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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] [What about the content of this article? (0)] [Affiliation(s)] [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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Hilgers L, Ghaffari Laleh N, West NP, Westwood A, Hewitt KJ, Quirke P, Grabsch HI, Carrero ZI, Matthaei E, Loeffler CML, Brinker TJ, Yuan T, Brenner H, Brobeil A, Hoffmeister M, Kather JN. Automated curation of large-scale cancer histopathology image datasets using deep learning. Histopathology 2024; 84:1139-1153. [PMID: 38409878 DOI: 10.1111/his.15159] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 12/29/2023] [Accepted: 02/09/2024] [Indexed: 02/28/2024]
Abstract
BACKGROUND Artificial intelligence (AI) has numerous applications in pathology, supporting diagnosis and prognostication in cancer. However, most AI models are trained on highly selected data, typically one tissue slide per patient. In reality, especially for large surgical resection specimens, dozens of slides can be available for each patient. Manually sorting and labelling whole-slide images (WSIs) is a very time-consuming process, hindering the direct application of AI on the collected tissue samples from large cohorts. In this study we addressed this issue by developing a deep-learning (DL)-based method for automatic curation of large pathology datasets with several slides per patient. METHODS We collected multiple large multicentric datasets of colorectal cancer histopathological slides from the United Kingdom (FOXTROT, N = 21,384 slides; CR07, N = 7985 slides) and Germany (DACHS, N = 3606 slides). These datasets contained multiple types of tissue slides, including bowel resection specimens, endoscopic biopsies, lymph node resections, immunohistochemistry-stained slides, and tissue microarrays. We developed, trained, and tested a deep convolutional neural network model to predict the type of slide from the slide overview (thumbnail) image. The primary statistical endpoint was the macro-averaged area under the receiver operating curve (AUROCs) for detection of the type of slide. RESULTS In the primary dataset (FOXTROT), with an AUROC of 0.995 [95% confidence interval [CI]: 0.994-0.996] the algorithm achieved a high classification performance and was able to accurately predict the type of slide from the thumbnail image alone. In the two external test cohorts (CR07, DACHS) AUROCs of 0.982 [95% CI: 0.979-0.985] and 0.875 [95% CI: 0.864-0.887] were observed, which indicates the generalizability of the trained model on unseen datasets. With a confidence threshold of 0.95, the model reached an accuracy of 94.6% (7331 classified cases) in CR07 and 85.1% (2752 classified cases) for the DACHS cohort. CONCLUSION Our findings show that using the low-resolution thumbnail image is sufficient to accurately classify the type of slide in digital pathology. This can support researchers to make the vast resource of existing pathology archives accessible to modern AI models with only minimal manual annotations.
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Affiliation(s)
- Lars Hilgers
- Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany
- Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, Technical University Dresden, Dresden, Germany
| | - Narmin Ghaffari Laleh
- Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany
- Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, Technical University Dresden, Dresden, Germany
| | - Nicholas P West
- Pathology & Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK
| | - Alice Westwood
- Pathology & Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK
| | - Katherine J Hewitt
- Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany
- Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, Technical University Dresden, Dresden, Germany
| | - Philip Quirke
- Pathology & Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK
| | - Heike I Grabsch
- Pathology & Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK
- Department of Pathology, GROW - Research Institute for Oncology and Reproduction, Maastricht University Medical Center+, Maastricht, The Netherlands
| | - Zunamys I Carrero
- Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, Technical University Dresden, Dresden, Germany
| | - Emylou Matthaei
- Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, Technical University Dresden, Dresden, Germany
| | - Chiara M L Loeffler
- Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, Technical University Dresden, Dresden, Germany
| | - Titus J Brinker
- Digital Biomarkers for Oncology Group, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Tanwei Yuan
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg, Germany
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Alexander Brobeil
- Institute of Pathology, University Hospital Heidelberg, Heidelberg, Germany
- Tissue Bank, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany
| | - Michael Hoffmeister
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Jakob Nikolas Kather
- Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, Technical University Dresden, Dresden, Germany
- Pathology & Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK
- Medical Oncology, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany
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3
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Jain E, Patel A, Parwani AV, Shafi S, Brar Z, Sharma S, Mohanty SK. Whole Slide Imaging Technology and Its Applications: Current and Emerging Perspectives. Int J Surg Pathol 2024; 32:433-448. [PMID: 37437093 DOI: 10.1177/10668969231185089] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/14/2023]
Abstract
Background. Whole slide imaging (WSI) represents a paradigm shift in pathology, serving as a necessary first step for a wide array of digital tools to enter the field. It utilizes virtual microscopy wherein glass slides are converted into digital slides and are viewed by pathologists by automated image analysis. Its impact on pathology workflow, reproducibility, dissemination of educational material, expansion of service to underprivileged areas, and institutional collaboration exemplifies a significant innovative movement. The recent US Food and Drug Administration approval to WSI for its use in primary surgical pathology diagnosis has opened opportunities for wider application of this technology in routine practice. Main Text. The ongoing technological advances in digital scanners, image visualization methods, and the integration of artificial intelligence-derived algorithms with these systems provide avenues to exploit its applications. Its benefits are innumerable such as ease of access through the internet, avoidance of physical storage space, and no risk of deterioration of staining quality or breakage of slides to name a few. Although the benefits of WSI to pathology practices are many, the complexities of implementation remain an obstacle to widespread adoption. Some barriers including the high cost, technical glitches, and most importantly professional hesitation to adopt a new technology have hindered its use in routine pathology. Conclusions. In this review, we summarize the technical aspects of WSI, its applications in diagnostic pathology, training, and research along with future perspectives. It also highlights improved understanding of the current challenges to implementation, as well as the benefits and successes of the technology. WSI provides a golden opportunity for pathologists to guide its evolution, standardization, and implementation to better acquaint them with the key aspects of this technology and its judicial use. Also, implementation of routine digital pathology is an extra step requiring resources which (currently) does not usually result increased efficiency or payment.
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Affiliation(s)
- Ekta Jain
- Department of Pathology and Laboratory Medicine, CORE Diagnostics, Gurgaon, India
| | - Ankush Patel
- Department of Pathology, Wexner Medical Center, Columbus, OH, USA
| | - Anil V Parwani
- Department of Pathology, Wexner Medical Center, Columbus, OH, USA
| | - Saba Shafi
- Department of Pathology, Wexner Medical Center, Columbus, OH, USA
| | - Zoya Brar
- Department of Pathology and Laboratory Medicine, CORE Diagnostics, Gurgaon, India
| | - Shivani Sharma
- Department of Pathology and Laboratory Medicine, CORE Diagnostics, Gurgaon, India
| | - Sambit K Mohanty
- Department of Pathology and Laboratory Medicine, CORE Diagnostics, Gurgaon, India
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4
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Bakırdöğen D, Görgülü K, Algül H. Hourglass, a compass navigating global and regional heterogeneity of pancreatic cancer †. J Pathol 2024; 263:5-7. [PMID: 38404051 DOI: 10.1002/path.6268] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2023] [Accepted: 01/25/2024] [Indexed: 02/27/2024]
Abstract
Advances in the digital pathology field have facilitated the characterization of histology samples for both clinical and preclinical research. However, uncovering subtle correlations between bioimaging, clinical and molecular parameters requires extensive statistical analysis. As a user-friendly software, Hourglass, simplifies multiparametric dataset analysis through intuitive data visualization and statistical tools. Systemic analysis of interleukin-6 (IL-6)/pStat3 signaling pathway through Hourglass revealed differences in regional immune cell composition within tumors. Moreover, these regional disparities were partially mediated by sex. Overall, Hourglass simplifies information extraction from complex datasets, resolving overlooked regional and global spatial tumor differences. © 2024 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)
- Derya Bakırdöğen
- Comprehensive Cancer Center Munich at Klinikum rechts der Isar, Technische Universität München, Munich, Germany
| | - Kıvanç Görgülü
- Comprehensive Cancer Center Munich at Klinikum rechts der Isar, Technische Universität München, Munich, Germany
| | - Hana Algül
- Comprehensive Cancer Center Munich at Klinikum rechts der Isar, Technische Universität München, Munich, Germany
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5
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Lee JH, Song G, Lee J, Kang S, Moon KM, Choi Y, Shen J, Noh M, Yang D. Prediction of immunochemotherapy response for diffuse large B-cell lymphoma using artificial intelligence digital pathology. J Pathol Clin Res 2024; 10:e12370. [PMID: 38584594 PMCID: PMC10999948 DOI: 10.1002/2056-4538.12370] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Revised: 02/13/2024] [Accepted: 03/04/2024] [Indexed: 04/09/2024]
Abstract
Diffuse large B-cell lymphoma (DLBCL) is a heterogeneous and prevalent subtype of aggressive non-Hodgkin lymphoma that poses diagnostic and prognostic challenges, particularly in predicting drug responsiveness. In this study, we used digital pathology and deep learning to predict responses to immunochemotherapy in patients with DLBCL. We retrospectively collected 251 slide images from 216 DLBCL patients treated with rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP), with their immunochemotherapy response labels. The digital pathology images were processed using contrastive learning for feature extraction. A multi-modal prediction model was developed by integrating clinical data and pathology image features. Knowledge distillation was employed to mitigate overfitting on gigapixel histopathology images to create a model that predicts responses based solely on pathology images. Based on the importance derived from the attention mechanism of the model, we extracted histological features that were considered key textures associated with drug responsiveness. The multi-modal prediction model achieved an impressive area under the ROC curve of 0.856, demonstrating significant associations with clinical variables such as Ann Arbor stage, International Prognostic Index, and bulky disease. Survival analyses indicated their effectiveness in predicting relapse-free survival. External validation using TCGA datasets supported the model's ability to predict survival differences. Additionally, pathology-based predictions show promise as independent prognostic indicators. Histopathological analysis identified centroblastic and immunoblastic features to be associated with treatment response, aligning with previous morphological classifications and highlighting the objectivity and reproducibility of artificial intelligence-based diagnosis. This study introduces a novel approach that combines digital pathology and clinical data to predict the response to immunochemotherapy in patients with DLBCL. This model shows great promise as a diagnostic and prognostic tool for clinical management of DLBCL. Further research and genomic data integration hold the potential to enhance its impact on clinical practice, ultimately improving patient outcomes.
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Affiliation(s)
- Jeong Hoon Lee
- Department of RadiologyStanford University School of MedicineStanfordCAUSA
| | - Ga‐Young Song
- Department of Hematology‐OncologyChonnam National University Hwasun HospitalHwasunRepublic of Korea
| | - Jonghyun Lee
- Department of Medical and Digital EngineeringHanyang University College of EngineeringSeoulRepublic of Korea
| | - Sae‐Ryung Kang
- Department of Nuclear MedicineChonnam National University Hwasun Hospital and Medical SchoolHwasun‐gunRepublic of Korea
| | - Kyoung Min Moon
- Division of Pulmonary and Allergy Medicine, Department of Internal MedicineChung‐Ang University Hospital, Chung‐Ang University College of MedicineSeoulRepublic of Korea
- Artificial Intelligence, Ziovision Co., Ltd.ChuncheonRepublic of Korea
| | - Yoo‐Duk Choi
- Department of PathologyChonnam National University Medical SchoolGwangjuRepublic of Korea
| | - Jeanne Shen
- Department of Pathology and Center for Artificial Intelligence in Medicine & ImagingStanford University School of MedicineStanfordCAUSA
| | - Myung‐Giun Noh
- Department of PathologyChonnam National University Medical SchoolGwangjuRepublic of Korea
- Department of PathologySchool of Medicine, Ajou UniversitySuwonRepublic of Korea
| | - Deok‐Hwan Yang
- Department of Hematology‐OncologyChonnam National University Hwasun HospitalHwasunRepublic of Korea
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6
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Zhou H, Watson M, Bernadt CT, Lin SS, Lin CY, Ritter JH, Wein A, Mahler S, Rawal S, Govindan R, Yang C, Cote RJ. AI-guided histopathology predicts brain metastasis in lung cancer patients. J Pathol 2024; 263:89-98. [PMID: 38433721 DOI: 10.1002/path.6263] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 11/30/2023] [Accepted: 01/16/2024] [Indexed: 03/05/2024]
Abstract
Brain metastases can occur in nearly half of patients with early and locally advanced (stage I-III) non-small cell lung cancer (NSCLC). There are no reliable histopathologic or molecular means to identify those who are likely to develop brain metastases. We sought to determine if deep learning (DL) could be applied to routine H&E-stained primary tumor tissue sections from stage I-III NSCLC patients to predict the development of brain metastasis. Diagnostic slides from 158 patients with stage I-III NSCLC followed for at least 5 years for the development of brain metastases (Met+, 65 patients) versus no progression (Met-, 93 patients) were subjected to whole-slide imaging. Three separate iterations were performed by first selecting 118 cases (45 Met+, 73 Met-) to train and validate the DL algorithm, while 40 separate cases (20 Met+, 20 Met-) were used as the test set. The DL algorithm results were compared to a blinded review by four expert pathologists. The DL-based algorithm was able to distinguish the eventual development of brain metastases with an accuracy of 87% (p < 0.0001) compared with an average of 57.3% by the four pathologists and appears to be particularly useful in predicting brain metastases in stage I patients. The DL algorithm appears to focus on a complex set of histologic features. DL-based algorithms using routine H&E-stained slides may identify patients who are likely to develop brain metastases from those who will remain disease free over extended (>5 year) follow-up and may thus be spared systemic therapy. © 2024 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)
- Haowen Zhou
- Department of Electrical Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Mark Watson
- Department of Pathology and Immunology, Washington University School of Medicine, Saint Louis, MO, USA
| | - Cory T Bernadt
- Department of Pathology and Immunology, Washington University School of Medicine, Saint Louis, MO, USA
| | - Steven Siyu Lin
- Department of Electrical Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Chieh-Yu Lin
- Department of Pathology and Immunology, Washington University School of Medicine, Saint Louis, MO, USA
| | - Jon H Ritter
- Department of Pathology and Immunology, Washington University School of Medicine, Saint Louis, MO, USA
| | - Alexander Wein
- Department of Pathology and Immunology, Washington University School of Medicine, Saint Louis, MO, USA
| | - Simon Mahler
- Department of Electrical Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Sid Rawal
- Department of Pathology and Immunology, Washington University School of Medicine, Saint Louis, MO, USA
| | - Ramaswamy Govindan
- Department of Medicine, Washington University School of Medicine, Saint Louis, MO, USA
| | - Changhuei Yang
- Department of Electrical Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Richard J Cote
- Department of Pathology and Immunology, Washington University School of Medicine, Saint Louis, MO, USA
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7
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van Diest PJ, Flach RN, van Dooijeweert C, Makineli S, Breimer GE, Stathonikos N, Pham P, Nguyen TQ, Veta M. Pros and cons of artificial intelligence implementation in diagnostic pathology. Histopathology 2024; 84:924-934. [PMID: 38433288 DOI: 10.1111/his.15153] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Revised: 12/29/2023] [Accepted: 01/19/2024] [Indexed: 03/05/2024]
Abstract
The rapid introduction of digital pathology has greatly facilitated development of artificial intelligence (AI) models in pathology that have shown great promise in assisting morphological diagnostics and quantitation of therapeutic targets. We are now at a tipping point where companies have started to bring algorithms to the market, and questions arise whether the pathology community is ready to implement AI in routine workflow. However, concerns also arise about the use of AI in pathology. This article reviews the pros and cons of introducing AI in diagnostic pathology.
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Affiliation(s)
- Paul J van Diest
- Department of Pathology, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Rachel N Flach
- Department of Pathology, University Medical Center Utrecht, Utrecht, the Netherlands
- Department of Oncological Urology, University Medical Center Utrecht, Utrecht, the Netherlands
| | | | - Seher Makineli
- Department of Pathology, University Medical Center Utrecht, Utrecht, the Netherlands
- Department of Surgical Oncology, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Gerben E Breimer
- Department of Pathology, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Nikolas Stathonikos
- Department of Pathology, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Paul Pham
- Department of Pathology, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Tri Q Nguyen
- Department of Pathology, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Mitko Veta
- Department of Oncological Urology, University Medical Center Utrecht, Utrecht, the Netherlands
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands
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8
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Prassas I, Clarke B, Youssef T, Phlamon J, Dimitrakopoulos L, Rofaeil A, Yousef GM. Computational pathology: an evolving concept. Clin Chem Lab Med 2024; 0:cclm-2023-1124. [PMID: 38646706 DOI: 10.1515/cclm-2023-1124] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Accepted: 04/10/2024] [Indexed: 04/23/2024]
Abstract
The initial enthusiasm about computational pathology (CP) and artificial intelligence (AI) was that they will replace pathologists entirely on the way to fully automated diagnostics. It is becoming clear that currently this is not the immediate model to pursue. On top of the legal and regulatory complexities surrounding its implementation, the majority of tested machine learning (ML)-based predictive algorithms do not display the exquisite performance needed to render them unequivocal, standalone decision makers for matters with direct implications to human health. We are thus moving into a different model of "computer-assisted diagnostics", where AI is there to provide support, rather than replacing, the pathologist. Herein we focus on the practical aspects of CP, from a pathologist perspective. There is a wide range of potential applications where CP can enhance precision of pathology diagnosis, tailor prognostic and predictive information, as well as save time. There are, however, a number of potential limitations for CP that currently hinder their wider adoption in the clinical setting. We address the key necessary steps towards clinical implementation of computational pathology, discuss the significant obstacles that hinders its adoption in the clinical context and summarize some proposed solutions. We conclude that the advancement of CP in the clinic is a promising resource-intensive endeavour that requires broad and inclusive collaborations between academia, industry, and regulatory bodies.
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Affiliation(s)
- Ioannis Prassas
- Laboratory Medicine Program, 7989 University Health Network , Toronto, ON, Canada
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada
| | - Blaise Clarke
- Laboratory Medicine Program, 7989 University Health Network , Toronto, ON, Canada
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada
| | - Timothy Youssef
- Laboratory Medicine Program, 7989 University Health Network , Toronto, ON, Canada
| | - Juliana Phlamon
- Laboratory Medicine Program, 7989 University Health Network , Toronto, ON, Canada
| | | | - Andrew Rofaeil
- Laboratory Medicine Program, 7989 University Health Network , Toronto, ON, Canada
| | - George M Yousef
- Laboratory Medicine Program, 7989 University Health Network , Toronto, ON, Canada
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada
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Giammanco A, Bychkov A, Schallenberg S, Tsvetkov T, Fukuoka J, Pryalukhin A, Mairinger F, Seper A, Hulla W, Klein S, Quaas A, Büttner R, Tolkach Y. Fast-track development and multi-institutional clinical validation of an artificial intelligence algorithm for detection of lymph node metastasis in colorectal cancer. Mod Pathol 2024:100496. [PMID: 38636778 DOI: 10.1016/j.modpat.2024.100496] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2023] [Revised: 03/24/2024] [Accepted: 04/10/2024] [Indexed: 04/20/2024]
Abstract
Lymph node metastasis (LNM) detection can be automated using artificial intelligence-based diagnostic tools. Only limited studies have addressed this task for colorectal cancer. The aim of this study was to develop of a clinical-grade digital pathology tool for LNM detection in colorectal cancer (CRC) using the original fast-track framework. The training cohort included 432 slides from one department. A segmentation algorithm detecting 8 relevant tissue classes was trained. The test cohorts consisted of materials from five pathology departments digitized by four different scanning systems. A high-quality, large training dataset was generated within 7 days, and a minimal amount of annotation work using fast-track principles. The AI tool showed very high accuracy for LNM detection in all cohorts, with sensitivity, negative predictive value, and specificity ranges of 0.980-1.000, 0.997-1.000, and 0.913-0.990, correspondingly. Only 5 of 14460 analyzed test slides with tumor cells over all cohorts were classified as false negative (3/5 representing clusters of tumor cells in lymphatic vessels). A clinical-grade tool was trained in a short time using fast-track development principles and validated using the largest international, multi-institutional, multi-scanner cohort of cases to date, showing very high precision for LNM detection in CRC. We are releasing a part of the test datasets to facilitate academic research.
