect real-time data to make inferences, mechanisms that close-the-loop by displaying inferences back to end-users within their workflow, monitoring modules that track performance of deployed models over time, silent deployment capabilities, and mechanisms to prospectively evaluate a deployed model's impact. RESULTS We demonstrate the use of DEPLOYR by silently deploying and prospectively evaluating 12 machine learning models trained using electronic medical record data that predict laboratory diagnostic results, triggered by clinician button-clicks in Stanford Health Care's electronic medical record. DISCUSSION Our study highlights the need and feasibility for such silent deployment, because prospectively measured performance varies from retrospective estimates. When possible, we recommend using prospectively estimated performance measures during silent trials to make final go decisions for model deployment. CONCLUSION Machine learning applications in healthcare are extensively researched, but successful translations to the bedside are rare. By describing DEPLOYR, we aim to inform machine learning deployment best practices and help bridge the model implementation gap.
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Affiliation(s)
- Conor K Corbin
- Department of Biomedical Data Science, Stanford, California, USA
| | - Rob Maclay
- Stanford Children’s Health, Palo Alto, California, USA
| | | | | | | | - Rahul Thapa
- Stanford Health Care, Palo Alto, California, USA
| | | | - Nigam H Shah
- Center for Biomedical Informatics Research, Division of Hospital Medicine, Department of Medicine, Stanford University, School of Medicine, Stanford, California, USA
| | - Jonathan H Chen
- Center for Biomedical Informatics Research, Division of Hospital Medicine, Department of Medicine, Stanford University, School of Medicine, Stanford, California, USA
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22
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Asif A, Rajpoot K, Graham S, Snead D, Minhas F, Rajpoot N. Unleashing the potential of AI for pathology: challenges and recommendations. J Pathol 2023; 260:564-577. [PMID: 37550878 PMCID: PMC10952719 DOI: 10.1002/path.6168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 06/21/2023] [Accepted: 06/22/2023] [Indexed: 08/09/2023]
Abstract
Computational pathology is currently witnessing a surge in the development of AI techniques, offering promise for achieving breakthroughs and significantly impacting the practices of pathology and oncology. These AI methods bring with them the potential to revolutionize diagnostic pipelines as well as treatment planning and overall patient care. Numerous peer-reviewed studies reporting remarkable performance across diverse tasks serve as a testimony to the potential of AI in the field. However, widespread adoption of these methods in clinical and pre-clinical settings still remains a challenge. In this review article, we present a detailed analysis of the major obstacles encountered during the development of effective models and their deployment in practice. We aim to provide readers with an overview of the latest developments, assist them with insights into identifying some specific challenges that may require resolution, and suggest recommendations and potential future research directions. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.
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Affiliation(s)
- Amina Asif
- Tissue Image Analytics Centre, Department of Computer ScienceUniversity of WarwickCoventryUK
| | - Kashif Rajpoot
- School of Computer ScienceUniversity of BirminghamBirminghamUK
| | - Simon Graham
- Histofy Ltd, Birmingham Business ParkBirminghamUK
| | - David Snead
- Histofy Ltd, Birmingham Business ParkBirminghamUK
- Department of PathologyUniversity Hospitals Coventry & Warwickshire NHS TrustCoventryUK
| | - Fayyaz Minhas
- Tissue Image Analytics Centre, Department of Computer ScienceUniversity of WarwickCoventryUK
- Cancer Research CentreUniversity of WarwickCoventryUK
| | - Nasir Rajpoot
- Tissue Image Analytics Centre, Department of Computer ScienceUniversity of WarwickCoventryUK
- Histofy Ltd, Birmingham Business ParkBirminghamUK
- Cancer Research CentreUniversity of WarwickCoventryUK
- The Alan Turing InstituteLondonUK
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23
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Pierre K, Gupta M, Raviprasad A, Sadat Razavi SM, Patel A, Peters K, Hochhegger B, Mancuso A, Forghani R. Medical imaging and multimodal artificial intelligence models for streamlining and enhancing cancer care: opportunities and challenges. Expert Rev Anticancer Ther 2023; 23:1265-1279. [PMID: 38032181 DOI: 10.1080/14737140.2023.2286001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Accepted: 11/16/2023] [Indexed: 12/01/2023]
Abstract
INTRODUCTION Artificial intelligence (AI) has the potential to transform oncologic care. There have been significant developments in AI applications in medical imaging and increasing interest in multimodal models. These are likely to enable improved oncologic care through more precise diagnosis, increasingly in a more personalized and less invasive manner. In this review, we provide an overview of the current state and challenges that clinicians, administrative personnel and policy makers need to be aware of and mitigate for the technology to reach its full potential. AREAS COVERED The article provides a brief targeted overview of AI, a high-level review of the current state and future potential AI applications in diagnostic radiology and to a lesser extent digital pathology, focusing on oncologic applications. This is followed by a discussion of emerging approaches, including multimodal models. The article concludes with a discussion of technical, regulatory challenges and infrastructure needs for AI to realize its full potential. EXPERT OPINION There is a large volume of promising research, and steadily increasing commercially available tools using AI. For the most advanced and promising precision diagnostic applications of AI to be used clinically, robust and comprehensive quality monitoring systems and informatics platforms will likely be required.