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Affiliation(s)
- Avri Giammanco
- Institute of Pathology, University Hospital Cologne, Cologne, Germany
| | - Andrey Bychkov
- Department of Pathology, Kameda Medical Center, Kamogawa, Japan; Department of Pathology Informatics, Nagasaki University, Nagasaki, Japan
| | | | - Tsvetan Tsvetkov
- Institute of Pathology, University Hospital Cologne, Cologne, Germany
| | - Junya Fukuoka
- Department of Pathology, Kameda Medical Center, Kamogawa, Japan; Department of Pathology Informatics, Nagasaki University, Nagasaki, Japan
| | - Alexey Pryalukhin
- Institute of Pathology, Wiener Neustadt State Hospital, Wiener Neustadt, Austria
| | - Fabian Mairinger
- Institute of Pathology, University Hospital Essen, Essen, Germany
| | - Alexander Seper
- Institute of Pathology, Wiener Neustadt State Hospital, Wiener Neustadt, Austria; Danube Private University, Wien, Austria
| | - Wolfgang Hulla
- Institute of Pathology, Wiener Neustadt State Hospital, Wiener Neustadt, Austria
| | - Sebastian Klein
- Institute of Pathology, University Hospital Cologne, Cologne, Germany
| | - Alexander Quaas
- Institute of Pathology, University Hospital Cologne, Cologne, Germany
| | - Reinhard Büttner
- Institute of Pathology, University Hospital Cologne, Cologne, Germany
| | - Yuri Tolkach
- Institute of Pathology, University Hospital Cologne, Cologne, Germany.
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Tucci F, Laurinavicius A, Kather JN, Eloy C. The digital revolution in pathology: Towards a smarter approach to research and treatment. Tumori 2024:3008916241231035. [PMID: 38606831 DOI: 10.1177/03008916241231035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/13/2024]
Abstract
Artificial intelligence (AI) applications in oncology are at the forefront of transforming healthcare during the Fourth Industrial Revolution, driven by the digital data explosion. This review provides an accessible introduction to the field of AI, presenting a concise yet structured overview of the foundations of AI, including expert systems, classical machine learning, and deep learning, along with their contextual application in clinical research and healthcare. We delve into the current applications of AI in oncology, with a particular focus on diagnostic imaging and pathology. Numerous AI tools have already received regulatory approval, and more are under active development, bringing clear benefits but not without challenges. We discuss the importance of data security, the need for transparent and interpretable models, and the ethical considerations that must guide AI development in healthcare. By providing a perspective on the opportunities and challenges, this review aims to inform and guide researchers, clinicians, and policymakers in the adoption of AI in oncology.
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Affiliation(s)
- Francesco Tucci
- School of Pathology, University of Milan, Milan, Italy
- European Institute of Oncology (IEO) IRCCS, Milan, Italy
| | - Arvydas Laurinavicius
- Department of Pathology, Forensic Medicine and Pharmacology, Faculty of Medicine, Institute of Biomedical Sciences, Vilnius University, Vilnius, Lithuania
- National Centre of Pathology, Affiliate of Vilnius University Hospital Santaros Clinics, Vilnius, Lithuania
| | - Jakob Nikolas Kather
- Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, Technical University Dresden, Dresden, Germany
- Medical Oncology, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany
| | - Catarina Eloy
- Ipatimup - Institute of Molecular Pathology and Immunology of University of Porto, Porto, Portugal
- Medical Faculty, University of Porto, Porto, Portugal
- i3S-Instituto de Investigação e Inovação em Saúde, Porto, Portugal
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11
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Elfer K, Gardecki E, Garcia V, Ly A, Hytopoulos E, Wen S, Hanna MG, Peeters DJE, Saltz J, Ehinger A, Dudgeon SN, Li X, Blenman KRM, Chen W, Green U, Birmingham R, Pan T, Lennerz JK, Salgado R, Gallas BD. Reproducible Reporting of the Collection and Evaluation of Annotations for Artificial Intelligence Models. Mod Pathol 2024; 37:100439. [PMID: 38286221 DOI: 10.1016/j.modpat.2024.100439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Revised: 12/14/2023] [Accepted: 01/21/2024] [Indexed: 01/31/2024]
Abstract
This work puts forth and demonstrates the utility of a reporting framework for collecting and evaluating annotations of medical images used for training and testing artificial intelligence (AI) models in assisting detection and diagnosis. AI has unique reporting requirements, as shown by the AI extensions to the Consolidated Standards of Reporting Trials (CONSORT) and Standard Protocol Items: Recommendations for Interventional Trials (SPIRIT) checklists and the proposed AI extensions to the Standards for Reporting Diagnostic Accuracy (STARD) and Transparent Reporting of a Multivariable Prediction model for Individual Prognosis or Diagnosis (TRIPOD) checklists. AI for detection and/or diagnostic image analysis requires complete, reproducible, and transparent reporting of the annotations and metadata used in training and testing data sets. In an earlier work by other researchers, an annotation workflow and quality checklist for computational pathology annotations were proposed. In this manuscript, we operationalize this workflow into an evaluable quality checklist that applies to any reader-interpreted medical images, and we demonstrate its use for an annotation effort in digital pathology. We refer to this quality framework as the Collection and Evaluation of Annotations for Reproducible Reporting of Artificial Intelligence (CLEARR-AI).
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Affiliation(s)
- Katherine Elfer
- United States Food and Drug Administration, Center for Devices and Radiological Health, Office of Science and Engineering Laboratories, Division of Imaging Diagnostics and Software Reliability, Silver Spring, Maryland; National Institutes of Health, National Cancer Institute, Division of Cancer Prevention, Cancer Prevention Fellowship Program, Bethesda, Maryland.
| | - Emma Gardecki
- United States Food and Drug Administration, Center for Devices and Radiological Health, Office of Science and Engineering Laboratories, Division of Imaging Diagnostics and Software Reliability, Silver Spring, Maryland
| | - Victor Garcia
- United States Food and Drug Administration, Center for Devices and Radiological Health, Office of Science and Engineering Laboratories, Division of Imaging Diagnostics and Software Reliability, Silver Spring, Maryland
| | - Amy Ly
- Department of Pathology, Massachusetts General Hospital, Boston, Massachusetts
| | | | - Si Wen
- United States Food and Drug Administration, Center for Devices and Radiological Health, Office of Science and Engineering Laboratories, Division of Imaging Diagnostics and Software Reliability, Silver Spring, Maryland
| | - Matthew G Hanna
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Dieter J E Peeters
- Department of Pathology, University Hospital Antwerp/University of Antwerp, Antwerp, Belgium; Department of Pathology, Sint-Maarten Hospital, Mechelen, Belgium
| | - Joel Saltz
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York
| | - Anna Ehinger
- Department of Clinical Genetics, Pathology and Molecular Diagnostics, Laboratory Medicine, Lund University, Lund, Sweden
| | - Sarah N Dudgeon
- Department of Laboratory Medicine, Yale School of Medicine, New Haven, Connecticut
| | - Xiaoxian Li
- Department of Pathology and Laboratory Medicine, Emory University School of Medicine, Atlanta, Georgia
| | - Kim R M Blenman
- Department of Internal Medicine, Section of Medical Oncology, Yale School of Medicine and Yale Cancer Center, Yale University, New Haven, Connecticut; Department of Computer Science, School of Engineering and Applied Science, Yale University, New Haven, Connecticut
| | - Weijie Chen
- United States Food and Drug Administration, Center for Devices and Radiological Health, Office of Science and Engineering Laboratories, Division of Imaging Diagnostics and Software Reliability, Silver Spring, Maryland
| | - Ursula Green
- Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, Georgia
| | - Ryan Birmingham
- United States Food and Drug Administration, Center for Devices and Radiological Health, Office of Science and Engineering Laboratories, Division of Imaging Diagnostics and Software Reliability, Silver Spring, Maryland; Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, Georgia
| | - Tony Pan
- Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, Georgia
| | - Jochen K Lennerz
- Department of Pathology, Center for Integrated Diagnostics, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - Roberto Salgado
- Division of Research, Peter Mac Callum Cancer Centre, Melbourne, Australia; Department of Pathology, GZA-ZNA Hospitals, Antwerp, Belgium
| | - Brandon D Gallas
- United States Food and Drug Administration, Center for Devices and Radiological Health, Office of Science and Engineering Laboratories, Division of Imaging Diagnostics and Software Reliability, Silver Spring, Maryland
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12
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Azam AS, Tsang YW, Thirlwall J, Kimani PK, Sah S, Gopalakrishnan K, Boyd C, Loughrey MB, Kelly PJ, Boyle DP, Salto-Tellez M, Clark D, Ellis IO, Ilyas M, Rakha E, Bickers A, Roberts ISD, Soares MF, Neil DAH, Takyi A, Raveendran S, Hero E, Evans H, Osman R, Fatima K, Hughes RW, McIntosh SA, Moran GW, Ortiz-Fernandez-Sordo J, Rajpoot NM, Storey B, Ahmed I, Dunn JA, Hiller L, Snead DRJ. Digital pathology for reporting histopathology samples, including cancer screening samples - definitive evidence from a multisite study. Histopathology 2024; 84:847-862. [PMID: 38233108 DOI: 10.1111/his.15129] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 11/28/2023] [Accepted: 12/13/2023] [Indexed: 01/19/2024]
Abstract
AIMS To conduct a definitive multicentre comparison of digital pathology (DP) with light microscopy (LM) for reporting histopathology slides including breast and bowel cancer screening samples. METHODS A total of 2024 cases (608 breast, 607 GI, 609 skin, 200 renal) were studied, including 207 breast and 250 bowel cancer screening samples. Cases were examined by four pathologists (16 study pathologists across the four speciality groups), using both LM and DP, with the order randomly assigned and 6 weeks between viewings. Reports were compared for clinical management concordance (CMC), meaning identical diagnoses plus differences which do not affect patient management. Percentage CMCs were computed using logistic regression models with crossed random-effects terms for case and pathologist. The obtained percentage CMCs were referenced to 98.3% calculated from previous studies. RESULTS For all cases LM versus DP comparisons showed the CMC rates were 99.95% [95% confidence interval (CI) = 99.90-99.97] and 98.96 (95% CI = 98.42-99.32) for cancer screening samples. In speciality groups CMC for LM versus DP showed: breast 99.40% (99.06-99.62) overall and 96.27% (94.63-97.43) for cancer screening samples; [gastrointestinal (GI) = 99.96% (99.89-99.99)] overall and 99.93% (99.68-99.98) for bowel cancer screening samples; skin 99.99% (99.92-100.0); renal 99.99% (99.57-100.0). Analysis of clinically significant differences revealed discrepancies in areas where interobserver variability is known to be high, in reads performed with both modalities and without apparent trends to either. CONCLUSIONS Comparing LM and DP CMC, overall rates exceed the reference 98.3%, providing compelling evidence that pathologists provide equivalent results for both routine and cancer screening samples irrespective of the modality used.
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Affiliation(s)
- Ayesha S Azam
- University Hospitals Coventry and Warwickshire NHS Trust, Coventry, UK
- Warwick Medical School, University of Warwick, Coventry, UK
| | - Yee-Wah Tsang
- University Hospitals Coventry and Warwickshire NHS Trust, Coventry, UK
| | | | - Peter K Kimani
- Warwick Medical School, University of Warwick, Coventry, UK
| | - Shatrughan Sah
- University Hospitals Coventry and Warwickshire NHS Trust, Coventry, UK
| | | | - Clinton Boyd
- Belfast Health and Social Care Trust, Belfast, UK
| | - Maurice B Loughrey
- Belfast Health and Social Care Trust, Belfast, UK
- Queen's University, Belfast, UK
| | - Paul J Kelly
- Belfast Health and Social Care Trust, Belfast, UK
| | | | | | - David Clark
- Nottingham University Hospital NHS Trust, Nottingham, UK
| | - Ian O Ellis
- Nottingham University Hospital NHS Trust, Nottingham, UK
- University of Nottingham, Nottingham, UK
| | - Mohammad Ilyas
- Nottingham University Hospital NHS Trust, Nottingham, UK
- University of Nottingham, Nottingham, UK
| | - Emad Rakha
- Nottingham University Hospital NHS Trust, Nottingham, UK
- University of Nottingham, Nottingham, UK
| | - Adam Bickers
- Northern Lincolnshire and Goole NHS Foundation Trust, Scunthorpe, UK
| | - Ian S D Roberts
- Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Maria F Soares
- Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | | | - Abi Takyi
- University Hospitals Coventry and Warwickshire NHS Trust, Coventry, UK
| | | | - Emily Hero
- University Hospitals Coventry and Warwickshire NHS Trust, Coventry, UK
- University Hospitals of Leicester NHS Trust, Leicester, UK
| | - Harriet Evans
- University Hospitals Coventry and Warwickshire NHS Trust, Coventry, UK
- Warwick Medical School, University of Warwick, Coventry, UK
| | - Rania Osman
- University Hospitals Coventry and Warwickshire NHS Trust, Coventry, UK
| | - Khunsha Fatima
- Warwick Medical School, University of Warwick, Coventry, UK
| | - Rhian W Hughes
- University Hospitals Coventry and Warwickshire NHS Trust, Coventry, UK
| | | | | | | | - Nasir M Rajpoot
- Computer Science Department, University of Warwick, Coventry, UK
| | - Ben Storey
- Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Imtiaz Ahmed
- University Hospitals Coventry and Warwickshire NHS Trust, Coventry, UK
| | - Janet A Dunn
- Warwick Medical School, University of Warwick, Coventry, UK
| | - Louise Hiller
- Warwick Medical School, University of Warwick, Coventry, UK
| | - David R J Snead
- University Hospitals Coventry and Warwickshire NHS Trust, Coventry, UK
- Warwick Medical School, University of Warwick, Coventry, UK
- Computer Science Department, University of Warwick, Coventry, UK
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13
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Ono D, Kawai H, Kuwahara H, Yokota T. Automated whole slide morphometry of sural nerve biopsy using machine learning. Neuropathol Appl Neurobiol 2024; 50:e12967. [PMID: 38448224 DOI: 10.1111/nan.12967] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Revised: 02/13/2024] [Accepted: 02/18/2024] [Indexed: 03/08/2024]
Abstract
AIM The morphometry of sural nerve biopsies, such as fibre diameter and myelin thickness, helps us understand the underlying mechanism of peripheral neuropathies. However, in current clinical practice, only a portion of the specimen is measured manually because of its labour-intensive nature. In this study, we aimed to develop a machine learning-based application that inputs a whole slide image (WSI) of the biopsied sural nerve and automatically performs morphometric analyses. METHODS Our application consists of three supervised learning models: (1) nerve fascicle instance segmentation, (2) myelinated fibre detection and (3) myelin sheath segmentation. We fine-tuned these models using 86 toluidine blue-stained slides from various neuropathies and developed an open-source Python library. RESULTS Performance evaluation showed (1) a mask average precision (AP) of 0.861 for fascicle segmentation, (2) box AP of 0.711 for fibre detection and (3) a mean intersection over union (mIoU) of 0.817 for myelin segmentation. Our software identified 323,298 nerve fibres and 782 fascicles in 70 WSIs. Small and large fibre populations were objectively determined based on clustering analysis. The demyelination group had large fibres with thinner myelin sheaths and higher g-ratios than the vasculitis group. The slope of the regression line from the scatter plots of the diameters and g-ratios was higher in the demyelination group than in the vasculitis group. CONCLUSION We developed an application that performs whole slide morphometry of human biopsy samples. Our open-source software can be used by clinicians and pathologists without specific machine learning skills, which we expect will facilitate data-driven analysis of sural nerve biopsies for a more detailed understanding of these diseases.
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Affiliation(s)
- Daisuke Ono
- Department of Neurology and Neurological Science, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japan
- Department of Neuroscience, Mayo Clinic, Jacksonville, Florida, USA
| | - Honami Kawai
- Department of Neurology and Neurological Science, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japan
| | - Hiroya Kuwahara
- Department of Neurology and Neurological Science, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japan
| | - Takanori Yokota
- Department of Neurology and Neurological Science, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japan
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14
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Yoshikawa AL, Omura T, Takahashi-Kanemitsu A, Susaki EA. Blueprints from plane to space: outlook of next-generation three-dimensional histopathology. Cancer Sci 2024; 115:1029-1038. [PMID: 38316137 PMCID: PMC11006986 DOI: 10.1111/cas.16095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2023] [Revised: 01/02/2024] [Accepted: 01/16/2024] [Indexed: 02/07/2024] Open
Abstract
Here, we summarize the literature relevant to recent advances in three-dimensional (3D) histopathology in relation to clinical oncology, highlighting serial sectioning, tissue clearing, light-sheet microscopy, and digital image analysis with artificial intelligence. We look forward to a future where 3D histopathology expands our understanding of human pathophysiology and improves patient care through cross-disciplinary collaboration and innovation.
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Affiliation(s)
- Akira Leon Yoshikawa
- Department of Biochemistry and Systems Biomedicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Department of Pathology Informatics, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki, Japan
- Department of Pathology, Kameda Medical Center, Chiba, Japan
| | - Takaki Omura
- Department of Biochemistry and Systems Biomedicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Department of Neurosurgery and Neuro-Oncology, National Cancer Center Hospital, Tokyo, Japan
| | - Atsushi Takahashi-Kanemitsu
- Department of Biochemistry and Systems Biomedicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Etsuo A Susaki
- Department of Biochemistry and Systems Biomedicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
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15
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Mecklenburg L, Luetjens CM, Romeike A, Garg R, Samanta P, Mohanty A, Thomas T, Weinbauer G. Deep Learning-Based Spermatogenic Staging in Tissue Sections of Cynomolgus Macaque Testes. Toxicol Pathol 2024:1926233241234059. [PMID: 38465599 DOI: 10.1177/01926233241234059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
The indirect assessment of adverse effects on fertility in cynomolgus monkeys requires that tissue sections of the testis be microscopically evaluated with awareness of the stage of spermatogenesis that a particular cross-section of a seminiferous tubule is in. This difficult and subjective task could very much benefit from automation. Using digital whole slide images (WSIs) from tissue sections of testis, we have developed a deep learning model that can annotate the stage of each tubule with high sensitivity, precision, and accuracy. The model was validated on six WSI using a six-stage spermatogenic classification system. Whole slide images contained an average number of 4938 seminiferous tubule cross-sections. On average, 78% of these tubules were staged with 29% in stage I-IV, 12% in stage V-VI, 4% in stage VII, 19% in stage VIII-IX, 18% in stage X-XI, and 17% in stage XII. The deep learning model supports pathologists in conducting a stage-aware evaluation of the testis. It also allows derivation of a stage-frequency map. The diagnostic value of this stage-frequency map is still unclear, as further data on its variability and relevance need to be generated for testes with spermatogenic disturbances.
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Affiliation(s)
| | | | | | - Rohit Garg
- Aira Matrix Pvt. Ltd., Dosti Pinnacle, Thane, India
| | | | | | - Tijo Thomas
- Aira Matrix Pvt. Ltd., Dosti Pinnacle, Thane, India
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16
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Magalhães G, Calisto R, Freire C, Silva R, Montezuma D, Canberk S, Schmitt F. Invisible for a few but essential for many: the role of Histotechnologists in the establishment of digital pathology. J Histotechnol 2024; 47:39-52. [PMID: 37869882 DOI: 10.1080/01478885.2023.2268297] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Accepted: 10/03/2023] [Indexed: 10/24/2023]
Abstract
Digital pathology (DP) is indisputably the future for histopathology laboratories. The process of digital implementation requires deep workflow reorganisation which involves an interdisciplinary team. This transformation may have the greatest impact on the Histotechnologist (HTL) profession. Our review of the literature has clearly revealed that the role of HTLs in the establishment of DP is being unnoticed and guidance is limited. This article aims to bring HTLs from behind-the-scenes into the spotlight. Our objective is to provide them guidance and practical recommendations to successfully contribute to the implementation of a new digital workflow. Furthermore, it also intends to contribute for improvement of study programs, ensuring the role of HTL in DP is addressed as part of graduate and post-graduate education. In our review, we report on the differences encountered between workflow schemes and the limitations observed in this process. The authors propose a digital workflow to achieve its limitless potential, focusing on the HTL's role. This article explores the novel responsibilities of HTLs during specimen gross dissection, embedding, microtomy, staining, digital scanning, and whole slide image quality control. Furthermore, we highlight the benefits and challenges that DP implementation might bring the HTLs career. HTLs have an important role in the digital workflow: the responsibility of achieving the perfect glass slide.