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Affiliation(s)
- Kevin Pierre
- Radiomics and Augmented Intelligence Laboratory (RAIL), Department of Radiology and the Norman Fixel Institute for Neurological Diseases, University of Florida College of Medicine, Gainesville, FL, USA
- Department of Radiology, University of Florida College of Medicine, Gainesville, FL, USA
| | - Manas Gupta
- Radiomics and Augmented Intelligence Laboratory (RAIL), Department of Radiology and the Norman Fixel Institute for Neurological Diseases, University of Florida College of Medicine, Gainesville, FL, USA
| | - Abheek Raviprasad
- Radiomics and Augmented Intelligence Laboratory (RAIL), Department of Radiology and the Norman Fixel Institute for Neurological Diseases, University of Florida College of Medicine, Gainesville, FL, USA
- University of Florida College of Medicine, Gainesville, FL, USA
| | - Seyedeh Mehrsa Sadat Razavi
- Radiomics and Augmented Intelligence Laboratory (RAIL), Department of Radiology and the Norman Fixel Institute for Neurological Diseases, University of Florida College of Medicine, Gainesville, FL, USA
- University of Florida College of Medicine, Gainesville, FL, USA
| | - Anjali Patel
- Radiomics and Augmented Intelligence Laboratory (RAIL), Department of Radiology and the Norman Fixel Institute for Neurological Diseases, University of Florida College of Medicine, Gainesville, FL, USA
- University of Florida College of Medicine, Gainesville, FL, USA
| | - Keith Peters
- Radiomics and Augmented Intelligence Laboratory (RAIL), Department of Radiology and the Norman Fixel Institute for Neurological Diseases, University of Florida College of Medicine, Gainesville, FL, USA
- Department of Radiology, University of Florida College of Medicine, Gainesville, FL, USA
| | - Bruno Hochhegger
- Radiomics and Augmented Intelligence Laboratory (RAIL), Department of Radiology and the Norman Fixel Institute for Neurological Diseases, University of Florida College of Medicine, Gainesville, FL, USA
- Department of Radiology, University of Florida College of Medicine, Gainesville, FL, USA
| | - Anthony Mancuso
- Radiomics and Augmented Intelligence Laboratory (RAIL), Department of Radiology and the Norman Fixel Institute for Neurological Diseases, University of Florida College of Medicine, Gainesville, FL, USA
- Department of Radiology, University of Florida College of Medicine, Gainesville, FL, USA
| | - Reza Forghani
- Radiomics and Augmented Intelligence Laboratory (RAIL), Department of Radiology and the Norman Fixel Institute for Neurological Diseases, University of Florida College of Medicine, Gainesville, FL, USA
- Department of Radiology, University of Florida College of Medicine, Gainesville, FL, USA
- Division of Medical Physics, University of Florida College of Medicine, Gainesville, FL, USA
- Department of Neurology, Division of Movement Disorders, University of Florida College of Medicine, Gainesville, FL, USA
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Azizi S, Culp L, Freyberg J, Mustafa B, Baur S, Kornblith S, Chen T, Tomasev N, Mitrović J, Strachan P, Mahdavi SS, Wulczyn E, Babenko B, Walker M, Loh A, Chen PHC, Liu Y, Bavishi P, McKinney SM, Winkens J, Roy AG, Beaver Z, Ryan F, Krogue J, Etemadi M, Telang U, Liu Y, Peng L, Corrado GS, Webster DR, Fleet D, Hinton G, Houlsby N, Karthikesalingam A, Norouzi M, Natarajan V. Robust and data-efficient generalization of self-supervised machine learning for diagnostic imaging. Nat Biomed Eng 2023:10.1038/s41551-023-01049-7. [PMID: 37291435 DOI: 10.1038/s41551-023-01049-7] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Accepted: 05/02/2023] [Indexed: 06/10/2023]
Abstract
Machine-learning models for medical tasks can match or surpass the performance of clinical experts. However, in settings differing from those of the training dataset, the performance of a model can deteriorate substantially. Here we report a representation-learning strategy for machine-learning models applied to medical-imaging tasks that mitigates such 'out of distribution' performance problem and that improves model robustness and training efficiency. The strategy, which we named REMEDIS (for 'Robust and Efficient Medical Imaging with Self-supervision'), combines large-scale supervised transfer learning on natural images and intermediate contrastive self-supervised learning on medical images and requires minimal task-specific customization. We show the utility of REMEDIS in a range of diagnostic-imaging tasks covering six imaging domains and 15 test datasets, and by simulating three realistic out-of-distribution scenarios. REMEDIS improved in-distribution diagnostic accuracies up to 11.5% with respect to strong supervised baseline models, and in out-of-distribution settings required only 1-33% of the data for retraining to match the performance of supervised models retrained using all available data. REMEDIS may accelerate the development lifecycle of machine-learning models for medical imaging.
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Affiliation(s)
| | | | | | | | | | | | - Ting Chen
- Google Research, Mountain View, CA, USA
| | | | | | | | | | | | | | | | - Aaron Loh
- Google Research, Mountain View, CA, USA
| | | | - Yuan Liu
- Google Research, Mountain View, CA, USA
| | | | | | | | | | | | - Fiona Ryan
- Georgia Institute of Technology, Computer Science, Atlanta, GA, USA
| | | | - Mozziyar Etemadi
- School of Medicine/School of Engineering, Northwestern University, Chicago, IL, USA
| | | | - Yun Liu
- Google Research, Mountain View, CA, USA
| | - Lily Peng
- Google Research, Mountain View, CA, USA
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25
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Wang LM, Ang TL. Optimizing endoscopic ultrasound guided fine needle aspiration through artificial intelligence. J Gastroenterol Hepatol 2023; 38:839-840. [PMID: 37264500 DOI: 10.1111/jgh.16242] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Affiliation(s)
- Lai Mun Wang
- Department of Anatomical Pathology, Changi General Hospital, SingHealth, Duke-NUS Medical School, Singapore
| | - Tiing Leong Ang
- Department of Gastroenterology and Hepatology, Changi General Hospital, SingHealth; Duke-NUS Medical School; Yong Loo Lin School of Medicine, National University of Singapore; Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore
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26
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Cussat-Blanc S, Castets-Renard C, Monsarrat P. Doctors in Medical Data Sciences: A New Curriculum. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 20:675. [PMID: 36612994 PMCID: PMC9819870 DOI: 10.3390/ijerph20010675] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/29/2022] [Revised: 12/15/2022] [Accepted: 12/21/2022] [Indexed: 06/17/2023]
Abstract
Machine Learning (ML), a branch of Artificial Intelligence, which is competing with human experts in many specialized biomedical fields and will play an increasing role in precision medicine. As with any other technological advances in medicine, the keys to understanding must be integrated into practitioner training. To respond to this challenge, this viewpoint discusses some necessary changes in the health studies curriculum that could help practitioners to interpret decisions the made by a machine and question them in relation to the patient's medical context. The complexity of technology and the inherent criticality of its use in medicine also necessitate a new medical profession. To achieve this objective, this viewpoint will propose new medical practitioners with skills in both medicine and data science: the Doctor in Medical Data Sciences.
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Affiliation(s)
- Sylvain Cussat-Blanc
- Artificial and Natural Intelligence Toulouse Institute ANITI, 31013 Toulouse, France
- Institute of Research in Informatics (IRIT) of Toulouse, CNRS—UMR5505, 31400 Toulouse, France
| | - Céline Castets-Renard
- Artificial and Natural Intelligence Toulouse Institute ANITI, 31013 Toulouse, France
- Civil Law Faculty, University of Ottawa, Ottawa, ON K1N 6N5, Canada
| | - Paul Monsarrat
- Artificial and Natural Intelligence Toulouse Institute ANITI, 31013 Toulouse, France
- RESTORE Research Center, Université de Toulouse, INSERM, CNRS, EFS, ENVT, Batiment INCERE, 4bis Avenue Hubert Curien, 31100 Toulouse, France
- Department of Oral Medicine, Toulouse University Hospital (CHU de Toulouse), CEDEX 9, 31062 Toulouse, France
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27
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Frank SJ. Accurate diagnostic tissue segmentation and concurrent disease subtyping with small datasets. J Pathol Inform 2022; 14:100174. [PMID: 36687530 PMCID: PMC9852683 DOI: 10.1016/j.jpi.2022.100174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Revised: 09/01/2022] [Accepted: 12/15/2022] [Indexed: 12/24/2022] Open
Abstract
Purpose To provide a flexible, end-to-end platform for visually distinguishing diseased from undiseased tissue in a medical image, in particular pathology slides, and classifying diseased regions by subtype. Highly accurate results are obtained using small training datasets and reduced-scale source images that can be easily shared. Approach An ensemble of lightweight convolutional neural networks (CNNs) is trained on different subsets of images derived from a relatively small number of annotated whole-slide histopathology images (WSIs). The WSIs are first reduced in scale in a manner that preserves anatomic features critical to analysis while also facilitating convenient handling and storage. The segmentation and subtyping tasks are performed sequentially on the reduced-scale images using the same basic workflow: generating and sifting tiles from the image, then classifying each tile with an ensemble of appropriately trained CNNs. For segmentation, the CNN predictions are combined using a function to favor a selected similarity metric, and a mask or map for a a candidate image is produced from tiles whose combined predictions exceed a decision boundary. For subtyping, the resulting mask is applied to the candidate image, and new tiles are derived from the unoccluded regions. These are classified by the subtyping CNNs to produce an overall subtype prediction. Results and conclusion This approach was applied successfully to two very different datasets of large WSIs, one (PAIP2020) involving multiple subtypes of colorectal cancer and the other (CAMELYON16) single-type breast cancer metastases. Scored using standard similarity metrics, the segmentations outperformed more complex models typifying the state of the art.
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28
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Differentiation of Urothelial Carcinoma in Histopathology Images Using Deep Learning and Visualisation. J Pathol Inform 2022; 14:100155. [DOI: 10.1016/j.jpi.2022.100155] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Revised: 10/16/2022] [Accepted: 11/02/2022] [Indexed: 11/09/2022] Open
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29
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Browning L, White K, Siiankoski D, Colling R, Roskell D, Fryer E, Hemsworth H, Roberts-Gant S, Roelofsen R, Rittscher J, Verrill C. RFID analysis of the complexity of cellular pathology workflow—An opportunity for digital pathology. Front Med (Lausanne) 2022; 9:933933. [PMID: 35979219 PMCID: PMC9377528 DOI: 10.3389/fmed.2022.933933] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2022] [Accepted: 07/07/2022] [Indexed: 12/02/2022] Open
Abstract
Digital pathology (DP) offers potential for time efficiency gains over an analog workflow however, to date, evidence supporting this claim is relatively lacking. Studies available concentrate on specific workflow points such as diagnostic reporting time, rather than overall efficiencies in slide logistics that might be expected. This is in part a result of the complexity and variation in analog working, and the challenge therefore in capturing this. We have utilized RFID technology to conduct a novel study capturing the movement of diagnostic cases within the analog pathway in a large teaching hospital setting, thus providing benchmark data for potential efficiency gains with DP. This technology overcomes the need to manually record data items and has facilitated the capture of both the physical journey of a case and the time associated with relevant components of the analog pathway predicted to be redundant in the digital setting. RFID tracking of 1,173 surgical pathology cases and over 30 staff in an analog cellular pathology workflow illustrates the complexity of the physical movement of slides within the department, which impacts on case traceability within the system. Detailed analysis of over 400 case journeys highlights redundant periods created by batching of slides at workflow points, including potentially 2–3 h for a case to become available for reporting after release from the lab, and variable lag-times prior to collection for reporting, and provides an illustration of patterns of lab and pathologist working within the analog setting. This study supports the challenge in evidencing efficiency gains to be anticipated with DP in the context of the variation and complexity of the analog pathway, but also evidences the efficiency gains that may be expected through a greater understanding of patterns of working and movement of cases. Such data may benefit other departments building a business case for DP.
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Affiliation(s)
- Lisa Browning
- Department of Cellular Pathology, Oxford University Hospitals NHS Trust, John Radcliffe Hospital, Oxford, United Kingdom
- NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom
- *Correspondence: Lisa Browning
| | - Kieron White
- Department of Cellular Pathology, Oxford University Hospitals NHS Trust, John Radcliffe Hospital, Oxford, United Kingdom
| | - Darrin Siiankoski
- Department of Cellular Pathology, Oxford University Hospitals NHS Trust, John Radcliffe Hospital, Oxford, United Kingdom
| | - Richard Colling
- Department of Cellular Pathology, Oxford University Hospitals NHS Trust, John Radcliffe Hospital, Oxford, United Kingdom
- Nuffield Department of Surgical Sciences, John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom
| | - Derek Roskell
- Department of Cellular Pathology, Oxford University Hospitals NHS Trust, John Radcliffe Hospital, Oxford, United Kingdom
| | - Eve Fryer
- Department of Cellular Pathology, Oxford University Hospitals NHS Trust, John Radcliffe Hospital, Oxford, United Kingdom
| | - Helen Hemsworth
- Department of Cellular Pathology, Oxford University Hospitals NHS Trust, John Radcliffe Hospital, Oxford, United Kingdom
| | - Sharon Roberts-Gant
- Department of Cellular Pathology, Oxford University Hospitals NHS Trust, John Radcliffe Hospital, Oxford, United Kingdom
| | - Ruud Roelofsen
- Philips Digital and Computational Pathology, Precision Diagnosis Solutions, Best, Netherlands
| | - Jens Rittscher
- NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom
- Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford, Oxford, United Kingdom
| | - Clare Verrill
- Department of Cellular Pathology, Oxford University Hospitals NHS Trust, John Radcliffe Hospital, Oxford, United Kingdom
- NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom
- Nuffield Department of Surgical Sciences, John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom
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30
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van der Kamp A, Waterlander TJ, de Bel T, van der Laak J, van den Heuvel-Eibrink MM, Mavinkurve-Groothuis AMC, de Krijger RR. Artificial Intelligence in Pediatric Pathology: The Extinction of a Medical Profession or the Key to a Bright Future? Pediatr Dev Pathol 2022; 25:380-387. [PMID: 35238696 DOI: 10.1177/10935266211059809] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Artificial Intelligence (AI) has become of increasing interest over the past decade. While digital image analysis (DIA) is already being used in radiology, it is still in its infancy in pathology. One of the reasons is that large-scale digitization of glass slides has only recently become available. With the advent of digital slide scanners, that digitize glass slides into whole slide images, many labs are now in a transition phase towards digital pathology. However, only few departments worldwide are currently fully digital. Digital pathology provides the ability to annotate large datasets and train computers to develop and validate robust algorithms, similar to radiology. In this opinionated overview, we will give a brief introduction into AI in pathology, discuss the potential positive and negative implications and speculate about the future role of AI in the field of pediatric pathology.