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Affiliation(s)
- Gisela Magalhães
- Histopathology Department, Portsmouth Hospital University NHS Trust, Portsmouth, UK
- Department of Pathological Anatomy, School of Health Polytechnic of Porto (ESS|P.PORTO), Porto, Portugal
| | - Rita Calisto
- Department of Pathological Anatomy, School of Health Polytechnic of Porto (ESS|P.PORTO), Porto, Portugal
- Department of Pathological Anatomy, Hospital do Divino Espírito Santo, Ponta Delgada, Portugal
| | - Catarina Freire
- Department of Pathological Anatomy, School of Health Polytechnic of Porto (ESS|P.PORTO), Porto, Portugal
- Department of Pathological Anatomy, Hospital do Divino Espírito Santo, Ponta Delgada, Portugal
| | - Regina Silva
- Department of Pathological Anatomy, School of Health Polytechnic of Porto (ESS|P.PORTO), Porto, Portugal
- Centro de Investigação em Saúde e Ambiente, ESS,P.PORTO, Porto, Portugal
| | - Diana Montezuma
- Research & Development Unit, IMP Diagnostics, Porto, Portugal
- School of Medicine and Biomedical Sciences, University of Porto (ICBAS-UP), Porto, Portugal
| | - Sule Canberk
- Institute for Research and Innovation in Health (i3S), University of Porto, Porto, Portugal
- Cancer Signalling & Metabolism, Institute of Molecular Pathology and Immunology of the University of Porto (Ipatimup), Porto, Portugal
- Faculty of Medicine of the University of Porto (FMUP), Porto, Portugal
| | - Fernando Schmitt
- Department of Pathology, Faculty of Medicine of University of Porto, Porto, Portugal
- CINTESIS@RISE, Health Research Network, Alameda Prof. Hernâni Monteiro, Portugal
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17
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Gambella A, Salvi M, Molinaro L, Patrono D, Cassoni P, Papotti M, Romagnoli R, Molinari F. Improved assessment of donor liver steatosis using Banff consensus recommendations and deep learning algorithms. J Hepatol 2024; 80:495-504. [PMID: 38036009 DOI: 10.1016/j.jhep.2023.11.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Revised: 10/23/2023] [Accepted: 11/03/2023] [Indexed: 12/02/2023]
Abstract
BACKGROUND & AIMS The Banff Liver Working Group recently published consensus recommendations for steatosis assessment in donor liver biopsy, but few studies reported their use and no automated deep-learning algorithms based on the proposed criteria have been developed so far. We evaluated Banff recommendations on a large monocentric series of donor liver needle biopsies by comparing pathologists' scores with those generated by convolutional neural networks (CNNs) we specifically developed for automated steatosis assessment. METHODS We retrospectively retrieved 292 allograft liver needle biopsies collected between January 2016 and January 2020 and performed steatosis assessment using a former intra-institution method (pre-Banff method) and the newly introduced Banff recommendations. Scores provided by pathologists and CNN models were then compared, and the degree of agreement was measured with the intraclass correlation coefficient (ICC). RESULTS Regarding the pre-Banff method, poor agreement was observed between the pathologist and CNN models for small droplet macrovesicular steatosis (ICC: 0.38), large droplet macrovesicular steatosis (ICC: 0.08), and the final combined score (ICC: 0.16) evaluation, but none of these reached statistically significance. Interestingly, significantly improved agreement was observed using the Banff approach: ICC was 0.93 for the low-power score (p <0.001), 0.89 for the high-power score (p <0.001), and 0.93 for the final score (p <0.001). Comparing the pre-Banff method with the Banff approach on the same biopsy, pathologist and CNN model assessment showed a mean (±SD) percentage of discrepancy of 26.89 (±22.16) and 1.20 (±5.58), respectively. CONCLUSIONS Our findings support the use of Banff recommendations in daily practice and highlight the need for a granular analysis of their effect on liver transplantation outcomes. IMPACT AND IMPLICATIONS We developed and validated the first automated deep-learning algorithms for standardized steatosis assessment based on the Banff Liver Working Group consensus recommendations. Our algorithm provides an unbiased automated evaluation of steatosis, which will lay the groundwork for granular analysis of steatosis's short- and long-term effects on organ viability, enabling the identification of clinically relevant steatosis cut-offs for donor organ acceptance. Implementing our algorithm in daily clinical practice will allow for a more efficient and safe allocation of donor organs, improving the post-transplant outcomes of patients.
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Affiliation(s)
- Alessandro Gambella
- Pathology Unit, Department of Medical Sciences, University of Turin, Turin, Italy; Division of Liver and Transplant Pathology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
| | - Massimo Salvi
- Department of Electronics and Telecommunications, PolitoBIOMed Lab, Politecnico di Torino, Biolab, Corso Duca degli Abruzzi 24, 10129 Turin, Italy
| | - Luca Molinaro
- Division of Pathology, AOU Città Della Salute e Della Scienza di Torino, Turin, Italy
| | - Damiano Patrono
- General Surgery 2U, Liver Transplant Center, AOU Città Della Salute e Della Scienza di Torino, University of Turin, Turin, Italy
| | - Paola Cassoni
- Pathology Unit, Department of Medical Sciences, University of Turin, Turin, Italy
| | - Mauro Papotti
- Division of Pathology, Department of Oncology, University of Turin, Turin, Italy
| | - Renato Romagnoli
- General Surgery 2U, Liver Transplant Center, AOU Città Della Salute e Della Scienza di Torino, University of Turin, Turin, Italy
| | - Filippo Molinari
- Department of Electronics and Telecommunications, PolitoBIOMed Lab, Politecnico di Torino, Biolab, Corso Duca degli Abruzzi 24, 10129 Turin, Italy
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Sandeep F, Kiran N, Rahaman Z, Devi P, Bendari A. Pathology in the Age of Artificial Intelligence (AI): Redefining Roles and Responsibilities for Tomorrow's Practitioners. Cureus 2024; 16:e56040. [PMID: 38606226 PMCID: PMC11008776 DOI: 10.7759/cureus.56040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/12/2024] [Indexed: 04/13/2024] Open
Abstract
The evolution of pathology from its rudimentary beginnings around 1700 BC to the present day has been marked by profound advancement in understanding and diagnosing diseases. This journey, from the earliest dissections to the modern era of histochemical analysis, sets the stage for the next transformative leap to the integration of artificial intelligence (AI) in pathology. Recent research highlights AI's significant potential to revolutionize healthcare within the next decade, with a particular impact on diagnostic processes. A majority of pathologists foresee AI becoming a cornerstone in diagnostic workflow, driven by the advent of image-based algorithms and computational pathology. These innovations promise to enhance the precision of disease diagnosis, particularly in complex cases, such as cancers, by offering detailed insights into the molecular and cellular mechanisms. Moreover, AI-assisted tools are improving the efficiency and accuracy of histological analysis by automating the evaluation of immunohistochemical biomarkers and tissue architecture. This shift not only accelerates diagnostic processes but also facilitates early disease management, crucial for improving patient outcomes. Furthermore, AI is reshaping educational paradigms in pathology, offering interactive learning environments that promise to enrich the training of future pathologists. Despite these advancements, the integration of AI in pathology raises ethical considerations regarding patient consent and data privacy. As pathology embarks on this AI-augmented era, it is imperative to navigate these challenges thoughtfully, ensuring that AI enhances rather than replaces the pathologist's role. This editorial discussed the historical progression of pathology, the current impact of AI on diagnostic practices, and the ethical implications of its adoption, underscoring the need for a symbiotic relationship between pathologists and AI to unlock the full potential of healthcare.
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Affiliation(s)
| | - Nfn Kiran
- Pathology, Northwell Health, New York, USA
| | - Zubair Rahaman
- Internal Medicine, University at Buffalo Jacobs School of Medicine and Biomedical Sciences, Buffalo, USA
| | - Pooja Devi
- Pathology/Hematopathology, University of Pennsylvania Health System, Philadelphia, USA
| | - Ahmed Bendari
- Pathology, Northwell/Lenox Hill Hospital, NewYork, USA
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19
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Dimitriou N, Arandjelović O, Harrison DJ. Magnifying Networks for Histopathological Images with Billions of Pixels. Diagnostics (Basel) 2024; 14:524. [PMID: 38472996 DOI: 10.3390/diagnostics14050524] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Revised: 02/25/2024] [Accepted: 02/26/2024] [Indexed: 03/14/2024] Open
Abstract
Amongst the other benefits conferred by the shift from traditional to digital pathology is the potential to use machine learning for diagnosis, prognosis, and personalization. A major challenge in the realization of this potential emerges from the extremely large size of digitized images, which are often in excess of 100,000 × 100,000 pixels. In this paper, we tackle this challenge head-on by diverging from the existing approaches in the literature-which rely on the splitting of the original images into small patches-and introducing magnifying networks (MagNets). By using an attention mechanism, MagNets identify the regions of the gigapixel image that benefit from an analysis on a finer scale. This process is repeated, resulting in an attention-driven coarse-to-fine analysis of only a small portion of the information contained in the original whole-slide images. Importantly, this is achieved using minimal ground truth annotation, namely, using only global, slide-level labels. The results from our tests on the publicly available Camelyon16 and Camelyon17 datasets demonstrate the effectiveness of MagNets-as well as the proposed optimization framework-in the task of whole-slide image classification. Importantly, MagNets process at least five times fewer patches from each whole-slide image than any of the existing end-to-end approaches.
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Affiliation(s)
- Neofytos Dimitriou
- Maritime Digitalisation Centre, Cyprus Marine and Maritime Institute, Larnaca 6300, Cyprus
- School of Computer Science, University of St Andrews, St Andrews KY16 9SX, UK
| | - Ognjen Arandjelović
- School of Computer Science, University of St Andrews, St Andrews KY16 9SX, UK
| | - David J Harrison
- School of Medicine, University of St Andrews, St Andrews KY16 9TF, UK
- NHS Lothian Pathology, Division of Laboratory Medicine, Royal Infirmary of Edinburgh, Edinburgh EH16 4SA, UK
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Angeloni M, van Doeveren T, Lindner S, Volland P, Schmelmer J, Foersch S, Matek C, Stoehr R, Geppert CI, Heers H, Wach S, Taubert H, Sikic D, Wullich B, van Leenders GJLH, Zaburdaev V, Eckstein M, Hartmann A, Boormans JL, Ferrazzi F, Bahlinger V. A deep-learning workflow to predict upper tract urothelial carcinoma protein-based subtypes from H&E slides supporting the prioritization of patients for molecular testing. J Pathol Clin Res 2024; 10:e12369. [PMID: 38504364 PMCID: PMC10951050 DOI: 10.1002/2056-4538.12369] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2023] [Revised: 02/08/2024] [Accepted: 02/26/2024] [Indexed: 03/21/2024]
Abstract
Upper tract urothelial carcinoma (UTUC) is a rare and aggressive, yet understudied, urothelial carcinoma (UC). The more frequent UC of the bladder comprises several molecular subtypes, associated with different targeted therapies and overlapping with protein-based subtypes. However, if and how these findings extend to UTUC remains unclear. Artificial intelligence-based approaches could help elucidate UTUC's biology and extend access to targeted treatments to a wider patient audience. Here, UTUC protein-based subtypes were identified, and a deep-learning (DL) workflow was developed to predict them directly from routine histopathological H&E slides. Protein-based subtypes in a retrospective cohort of 163 invasive tumors were assigned by hierarchical clustering of the immunohistochemical expression of three luminal (FOXA1, GATA3, and CK20) and three basal (CD44, CK5, and CK14) markers. Cluster analysis identified distinctive luminal (N = 80) and basal (N = 42) subtypes. The luminal subtype mostly included pushing, papillary tumors, whereas the basal subtype diffusely infiltrating, non-papillary tumors. DL model building relied on a transfer-learning approach by fine-tuning a pre-trained ResNet50. Classification performance was measured via three-fold repeated cross-validation. A mean area under the receiver operating characteristic curve of 0.83 (95% CI: 0.67-0.99), 0.8 (95% CI: 0.62-0.99), and 0.81 (95% CI: 0.65-0.96) was reached in the three repetitions. High-confidence DL-based predicted subtypes showed significant associations (p < 0.001) with morphological features, i.e. tumor type, histological subtypes, and infiltration type. Furthermore, a significant association was found with programmed cell death ligand 1 (PD-L1) combined positive score (p < 0.001) and FGFR3 mutational status (p = 0.002), with high-confidence basal predictions containing a higher proportion of PD-L1 positive samples and high-confidence luminal predictions a higher proportion of FGFR3-mutated samples. Testing of the DL model on an independent cohort highlighted the importance to accommodate histological subtypes. Taken together, our DL workflow can predict protein-based UTUC subtypes, associated with the presence of targetable alterations, directly from H&E slides.
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Affiliation(s)
- Miriam Angeloni
- Institute of Pathology, University Hospital Erlangen‐Nürnberg, Friedrich‐Alexander‐Universität Erlangen‐Nürnberg (FAU)ErlangenGermany
- Comprehensive Cancer Center Erlangen‐EMN (CCC ER‐EMN)ErlangenGermany
- Bavarian Cancer Research Center (BZKF)ErlangenGermany
| | - Thomas van Doeveren
- Department of UrologyErasmus MC Urothelial Cancer Research GroupRotterdamThe Netherlands
| | - Sebastian Lindner
- Institute of Pathology, University Hospital Erlangen‐Nürnberg, Friedrich‐Alexander‐Universität Erlangen‐Nürnberg (FAU)ErlangenGermany
- Comprehensive Cancer Center Erlangen‐EMN (CCC ER‐EMN)ErlangenGermany
- Bavarian Cancer Research Center (BZKF)ErlangenGermany
| | - Patrick Volland
- Institute of Pathology, University Hospital Erlangen‐Nürnberg, Friedrich‐Alexander‐Universität Erlangen‐Nürnberg (FAU)ErlangenGermany
- Comprehensive Cancer Center Erlangen‐EMN (CCC ER‐EMN)ErlangenGermany
- Bavarian Cancer Research Center (BZKF)ErlangenGermany
| | - Jorina Schmelmer
- Institute of Pathology, University Hospital Erlangen‐Nürnberg, Friedrich‐Alexander‐Universität Erlangen‐Nürnberg (FAU)ErlangenGermany
- Comprehensive Cancer Center Erlangen‐EMN (CCC ER‐EMN)ErlangenGermany
- Bavarian Cancer Research Center (BZKF)ErlangenGermany
| | | | - Christian Matek
- Institute of Pathology, University Hospital Erlangen‐Nürnberg, Friedrich‐Alexander‐Universität Erlangen‐Nürnberg (FAU)ErlangenGermany
- Comprehensive Cancer Center Erlangen‐EMN (CCC ER‐EMN)ErlangenGermany
- Bavarian Cancer Research Center (BZKF)ErlangenGermany
| | - Robert Stoehr
- Institute of Pathology, University Hospital Erlangen‐Nürnberg, Friedrich‐Alexander‐Universität Erlangen‐Nürnberg (FAU)ErlangenGermany
- Comprehensive Cancer Center Erlangen‐EMN (CCC ER‐EMN)ErlangenGermany
- Bavarian Cancer Research Center (BZKF)ErlangenGermany
| | - Carol I Geppert
- Institute of Pathology, University Hospital Erlangen‐Nürnberg, Friedrich‐Alexander‐Universität Erlangen‐Nürnberg (FAU)ErlangenGermany
- Comprehensive Cancer Center Erlangen‐EMN (CCC ER‐EMN)ErlangenGermany
- Bavarian Cancer Research Center (BZKF)ErlangenGermany
| | - Hendrik Heers
- Department of UrologyPhilipps‐Universität MarburgMarburgGermany
| | - Sven Wach
- Comprehensive Cancer Center Erlangen‐EMN (CCC ER‐EMN)ErlangenGermany
- Bavarian Cancer Research Center (BZKF)ErlangenGermany
- Department of Urology and Pediatric UrologyUniversity Hospital Erlangen, Friedrich‐Alexander Universität Erlangen‐Nürnberg (FAU)ErlangenGermany
| | - Helge Taubert
- Comprehensive Cancer Center Erlangen‐EMN (CCC ER‐EMN)ErlangenGermany
- Bavarian Cancer Research Center (BZKF)ErlangenGermany
- Department of Urology and Pediatric UrologyUniversity Hospital Erlangen, Friedrich‐Alexander Universität Erlangen‐Nürnberg (FAU)ErlangenGermany
| | - Danijel Sikic
- Comprehensive Cancer Center Erlangen‐EMN (CCC ER‐EMN)ErlangenGermany
- Bavarian Cancer Research Center (BZKF)ErlangenGermany
- Department of Urology and Pediatric UrologyUniversity Hospital Erlangen, Friedrich‐Alexander Universität Erlangen‐Nürnberg (FAU)ErlangenGermany
| | - Bernd Wullich
- Comprehensive Cancer Center Erlangen‐EMN (CCC ER‐EMN)ErlangenGermany
- Bavarian Cancer Research Center (BZKF)ErlangenGermany
- Department of Urology and Pediatric UrologyUniversity Hospital Erlangen, Friedrich‐Alexander Universität Erlangen‐Nürnberg (FAU)ErlangenGermany
| | - Geert JLH van Leenders
- Department of PathologyErasmus MC Cancer Institute, University Medical CentreRotterdamthe Netherlands
| | - Vasily Zaburdaev
- Department of BiologyFriedrich‐Alexander‐Universität Erlangen‐Nürnberg (FAU)ErlangenGermany
- Max‐Planck‐Zentrum für Physik und MedizinErlangenGermany
| | - Markus Eckstein
- Institute of Pathology, University Hospital Erlangen‐Nürnberg, Friedrich‐Alexander‐Universität Erlangen‐Nürnberg (FAU)ErlangenGermany
- Comprehensive Cancer Center Erlangen‐EMN (CCC ER‐EMN)ErlangenGermany
- Bavarian Cancer Research Center (BZKF)ErlangenGermany
| | - Arndt Hartmann
- Institute of Pathology, University Hospital Erlangen‐Nürnberg, Friedrich‐Alexander‐Universität Erlangen‐Nürnberg (FAU)ErlangenGermany
- Comprehensive Cancer Center Erlangen‐EMN (CCC ER‐EMN)ErlangenGermany
- Bavarian Cancer Research Center (BZKF)ErlangenGermany
| | - Joost L Boormans
- Department of UrologyErasmus MC Urothelial Cancer Research GroupRotterdamThe Netherlands
| | - Fulvia Ferrazzi
- Institute of Pathology, University Hospital Erlangen‐Nürnberg, Friedrich‐Alexander‐Universität Erlangen‐Nürnberg (FAU)ErlangenGermany
- Comprehensive Cancer Center Erlangen‐EMN (CCC ER‐EMN)ErlangenGermany
- Bavarian Cancer Research Center (BZKF)ErlangenGermany
- Department of NephropathologyInstitute of Pathology, University Hospital Erlangen‐Nürnberg, Friedrich‐Alexander‐Universität Erlangen‐Nürnberg (FAU)ErlangenGermany
| | - Veronika Bahlinger
- Institute of Pathology, University Hospital Erlangen‐Nürnberg, Friedrich‐Alexander‐Universität Erlangen‐Nürnberg (FAU)ErlangenGermany
- Comprehensive Cancer Center Erlangen‐EMN (CCC ER‐EMN)ErlangenGermany
- Bavarian Cancer Research Center (BZKF)ErlangenGermany
- Department of Pathology and NeuropathologyUniversity Hospital and Comprehensive Cancer Center TübingenTübingenGermany
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21
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Lozano MD, Argueta A, de Andrea C. Immunotherapy and lung cytopathology: Overview and possibilities. Cytopathology 2024; 35:213-217. [PMID: 37968806 DOI: 10.1111/cyt.13335] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Accepted: 10/31/2023] [Indexed: 11/17/2023]
Abstract
Immunotherapy has become a promising cancer treatment in the past decade, and IHC is the most commonly used testing method for PDL-1/PD1 evaluation. In general, PD-L1 assays can be performed on both FFPE specimens and cytological samples. However, their use on smears is not yet well-established or validated. Nowadays, digital images and advanced algorithms can aid in interpreting PD-L1 in cytological samples. Understanding the immune environment of non-small cell lung cancer (NSCLC) is critical in developing successful anticancer immunotherapies. The use of a multiplexed immunofluorescence (mIF) assay on cytological samples obtained through minimally invasive methods appears to be a viable option for investigating the immune environment of NSCLC. This review aims to briefly summarize the knowledge of the role of cytopathology in the analysis of PD-L1 by immunocytochemistry (ICC) and future directions of cytopathology in the immunotherapy setting.