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Affiliation(s)
- Ananda van der Kamp
- 541199Princess Máxima Center for Pediatric Oncology, Utrecht, the Netherlands
| | - Tomas J Waterlander
- 541199Princess Máxima Center for Pediatric Oncology, Utrecht, the Netherlands
| | - Thomas de Bel
- Department of Pathology, 234134Radboud University Medical Center, Nijmegen, the Netherlands
| | - Jeroen van der Laak
- Department of Pathology, 234134Radboud University Medical Center, Nijmegen, the Netherlands.,Center for Medical Image Science and Visualization, 4566Linköping University, Linköping, Sweden
| | | | | | - Ronald R de Krijger
- 541199Princess Máxima Center for Pediatric Oncology, Utrecht, the Netherlands.,Department of Pathology, University Medical Center Utrecht, Utrecht, the Netherlands
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31
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Kouzu K, Nearchou IP, Kajiwara Y, Tsujimoto H, Lillard K, Kishi Y, Ueno H. Deep-learning-based classification of desmoplastic reaction on H&E predicts poor prognosis in oesophageal squamous cell carcinoma. Histopathology 2022; 81:255-263. [PMID: 35758184 DOI: 10.1111/his.14708] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Revised: 05/16/2022] [Accepted: 05/31/2022] [Indexed: 12/24/2022]
Abstract
AIMS Desmoplastic reaction (DR) categorisation has been shown to be a promising prognostic factor in oesophageal squamous cell carcinoma (ESCC). The usual DR evaluation is performed using semiquantitative scores, which can be subjective. This study aimed to investigate whether a deep-learning classifier could be used for DR classification. We further assessed the prognostic significance of the deep-learning classifier and compared it to that of manual DR reporting and other pathological factors currently used in the clinic. METHODS AND RESULTS From 222 surgically resected ESCC cases, 31 randomly selected haematoxylin-eosin-digitised whole slides of patients with immature DR were used to train and develop a deep-learning classifier. The classifier was trained for 89 370 iterations. The accuracy of the deep-learning classifier was assessed to 30 unseen cases, and the results revealed a Dice coefficient score of 0.81. For survival analysis, the classifier was then applied to the entire cohort of patients, which was split into a training (n = 156) and a test (n = 66) cohort. The automated DR classification had a higher prognostic significance for disease-specific survival than the manually classified DR in both the training and test cohorts. In addition, the automated DR classification outperformed the prognostic accuracy of the gold-standard factors of tumour depth and lymph node metastasis. CONCLUSIONS This study demonstrated that DR can be objectively and quantitatively assessed in ESCC using a deep-learning classifier and that automatically classed DR has a higher prognostic significance than manual DR and other features currently used in the clinic.
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Affiliation(s)
- Keita Kouzu
- Department of Surgery, National Defense Medical College, Saitama, Japan
| | - Ines P Nearchou
- Department of Surgery, National Defense Medical College, Saitama, Japan
| | - Yoshiki Kajiwara
- Department of Surgery, National Defense Medical College, Saitama, Japan
| | | | | | - Yoji Kishi
- Department of Surgery, National Defense Medical College, Saitama, Japan
| | - Hideki Ueno
- Department of Surgery, National Defense Medical College, Saitama, Japan
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32
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Neto PC, Oliveira SP, Montezuma D, Fraga J, Monteiro A, Ribeiro L, Gonçalves S, Pinto IM, Cardoso JS. iMIL4PATH: A Semi-Supervised Interpretable Approach for Colorectal Whole-Slide Images. Cancers (Basel) 2022; 14:cancers14102489. [PMID: 35626093 PMCID: PMC9139905 DOI: 10.3390/cancers14102489] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Revised: 05/13/2022] [Accepted: 05/17/2022] [Indexed: 02/04/2023] Open
Abstract
Simple Summary Nowadays, colorectal cancer is the third most incident cancer worldwide and, although it can be detected by imaging techniques, diagnosis is always based on biopsy samples. This assessment includes neoplasia grading, a subjective yet important task for pathologists. With the growing availability of digital slides, the development of robust and high-performance computer vision algorithms can help to tackle such a task. In this work, we propose an approach to automatically detect and grade lesions in colorectal biopsies with high sensitivity. The presented model attempts to support slide decision reasoning in terms of the spatial distribution of lesions, focusing the pathologist’s attention on key areas. Thus, it can be integrated into clinical practice as a second opinion or as a flag for details that may have been missed at first glance. Abstract Colorectal cancer (CRC) diagnosis is based on samples obtained from biopsies, assessed in pathology laboratories. Due to population growth and ageing, as well as better screening programs, the CRC incidence rate has been increasing, leading to a higher workload for pathologists. In this sense, the application of AI for automatic CRC diagnosis, particularly on whole-slide images (WSI), is of utmost relevance, in order to assist professionals in case triage and case review. In this work, we propose an interpretable semi-supervised approach to detect lesions in colorectal biopsies with high sensitivity, based on multiple-instance learning and feature aggregation methods. The model was developed on an extended version of the recent, publicly available CRC dataset (the CRC+ dataset with 4433 WSI), using 3424 slides for training and 1009 slides for evaluation. The proposed method attained 90.19% classification ACC, 98.8% sensitivity, 85.7% specificity, and a quadratic weighted kappa of 0.888 at slide-based evaluation. Its generalisation capabilities are also studied on two publicly available external datasets.
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Affiliation(s)
- Pedro C. Neto
- Institute for Systems and Computer Engineering, Technology and Science (INESC TEC), 4200-465 Porto, Portugal; (S.P.O.); (J.S.C.)
- Faculty of Engineering, University of Porto (FEUP), 4200-465 Porto, Portugal
- Correspondence:
| | - Sara P. Oliveira
- Institute for Systems and Computer Engineering, Technology and Science (INESC TEC), 4200-465 Porto, Portugal; (S.P.O.); (J.S.C.)
- Faculty of Engineering, University of Porto (FEUP), 4200-465 Porto, Portugal
| | - Diana Montezuma
- IMP Diagnostics, 4150-146 Porto, Portugal; (D.M.); (A.M.); (L.R.); (S.G.); (I.M.P.)
- School of Medicine and Biomedical Sciences, University of Porto (ICBAS), 4050-313 Porto, Portugal
- Cancer Biology and Epigenetics Group, IPO-Porto, 4200-072 Porto, Portugal
| | - João Fraga
- Department of Pathology, IPO-Porto, 4200-072 Porto, Portugal;
| | - Ana Monteiro
- IMP Diagnostics, 4150-146 Porto, Portugal; (D.M.); (A.M.); (L.R.); (S.G.); (I.M.P.)
| | - Liliana Ribeiro
- IMP Diagnostics, 4150-146 Porto, Portugal; (D.M.); (A.M.); (L.R.); (S.G.); (I.M.P.)
| | - Sofia Gonçalves
- IMP Diagnostics, 4150-146 Porto, Portugal; (D.M.); (A.M.); (L.R.); (S.G.); (I.M.P.)
| | - Isabel M. Pinto
- IMP Diagnostics, 4150-146 Porto, Portugal; (D.M.); (A.M.); (L.R.); (S.G.); (I.M.P.)
| | - Jaime S. Cardoso
- Institute for Systems and Computer Engineering, Technology and Science (INESC TEC), 4200-465 Porto, Portugal; (S.P.O.); (J.S.C.)