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Affiliation(s)
- Maria D Lozano
- Department of Pathology, Clínica Universidad de Navarra, University of Navarra, Pamplona, Spain
- Centro de Investigación Biomedica en Red de Oncología (CIBERONC), Madrid, Spain
- Instituto de Investigación Sanitaria de Navarra (IDISNA), Pamplona, Spain
| | - Allan Argueta
- Department of Pathology, Clínica Universidad de Navarra, University of Navarra, Pamplona, Spain
| | - Carlos de Andrea
- Department of Pathology, Clínica Universidad de Navarra, University of Navarra, Pamplona, Spain
- Centro de Investigación Biomedica en Red de Oncología (CIBERONC), Madrid, Spain
- Instituto de Investigación Sanitaria de Navarra (IDISNA), Pamplona, Spain
- Department of Histology and Pathology, University of Navarra, Pamplona, Spain
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22
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Oon ML, Syn NL, Tan CL, Tan KB, Ng SB. Bridging bytes and biopsies: A comparative analysis of ChatGPT and histopathologists in pathology diagnosis and collaborative potential. Histopathology 2024; 84:601-613. [PMID: 38032062 DOI: 10.1111/his.15100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2023] [Revised: 10/03/2023] [Accepted: 11/02/2023] [Indexed: 12/01/2023]
Abstract
BACKGROUND AND AIMS ChatGPT is a powerful artificial intelligence (AI) chatbot developed by the OpenAI research laboratory which is capable of analysing human input and generating human-like responses. Early research into the potential application of ChatGPT in healthcare has focused mainly on clinical and administrative functions. The diagnostic ability and utility of ChatGPT in histopathology is not well defined. We benchmarked the performance of ChatGPT against pathologists in diagnostic histopathology, and evaluated the collaborative potential between pathologists and ChatGPT to deliver more accurate diagnoses. METHODS AND RESULTS In Part 1 of the study, pathologists and ChatGPT were subjected to a series of questions encompassing common diagnostic conundrums in histopathology. For Part 2, pathologists reviewed a series of challenging virtual slides and provided their diagnoses before and after consultation with ChatGPT. We found that ChatGPT performed worse than pathologists in reaching the correct diagnosis. Consultation with ChatGPT provided limited help and information generated from ChatGPT is dependent on the prompts provided by the pathologists and is not always correct. Finally, we surveyed pathologists who rated the diagnostic accuracy of ChatGPT poorly, but found it useful as an advanced search engine. CONCLUSIONS The use of ChatGPT4 as a diagnostic tool in histopathology is limited by its inherent shortcomings. Judicious evaluation of the information and histopathology diagnosis generated from ChatGPT4 is essential and cannot replace the acuity and judgement of a pathologist. However, future advances in generative AI may expand its role in the field of histopathology.
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Affiliation(s)
- Ming Liang Oon
- Department of Pathology, National University Hospital, Singapore, Singapore
| | - Nicholas L Syn
- Department of Pathology, National University Hospital, Singapore, Singapore
| | - Char Loo Tan
- Department of Pathology, National University Hospital, Singapore, Singapore
| | - Kong-Bing Tan
- Department of Pathology, National University Hospital, Singapore, Singapore
- Department of Pathology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Siok-Bian Ng
- Department of Pathology, National University Hospital, Singapore, Singapore
- Department of Pathology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Cancer Science Institute of Singapore, National University of Singapore, Singapore, Singapore
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23
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Irmakci I, Nateghi R, Zhou R, Vescovo M, Saft M, Ross AE, Yang XJ, Cooper LAD, Goldstein JA. Tissue Contamination Challenges the Credibility of Machine Learning Models in Real World Digital Pathology. Mod Pathol 2024; 37:100422. [PMID: 38185250 PMCID: PMC10960671 DOI: 10.1016/j.modpat.2024.100422] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 11/13/2023] [Accepted: 12/15/2023] [Indexed: 01/09/2024]
Abstract
Machine learning (ML) models are poised to transform surgical pathology practice. The most successful use attention mechanisms to examine whole slides, identify which areas of tissue are diagnostic, and use them to guide diagnosis. Tissue contaminants, such as floaters, represent unexpected tissue. Although human pathologists are extensively trained to consider and detect tissue contaminants, we examined their impact on ML models. We trained 4 whole-slide models. Three operate in placenta for the following functions: (1) detection of decidual arteriopathy, (2) estimation of gestational age, and (3) classification of macroscopic placental lesions. We also developed a model to detect prostate cancer in needle biopsies. We designed experiments wherein patches of contaminant tissue are randomly sampled from known slides and digitally added to patient slides and measured model performance. We measured the proportion of attention given to contaminants and examined the impact of contaminants in the t-distributed stochastic neighbor embedding feature space. Every model showed performance degradation in response to one or more tissue contaminants. Decidual arteriopathy detection--balanced accuracy decreased from 0.74 to 0.69 ± 0.01 with addition of 1 patch of prostate tissue for every 100 patches of placenta (1% contaminant). Bladder, added at 10% contaminant, raised the mean absolute error in estimating gestational age from 1.626 weeks to 2.371 ± 0.003 weeks. Blood, incorporated into placental sections, induced false-negative diagnoses of intervillous thrombi. Addition of bladder to prostate cancer needle biopsies induced false positives, a selection of high-attention patches, representing 0.033 mm2, and resulted in a 97% false-positive rate when added to needle biopsies. Contaminant patches received attention at or above the rate of the average patch of patient tissue. Tissue contaminants induce errors in modern ML models. The high level of attention given to contaminants indicates a failure to encode biological phenomena. Practitioners should move to quantify and ameliorate this problem.
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Affiliation(s)
- Ismail Irmakci
- Department of Pathology, Northwestern University, Feinberg School of Medicine, Chicago, Illinois
| | - Ramin Nateghi
- Department of Pathology, Northwestern University, Feinberg School of Medicine, Chicago, Illinois
| | - Rujoi Zhou
- Department of Pathology, Northwestern University, Feinberg School of Medicine, Chicago, Illinois
| | - Mariavittoria Vescovo
- Department of Pathology, Northwestern University, Feinberg School of Medicine, Chicago, Illinois
| | - Madeline Saft
- Department of Pathology, Northwestern University, Feinberg School of Medicine, Chicago, Illinois
| | - Ashley E Ross
- Department of Pathology, Northwestern University, Feinberg School of Medicine, Chicago, Illinois
| | - Ximing J Yang
- Department of Pathology, Northwestern University, Feinberg School of Medicine, Chicago, Illinois
| | - Lee A D Cooper
- Department of Pathology, Northwestern University, Feinberg School of Medicine, Chicago, Illinois
| | - Jeffery A Goldstein
- Department of Pathology, Northwestern University, Feinberg School of Medicine, Chicago, Illinois.
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24
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Yilmaz F, Brickman A, Najdawi F, Yakirevich E, Egger R, Resnick MB. Advancing Artificial Intelligence Integration Into the Pathology Workflow: Exploring Opportunities in Gastrointestinal Tract Biopsies. J Transl Med 2024; 104:102043. [PMID: 38431118 DOI: 10.1016/j.labinv.2024.102043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Revised: 02/14/2024] [Accepted: 02/26/2024] [Indexed: 03/05/2024] Open
Abstract
This review aims to present a comprehensive overview of the current landscape of artificial intelligence (AI) applications in the analysis of tubular gastrointestinal biopsies. These publications cover a spectrum of conditions, ranging from inflammatory ailments to malignancies. Moving beyond the conventional diagnosis based on hematoxylin and eosin-stained whole-slide images, the review explores additional implications of AI, including its involvement in interpreting immunohistochemical results, molecular subtyping, and the identification of cellular spatial biomarkers. Furthermore, the review examines how AI can contribute to enhancing the quality and control of diagnostic processes, introducing new workflow options, and addressing the limitations and caveats associated with current AI platforms in this context.
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Affiliation(s)
- Fazilet Yilmaz
- The Warren Alpert Medical School of Brown University, Rhode Island Hospital, Providence, Rhode Island
| | - Arlen Brickman
- The Warren Alpert Medical School of Brown University, Rhode Island Hospital, Providence, Rhode Island
| | - Fedaa Najdawi
- The Warren Alpert Medical School of Brown University, Rhode Island Hospital, Providence, Rhode Island
| | - Evgeny Yakirevich
- The Warren Alpert Medical School of Brown University, Rhode Island Hospital, Providence, Rhode Island
| | | | - Murray B Resnick
- The Warren Alpert Medical School of Brown University, Rhode Island Hospital, Providence, Rhode Island.
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25
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Ollen-Bittle N, Pejhan S, Pasternak SH, Keene CD, Zhang Q, Whitehead SN. Co-registration of MALDI-MSI and histology demonstrates gangliosides co-localize with amyloid beta plaques in Alzheimer's disease. Res Sq 2024:rs.3.rs-3985371. [PMID: 38463949 PMCID: PMC10925476 DOI: 10.21203/rs.3.rs-3985371/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
Alzheimer's disease (AD) is a progressive neurological condition characterized by impaired cognitive function and behavioural alterations. While AD research historically centered around mis-folded proteins, advances in mass spectrometry techniques have triggered increased exploration of the AD lipidome with lipid dysregulation emerging as a critical player in AD pathogenesis. Gangliosides are a class of glycosphingolipids enriched within the central nervous system. Previous work has suggested a shift in a-series gangliosides from complex (GM1) to simple (GM2 and GM3) species may be related to the development of neurodegenerative disease. Additionally, complex gangliosides with 20 carbon sphingosine chains have been shown to increase in the aging brain. In this study, we utilized matrix assisted laser desorption ionization mass spectrometry imaging (MALDI-MSI) to interrogate the in situ relationship of a-series gangliosides with either 18 or 20 carbon sphingosine chains (d18:1 or d20:1 respectively) in the post-mortem human AD brain. Here, we expanded upon previous literature and demonstrated a significant decrease in the GM1 d20:1:GM1 d18:1 ratio in regions of the dentate gyrus and entorhinal cortex in AD relative to control brain tissue. Then we demonstrated that the MALDI-MSI profile of GM3 co-localizes with histologically confirmed amyloid beta (Aβ) plaques and found a significant increase in both GM1 and GM3 in proximity to Aβ plaques. Collectively these results support past literature and demonstrate a perturbation of the ganglioside profile in AD. Moreover, this work validates a pipeline for MALDI-MSI and classic histological staining in the same tissue sections. This demonstrates feasibility for integrating untargeted mass spectrometry imaging approaches into a digital pathology framework.
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Affiliation(s)
- Nikita Ollen-Bittle
- Department of Anatomy and Cell Biology, Schulich School of Medicine and Dentistry, Western University, London, Ontario, N6A 5C1
| | - Shervin Pejhan
- Department of Pathology and Laboratory Medicine, London Health Sciences Centre, London, ON, Canada
| | - Stephen H Pasternak
- Robarts Research Institute, Schulich School of Medicine and Dentistry, London, ON, Canada
- Department of Clinical Neurological Sciences, Schulich School of Medicine and Dentistry, Western University, London, Ontario, N6A 5C1
| | - C Dirk Keene
- Department of Laboratory Medicine & Pathology, Division of Neuropathology, University of Washington School of Medicine, Seattle, WA, USA
| | - Qi Zhang
- Department of Pathology and Laboratory Medicine, London Health Sciences Centre, London, ON, Canada
| | - Shawn N Whitehead
- Department of Anatomy and Cell Biology, Schulich School of Medicine and Dentistry, Western University, London, Ontario, N6A 5C1
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26
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Canini V, Eccher A, d’Amati G, Fusco N, Maffini F, Lepanto D, Martini M, Cazzaniga G, Paliogiannis P, Lobrano R, L’Imperio V, Pagni F. Digital Pathology Applications for PD-L1 Scoring in Head and Neck Squamous Cell Carcinoma: A Challenging Series. J Clin Med 2024; 13:1240. [PMID: 38592086 PMCID: PMC10932078 DOI: 10.3390/jcm13051240] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Revised: 01/29/2024] [Accepted: 02/20/2024] [Indexed: 04/10/2024] Open
Abstract
The assessment of programmed death-ligand 1 (PD-L1) combined positive scoring (CPS) in head and neck squamous cell carcinoma (HNSCC) is challenged by pre-analytical and inter-observer variabilities. An educational program to compare the diagnostic performances between local pathologists and a board of pathologists on 11 challenging cases from different Italian pathology centers stained with PD-L1 immunohistochemistry on a digital pathology platform is reported. A laboratory-developed test (LDT) using both 22C3 (Dako) and SP263 (Ventana) clones on Dako or Ventana platforms was compared with the companion diagnostic (CDx) Dako 22C3 pharm Dx assay. A computational approach was performed to assess possible correlations between stain features and pathologists' visual assessments. Technical discordances were noted in five cases (LDT vs. CDx, 45%), due to an abnormal nuclear/cytoplasmic diaminobenzidine (DAB) stain in LDT (n = 2, 18%) and due to variation in terms of intensity, dirty background, and DAB droplets (n = 3, 27%). Interpretative discordances were noted in six cases (LDT vs. CDx, 54%). CPS remained unchanged, increased, or decreased from LDT to CDx in three (27%) cases, two (18%) cases, and one (9%) case, respectively, around relevant cutoffs (1 and 20, k = 0.63). Differences noted in DAB intensity/distribution using computational pathology partly explained the LDT vs. CDx differences in two cases (18%). Digital pathology may help in PD-L1 scoring, serving as a second opinion consultation platform in challenging cases. Computational and artificial intelligence tools will improve clinical decision-making and patient outcomes.
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Affiliation(s)
- Valentina Canini
- Department of Medicine and Surgery, Pathology, IRCCS Fondazione San Gerardo dei Tintori, University of Milano-Bicocca, 20126 Milan, Italy; (V.C.); (G.C.); (V.L.); (F.P.)
| | - Albino Eccher
- Department of Medical and Surgical Sciences for Children and Adults, University of Modena and Reggio Emilia, University Hospital of Modena, 41124 Modena, Italy
| | - Giulia d’Amati
- Department of Radiological, Oncological and Pathological Sciences, Sapienza University of Roma, 00185 Rome, Italy;
| | - Nicola Fusco
- Division of Pathology, European Institute of Oncology IRCCS, 20141 Milan, Italy; (N.F.); (F.M.); (D.L.)
- Department of Oncology and Hemato-Oncology, University of Milan, 20122 Milan, Italy
| | - Fausto Maffini
- Division of Pathology, European Institute of Oncology IRCCS, 20141 Milan, Italy; (N.F.); (F.M.); (D.L.)
| | - Daniela Lepanto
- Division of Pathology, European Institute of Oncology IRCCS, 20141 Milan, Italy; (N.F.); (F.M.); (D.L.)
| | - Maurizio Martini
- Department of Pathology, University of Messina, 98122 Messina, Italy;
| | - Giorgio Cazzaniga
- Department of Medicine and Surgery, Pathology, IRCCS Fondazione San Gerardo dei Tintori, University of Milano-Bicocca, 20126 Milan, Italy; (V.C.); (G.C.); (V.L.); (F.P.)
| | - Panagiotis Paliogiannis
- Anatomic Pathology and Histology, Department of Medical, Surgical and Experimental Sciences, University of Sassari, 07100 Sassari, Italy; (P.P.); (R.L.)
| | - Renato Lobrano
- Anatomic Pathology and Histology, Department of Medical, Surgical and Experimental Sciences, University of Sassari, 07100 Sassari, Italy; (P.P.); (R.L.)
| | - Vincenzo L’Imperio
- Department of Medicine and Surgery, Pathology, IRCCS Fondazione San Gerardo dei Tintori, University of Milano-Bicocca, 20126 Milan, Italy; (V.C.); (G.C.); (V.L.); (F.P.)
| | - Fabio Pagni
- Department of Medicine and Surgery, Pathology, IRCCS Fondazione San Gerardo dei Tintori, University of Milano-Bicocca, 20126 Milan, Italy; (V.C.); (G.C.); (V.L.); (F.P.)
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27
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Marletta S, Eccher A, Martelli FM, Santonicco N, Girolami I, Scarpa A, Pagni F, L'Imperio V, Pantanowitz L, Gobbo S, Seminati D, Dei Tos AP, Parwani A. Artificial intelligence-based algorithms for the diagnosis of prostate cancer: A systematic review. Am J Clin Pathol 2024:aqad182. [PMID: 38381582 DOI: 10.1093/ajcp/aqad182] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Accepted: 12/16/2023] [Indexed: 02/23/2024] Open
Abstract
OBJECTIVES The high incidence of prostate cancer causes prostatic samples to significantly affect pathology laboratories workflow and turnaround times (TATs). Whole-slide imaging (WSI) and artificial intelligence (AI) have both gained approval for primary diagnosis in prostate pathology, providing physicians with novel tools for their daily routine. METHODS A systematic review according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines was carried out in electronic databases to gather the available evidence on the application of AI-based algorithms to prostate cancer. RESULTS Of 6290 articles, 80 were included, mostly (59%) dealing with biopsy specimens. Glass slides were digitized to WSI in most studies (89%), roughly two-thirds of which (66%) exploited convolutional neural networks for computational analysis. The algorithms achieved good to excellent results about cancer detection and grading, along with significantly reduced TATs. Furthermore, several studies showed a relevant correlation between AI-identified histologic features and prognostic predictive variables such as biochemical recurrence, extraprostatic extension, perineural invasion, and disease-free survival. CONCLUSIONS The published evidence suggests that AI can be reliably used for prostate cancer detection and grading, assisting pathologists in the time-consuming screening of slides. Further technologic improvement would help widening AI's adoption in prostate pathology, as well as expanding its prognostic predictive potential.
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Affiliation(s)
- Stefano Marletta
- Department of Diagnostics and Public Health, Section of Pathology, University of Verona, Verona, Italy
- Division of Pathology, Humanitas Istituto Clinico Catanese, Catania, Italy
| | - Albino Eccher
- Section of Pathology, Department of Medical and Surgical Sciences for Children and Adults, University of Modena and Reggio Emilia, University Hospital of Modena, Modena, Italy
| | - Filippo Maria Martelli
- Department of Diagnostics and Public Health, Section of Pathology, University of Verona, Verona, Italy
| | - Nicola Santonicco
- Department of Diagnostics and Public Health, Section of Pathology, University of Verona, Verona, Italy
| | - Ilaria Girolami
- Department of Pathology, Provincial Hospital of Bolzano (SABES-ASDAA), Bolzano-Bozen, Italy
| | - Aldo Scarpa
- Department of Diagnostics and Public Health, Section of Pathology, University of Verona, Verona, Italy
| | - Fabio Pagni
- Department of Medicine and Surgery, Pathology, University of Milano-Bicocca, IRCCS (Scientific Institute for Research, Hospitalization and Healthcare) Fondazione San Gerardo dei Tintori, Monza, Italy
| | - Vincenzo L'Imperio
- Department of Medicine and Surgery, Pathology, University of Milano-Bicocca, IRCCS (Scientific Institute for Research, Hospitalization and Healthcare) Fondazione San Gerardo dei Tintori, Monza, Italy
| | - Liron Pantanowitz
- Department of Pathology, University of Pittsburgh, Pittsburgh, PA, US
| | - Stefano Gobbo
- Department of Translational Medicine, University of Ferrara, Ferrara, Italy
| | - Davide Seminati
- Department of Medicine and Surgery, Pathology, University of Milano-Bicocca, IRCCS (Scientific Institute for Research, Hospitalization and Healthcare) Fondazione San Gerardo dei Tintori, Monza, Italy
| | - Angelo Paolo Dei Tos
- Surgical Pathology & Cytopathology Unit, Department of Medicine-DIMED, University of Padua, Padua, Italy
| | - Anil Parwani
- Department of Pathology and Laboratory Medicine, Ohio State University Wexner Medical Center, Columbus, OH, US
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Cai M, Zhao K, Wu L, Huang Y, Zhao M, Hu Q, Chen Q, Yao S, Li Z, Fan X, Liu Z. Artificial intelligence-based analysis of tumor-infiltrating lymphocyte spatial distribution for colorectal cancer prognosis. Chin Med J (Engl) 2024; 137:421-430. [PMID: 38238158 PMCID: PMC10876244 DOI: 10.1097/cm9.0000000000002964] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Indexed: 02/21/2024] Open
Abstract
BACKGROUND Artificial intelligence (AI) technology represented by deep learning has made remarkable achievements in digital pathology, enhancing the accuracy and reliability of diagnosis and prognosis evaluation. The spatial distribution of CD3 + and CD8 + T cells within the tumor microenvironment has been demonstrated to have a significant impact on the prognosis of colorectal cancer (CRC). This study aimed to investigate CD3 CT (CD3 + T cells density in the core of the tumor [CT]) prognostic ability in patients with CRC by using AI technology. METHODS The study involved the enrollment of 492 patients from two distinct medical centers, with 358 patients assigned to the training cohort and an additional 134 patients allocated to the validation cohort. To facilitate tissue segmentation and T-cells quantification in whole-slide images (WSIs), a fully automated workflow based on deep learning was devised. Upon the completion of tissue segmentation and subsequent cell segmentation, a comprehensive analysis was conducted. RESULTS The evaluation of various positive T cell densities revealed comparable discriminatory ability between CD3 CT and CD3-CD8 (the combination of CD3 + and CD8 + T cells density within the CT and invasive margin) in predicting mortality (C-index in training cohort: 0.65 vs. 0.64; validation cohort: 0.69 vs. 0.69). The CD3 CT was confirmed as an independent prognostic factor, with high CD3 CT density associated with increased overall survival (OS) in the training cohort (hazard ratio [HR] = 0.22, 95% confidence interval [CI]: 0.12-0.38, P <0.001) and validation cohort (HR = 0.21, 95% CI: 0.05-0.92, P = 0.037). CONCLUSIONS We quantify the spatial distribution of CD3 + and CD8 + T cells within tissue regions in WSIs using AI technology. The CD3 CT confirmed as a stage-independent predictor for OS in CRC patients. Moreover, CD3 CT shows promise in simplifying the CD3-CD8 system and facilitating its practical application in clinical settings.
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Affiliation(s)
- Ming Cai
- Department of Radiology, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong 510080, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, Guangdong 510080, China
| | - Ke Zhao
- Guangdong Cardiovascular Institute, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong 510080, China
- Medical Research Institute, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong 510080, China
| | - Lin Wu
- Department of Pathology, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, Yunnan 650118, China
| | - Yanqi Huang
- Department of Radiology, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong 510080, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, Guangdong 510080, China
| | - Minning Zhao
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou, Guangdong 510515, China
| | - Qingru Hu
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou, Guangdong 510515, China
| | - Qicong Chen
- Institute of Computing Science and Technology, Guangzhou University, Guangzhou, Guangdong 510006, China
| | - Su Yao
- Department of Pathology, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong 510080, China
| | - Zhenhui Li
- Department of Pathology, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, Yunnan 650118, China
| | - Xinjuan Fan
- Department of Pathology, The Sixth Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong 510655, China
| | - Zaiyi Liu
- Department of Radiology, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong 510080, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, Guangdong 510080, China
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Delgado-Coka LA, Horowitz M, Torrente-Goncalves M, Roa-Peña L, Leiton CV, Hasan M, Babu S, Fassler D, Oentoro J, Karen Bai JD, Petricoin EF, Matrisian LM, Blais EM, Marchenko N, Allard FD, Jiang W, Larson B, Hendifar A, Chen C, Abousamra S, Samaras D, Kurc T, Saltz J, Escobar-Hoyos LF, Shroyer K. Keratin 17 modulates the immune topography of pancreatic cancer. Res Sq 2024:rs.3.rs-3886691. [PMID: 38464123 PMCID: PMC10925455 DOI: 10.21203/rs.3.rs-3886691/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
Background The immune microenvironment impacts tumor growth, invasion, metastasis, and patient survival and may provide opportunities for therapeutic intervention in pancreatic ductal adenocarcinoma (PDAC). Although never studied as a potential modulator of the immune response in most cancers, Keratin 17 (K17), a biomarker of the most aggressive (basal) molecular subtype of PDAC, is intimately involved in the histogenesis of the immune response in psoriasis, basal cell carcinoma, and cervical squamous cell carcinoma. Thus, we hypothesized that K17 expression could also impact the immune cell response in PDAC, and that uncovering this relationship could provide insight to guide the development of immunotherapeutic opportunities to extend patient survival. Methods Multiplex immunohistochemistry (mIHC) and automated image analysis based on novel computational imaging technology were used to decipher the abundance and spatial distribution of T cells, macrophages, and tumor cells, relative to K17 expression in 235 PDACs. Results K17 expression had profound effects on the exclusion of intratumoral CD8 + T cells and was also associated with decreased numbers of peritumoral CD8 + T cells, CD16 + macrophages, and CD163 + macrophages (p < 0.0001). The differences in the intratumor and peritumoral CD8 + T cell abundance were not impacted by neoadjuvant therapy, tumor stage, grade, lymph node status, histologic subtype, nor KRAS, p53, SMAD4, or CDKN2A mutations. Conclusions Thus, K17 expression correlates with major differences in the immune microenvironment that are independent of any tested clinicopathologic or tumor intrinsic variables, suggesting that targeting K17-mediated immune effects on the immune system could restore the innate immunologic response to PDAC and might provide novel opportunities to restore immunotherapeutic approaches for this most deadly form of cancer.
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Cabibi D, Giannone AG, Quattrocchi A, Calvaruso V, Porcasi R, Di Grusa D, Pavone AM, Comelli A, Petta S. Quantitative Evaluation by Digital Pathology of Immunohistochemical Expression of CK7, CK19, and EpCAM in Advanced Stages of NASH. Biomedicines 2024; 12:440. [PMID: 38398042 PMCID: PMC10887071 DOI: 10.3390/biomedicines12020440] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2024] [Revised: 02/09/2024] [Accepted: 02/13/2024] [Indexed: 02/25/2024] Open
Abstract
(1) Background: Nonalcoholic Steatohepatitis/Nonalcoholic Fatty Liver Disease (NASH/NAFLD) is the most recurrent chronic liver disease. NASH could present with a cholestatic (C) or hepatic (H) pattern of damage. Recently, we observed that increased Epithelial Cell Adhesion Molecule (EpCAM) expression was the main immunohistochemical feature to distinguish C from H pattern in NASH. (2) Methods: In the present study, we used digital pathology to compare the quantitative results of digital image analysis by QuPath software (Q-results), with the semi-quantitative results of observer assessment (S-results) for cytokeratin 7 and 19, (CK7, CK19) as well as EpCAM expression. Patients were classified into H or C group on the basis of the ratio between alanine transaminase (ALT) and alkaline phosphatase (ALP) values, using the "R-ratio formula". (3) Results: Q- and S-results showed a significant correlation for all markers (p < 0.05). Q-EpCAM expression was significantly higher in the C group than in the H group (p < 0.05). Importantly ALP, an indicator of hepatobiliary disorder, was the only biochemical parameter significantly correlated with Q-EpCAM. Instead, Q-CK7, but not Q-CK19, correlated only with γGlutamyl-Transferase (γGT). Of note, Stage 4 fibrosis correlated with Q-EpCAM, Q-CK19, and ALP but not with γGT or ALT. Conclusions: Image analysis confirms the relation between cholestatic-like pattern, associated with a worse prognosis, with increased ALP values, EpCAM positive biliary metaplasia, and advanced fibrosis. These preliminary data could be useful for the implementation of AI algorithms for the assessment of cholestatic NASH.
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Affiliation(s)
- Daniela Cabibi
- Unit of Anatomic Pathology, Department of Health Promotion Mother and Child Care Internal Medicine and Medical Specialties (PROMISE), University Hospital AOU Policlinico “P. Giaccone”, University of Palermo, Via del Vespro 129, 90127 Palermo, Italy; (D.C.); (A.Q.); (R.P.)
| | - Antonino Giulio Giannone
- Unit of Anatomic Pathology, Department of Health Promotion Mother and Child Care Internal Medicine and Medical Specialties (PROMISE), University Hospital AOU Policlinico “P. Giaccone”, University of Palermo, Via del Vespro 129, 90127 Palermo, Italy; (D.C.); (A.Q.); (R.P.)
| | - Alberto Quattrocchi
- Unit of Anatomic Pathology, Department of Health Promotion Mother and Child Care Internal Medicine and Medical Specialties (PROMISE), University Hospital AOU Policlinico “P. Giaccone”, University of Palermo, Via del Vespro 129, 90127 Palermo, Italy; (D.C.); (A.Q.); (R.P.)
| | - Vincenza Calvaruso
- Section of Gastroenterology and Hepatology, Department of Health Promotion Mother and Child Care Internal Medicine and Medical Specialties (PROMISE), University Hospital AOU Policlinico “P. Giaccone”, University of Palermo, Via del Vespro 129, 90127 Palermo, Italy
| | - Rossana Porcasi
- Unit of Anatomic Pathology, Department of Health Promotion Mother and Child Care Internal Medicine and Medical Specialties (PROMISE), University Hospital AOU Policlinico “P. Giaccone”, University of Palermo, Via del Vespro 129, 90127 Palermo, Italy; (D.C.); (A.Q.); (R.P.)
| | - Domenico Di Grusa
- Unit of Anatomic Pathology, Department of Health Promotion Mother and Child Care Internal Medicine and Medical Specialties (PROMISE), University Hospital AOU Policlinico “P. Giaccone”, University of Palermo, Via del Vespro 129, 90127 Palermo, Italy; (D.C.); (A.Q.); (R.P.)
| | - Anna Maria Pavone
- Ri.MED Foundation, Via Bandiera 11, 90133 Palermo, Italy; (A.M.P.); (A.C.)
| | - Albert Comelli
- Ri.MED Foundation, Via Bandiera 11, 90133 Palermo, Italy; (A.M.P.); (A.C.)
| | - Salvatore Petta
- Section of Gastroenterology and Hepatology, Department of Health Promotion Mother and Child Care Internal Medicine and Medical Specialties (PROMISE), University Hospital AOU Policlinico “P. Giaccone”, University of Palermo, Via del Vespro 129, 90127 Palermo, Italy
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Demetris AJ, Lesniak AJ, Popp BA, Frencho RJ, Minervini MI, Nalesnik MA, El Hag MI, Hariharan S, Randhawa PS. Banff scoring of kidney allograft biopsies: "Manual" application vs software-assisted sign-out. Am J Clin Pathol 2024:aqad180. [PMID: 38340346 DOI: 10.1093/ajcp/aqad180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Accepted: 12/06/2023] [Indexed: 02/12/2024] Open
Abstract
OBJECTIVES Pathologists interpreting kidney allograft biopsies using the Banff system usually start by recording component scores (eg, i, t, cg) using histopathologic criteria committed to memory. Component scores are then melded into diagnoses using the same manual/mental processes. This approach to complex Banff rules during routine sign-out produces a lack of fidelity and needs improvement. METHODS We constructed a web-based "smart template" (software-assisted sign-out) system that uniquely starts with upstream Banff-defined additional diagnostic parameters (eg, infection) and histopathologic criteria (eg, percent interstitial inflammation) collectively referred to as feeder data that is then translated into component scores and integrated into final diagnoses using software-encoded decision trees. RESULTS Software-assisted sign-out enables pathologists to (1) accurately and uniformly apply Banff rules, thereby eliminating human inconsistencies (present in 25% of the cohort); (2) document areas of improvement; (3) show improved correlation with function; (4) examine t-Distributed Stochastic Neighbor Embedding clustering for diagnosis stratification; and (5) ready upstream incorporation of artificial intelligence-assisted scoring of biopsies. CONCLUSIONS Compared with the legacy approach, software-assisted sign-out improves Banff accuracy and fidelity, more closely correlates with kidney function, is practical for routine clinical work and translational research studies, facilitates downstream integration with nonpathology data, and readies biopsy scoring for artificial intelligence algorithms.
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Affiliation(s)
- Anthony J Demetris
- Department of Pathology, Division of Transplantation, University of Pittsburgh, Pittsburgh, PA, US
| | - Andrew J Lesniak
- Department of Pathology, Division of Transplantation, University of Pittsburgh, Pittsburgh, PA, US
| | - Benjamin A Popp
- Department of Pathology, Division of Transplantation, University of Pittsburgh, Pittsburgh, PA, US
| | | | - Marta I Minervini
- Department of Pathology, Division of Transplantation, University of Pittsburgh, Pittsburgh, PA, US
| | - Michael A Nalesnik
- Department of Pathology, Division of Transplantation, University of Pittsburgh, Pittsburgh, PA, US
| | - Mohamed I El Hag
- Department of Pathology, Division of Transplantation, University of Pittsburgh, Pittsburgh, PA, US
| | - Sundaram Hariharan
- Division of Transplant Nephrology, University of Pittsburgh Medical Center, Pittsburgh, PA, US
| | - Parmjeet S Randhawa
- Department of Pathology, Division of Transplantation, University of Pittsburgh, Pittsburgh, PA, US
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Rai T, Morisi A, Bacci B, Bacon NJ, Dark MJ, Aboellail T, Thomas SA, La Ragione RM, Wells K. Keeping Pathologists in the Loop and an Adaptive F1-Score Threshold Method for Mitosis Detection in Canine Perivascular Wall Tumours. Cancers (Basel) 2024; 16:644. [PMID: 38339394 PMCID: PMC10854568 DOI: 10.3390/cancers16030644] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 01/17/2024] [Accepted: 01/29/2024] [Indexed: 02/12/2024] Open
Abstract
Performing a mitosis count (MC) is the diagnostic task of histologically grading canine Soft Tissue Sarcoma (cSTS). However, mitosis count is subject to inter- and intra-observer variability. Deep learning models can offer a standardisation in the process of MC used to histologically grade canine Soft Tissue Sarcomas. Subsequently, the focus of this study was mitosis detection in canine Perivascular Wall Tumours (cPWTs). Generating mitosis annotations is a long and arduous process open to inter-observer variability. Therefore, by keeping pathologists in the loop, a two-step annotation process was performed where a pre-trained Faster R-CNN model was trained on initial annotations provided by veterinary pathologists. The pathologists reviewed the output false positive mitosis candidates and determined whether these were overlooked candidates, thus updating the dataset. Faster R-CNN was then trained on this updated dataset. An optimal decision threshold was applied to maximise the F1-score predetermined using the validation set and produced our best F1-score of 0.75, which is competitive with the state of the art in the canine mitosis domain.
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Affiliation(s)
- Taranpreet Rai
- Centre for Vision, Speech and Signal Processing, University of Surrey, Guildford GU2 7XH, UK;
- Surrey DataHub, University of Surrey, Guildford GU2 7AL, UK
| | - Ambra Morisi
- School of Veterinary Medicine, University of Surrey, Guildford GU2 7AL, UK; (A.M.); (R.M.L.R.)
| | - Barbara Bacci
- Department of Veterinary Medical Sciences, University of Bologna, 40126 Bologna, Italy;
| | | | - Michael J. Dark
- Department of Comparative, Diagnostic and Population Medicine, College of Veterinary Medicine, University of Florida, Gainesville, FL 32611, USA;
| | - Tawfik Aboellail
- Department of Diagnostic Pathology and Pathobiology, Kansas State University, Manhattan, KS 66506, USA;
| | - Spencer A. Thomas
- Department of Computer Science, University of Surrey, Guildford GU2 7XH, UK;
- National Physical Laboratory, London TW11 0LW, UK
| | - Roberto M. La Ragione
- School of Veterinary Medicine, University of Surrey, Guildford GU2 7AL, UK; (A.M.); (R.M.L.R.)
- School of Biosciences, University of Surrey, Guildford GU2 7XH, UK
| | - Kevin Wells
- Centre for Vision, Speech and Signal Processing, University of Surrey, Guildford GU2 7XH, UK;
- Surrey DataHub, University of Surrey, Guildford GU2 7AL, UK
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Shafique A, Gonzalez R, Pantanowitz L, Tan PH, Machado A, Cree IA, Tizhoosh HR. A Preliminary Investigation into Search and Matching for Tumor Discrimination in World Health Organization Breast Taxonomy Using Deep Networks. Mod Pathol 2024; 37:100381. [PMID: 37939901 PMCID: PMC10891482 DOI: 10.1016/j.modpat.2023.100381] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Revised: 10/26/2023] [Accepted: 10/31/2023] [Indexed: 11/10/2023]
Abstract
Breast cancer is one of the most common cancers affecting women worldwide. It includes a group of malignant neoplasms with a variety of biological, clinical, and histopathologic characteristics. There are more than 35 different histologic forms of breast lesions that can be classified and diagnosed histologically according to cell morphology, growth, and architecture patterns. Recently, deep learning, in the field of artificial intelligence, has drawn a lot of attention for the computerized representation of medical images. Searchable digital atlases can provide pathologists with patch-matching tools, allowing them to search among evidently diagnosed and treated archival cases, a technology that may be regarded as computational second opinion. In this study, we indexed and analyzed the World Health Organization breast taxonomy (Classification of Tumors fifth ed.) spanning 35 tumor types. We visualized all tumor types using deep features extracted from a state-of-the-art deep-learning model, pretrained on millions of diagnostic histopathology images from the Cancer Genome Atlas repository. Furthermore, we tested the concept of a digital "atlas" as a reference for search and matching with rare test cases. The patch similarity search within the World Health Organization breast taxonomy data reached >88% accuracy when validating through "majority vote" and >91% accuracy when validating using top n tumor types. These results show for the first time that complex relationships among common and rare breast lesions can be investigated using an indexed digital archive.
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Affiliation(s)
- Abubakr Shafique
- Rhazes Lab, Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, Minnesota; Kimia Lab, University of Waterloo, Waterloo, Ontario, Canada
| | - Ricardo Gonzalez
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota
| | - Liron Pantanowitz
- Department of Pathology, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania
| | - Puay Hoon Tan
- Women's Imaging Centre, Luma Medical Centre, Singapore
| | - Alberto Machado
- WHO Classification of Tumours Group, International Agency for Research on Cancer, Lyon, France
| | - Ian A Cree
- WHO Classification of Tumours Group, International Agency for Research on Cancer, Lyon, France
| | - Hamid R Tizhoosh
- Rhazes Lab, Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, Minnesota; Kimia Lab, University of Waterloo, Waterloo, Ontario, Canada.