- Faculty of Engineering, University of Porto (FEUP), 4200-465 Porto, Portugal
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Bankhead P. Developing image analysis methods for digital pathology. J Pathol 2022; 257:391-402. [PMID: 35481680 PMCID: PMC9324951 DOI: 10.1002/path.5921] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 04/22/2022] [Accepted: 04/25/2022] [Indexed: 12/04/2022]
Abstract
The potential to use quantitative image analysis and artificial intelligence is one of the driving forces behind digital pathology. However, despite novel image analysis methods for pathology being described across many publications, few become widely adopted and many are not applied in more than a single study. The explanation is often straightforward: software implementing the method is simply not available, or is too complex, incomplete, or dataset‐dependent for others to use. The result is a disconnect between what seems already possible in digital pathology based upon the literature, and what actually is possible for anyone wishing to apply it using currently available software. This review begins by introducing the main approaches and techniques involved in analysing pathology images. I then examine the practical challenges inherent in taking algorithms beyond proof‐of‐concept, from both a user and developer perspective. I describe the need for a collaborative and multidisciplinary approach to developing and validating meaningful new algorithms, and argue that openness, implementation, and usability deserve more attention among digital pathology researchers. The review ends with a discussion about how digital pathology could benefit from interacting with and learning from the wider bioimage analysis community, particularly with regard to sharing data, software, and ideas. © 2022 The Author. 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)
- Peter Bankhead
- Edinburgh Pathology, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK.,Centre for Genomic & Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK.,Cancer Research UK Edinburgh Centre, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
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34
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Flach RN, Fransen NL, Sonnen AFP, Nguyen TQ, Breimer GE, Veta M, Stathonikos N, van Dooijeweert C, van Diest PJ. Implementation of Artificial Intelligence in Diagnostic Practice as a Next Step after Going Digital: The UMC Utrecht Perspective. Diagnostics (Basel) 2022; 12:diagnostics12051042. [PMID: 35626198 PMCID: PMC9140005 DOI: 10.3390/diagnostics12051042] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Revised: 04/13/2022] [Accepted: 04/19/2022] [Indexed: 01/31/2023] Open
Abstract
Building on a growing number of pathology labs having a full digital infrastructure for pathology diagnostics, there is a growing interest in implementing artificial intelligence (AI) algorithms for diagnostic purposes. This article provides an overview of the current status of the digital pathology infrastructure at the University Medical Center Utrecht and our roadmap for implementing AI algorithms in the next few years.
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Affiliation(s)
- Rachel N. Flach
- Department of Pathology, University Medical Center Utrecht, 3508 GA Utrecht, The Netherlands; (R.N.F.); (N.L.F.); (A.F.P.S.); (T.Q.N.); (G.E.B.); (M.V.); (N.S.); (C.v.D.)
| | - Nina L. Fransen
- Department of Pathology, University Medical Center Utrecht, 3508 GA Utrecht, The Netherlands; (R.N.F.); (N.L.F.); (A.F.P.S.); (T.Q.N.); (G.E.B.); (M.V.); (N.S.); (C.v.D.)
| | - Andreas F. P. Sonnen
- Department of Pathology, University Medical Center Utrecht, 3508 GA Utrecht, The Netherlands; (R.N.F.); (N.L.F.); (A.F.P.S.); (T.Q.N.); (G.E.B.); (M.V.); (N.S.); (C.v.D.)
| | - Tri Q. Nguyen
- Department of Pathology, University Medical Center Utrecht, 3508 GA Utrecht, The Netherlands; (R.N.F.); (N.L.F.); (A.F.P.S.); (T.Q.N.); (G.E.B.); (M.V.); (N.S.); (C.v.D.)
| | - Gerben E. Breimer
- Department of Pathology, University Medical Center Utrecht, 3508 GA Utrecht, The Netherlands; (R.N.F.); (N.L.F.); (A.F.P.S.); (T.Q.N.); (G.E.B.); (M.V.); (N.S.); (C.v.D.)
| | - Mitko Veta
- Department of Pathology, University Medical Center Utrecht, 3508 GA Utrecht, The Netherlands; (R.N.F.); (N.L.F.); (A.F.P.S.); (T.Q.N.); (G.E.B.); (M.V.); (N.S.); (C.v.D.)
- Department of Biomedical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands
| | - Nikolas Stathonikos
- Department of Pathology, University Medical Center Utrecht, 3508 GA Utrecht, The Netherlands; (R.N.F.); (N.L.F.); (A.F.P.S.); (T.Q.N.); (G.E.B.); (M.V.); (N.S.); (C.v.D.)
| | - Carmen van Dooijeweert
- Department of Pathology, University Medical Center Utrecht, 3508 GA Utrecht, The Netherlands; (R.N.F.); (N.L.F.); (A.F.P.S.); (T.Q.N.); (G.E.B.); (M.V.); (N.S.); (C.v.D.)
| | - Paul J. van Diest
- Department of Pathology, University Medical Center Utrecht, 3508 GA Utrecht, The Netherlands; (R.N.F.); (N.L.F.); (A.F.P.S.); (T.Q.N.); (G.E.B.); (M.V.); (N.S.); (C.v.D.)
- Correspondence:
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35
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Battino M. Feature Paper Special Issue for Editorial Board Members (EBMs) of Diseases. Diseases 2022; 10:diseases10020018. [PMID: 35466188 PMCID: PMC9036201 DOI: 10.3390/diseases10020018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Accepted: 03/17/2022] [Indexed: 11/28/2022] Open
Affiliation(s)
- Maurizio Battino
- Department of Clinical Sciences, Polytechnic University of Marche, 60131 Ancona, Italy; ; Tel.: +39-071-220-4146
- International Joint Research Laboratory of Intelligent Agriculture and Agri-Product Processing, Jiangsu University, Zhenjiang 212013, China
- Research Group on Foods, Nutritional Biochemistry and Health, Universidad Europea del Atlántico, 39011 Santander, Spain
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El Agouri H, Azizi M, El Attar H, El Khannoussi M, Ibrahimi A, Kabbaj R, Kadiri H, BekarSabein S, EchCharif S, Mounjid C, El Khannoussi B. Assessment of deep learning algorithms to predict histopathological diagnosis of breast cancer: first Moroccan prospective study on a private dataset. BMC Res Notes 2022; 15:66. [PMID: 35183227 PMCID: PMC8857730 DOI: 10.1186/s13104-022-05936-1] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2021] [Accepted: 01/29/2022] [Indexed: 11/18/2022] Open
Abstract
Objective Breast cancer is a critical public health issue and a leading cause of cancer-related deaths among women worldwide. Its early diagnosis and detection can effectively help in increasing the chances of survival rate. For this reason, the diagnosis and classification of breast cancer using Deep learning algorithms have attracted a lot of attention. Therefore, our study aimed to design a computational approach based on deep convolutional neural networks for an efficient classification of breast cancer histopathological images by using our own created dataset. We collected overall 328 digital slides, from 116 of surgical breast specimens diagnosed with invasive breast carcinoma of non-specific type, and referred to the histopathology department of the National Institute of Oncology in Rabat, Morocco. We used two models of deep neural network architectures in order to accurately classify the images into one of three categories: normal tissue-benign lesions, in situ carcinoma or invasive carcinoma. Results Both Resnet50 and Xception models achieved comparable results, with a small advantage to Xception extracted features. We reported high degrees of overall correct classification accuracy (88%), and sensitivity (95%) for detection of carcinoma cases, which is important for diagnostic pathology workflow in order to assist pathologists for diagnosing breast cancer with precision. The results of the present study showed that the designed classification model has a good generalization performance in predicting diagnosis of breast cancer, in spite of the limited size of the data. To our knowledge, this approach can be highly compared with other common methods in the automated analysis of breast cancer images reported in literature.