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Ratziu V, Hompesch M, Petitjean M, Serdjebi C, Iyer JS, Parwani AV, Tai D, Bugianesi E, Cusi K, Friedman SL, Lawitz E, Romero-Gómez M, Schuppan D, Loomba R, Paradis V, Behling C, Sanyal AJ. Artificial intelligence-assisted digital pathology for non-alcoholic steatohepatitis: current status and future directions. J Hepatol 2024; 80:335-351. [PMID: 37879461 DOI: 10.1016/j.jhep.2023.10.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 08/28/2023] [Accepted: 10/09/2023] [Indexed: 10/27/2023]
Abstract
The worldwide prevalence of non-alcoholic steatohepatitis (NASH) is increasing, causing a significant medical burden, but no approved therapeutics are currently available. NASH drug development requires histological analysis of liver biopsies by expert pathologists for trial enrolment and efficacy assessment, which can be hindered by multiple issues including sample heterogeneity, inter-reader and intra-reader variability, and ordinal scoring systems. Consequently, there is a high unmet need for accurate, reproducible, quantitative, and automated methods to assist pathologists with histological analysis to improve the precision around treatment and efficacy assessment. Digital pathology (DP) workflows in combination with artificial intelligence (AI) have been established in other areas of medicine and are being actively investigated in NASH to assist pathologists in the evaluation and scoring of NASH histology. DP/AI models can be used to automatically detect, localise, quantify, and score histological parameters and have the potential to reduce the impact of scoring variability in NASH clinical trials. This narrative review provides an overview of DP/AI tools in development for NASH, highlights key regulatory considerations, and discusses how these advances may impact the future of NASH clinical management and drug development. This should be a high priority in the NASH field, particularly to improve the development of safe and effective therapeutics.
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Affiliation(s)
- Vlad Ratziu
- Sorbonne Université, ICAN Institute for Cardiometabolism and Nutrition, Hospital Pitié-Salpêtrière, INSERM UMRS 1138 CRC, Paris, France.
| | | | | | | | | | - Anil V Parwani
- Department of Pathology, The Ohio State University, Columbus, OH, USA
| | | | | | - Kenneth Cusi
- Division of Endocrinology, Diabetes and Metabolism, University of Florida, Gainesville, FL, USA
| | - Scott L Friedman
- Division of Liver Diseases, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Eric Lawitz
- Texas Liver Institute, University of Texas Health San Antonio, San Antonio, TX, USA
| | - Manuel Romero-Gómez
- Hospital Universitario Virgen del Rocío, CiberEHD, Insituto de Biomedicina de Sevilla (HUVR/CSIC/US), Universidad de Sevilla, Seville, Spain
| | - Detlef Schuppan
- Institute of Translational Immunology and Department of Medicine, University Medical Center, Mainz, Germany; Department of Hepatology and Gastroenterology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Rohit Loomba
- NAFLD Research Center, University of California at San Diego, San Diego, CA, USA
| | - Valérie Paradis
- Université Paris Cité, Service d'Anatomie Pathologique, Hôpital Beaujon, Paris, France
| | | | - Arun J Sanyal
- Division of Gastroenterology, Hepatology and Nutrition, Virginia Commonwealth University, Richmond, VA, USA
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Yüceer RO, Başpınar Ş. Investigation of Ki67 and Phospho-Histone H3 Expressions in Urothelial Carcinoma of the Bladder by Immunohistochemical Method. Cureus 2024; 16:e55297. [PMID: 38558732 PMCID: PMC10981782 DOI: 10.7759/cureus.55297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/29/2024] [Indexed: 04/04/2024] Open
Abstract
BACKGROUND In our study, it is aimed to investigate the relationship between Ki67 and phospho-histone H3 (pHH3) expressions in bladder urothelial carcinomas, with clinicopathological parameters and survival, which have prognostic value. METHODS The study included 44 cases of high-grade urothelial carcinoma (HGUC), 37 cases of low-grade urothelial carcinoma (LGUC), and 11 nontumoral bladder cases. Ki67 and pHH3 were applied to the paraffin blocks of the tissues of 81 urothelial carcinoma and 11 nontumoral bladder cases by immunohistochemical method. Percentages of Ki67 and pHH3 expressions were evaluated by digital imaging analysis method. Expression percentages were compared with various clinicopathological parameters, and the relationship between them was evaluated. RESULTS Ki67 was expressed in 28% of urothelial carcinoma cases and 1% of nontumoral cases. pHH3 was expressed in 10.32% of urothelial carcinoma cases and 0.16% of nontumoral cases. In our study, we found significantly higher Ki67 and pHH3 expressions in urothelial carcinoma compared to nontumoral cases. There was a statistically significant relationship (p < 0.05) and a positive correlation between Ki67 expression and lymphovascular invasion, pT stage, and histological grade. A statistically significant relationship (p < 0.05) and a positive correlation were found between pHH3 expression and lymphovascular invasion, pT stage, recurrence, and histological grade. In addition, a statistically significant relationship was found between Ki67 and pHH3 expressions. In our study, survival was found to be low in high-grade urothelial carcinoma cases with lymphovascular invasion, advanced age (65 years and older), and high Ki67 and pHH3 expression rates. CONCLUSIONS According to our findings, high Ki67 and pHH3 expressions were found to be associated with poor prognostic parameters such as advanced pathologic stage, high histologic grade, and low survival. Our findings suggest that Ki67 and pHH3 may play a role in the differentiation, progression, and aggressive behavior of urothelial carcinoma. However, further studies are needed to confirm our findings and determine the role of these markers in urothelial carcinoma.
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Affiliation(s)
| | - Şirin Başpınar
- Medical Pathology, Süleyman Demirel University Faculty of Medicine, Isparta, TUR
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Vermorgen S, Gelton T, Bult P, Kusters-Vandevelde HVN, Hausnerová J, Van de Vijver K, Davidson B, Stefansson IM, Kooreman LFS, Qerimi A, Huvila J, Gilks B, Shahi M, Zomer S, Bartosch C, Pijnenborg JMA, Bulten J, Ciompi F, Simons M. Endometrial Pipelle Biopsy Computer-Aided Diagnosis: A Feasibility Study. Mod Pathol 2024; 37:100417. [PMID: 38154654 DOI: 10.1016/j.modpat.2023.100417] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Revised: 12/02/2023] [Accepted: 12/19/2023] [Indexed: 12/30/2023]
Abstract
Endometrial biopsies are important in the diagnostic workup of women who present with abnormal uterine bleeding or hereditary risk of endometrial cancer. In general, approximately 10% of all endometrial biopsies demonstrate endometrial (pre)malignancy that requires specific treatment. As the diagnostic evaluation of mostly benign cases results in a substantial workload for pathologists, artificial intelligence (AI)-assisted preselection of biopsies could optimize the workflow. This study aimed to assess the feasibility of AI-assisted diagnosis for endometrial biopsies (endometrial Pipelle biopsy computer-aided diagnosis), trained on daily-practice whole-slide images instead of highly selected images. Endometrial biopsies were classified into 6 clinically relevant categories defined as follows: nonrepresentative, normal, nonneoplastic, hyperplasia without atypia, hyperplasia with atypia, and malignant. The agreement among 15 pathologists, within these classifications, was evaluated in 91 endometrial biopsies. Next, an algorithm (trained on a total of 2819 endometrial biopsies) rated the same 91 cases, and we compared its performance using the pathologist's classification as the reference standard. The interrater reliability among pathologists was moderate with a mean Cohen's kappa of 0.51, whereas for a binary classification into benign vs (pre)malignant, the agreement was substantial with a mean Cohen's kappa of 0.66. The AI algorithm performed slightly worse for the 6 categories with a moderate Cohen's kappa of 0.43 but was comparable for the binary classification with a substantial Cohen's kappa of 0.65. AI-assisted diagnosis of endometrial biopsies was demonstrated to be feasible in discriminating between benign and (pre)malignant endometrial tissues, even when trained on unselected cases. Endometrial premalignancies remain challenging for both pathologists and AI algorithms. Future steps to improve reliability of the diagnosis are needed to achieve a more refined AI-assisted diagnostic solution for endometrial biopsies that covers both premalignant and malignant diagnoses.
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Affiliation(s)
- Sanne Vermorgen
- Department of Pathology, Radboudumc, Nijmegen, the Netherlands
| | - Thijs Gelton
- Department of Pathology, Radboudumc, Nijmegen, the Netherlands
| | - Peter Bult
- Department of Pathology, Radboudumc, Nijmegen, the Netherlands
| | | | - Jitka Hausnerová
- Department of Pathology, University Hospital Brno, Brno, Czech Republic
| | | | - Ben Davidson
- Department of Pathology, Oslo University Hospital, Norwegian Radium Hospital, Oslo, Norway; University of Oslo, Faculty of Medicine, Institute of Clinical Medicine, Oslo, Norway
| | - Ingunn Marie Stefansson
- Centre for Cancer Biomarkers CCBIO, Department of Clinical Medicine, Section for Pathology, University of Bergen, Bergen, Norway; Department of Pathology, Haukeland University Hospital Bergen, Bergen, Norway
| | - Loes F S Kooreman
- Department of Pathology, GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, the Netherlands
| | - Adelina Qerimi
- Department of Pathology, ViraTherapeutics GmbH, Innsbruck, Austria
| | - Jutta Huvila
- Department of Pathology, University of Turku, Turku University Hospital, Turku, Finland
| | - Blake Gilks
- Department of Pathology, University of British Columbia, Vancouver, Canada
| | - Maryam Shahi
- Department of Pathology, Mayo Clinic, Rochester, Minnesota
| | - Saskia Zomer
- Department of Pathology, Canisius-Wilhelmina Hospital, Nijmegen, the Netherlands
| | - Carla Bartosch
- Department of Pathology, Portuguese Oncology Institute Lisbon, Lisbon, Portugal
| | | | - Johan Bulten
- Department of Pathology, Radboudumc, Nijmegen, the Netherlands
| | | | - Michiel Simons
- Department of Pathology, Radboudumc, Nijmegen, the Netherlands.
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Dawe M, Shi W, Liu TY, Lajkosz K, Shibahara Y, Gopal NEK, Geread R, Mirjahanmardi S, Wei CX, Butt S, Abdalla M, Manolescu S, Liang SB, Chadwick D, Roehrl MHA, McKee TD, Adeoye A, McCready D, Khademi A, Liu FF, Fyles A, Done SJ. Reliability and Variability of Ki-67 Digital Image Analysis Methods for Clinical Diagnostics in Breast Cancer. J Transl Med 2024; 104:100341. [PMID: 38280634 DOI: 10.1016/j.labinv.2024.100341] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Revised: 11/20/2023] [Accepted: 01/19/2024] [Indexed: 01/29/2024] Open
Abstract
Ki-67 is a nuclear protein associated with proliferation, and a strong potential biomarker in breast cancer, but is not routinely measured in current clinical management owing to a lack of standardization. Digital image analysis (DIA) is a promising technology that could allow high-throughput analysis and standardization. There is a dearth of data on the clinical reliability as well as intra- and interalgorithmic variability of different DIA methods. In this study, we scored and compared a set of breast cancer cases in which manually counted Ki-67 has already been demonstrated to have prognostic value (n = 278) to 5 DIA methods, namely Aperio ePathology (Lieca Biosystems), Definiens Tissue Studio (Definiens AG), Qupath, an unsupervised immunohistochemical color histogram algorithm, and a deep-learning pipeline piNET. The piNET system achieved high agreement (interclass correlation coefficient: 0.850) and correlation (R = 0.85) with the reference score. The Qupath algorithm exhibited a high degree of reproducibility among all rater instances (interclass correlation coefficient: 0.889). Although piNET performed well against absolute manual counts, none of the tested DIA methods classified common Ki-67 cutoffs with high agreement or reached the clinically relevant Cohen's κ of at least 0.8. The highest agreement achieved was a Cohen's κ statistic of 0.73 for cutoffs 20% and 25% by the piNET system. The main contributors to interalgorithmic variation and poor cutoff characterization included heterogeneous tumor biology, varying algorithm implementation, and setting assignments. It appears that image segmentation is the primary explanation for semiautomated intra-algorithmic variation, which involves significant manual intervention to correct. Automated pipelines, such as piNET, may be crucial in developing robust and reproducible unbiased DIA approaches to accurately quantify Ki-67 for clinical diagnosis in the future.
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Affiliation(s)
- Melanie Dawe
- Princess Margaret Cancer Centre, University Health Network, Toronto, Canada
| | - Wei Shi
- Princess Margaret Cancer Centre, University Health Network, Toronto, Canada
| | - Tian Y Liu
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Canada
| | - Katherine Lajkosz
- Princess Margaret Cancer Centre, University Health Network, Toronto, Canada
| | - Yukiko Shibahara
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Canada; Laboratory Medicine Program, University Health Network, Toronto, Canada
| | - Nakita E K Gopal
- Princess Margaret Cancer Centre, University Health Network, Toronto, Canada; Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Canada
| | - Rokshana Geread
- Department of Electrical, Computer and Biomedical Engineering, Toronto Metropolitan University, Toronto, Canada
| | - Seyed Mirjahanmardi
- Department of Electrical, Computer and Biomedical Engineering, Toronto Metropolitan University, Toronto, Canada; Division of Medical Physics, Department of Radiation Oncology, Stanford University, Stanford, California
| | - Carrie X Wei
- Princess Margaret Cancer Centre, University Health Network, Toronto, Canada
| | - Sehrish Butt
- STTARR Innovation Centre, University Health Network, Toronto, Canada
| | - Moustafa Abdalla
- Princess Margaret Cancer Centre, University Health Network, Toronto, Canada
| | - Sabrina Manolescu
- Princess Margaret Cancer Centre, University Health Network, Toronto, Canada
| | - Sheng-Ben Liang
- Princess Margaret Cancer Biobank, University Health Network, Toronto, Canada
| | - Dianne Chadwick
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Canada; Laboratory Medicine Program, University Health Network, Toronto, Canada; Princess Margaret Cancer Biobank, University Health Network, Toronto, Canada; Ontario Tumour Bank, Ontario Institute for Cancer Research, Toronto, Canada
| | - Michael H A Roehrl
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Canada; Laboratory Medicine Program, University Health Network, Toronto, Canada; Princess Margaret Cancer Biobank, University Health Network, Toronto, Canada; Department of Pathology, Beth Israel Deaconess Medical Center, Boston, Massachusetts
| | - Trevor D McKee
- STTARR Innovation Centre, University Health Network, Toronto, Canada
| | - Adewunmi Adeoye
- Princess Margaret Cancer Centre, University Health Network, Toronto, Canada
| | - David McCready
- Princess Margaret Cancer Centre, University Health Network, Toronto, Canada
| | - April Khademi
- Department of Electrical, Computer and Biomedical Engineering, Toronto Metropolitan University, Toronto, Canada; St Michael's Hospital, Unity Health Network, Toronto, Canada
| | - Fei-Fei Liu
- Princess Margaret Cancer Centre, University Health Network, Toronto, Canada
| | - Anthony Fyles
- Princess Margaret Cancer Centre, University Health Network, Toronto, Canada
| | - Susan J Done
- Princess Margaret Cancer Centre, University Health Network, Toronto, Canada; Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Canada; Laboratory Medicine Program, University Health Network, Toronto, Canada.
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Lee J, Cha S, Kim J, Kim JJ, Kim N, Jae Gal SG, Kim JH, Lee JH, Choi YD, Kang SR, Song GY, Yang DH, Lee JH, Lee KH, Ahn S, Moon KM, Noh MG. Ensemble Deep Learning Model to Predict Lymphovascular Invasion in Gastric Cancer. Cancers (Basel) 2024; 16:430. [PMID: 38275871 PMCID: PMC10814827 DOI: 10.3390/cancers16020430] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Revised: 01/11/2024] [Accepted: 01/16/2024] [Indexed: 01/27/2024] Open
Abstract
Lymphovascular invasion (LVI) is one of the most important prognostic factors in gastric cancer as it indicates a higher likelihood of lymph node metastasis and poorer overall outcome for the patient. Despite its importance, the detection of LVI(+) in histopathology specimens of gastric cancer can be a challenging task for pathologists as invasion can be subtle and difficult to discern. Herein, we propose a deep learning-based LVI(+) detection method using H&E-stained whole-slide images. The ConViT model showed the best performance in terms of both AUROC and AURPC among the classification models (AUROC: 0.9796; AUPRC: 0.9648). The AUROC and AUPRC of YOLOX computed based on the augmented patch-level confidence score were slightly lower (AUROC: -0.0094; AUPRC: -0.0225) than those of the ConViT classification model. With weighted averaging of the patch-level confidence scores, the ensemble model exhibited the best AUROC, AUPRC, and F1 scores of 0.9880, 0.9769, and 0.9280, respectively. The proposed model is expected to contribute to precision medicine by potentially saving examination-related time and labor and reducing disagreements among pathologists.
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Affiliation(s)
- Jonghyun Lee
- Department of Medical and Digital Engineering, Hanyang University College of Engineering, Seoul 04763, Republic of Korea;
| | - Seunghyun Cha
- Department of Pre-Medicine, Chonnam National University Medical School, 322 Seoyang-ro, Hwasun-eup, Hwasun-gun, Gwangju 58128, Republic of Korea;
| | - Jiwon Kim
- NetTargets, 495 Sinseong-dong, Yuseong, Daejeon 34109, Republic of Korea
| | - Jung Joo Kim
- AMGINE, Inc., Jeongui-ro 8-gil 13, Seoul 05836, Republic of Korea;
| | - Namkug Kim
- Department of Convergence Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul 25440, Republic of Korea; (N.K.); (S.G.J.G.)
| | - Seong Gyu Jae Gal
- Department of Convergence Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul 25440, Republic of Korea; (N.K.); (S.G.J.G.)
| | - Ju Han Kim
- Division of Biomedical Informatics, Seoul National University Biomedical Informatics (SNUBI), Seoul National University College of Medicine, Seoul 03080, Republic of Korea;
| | - Jeong Hoon Lee
- Department of Radiology, Stanford University School of Medicine, Stanford, CA 94305-5101, USA;
| | - Yoo-Duk Choi
- Department of Pathology, Chonnam National University Medical School, Gwangju 61469, Republic of Korea;
| | - Sae-Ryung Kang
- Department of Nuclear Medicine, Clinical Medicine Research Center, Chonnam National University Hospital, 671 Jebongno, Gwangju 61469, Republic of Korea;
| | - Ga-Young Song
- Departments of Hematology-Oncology, Chonnam National University Hwasun Hospital, 322 Seoyangro, Hwasun 58128, Republic of Korea; (G.-Y.S.); (D.-H.Y.)
| | - Deok-Hwan Yang
- Departments of Hematology-Oncology, Chonnam National University Hwasun Hospital, 322 Seoyangro, Hwasun 58128, Republic of Korea; (G.-Y.S.); (D.-H.Y.)
| | - Jae-Hyuk Lee
- Department of Pathology, Chonnam National University Hwasun Hospital and Medical School, 322 Seoyang-ro, Hwasun-eup, Hwasun-gun, Hwasun 58128, Republic of Korea (K.-H.L.)
| | - Kyung-Hwa Lee
- Department of Pathology, Chonnam National University Hwasun Hospital and Medical School, 322 Seoyang-ro, Hwasun-eup, Hwasun-gun, Hwasun 58128, Republic of Korea (K.-H.L.)
| | - Sangjeong Ahn
- Department of Pathology, Korea University Anam Hospital, Korea University College of Medicine, 73 Goryeodae-ro, Seongbuk-gu, Seoul 02841, Republic of Korea;
| | - Kyoung Min Moon
- Division of Pulmonary and Allergy Medicine, Department of Internal Medicine, Chung-Ang University Hospital, Chung-Ang University College of Medicine, Seoul 06973, Republic of Korea
- Artificial Intelligence, ZIOVISION Co., Ltd., Chuncheon 24341, Republic of Korea
| | - Myung-Giun Noh
- Department of Pathology, Chonnam National University Hwasun Hospital and Medical School, 322 Seoyang-ro, Hwasun-eup, Hwasun-gun, Hwasun 58128, Republic of Korea (K.-H.L.)
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Pateras IS, Igea A, Nikas IP, Leventakou D, Koufopoulos NI, Ieronimaki AI, Bergonzini A, Ryu HS, Chatzigeorgiou A, Frisan T, Kittas C, Panayiotides IG. Diagnostic Challenges during Inflammation and Cancer: Current Biomarkers and Future Perspectives in Navigating through the Minefield of Reactive versus Dysplastic and Cancerous Lesions in the Digestive System. Int J Mol Sci 2024; 25:1251. [PMID: 38279253 PMCID: PMC10816510 DOI: 10.3390/ijms25021251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Revised: 01/12/2024] [Accepted: 01/16/2024] [Indexed: 01/28/2024] Open
Abstract
In the setting of pronounced inflammation, changes in the epithelium may overlap with neoplasia, often rendering it impossible to establish a diagnosis with certainty in daily clinical practice. Here, we discuss the underlying molecular mechanisms driving tissue response during persistent inflammatory signaling along with the potential association with cancer in the gastrointestinal tract, pancreas, extrahepatic bile ducts, and liver. We highlight the histopathological challenges encountered in the diagnosis of chronic inflammation in routine practice and pinpoint tissue-based biomarkers that could complement morphology to differentiate reactive from dysplastic or cancerous lesions. We refer to the advantages and limitations of existing biomarkers employing immunohistochemistry and point to promising new markers, including the generation of novel antibodies targeting mutant proteins, miRNAs, and array assays. Advancements in experimental models, including mouse and 3D models, have improved our understanding of tissue response. The integration of digital pathology along with artificial intelligence may also complement routine visual inspections. Navigating through tissue responses in various chronic inflammatory contexts will help us develop novel and reliable biomarkers that will improve diagnostic decisions and ultimately patient treatment.
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Affiliation(s)
- Ioannis S. Pateras
- 2nd Department of Pathology, “Attikon” University Hospital, Medical School, National and Kapodistrian University of Athens, 124 62 Athens, Greece; (D.L.); (N.I.K.); (A.I.I.); (I.G.P.)
| | - Ana Igea
- Instituto de Investigación Sanitaria de Santiago de Compostela (IDIS), 15706 Santiago de Compostela, Spain;
- Mobile Genomes, Centre for Research in Molecular Medicine and Chronic Diseases (CiMUS), University of Santiago de Compostela (USC), 15706 Santiago de Compostela, Spain
| | - Ilias P. Nikas
- Medical School, University of Cyprus, 2029 Nicosia, Cyprus
| | - Danai Leventakou
- 2nd Department of Pathology, “Attikon” University Hospital, Medical School, National and Kapodistrian University of Athens, 124 62 Athens, Greece; (D.L.); (N.I.K.); (A.I.I.); (I.G.P.)
| | - Nektarios I. Koufopoulos
- 2nd Department of Pathology, “Attikon” University Hospital, Medical School, National and Kapodistrian University of Athens, 124 62 Athens, Greece; (D.L.); (N.I.K.); (A.I.I.); (I.G.P.)
| | - Argyro Ioanna Ieronimaki
- 2nd Department of Pathology, “Attikon” University Hospital, Medical School, National and Kapodistrian University of Athens, 124 62 Athens, Greece; (D.L.); (N.I.K.); (A.I.I.); (I.G.P.)
| | - Anna Bergonzini
- Center for Infectious Medicine, Department of Medicine Huddinge, Karolinska Institutet, Karolinska University Hospital, Alfred Nobels Allé 8, 141 52 Stockholm, Sweden;
- Department of Molecular Biology and Umeå Centre for Microbial Research (UCMR), Umeå University, 901 87 Umeå, Sweden;
| | - Han Suk Ryu
- Department of Pathology, Seoul National University Hospital, Seoul 03080, Republic of Korea;
| | - Antonios Chatzigeorgiou
- Department of Physiology, Medical School, National and Kapodistrian University of Athens, 115 27 Athens, Greece;
| | - Teresa Frisan
- Department of Molecular Biology and Umeå Centre for Microbial Research (UCMR), Umeå University, 901 87 Umeå, Sweden;
| | - Christos Kittas
- Department of Histopathology, Biomedicine Group of Health Company, 156 26 Athens, Greece;
| | - Ioannis G. Panayiotides
- 2nd Department of Pathology, “Attikon” University Hospital, Medical School, National and Kapodistrian University of Athens, 124 62 Athens, Greece; (D.L.); (N.I.K.); (A.I.I.); (I.G.P.)
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Zdrenka M, Kowalewski A, Ahmadi N, Sadiqi RU, Chmura Ł, Borowczak J, Maniewski M, Szylberg Ł. Refining PD-1/PD-L1 assessment for biomarker-guided immunotherapy: A review. Biomol Biomed 2024; 24:14-29. [PMID: 37877810 PMCID: PMC10787614 DOI: 10.17305/bb.2023.9265] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 06/23/2023] [Accepted: 06/23/2023] [Indexed: 10/26/2023]
Abstract
Anti-programmed cell death ligand 1 (anti-PD-L1) immunotherapy is an increasingly crucial in cancer treatment. To date, the Federal Drug Administration (FDA) has approved four PD-L1 immunohistochemistry (IHC) staining protocols, commercially available in the form of "kits", facilitating testing for PD-L1 expression. These kits comprise four PD-L1 antibodies on two separate IHC platforms, each utilizing distinct, non-interchangeable scoring systems. Several factors, including tumor heterogeneity and the size of the tissue specimens assessed, can lead to PD-L1 status misclassification, potentially hindering the initiation of therapy. Therefore, the development of more accurate predictive biomarkers to distinguish between responders and non-responders prior to anti-PD-1/PD-L1 therapy warrants further research. Achieving this goal necessitates refining sampling criteria, enhancing current methods of PD-L1 detection, and deepening our understanding of the impact of additional biomarkers. In this article, we review potential solutions to improve the predictive accuracy of PD-L1 assessment in order to more precisely anticipate patients' responses to anti-PD-1/PD-L1 therapy, monitor disease progression and predict clinical outcomes.
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Affiliation(s)
- Marek Zdrenka
- Department of Tumor Pathology and Pathomorphology, Oncology Centre-Prof. Franciszek Łukaszczyk Memorial Hospital, Bydgoszcz, Poland
| | - Adam Kowalewski
- Department of Tumor Pathology and Pathomorphology, Oncology Centre-Prof. Franciszek Łukaszczyk Memorial Hospital, Bydgoszcz, Poland
| | - Navid Ahmadi
- Department of Cardiothoracic Surgery, Royal Papworth Hospital, Cambridge, UK
| | | | - Łukasz Chmura
- Department of Pathomorphology, Jagiellonian University Medical College, Kraków, Poland
| | - Jędrzej Borowczak
- Department of Obstetrics, Gynaecology and Oncology, Chair of Pathomorphology and Clinical Placentology, Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Torun, Bydgoszcz, Poland
| | - Mateusz Maniewski
- Department of Obstetrics, Gynaecology and Oncology, Chair of Pathomorphology and Clinical Placentology, Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Torun, Bydgoszcz, Poland
| | - Łukasz Szylberg
- Department of Tumor Pathology and Pathomorphology, Oncology Centre-Prof. Franciszek Łukaszczyk Memorial Hospital, Bydgoszcz, Poland
- Department of Obstetrics, Gynaecology and Oncology, Chair of Pathomorphology and Clinical Placentology, Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Torun, Bydgoszcz, Poland
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Chen W, Ziebell J, Arole V, Parkinson B, Yu L, Dai H, Frankel WL, Yearsley M, Esnakula A, Sun S, Gamble D, Vazzano J, Mishra M, Schoenfield L, Kneile J, Reuss S, Schumacher M, Satturwar S, Li Z, Parwani A, Lujan G. Comparing Accuracy of Helicobacter pylori Identification Using Traditional Hematoxylin and Eosin-Stained Glass Slides With Digital Whole Slide Imaging. J Transl Med 2024; 104:100262. [PMID: 37839639 DOI: 10.1016/j.labinv.2023.100262] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2022] [Revised: 10/05/2023] [Accepted: 10/06/2023] [Indexed: 10/17/2023] Open
Abstract
With advancements in the field of digital pathology, there has been a growing need to compare the diagnostic abilities of pathologists using digitized whole slide images against those when using traditional hematoxylin and eosin (H&E)-stained glass slides for primary diagnosis. One of the most common specimens received in pathology practices is an endoscopic gastric biopsy with a request to rule out Helicobacter pylori (H. pylori) infection. The current standard of care is the identification of the organisms on H&E-stained slides. Immunohistochemical or histochemical stains are used selectively. However, due to their small size (2-4 μm in length by 0.5-1 μm in width), visualization of the organisms can present a diagnostic challenge. The goal of the study was to compare the ability of pathologists to identify H. pylori on H&E slides using a digital platform against the gold standard of H&E glass slides using routine light microscopy. Diagnostic accuracy rates using glass slides vs digital slides were 81% vs 72% (P = .0142) based on H&E slides alone. When H. pylori immunohistochemical slides were provided, the diagnostic accuracy was significantly improved to comparable rates (96% glass vs 99% digital, P = 0.2199). Furthermore, differences in practice settings (academic/subspecialized vs community/general) and the duration of sign-out experience did not significantly impact the accuracy of detecting H. pylori on digital slides. We concluded that digital whole slide images, although amenable in different practice settings and teaching environments, does present some shortcomings in accuracy and precision, especially in certain circumstances and thus is not yet fully capable of completely replacing glass slide review for identification of H. pylori. We specifically recommend reviewing glass slides and/or performing ancillary stains, especially when there is a discrepancy between the degree of inflammation and the presence of microorganisms on digital images.
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Affiliation(s)
- Wei Chen
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, Ohio
| | - Jennifer Ziebell
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, Ohio
| | - Vidya Arole
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, Ohio
| | - Bryce Parkinson
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, Ohio
| | - Lianbo Yu
- Department of Biomedical Informatics, The Ohio State University, Columbus, Ohio
| | - Harrison Dai
- Eastern Virginia Medical School, Norfolk, Virginia
| | - Wendy L Frankel
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, Ohio
| | - Martha Yearsley
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, Ohio
| | - Ashwini Esnakula
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, Ohio
| | - Shaoli Sun
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, Ohio
| | - Denise Gamble
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, Ohio
| | - Jennifer Vazzano
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, Ohio
| | - Manisha Mishra
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, Ohio
| | - Lynn Schoenfield
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, Ohio
| | - Jeffrey Kneile
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, Ohio
| | - Sarah Reuss
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, Ohio
| | - Melinda Schumacher
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, Ohio
| | - Swati Satturwar
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, Ohio
| | - Zaibo Li
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, Ohio
| | - Anil Parwani
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, Ohio
| | - Giovanni Lujan
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, Ohio.
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Chen C, Lu C, Viswanathan V, Maveal B, Maheshwari B, Willis J, Madabhushi A. Identifying primary tumor site of origin for liver metastases via a combination of handcrafted and deep learning features. J Pathol Clin Res 2024; 10:e344. [PMID: 37822044 PMCID: PMC10766034 DOI: 10.1002/cjp2.344] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 08/14/2023] [Accepted: 08/28/2023] [Indexed: 10/13/2023]
Abstract
Liver is one of the most common sites for metastases, which can occur on account of primary tumors from multiple sites of origin. Identifying the primary site of origin (PSO) of a metastasis can help in guiding therapeutic options for liver metastases. In this pilot study, we hypothesized that computer extracted handcrafted (HC) histomorphometric features can be utilized to identify the PSO of liver metastases. Cellular features, including tumor nuclei morphological and graph features as well as cytoplasm texture features, were extracted by computer algorithms from 175 slides (114 patients). The study comprised three experiments: (1) comparing and (2) fusing a machine learning (ML) model trained with HC pathomic features and deep learning (DL)-based classifiers to predict site of origin; (3) identifying the section of the primary tumor from which metastases were derived. For experiment 1, we divided the cohort into training sets composed of primary and matched liver metastases [60 patients, 121 whole slide images (WSIs)], and a hold-out validation set (54 patients, 54 WSIs) composed solely of liver metastases of known site of origin. Using the extracted HC features of the training set, a combination of supervised machine classifiers and unsupervised clustering was applied to identify the PSO. A random forest classifier achieved areas under the curve (AUCs) of 0.83, 0.64, 0.82, and 0.64 in classifying the metastatic tumor from colon, esophagus, breast, and pancreas on the validation set. The top features related to nuclear and peri-nuclear shape and textural attributes. We also trained a DL network to serve as a direct comparison to our method. The DL model achieved AUCs for colon: 0.94, esophagus: 0.66, breast: 0.79, and pancreas: 0.67 in identifying PSO. A decision fusion-based strategy was deployed to fuse the trained ML and DL classifiers and achieved slightly better results than ML or DL classifier alone (colon: 0.93, esophagus: 0.68, breast: 0.81, and pancreas: 0.69). For the third experiment, WSI-level attention maps were also generated using a trained DL network to generate a composite feature similarity heat map between paired primaries and their associated metastases. Our experiments revealed that epithelium-rich and moderately differentiated tumor regions of primary tumors were quantitatively similar to paired metastatic tumors. Our findings suggest that a combination of HC and DL features could potentially help identify the PSO for liver metastases while at the same time also potentially identify the spatial sites of origin for the metastases within primary tumors.
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Affiliation(s)
- Chuheng Chen
- Department of Biomedical EngineeringCase Western Reserve UniversityClevelandOHUSA
| | - Cheng Lu
- Department of Biomedical EngineeringCase Western Reserve UniversityClevelandOHUSA
| | - Vidya Viswanathan
- Wallace H. Coulter Department of Biomedical EngineeringGeorgia Institute of Technology and Emory UniversityAtlantaGAUSA
| | - Brandon Maveal
- Department of PathologyUniversity Hospitals Cleveland Medical Center and Case Western Reserve UniversityClevelandOHUSA
| | - Bhunesh Maheshwari
- Department of PathologyUniversity Hospitals Cleveland Medical Center and Case Western Reserve UniversityClevelandOHUSA
| | - Joseph Willis
- Department of Biomedical EngineeringCase Western Reserve UniversityClevelandOHUSA
- Department of PathologyUniversity Hospitals Cleveland Medical Center and Case Western Reserve UniversityClevelandOHUSA
| | - Anant Madabhushi
- Wallace H. Coulter Department of Biomedical EngineeringGeorgia Institute of Technology and Emory UniversityAtlantaGAUSA
- Radiology and Imaging Sciences, Biomedical Informatics (BMI) and PathologyGeorgia Institute of Technology and Emory UniversityAtlantaGAUSA
- Atlanta Veterans Administration Medical CenterAtlantaGAUSA
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Liu JTC, Chow SSL, Colling R, Downes MR, Farré X, Humphrey P, Janowczyk A, Mirtti T, Verrill C, Zlobec I, True LD. Engineering the future of 3D pathology. J Pathol Clin Res 2024; 10:e347. [PMID: 37919231 PMCID: PMC10807588 DOI: 10.1002/cjp2.347] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Revised: 10/06/2023] [Accepted: 10/15/2023] [Indexed: 11/04/2023]
Abstract
In recent years, technological advances in tissue preparation, high-throughput volumetric microscopy, and computational infrastructure have enabled rapid developments in nondestructive 3D pathology, in which high-resolution histologic datasets are obtained from thick tissue specimens, such as whole biopsies, without the need for physical sectioning onto glass slides. While 3D pathology generates massive datasets that are attractive for automated computational analysis, there is also a desire to use 3D pathology to improve the visual assessment of tissue histology. In this perspective, we discuss and provide examples of potential advantages of 3D pathology for the visual assessment of clinical specimens and the challenges of dealing with large 3D datasets (of individual or multiple specimens) that pathologists have not been trained to interpret. We discuss the need for artificial intelligence triaging algorithms and explainable analysis methods to assist pathologists or other domain experts in the interpretation of these novel, often complex, large datasets.
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Affiliation(s)
- Jonathan TC Liu
- Department of Mechanical EngineeringUniversity of WashingtonSeattleWAUSA
- Department of Laboratory Medicine & PathologyUniversity of Washington School of MedicineSeattleUSA
- Department of BioengineeringUniversity of WashingtonSeattleUSA
| | - Sarah SL Chow
- Department of Mechanical EngineeringUniversity of WashingtonSeattleWAUSA
| | | | | | | | - Peter Humphrey
- Department of UrologyYale School of MedicineNew HavenCTUSA
| | - Andrew Janowczyk
- Wallace H Coulter Department of Biomedical EngineeringEmory University and Georgia Institute of TechnologyAtlantaGAUSA
- Geneva University HospitalsGenevaSwitzerland
| | - Tuomas Mirtti
- Helsinki University Hospital and University of HelsinkiHelsinkiFinland
- Emory University School of MedicineAtlantaGAUSA
| | - Clare Verrill
- John Radcliffe HospitalUniversity of OxfordOxfordUK
- NIHR Oxford Biomedical Research CentreOxford University Hospitals NHS Foundation TrustOxfordUK
| | - Inti Zlobec
- Institute for Tissue Medicine and PathologyUniversity of BernBernSwitzerland
| | - Lawrence D True
- Department of Laboratory Medicine & PathologyUniversity of Washington School of MedicineSeattleUSA
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Mudhar HS, Krishna Y, Cross S, Auw-Haedrich C, Barnhill R, Cherepanoff S, Eagle R, Farmer J, Folberg R, Grossniklaus H, Herwig-Carl MC, Hyrcza M, Lassalle S, Loeffler KU, Moulin A, Milman T, Verdijk RM, Heegaard S, Coupland SE. A Multicenter Study Validates the WHO 2022 Classification for Conjunctival Melanocytic Intraepithelial Lesions With Clinical and Prognostic Relevance. J Transl Med 2024; 104:100281. [PMID: 37924948 DOI: 10.1016/j.labinv.2023.100281] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2023] [Revised: 09/16/2023] [Accepted: 10/27/2023] [Indexed: 11/06/2023] Open
Abstract
Several nomenclature and grading systems have been proposed for conjunctival melanocytic intraepithelial lesions (C-MIL). The fourth "WHO Classification of Eye Tumors" (WHO-EYE04) proposed a C-MIL classification, capturing the progression of noninvasive neoplastic melanocytes from low- to high-grade lesions, onto melanoma in situ (MIS), and then to invasive melanoma. This proposal was revised to the WHO-EYE05 C-MIL system, which simplified the high-grade C-MIL, whereby MIS was subsumed into high-grade C-MIL. Our aim was to validate the WHO-EYE05 C-MIL system using digitized images of C-MIL, stained with hematoxylin and eosin and immunohistochemistry. However, C-MIL cases were retrieved from 3 supraregional ocular pathology centers. Adequate conjunctival biopsies were stained with hematoxylin and eosin, Melan-A, SOX10, and PReferentially expressed Antigen in Melanoma. Digitized slides were uploaded on the SmartZoom platform and independently scored by 4 ocular pathologists to obtain a consensus score, before circulating to 14 expert eye pathologists for independent scoring. In total, 105 cases from 97 patients were evaluated. The initial consensus diagnoses using the WHO-EYE04 C-MIL system were as follows: 28 benign conjunctival melanoses, 13 low-grade C-MIL, 37 high-grade C-MIL, and 27 conjunctival MIS. Using this system resulted in 93% of the pathologists showing only fair-to-moderate agreement (kappa statistic) with the consensus score. The WHO-EYE05 C-MIL system (with high-grade C-MIL and MIS combined) improved consistency between pathologists, with the greatest level of agreement being seen with benign melanosis (74.5%) and high-grade C-MIL (85.4%). Lowest agreements remained between pathologists for low-grade C-MIL (38.7%). Regarding WHO-EYE05 C-MIL scoring and clinical outcomes, local recurrences of noninvasive lesions developed in 8% and 34% of the low- and high-grade cases. Invasive melanoma only occurred in 47% of the cases that were assessed as high-grade C-MIL. This extensive international collaborative study is the first to undertake a comprehensive review of the WHO-EYE05 C-MIL scoring system, which showed good interobserver agreement and reproducibility.