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Affiliation(s)
- H El Agouri
- Pathology Department, Oncology National Institute, Faculty of Medicine and Pharmacy, Mohammed V University, 10100, Rabat, Morocco.
| | - M Azizi
- Datapathology, 20000, Casablanca, Morocco
| | - H El Attar
- Anatomic Pathology Laboratory Ennassr, 24000, El Jadida, Morocco
| | | | - A Ibrahimi
- Medical Biotechnology Laboratory (MedBiotech), Bioinova Research Center, Rabat Medical & Pharmacy School, Mohammed Vth University in Rabat, 10100, Rabat, Morocco
| | - R Kabbaj
- Pathology Department, Oncology National Institute, Faculty of Medicine and Pharmacy, Mohammed V University, 10100, Rabat, Morocco
| | - H Kadiri
- Pathology Department, Oncology National Institute, Faculty of Medicine and Pharmacy, Mohammed V University, 10100, Rabat, Morocco
| | - S BekarSabein
- Pathology Department, Oncology National Institute, Faculty of Medicine and Pharmacy, Mohammed V University, 10100, Rabat, Morocco
| | - S EchCharif
- Pathology Department, Oncology National Institute, Faculty of Medicine and Pharmacy, Mohammed V University, 10100, Rabat, Morocco
| | - C Mounjid
- Pathology Department, Oncology National Institute, Faculty of Sciences, Mohammed V University, 10100, Rabat, Morocco
| | - B El Khannoussi
- Pathology Department, Oncology National Institute, Faculty of Medicine and Pharmacy, Mohammed V University, 10100, Rabat, Morocco
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37
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A promising deep learning-assistive algorithm for histopathological screening of colorectal cancer. Sci Rep 2022; 12:2222. [PMID: 35140318 PMCID: PMC8828883 DOI: 10.1038/s41598-022-06264-x] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Accepted: 01/24/2022] [Indexed: 02/06/2023] Open
Abstract
Colorectal cancer is one of the most common cancers worldwide, accounting for an annual estimated 1.8 million incident cases. With the increasing number of colonoscopies being performed, colorectal biopsies make up a large proportion of any histopathology laboratory workload. We trained and validated a unique artificial intelligence (AI) deep learning model as an assistive tool to screen for colonic malignancies in colorectal specimens, in order to improve cancer detection and classification; enabling busy pathologists to focus on higher order decision-making tasks. The study cohort consists of Whole Slide Images (WSI) obtained from 294 colorectal specimens. Qritive’s unique composite algorithm comprises both a deep learning model based on a Faster Region Based Convolutional Neural Network (Faster-RCNN) architecture for instance segmentation with a ResNet-101 feature extraction backbone that provides glandular segmentation, and a classical machine learning classifier. The initial training used pathologists’ annotations on a cohort of 66,191 image tiles extracted from 39 WSIs. A subsequent application of a classical machine learning-based slide classifier sorted the WSIs into ‘low risk’ (benign, inflammation) and ‘high risk’ (dysplasia, malignancy) categories. We further trained the composite AI-model’s performance on a larger cohort of 105 resections WSIs and then validated our findings on a cohort of 150 biopsies WSIs against the classifications of two independently blinded pathologists. We evaluated the area under the receiver-operator characteristic curve (AUC) and other performance metrics. The AI model achieved an AUC of 0.917 in the validation cohort, with excellent sensitivity (97.4%) in detection of high risk features of dysplasia and malignancy. We demonstrate an unique composite AI-model incorporating both a glandular segmentation deep learning model and a classical machine learning classifier, with excellent sensitivity in picking up high risk colorectal features. As such, AI plays a role as a potential screening tool in assisting busy pathologists by outlining the dysplastic and malignant glands.
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38
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Quintana DMVO, Dedivitis RA, Kowalski LP. Prognostic impact of perineural invasion in oral cancer: a systematic review. ACTA OTORHINOLARYNGOLOGICA ITALICA : ORGANO UFFICIALE DELLA SOCIETA ITALIANA DI OTORINOLARINGOLOGIA E CHIRURGIA CERVICO-FACCIALE 2022; 42:17-25. [PMID: 35292785 PMCID: PMC9058930 DOI: 10.14639/0392-100x-n1653] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Accepted: 11/21/2021] [Indexed: 11/24/2022]
Abstract
Introduction Numerous studies have evaluated the prognostic significance of perineural invasion (PNI) in oral cancer; however, the results are inconclusive. Purpose To identify the prognostic value of PNI in oral cancer through a metanalysis. Methods A literature review was carried out, searching the MedLine databases via Pubmed, Scielo, Lilacs, Cochrane and Websco. Results A total of 56 studies were included. The results indicate that PNI in oral cancer has an incidence of 28% (95% confidence interval (CI) 24-31%); 5-year survival with relative risk (RR) 0.67 (0.59-0.75); 5-year disease-free survival RR 0.71 (0.68-0.75); locoregional recurrence with RR 2.09 (1.86-2.35). Conclusions PNI is a negative prognostic factor in oral cancer.
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Affiliation(s)
| | - Rogerio Aparecido Dedivitis
- Department of Head and Neck Surgery, Hospital das Clinicas, University of São Paulo School of Medicine, São Paulo, Brazil
| | - Luiz Paulo Kowalski
- Head of the Department of Head and Neck Surgery, Hospital das Clinicas, University of São Paulo School of Medicine, São Paulo, Brazil
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39
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Kazdal D, Hofman V, Christopoulos P, Ilié M, Stenzinger A, Hofman P. Fusion-positive non-small cell lung carcinoma: Biological principles, clinical practice, and diagnostic implications. Genes Chromosomes Cancer 2022; 61:244-260. [PMID: 34997651 DOI: 10.1002/gcc.23022] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Revised: 12/26/2021] [Accepted: 12/27/2021] [Indexed: 12/11/2022] Open
Abstract
Based on superior efficacy and tolerability, targeted therapy is currently preferred over chemotherapy and/or immunotherapy for actionable gene fusions that occur in late-stage non-small cell lung carcinoma (NSCLC). Consequently, current clinical practice guidelines mandate testing for ALK, ROS1, NTRK, and RET gene fusions in all patients with newly diagnosed advanced non-squamous NSCLC (NS-NSCLC). Gene fusions can be detected using different approaches, but today RNA next-generation sequencing (NGS) or combined DNA/RNA NGS is the method of choice. The discovery of other gene fusions (involving, eg, NRG1, NUT, FGFR1, FGFR2, MET, BRAF, EGFR, SMARC fusions) and their partners has increased progressively in recent years, leading to the development of new and promising therapies and mandating the development and implementation of comprehensive detection methods. The purpose of this review is to focus on recent data concerning the main gene fusions identified in NSCLC, followed by the discussion of major challenges in this domain.
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Affiliation(s)
- Daniel Kazdal
- Institute of Pathology, University Hospital Heidelberg, Heidelberg, Germany.,Translational Lung Research Center (TLRC) Heidelberg, Heidelberg, Germany.,German Center for Lung Research (DZL), Heidelberg, Germany
| | - Véronique Hofman
- Laboratory of Clinical and Experimental Pathology, Pasteur Hospital, Université Côte d'Azur, FHU OncoAge, Nice, France.,Centre Antoine Lacassagne Cancer Center, Université Côte d'Azur, CNRS, INSERM, IRCAN, FHU OncoAge, Nice, France.,Hospital-Integrated Biobank BB-0033-00025, Université Côte d'Azur, CHU Nice, FHU OncoAge, Nice, France
| | - Petros Christopoulos
- Translational Lung Research Center (TLRC) Heidelberg, Heidelberg, Germany.,German Center for Lung Research (DZL), Heidelberg, Germany.,Thoraxklinik and National Center for Tumor Diseases, Heidelberg University Hospital, Heidelberg, Germany
| | - Marius Ilié
- Laboratory of Clinical and Experimental Pathology, Pasteur Hospital, Université Côte d'Azur, FHU OncoAge, Nice, France.,Centre Antoine Lacassagne Cancer Center, Université Côte d'Azur, CNRS, INSERM, IRCAN, FHU OncoAge, Nice, France.,Hospital-Integrated Biobank BB-0033-00025, Université Côte d'Azur, CHU Nice, FHU OncoAge, Nice, France
| | - Albrecht Stenzinger
- Institute of Pathology, University Hospital Heidelberg, Heidelberg, Germany.,German Center for Lung Research (DZL), Heidelberg, Germany.,German Cancer Consortium (DKTK), Heidelberg, Germany
| | - Paul Hofman
- Laboratory of Clinical and Experimental Pathology, Pasteur Hospital, Université Côte d'Azur, FHU OncoAge, Nice, France.,Centre Antoine Lacassagne Cancer Center, Université Côte d'Azur, CNRS, INSERM, IRCAN, FHU OncoAge, Nice, France.,Hospital-Integrated Biobank BB-0033-00025, Université Côte d'Azur, CHU Nice, FHU OncoAge, Nice, France
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40
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OUP accepted manuscript. Bioinformatics 2022; 38:3312-3314. [DOI: 10.1093/bioinformatics/btac315] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Revised: 04/21/2022] [Accepted: 05/03/2022] [Indexed: 11/12/2022] Open
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41
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McGenity C, Wright A, Treanor D. AIM in Surgical Pathology. Artif Intell Med 2022. [DOI: 10.1007/978-3-030-64573-1_278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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42
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Atallah NM, Toss MS, Verrill C, Salto-Tellez M, Snead D, Rakha EA. Potential quality pitfalls of digitalized whole slide image of breast pathology in routine practice. Mod Pathol 2022; 35:903-910. [PMID: 34961765 PMCID: PMC8711290 DOI: 10.1038/s41379-021-01000-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Revised: 12/11/2021] [Accepted: 12/12/2021] [Indexed: 12/26/2022]
Abstract
Using digitalized whole slide images (WSI) in routine histopathology practice is a revolutionary technology. This study aims to assess the clinical impacts of WSI quality and representation of the corresponding glass slides. 40,160 breast WSIs were examined and compared with their corresponding glass slides. The presence, frequency, location, tissue type, and the clinical impacts of missing tissue were assessed. Scanning time, type of the specimens, time to WSIs implementation, and quality control (QC) measures were also considered. The frequency of missing tissue ranged from 2% to 19%. The area size of the missed tissue ranged from 1-70%. In most cases (>75%), the missing tissue area size was <10% and peripherally located. In all cases the missed tissue was fat with or without small entrapped normal breast parenchyma. No missing tissue was identified in WSIs of the core biopsy specimens. QC measures improved images quality and reduced WSI failure rates by seven-fold. A negative linear correlation between the frequency of missing tissue and both the scanning time and the image file size was observed (p < 0.05). None of the WSI with missing tissues resulted in a change in the final diagnosis. Missing tissue on breast WSI is observed but with variable frequency and little diagnostic consequence. Balancing between WSI quality and scanning time/image file size should be considered and pathology laboratories should undertake their own assessments of risk and provide the relevant mitigations with the appropriate level of caution.
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Affiliation(s)
- Nehal M. Atallah
- grid.4563.40000 0004 1936 8868Department of Histopathology, School of Medicine, the University of Nottingham and Nottingham University Hospitals NHS Trust, Nottingham, UK ,grid.411775.10000 0004 0621 4712Department of Pathology, Faculty of Medicine, Menoufia University, Shebin Elkom, Al-Menoufia, Egypt
| | - Michael S. Toss
- grid.4563.40000 0004 1936 8868Department of Histopathology, School of Medicine, the University of Nottingham and Nottingham University Hospitals NHS Trust, Nottingham, UK
| | - Clare Verrill
- grid.4991.50000 0004 1936 8948Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK ,grid.4991.50000 0004 1936 8948NIHR Oxford Biomedical Research Centre, University of Oxford, John Radcliffe Hospital, Oxford, UK
| | - Manuel Salto-Tellez
- grid.4777.30000 0004 0374 7521Precision Medicine Centre of Excellence, The Patrick G Johnston Centre for Cancer Research, Queen’s University, Belfast, UK
| | - David Snead
- grid.15628.380000 0004 0393 1193Cellular Pathology, University Hospitals Coventry and Warwickshire NHS Trust, Coventry, Coventry, UK
| | - Emad A. Rakha
- grid.4563.40000 0004 1936 8868Department of Histopathology, School of Medicine, the University of Nottingham and Nottingham University Hospitals NHS Trust, Nottingham, UK ,grid.411775.10000 0004 0621 4712Department of Pathology, Faculty of Medicine, Menoufia University, Shebin Elkom, Al-Menoufia, Egypt
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Intelligent Computing with Levenberg-Marquardt Backpropagation Neural Networks for Third-Grade Nanofluid Over a Stretched Sheet with Convective Conditions. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2021; 47:8211-8229. [PMID: 34603929 PMCID: PMC8479501 DOI: 10.1007/s13369-021-06202-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Accepted: 09/09/2021] [Indexed: 01/04/2023]
Abstract
This article discussed the influence of activation energy on MHD flow of third-grade nanofluid model (MHD-TGNFM) along with the convective conditions and used the technique of backpropagation in artificial neural network using Levenberg–Marquardt technique (BANN-LMT). The PDEs representing (MHD-TGNFM) transformed into the system of ODEs. The dataset for BANN-LMT is computed for the six scenarios by using the Adam numerical method by varying the local Hartman number (Ha), Prandtl number (Pr), local chemical reaction parameter (σ), Schmidt number (Sc), concentration Biot number (γ2) and thermal Biot number (γ1). By testing, validation and training process of (BANN-LMT), the estimated solutions are interpreted for (MHD-TGNFM). The validation of the performance of (BANN-LMT) is done through the MSE, error histogram and regression analysis. The concentration profile increases when there is an increase in Biot number and the local Hartmann number; meanwhile, it decreases for the higher values of Schmidt number and the local chemical reaction parameter.
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Oliveira SP, Neto PC, Fraga J, Montezuma D, Monteiro A, Monteiro J, Ribeiro L, Gonçalves S, Pinto IM, Cardoso JS. CAD systems for colorectal cancer from WSI are still not ready for clinical acceptance. Sci Rep 2021; 11:14358. [PMID: 34257363 PMCID: PMC8277780 DOI: 10.1038/s41598-021-93746-z] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Accepted: 06/28/2021] [Indexed: 02/07/2023] Open
Abstract
Most oncological cases can be detected by imaging techniques, but diagnosis is based on pathological assessment of tissue samples. In recent years, the pathology field has evolved to a digital era where tissue samples are digitised and evaluated on screen. As a result, digital pathology opened up many research opportunities, allowing the development of more advanced image processing techniques, as well as artificial intelligence (AI) methodologies. Nevertheless, despite colorectal cancer (CRC) being the second deadliest cancer type worldwide, with increasing incidence rates, the application of AI for CRC diagnosis, particularly on whole-slide images (WSI), is still a young field. In this review, we analyse some relevant works published on this particular task and highlight the limitations that hinder the application of these works in clinical practice. We also empirically investigate the feasibility of using weakly annotated datasets to support the development of computer-aided diagnosis systems for CRC from WSI. Our study underscores the need for large datasets in this field and the use of an appropriate learning methodology to gain the most benefit from partially annotated datasets. The CRC WSI dataset used in this study, containing 1,133 colorectal biopsy and polypectomy samples, is available upon reasonable request.
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Affiliation(s)
- Sara P Oliveira
- INESCTEC, 4200-465, Porto, Portugal.
- Faculty of Engineering (FEUP), University of Porto, 4200-465, Porto, Portugal.
| | - Pedro C Neto
- INESCTEC, 4200-465, Porto, Portugal
- Faculty of Engineering (FEUP), University of Porto, 4200-465, Porto, Portugal
| | - João Fraga
- IMP Diagnostics, 4150-146, Porto, Portugal
| | - Diana Montezuma
- IMP Diagnostics, 4150-146, Porto, Portugal
- ICBAS, University of Porto, 4050-313, Porto , Portugal
- Cancer Biology and Epigenetics Group, IPO-Porto, 4200-072, Porto, Portugal
| | | | | | | | | | | | - Jaime S Cardoso
- INESCTEC, 4200-465, Porto, Portugal
- Faculty of Engineering (FEUP), University of Porto, 4200-465, Porto, Portugal
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Montironi R, Cimadamore A, Cheng L, Lopez-Beltran A, Scarpelli M. Lesson from the COVID-19 pandemic: pathologists need to build their confidence on working in a digital microscopy environment. Virchows Arch 2021; 479:227-229. [PMID: 34032916 PMCID: PMC8144693 DOI: 10.1007/s00428-021-03123-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Revised: 04/06/2021] [Accepted: 05/14/2021] [Indexed: 11/30/2022]
Affiliation(s)
- Rodolfo Montironi
- Section of Pathological Anatomy, Polytechnic University of the Marche Region, School of Medicine, United Hospitals, Via Conca 71, 60126, Ancona, Italy.
| | - Alessia Cimadamore
- Section of Pathological Anatomy, Polytechnic University of the Marche Region, School of Medicine, United Hospitals, Via Conca 71, 60126, Ancona, Italy
| | - Liang Cheng
- Department of Pathology and Laboratory Medicine, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Antonio Lopez-Beltran
- Department of Morphological Sciences, Cordoba University Medical School, Cordoba, Spain
| | - Marina Scarpelli
- Section of Pathological Anatomy, Polytechnic University of the Marche Region, School of Medicine, United Hospitals, Via Conca 71, 60126, Ancona, Italy
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46
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Pillay TS. Artificial intelligence in pathology and laboratory medicine. J Clin Pathol 2021; 74:407-408. [PMID: 34031137 DOI: 10.1136/jclinpath-2021-207682] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Accepted: 05/10/2021] [Indexed: 11/04/2022]
Affiliation(s)
- Tahir S Pillay
- Department of Chemical Pathology, University of Pretoria & National Health Laboratory Service, Pretoria, South Africa
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47
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Cimadamore A, Cheng L, Scarpelli M, Lopez-Beltran A, Montironi R. Digital diagnostics and artificial intelligence in prostate cancer treatment in 5 years from now. Transl Androl Urol 2021; 10:1499-1505. [PMID: 33850784 PMCID: PMC8039614 DOI: 10.21037/tau-2021-01] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Affiliation(s)
- Alessia Cimadamore
- Section of Pathological Anatomy, Polytechnic University of the Marche Region, School of Medicine, United Hospitals, Ancona, Italy
| | - Liang Cheng
- Department of Pathology and Laboratory Medicine, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Marina Scarpelli
- Section of Pathological Anatomy, Polytechnic University of the Marche Region, School of Medicine, United Hospitals, Ancona, Italy
| | | | - Rodolfo Montironi
- Section of Pathological Anatomy, Polytechnic University of the Marche Region, School of Medicine, United Hospitals, Ancona, Italy
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48
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AIM in Surgical Pathology. Artif Intell Med 2021. [DOI: 10.1007/978-3-030-58080-3_278-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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49
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Montironi R, Cimadamore A, Lopez-Beltran A, Cheng L, Scarpelli M. Exciting experiences in the ' Rocky road to digital diagnostics'. J Clin Pathol 2020; 74:5-6. [PMID: 33132214 DOI: 10.1136/jclinpath-2020-207161] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Accepted: 10/07/2020] [Indexed: 11/04/2022]
Affiliation(s)
- Rodolfo Montironi
- Section of Pathological Anatomy, Polytechnic University of Marche, Ancona, Italy
| | - Alessia Cimadamore
- Section of Pathological Anatomy, Polytechnic University of Marche, Ancona, Italy
| | - Antonio Lopez-Beltran
- Pathology and Surgery, Universidad de Cordoba Facultad de Medicina y Enfermeria, Cordoba, Spain
| | - Liang Cheng
- Pathology & Laboratory Medicine, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Marina Scarpelli
- Section of Pathological Anatomy, Polytechnic University of Marche, Ancona, Italy
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