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Affiliation(s)
- Hardeep Singh Mudhar
- National Specialist Ophthalmic Pathology Service, Department of Histopathology, E-Floor, Royal Hallamshire Hospital, Sheffield, UK
| | - Yamini Krishna
- National Specialist Ophthalmic Pathology Service, Liverpool Clinical Laboratories, Liverpool University Hospitals NHS Foundation Trust, Liverpool, UK; Liverpool Ocular Oncology Research Group, Department of Molecular and Clinical Cancer Medicine, Institute of System Molecular and Integrative Biology, University of Liverpool, Liverpool, UK
| | - Simon Cross
- Academic Unit of Pathology, Department of Neuroscience, University of Sheffield, Sheffield, UK
| | | | - Raymond Barnhill
- Department of Translational Research, Institut Curie, Paris Sciences and Lettres Research University, and Faculty of Medicine University of Paris Descartes, Paris, France
| | - Svetlana Cherepanoff
- Sydpath, Department of Anatomical Pathology, St Vincent's Hospital, Sydney, New South Wales, Australia; Faculty of Medicine, University of New South Wales, Sydney, New South Wales, Australia
| | - Ralph Eagle
- Department of Pathology, Wills Eye Hospital, Sidney Kimmel Medical College of Thomas Jefferson University, Philadelphia, Pennsylvania; Department of Ophthalmology, Wills Eye Hospital, Sidney Kimmel Medical College of Thomas Jefferson University, Philadelphia, Pennsylvania
| | - James Farmer
- Departments of Ophthalmology and Pathology and Molecular Medicine, Queen's University, Kingston, Ontario, Canada; Departments of Pathology and Laboratory Medicine and Ophthalmology, University of Ottawa, Ottawa, Ontario, Canada
| | - Robert Folberg
- Departments of Ophthalmology and Pathology, Oakland University William Beaumont School of Medicine, Rochester, Michigan; Departments of Ophthalmology and Pathology, Corewell Health William Beaumont University Hospital, Royal Oak, Michigan
| | - Hans Grossniklaus
- Department of Ophthalmology, Ocular Oncology and Pathology Section, Emory Eye Center, Emory University School of Medicine, Atlanta, Georgia
| | - Martina C Herwig-Carl
- Department of Ophthalmology, Division of Ophthalmic Pathology, University Hospital Bonn, Bonn, Germany
| | - Martin Hyrcza
- Department of Pathology and Laboratory Medicine, University of Calgary, Arnie Charbonneau Cancer Institute, Calgary, Alberta, Canada
| | - Sandra Lassalle
- Laboratory of Clinical and Experimental Pathology, Hospital-Related Biobank (BB-0033-00025), Pasteur Hospital, Centre Hospitalier Universitaire de Nice and Institute of Research on Cancer and Aging, FHU OncoAge, Université Côte d'Azur, Nice, France
| | - Karin U Loeffler
- Department of Ophthalmology, Division of Ophthalmic Pathology, University Hospital Bonn, Bonn, Germany
| | - Alexandre Moulin
- Jules-Gonin Eye Hospital, Fondation Asile des Aveugles, University of Lausanne, Lausanne, Switzerland
| | - Tatyana Milman
- Department of Pathology, Wills Eye Hospital, Sidney Kimmel Medical College of Thomas Jefferson University, Philadelphia, Pennsylvania; Department of Ophthalmology, Wills Eye Hospital, Sidney Kimmel Medical College of Thomas Jefferson University, Philadelphia, Pennsylvania
| | - Robert M Verdijk
- Department of Pathology, Section of Ophthalmic Pathology, Erasmus MC University Medical Center Rotterdam, Rotterdam, The Netherlands; Department of Pathology, Leiden University Medical Center, Leiden, The Netherlands
| | - Steffen Heegaard
- Department of Pathology, Eye Pathology Section, and Ophthalmology, Rigshospitalet, University of Copenhagen, Denmark
| | - Sarah E Coupland
- National Specialist Ophthalmic Pathology Service, Liverpool Clinical Laboratories, Liverpool University Hospitals NHS Foundation Trust, Liverpool, UK; Liverpool Ocular Oncology Research Group, Department of Molecular and Clinical Cancer Medicine, Institute of System Molecular and Integrative Biology, University of Liverpool, Liverpool, UK.
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Guitton T, Allaume P, Rabilloud N, Rioux-Leclercq N, Henno S, Turlin B, Galibert-Anne MD, Lièvre A, Lespagnol A, Pécot T, Kammerer-Jacquet SF. Artificial Intelligence in Predicting Microsatellite Instability and KRAS, BRAF Mutations from Whole-Slide Images in Colorectal Cancer: A Systematic Review. Diagnostics (Basel) 2023; 14:99. [PMID: 38201408 PMCID: PMC10795725 DOI: 10.3390/diagnostics14010099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Revised: 12/22/2023] [Accepted: 12/24/2023] [Indexed: 01/12/2024] Open
Abstract
Mismatch repair deficiency (d-MMR)/microsatellite instability (MSI), KRAS, and BRAF mutational status are crucial for treating advanced colorectal cancer patients. Traditional methods like immunohistochemistry or polymerase chain reaction (PCR) can be challenged by artificial intelligence (AI) based on whole slide images (WSI) to predict tumor status. In this systematic review, we evaluated the role of AI in predicting MSI status, KRAS, and BRAF mutations in colorectal cancer. Studies published in PubMed up to June 2023 were included (n = 17), and we reported the risk of bias and the performance for each study. Some studies were impacted by the reduced number of slides included in the data set and the lack of external validation cohorts. Deep learning models for the d-MMR/MSI status showed a good performance in training cohorts (mean AUC = 0.89, [0.74-0.97]) but slightly less than expected in the validation cohort when available (mean AUC = 0.82, [0.63-0.98]). Contrary to the MSI status, the prediction of KRAS and BRAF mutations was less explored with a less robust methodology. The performance was lower, with a maximum of 0.77 in the training cohort, 0.58 in the validation cohort for KRAS, and 0.82 AUC in the training cohort for BRAF.
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Affiliation(s)
- Theo Guitton
- Department of Pathology CHU de Rennes, Rennes 1 University, Pontchaillou Hospital, 2 Rue Henri Le Guilloux, CEDEX 09, 35033 Rennes, France; (P.A.); (N.R.-L.); (S.-F.K.-J.)
| | - Pierre Allaume
- Department of Pathology CHU de Rennes, Rennes 1 University, Pontchaillou Hospital, 2 Rue Henri Le Guilloux, CEDEX 09, 35033 Rennes, France; (P.A.); (N.R.-L.); (S.-F.K.-J.)
| | - Noémie Rabilloud
- Impact TEAM, Laboratoire Traitement du Signal et de l’Image (LTSI) INSERM, Rennes 1 University, Pontchaillou Hospital, CEDEX 09, 35033 Rennes, France
| | - Nathalie Rioux-Leclercq
- Department of Pathology CHU de Rennes, Rennes 1 University, Pontchaillou Hospital, 2 Rue Henri Le Guilloux, CEDEX 09, 35033 Rennes, France; (P.A.); (N.R.-L.); (S.-F.K.-J.)
| | - Sébastien Henno
- Department of Pathology CHU de Rennes, Rennes 1 University, Pontchaillou Hospital, 2 Rue Henri Le Guilloux, CEDEX 09, 35033 Rennes, France; (P.A.); (N.R.-L.); (S.-F.K.-J.)
| | - Bruno Turlin
- Department of Pathology CHU de Rennes, Rennes 1 University, Pontchaillou Hospital, 2 Rue Henri Le Guilloux, CEDEX 09, 35033 Rennes, France; (P.A.); (N.R.-L.); (S.-F.K.-J.)
| | - Marie-Dominique Galibert-Anne
- Department of Molecular Genetics and Medical Genomics CHU de Rennes, Rennes 1 University, Pontchaillou Hospital, 2 Rue Henri Le Guilloux, CEDEX 09, 35033 Rennes, France; (M.-D.G.-A.); (A.L.)
| | - Astrid Lièvre
- Department of Gastro-Entrology CHU de Rennes, Rennes 1 University, Pontchaillou Hospital, 2 Rue Henri Le Guilloux, CEDEX 09, 35033 Rennes, France;
| | - Alexandra Lespagnol
- Department of Molecular Genetics and Medical Genomics CHU de Rennes, Rennes 1 University, Pontchaillou Hospital, 2 Rue Henri Le Guilloux, CEDEX 09, 35033 Rennes, France; (M.-D.G.-A.); (A.L.)
| | - Thierry Pécot
- Facility for Artificial Intelligence and Image Analysis (FAIIA), Biosit UAR 3480 CNRS-US18 INSERM, Rennes University, 2 Avenue du Professeur Léon Bernard, 35042 Rennes, France
| | - Solène-Florence Kammerer-Jacquet
- Department of Pathology CHU de Rennes, Rennes 1 University, Pontchaillou Hospital, 2 Rue Henri Le Guilloux, CEDEX 09, 35033 Rennes, France; (P.A.); (N.R.-L.); (S.-F.K.-J.)
- Impact TEAM, Laboratoire Traitement du Signal et de l’Image (LTSI) INSERM, Rennes 1 University, Pontchaillou Hospital, CEDEX 09, 35033 Rennes, France
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Stulpinas R, Morkunas M, Rasmusson A, Drachneris J, Augulis R, Gulla A, Strupas K, Laurinavicius A. Improving HCC Prognostic Models after Liver Resection by AI-Extracted Tissue Fiber Framework Analytics. Cancers (Basel) 2023; 16:106. [PMID: 38201532 PMCID: PMC10778366 DOI: 10.3390/cancers16010106] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 12/11/2023] [Accepted: 12/21/2023] [Indexed: 01/12/2024] Open
Abstract
Despite advances in diagnostic and treatment technologies, predicting outcomes of patients with hepatocellular carcinoma (HCC) remains a challenge. Prognostic models are further obscured by the variable impact of the tumor properties and the remaining liver parenchyma, often affected by cirrhosis or non-alcoholic fatty liver disease that tend to precede HCC. This study investigated the prognostic value of reticulin and collagen microarchitecture in liver resection samples. We analyzed 105 scanned tissue sections that were stained using a Gordon and Sweet's silver impregnation protocol combined with Picric Acid-Sirius Red. A convolutional neural network was utilized to segment the red-staining collagen and black linear reticulin strands, generating a detailed map of the fiber structure within the HCC and adjacent liver tissue. Subsequent hexagonal grid subsampling coupled with automated epithelial edge detection and computational fiber morphometry provided the foundation for region-specific tissue analysis. Two penalized Cox regression models using LASSO achieved a concordance index (C-index) greater than 0.7. These models incorporated variables such as patient age, tumor multifocality, and fiber-derived features from the epithelial edge in both the tumor and liver compartments. The prognostic value at the tumor edge was derived from the reticulin structure, while collagen characteristics were significant at the epithelial edge of peritumoral liver. The prognostic performance of these models was superior to models solely reliant on conventional clinicopathologic parameters, highlighting the utility of AI-extracted microarchitectural features for the management of HCC.
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Affiliation(s)
- Rokas Stulpinas
- Faculty of Medicine, Institute of Biomedical Sciences, Department of Pathology and Forensic Medicine, Vilnius University, 03101 Vilnius, Lithuania (A.L.)
- National Center of Pathology, Affiliate of Vilnius University Hospital Santaros Klinikos, 08406 Vilnius, Lithuania;
| | - Mindaugas Morkunas
- National Center of Pathology, Affiliate of Vilnius University Hospital Santaros Klinikos, 08406 Vilnius, Lithuania;
- Vilnius Santaros Klinikos Biobank, Vilnius University Hospital Santaros Klinikos, 08661 Vilnius, Lithuania
| | - Allan Rasmusson
- Faculty of Medicine, Institute of Biomedical Sciences, Department of Pathology and Forensic Medicine, Vilnius University, 03101 Vilnius, Lithuania (A.L.)
- National Center of Pathology, Affiliate of Vilnius University Hospital Santaros Klinikos, 08406 Vilnius, Lithuania;
| | - Julius Drachneris
- Faculty of Medicine, Institute of Biomedical Sciences, Department of Pathology and Forensic Medicine, Vilnius University, 03101 Vilnius, Lithuania (A.L.)
- National Center of Pathology, Affiliate of Vilnius University Hospital Santaros Klinikos, 08406 Vilnius, Lithuania;
| | - Renaldas Augulis
- Faculty of Medicine, Institute of Biomedical Sciences, Department of Pathology and Forensic Medicine, Vilnius University, 03101 Vilnius, Lithuania (A.L.)
- National Center of Pathology, Affiliate of Vilnius University Hospital Santaros Klinikos, 08406 Vilnius, Lithuania;
| | - Aiste Gulla
- Faculty of Medicine, Institute of Clinical Medicine, Vilnius University, 03101 Vilnius, Lithuania
- Faculty of Medicine, Centre for Visceral Medicine and Translational Research, Vilnius University, 03101 Vilnius, Lithuania
- Center of Abdominal Surgery, Vilnius University Hospital Santaros Klinikos, 08410 Vilnius, Lithuania
| | - Kestutis Strupas
- Faculty of Medicine, Institute of Clinical Medicine, Vilnius University, 03101 Vilnius, Lithuania
- Faculty of Medicine, Centre for Visceral Medicine and Translational Research, Vilnius University, 03101 Vilnius, Lithuania
- Center of Abdominal Surgery, Vilnius University Hospital Santaros Klinikos, 08410 Vilnius, Lithuania
| | - Arvydas Laurinavicius
- Faculty of Medicine, Institute of Biomedical Sciences, Department of Pathology and Forensic Medicine, Vilnius University, 03101 Vilnius, Lithuania (A.L.)
- National Center of Pathology, Affiliate of Vilnius University Hospital Santaros Klinikos, 08406 Vilnius, Lithuania;
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Sobral-Leite M, Castillo S, Vonk S, Melillo X, Lam N, de Bruijn B, Hagos Y, Sanders J, Almekinders M, Visser L, Groen E, Kristel P, Ercan C, Azarang L, Yuan Y, Menezes R, Lips E, Wesseling J. Artificial intelligence-based morphometric signature to identify ductal carcinoma in situ with low risk of progression to invasive breast cancer. Res Sq 2023:rs.3.rs-3639521. [PMID: 38168198 PMCID: PMC10760295 DOI: 10.21203/rs.3.rs-3639521/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
Ductal carcinoma in situ (DCIS) may progress to ipsilateral invasive breast cancer (iIBC), but often never will. Because DCIS is treated as early breast cancer, many women with harmless DCIS face overtreatment. To identify these women that may forego treatment, we hypothesized that DCIS morphometric features relate to the risk of subsequent iIBC. We developed an artificial intelligence-based DCIS morphometric analysis pipeline (AIDmap) to detect DCIS as a pathologist and measure morphological structures in hematoxylin-eosin-stained (H&E) tissue sections. These were from a case-control study of patients diagnosed with primary DCIS, treated by breast-conserving surgery without radiotherapy. We analyzed 689 WSIs of DCIS of which 226 were diagnosed with subsequent iIBC (cases) and 463 were not (controls). The distribution of 15 duct morphological measurements in each H&E was summarized in 55 morphometric variables. A ridge regression classifier with cross validation predicted 5-years-free of iIBC with an area-under the curve of 0.65 (95% CI 0.55-0.76). A morphometric signature based on the 30 variables most associated with outcome, identified lesions containing small-sized ducts, low number of cells and low DCIS/stroma area ratio. This signature was associated with lower iIBC risk in a multivariate regression model including grade, ER, HER2 and COX-2 expression (HR = 0.56; 95% CI 0.28-0.78). AIDmap has potential to identify harmless DCIS that may not need treatment.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | | | - Caner Ercan
- The University of Texas MD Anderson Cancer Center
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Vazzano J, Johansson D, Hu K, Eurén K, Elfwing S, Parwani A, Zhou M. Evaluation of A Computer-Aided Detection Software for Prostate Cancer Prediction: Excellent Diagnostic Accuracy Independent of Preanalytical Factors. J Transl Med 2023; 103:100257. [PMID: 37813279 DOI: 10.1016/j.labinv.2023.100257] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 09/18/2023] [Accepted: 10/01/2023] [Indexed: 10/11/2023] Open
Abstract
Prostate cancer (PCa) is the most common noncutaneous cancer in men in the Western world. In addition to accurate diagnosis, Gleason grading and tumor volume estimates are critical for patient management. Computer-aided detection (CADe) software can be used to facilitate the diagnosis and improve the diagnostic accuracy and reporting consistency. However, preanalytical factors such as fixation and staining of prostate biopsy specimens and whole slide images (WSI) on scanners can vary significantly between pathology laboratories and may, therefore, impact the quality of WSI and utility of CADe algorithms. We evaluated the performance of a CADe software in predicting PCa on WSIs of prostate biopsy specimens and focused on whether there were any significant differences in image quality between WSIs obtained on different scanners and specimens from different histopathology laboratories. Thirty prostate biopsy specimens from 2 histopathology laboratories in the United States were included in this study. The hematoxylin and eosin slides of the biopsy specimens were scanned on 3 scanners, generating 90 WSIs. These WSIs were then analyzed using a CADe software (INIFY Prostate, Algorithm), which identified and annotated all areas suspicious for PCa and calculated the tumor volume (percentage area of the biopsy core involved). Study pathologists then reviewed the Algorithm's annotations and tumor volume calculation to confirm the diagnosis and identify benign glands that were misclassified as cancer (false positive) and cancer glands that were misclassified as benign (false negative). The CADe software worked equally well on WSIs from all 3 scanners and from both laboratories, with similar sensitivity and specificity. The overall sensitivity was 99.4%, and specificity was 97%. The percentage of suspicious cancer areas calculated by the Algorithm was similar for all 3 scanners. For WSIs with small foci of cancer (<1 mm), the Algorithm identified all cancer glands (sensitivity, 100%). Preanalytical factors had no significant impact on whole slide imaging and subsequent application of a CADe software. Prediction accuracy could potentially be further improved by processing biopsy specimens in a centralized histology laboratory and training the Algorithm on WSIs from the same laboratory in order to minimize variations in preanalytical factors and optimize the diagnostic performance of the Algorithm.
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Affiliation(s)
- Jennifer Vazzano
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, Ohio
| | - Dorota Johansson
- Inify Laboratories AB, Stockholm, Sweden (previously part of ContextVision)
| | - Kun Hu
- Department of Pathology, Tufts Medical Center, Tufts University School of Medicine, Boston, Massachusetts
| | - Kristian Eurén
- Inify Laboratories AB, Stockholm, Sweden (previously part of ContextVision)
| | - Stefan Elfwing
- Inify Laboratories AB, Stockholm, Sweden (previously part of ContextVision)
| | - Anil Parwani
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, Ohio.
| | - Ming Zhou
- Department of Pathology, Tufts Medical Center, Tufts University School of Medicine, Boston, Massachusetts.
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Rinck D, Dittmer M, Tinker D, Smith K, Heinecke G. National resident survey in dermatopathology: The role of slide scanners in resident learning. J Cutan Pathol 2023; 50:1078-1082. [PMID: 37749824 PMCID: PMC10843035 DOI: 10.1111/cup.14538] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Revised: 08/07/2023] [Accepted: 09/13/2023] [Indexed: 09/27/2023]
Abstract
BACKGROUND Dermatology residents gain exposure to dermatopathology through a variety of educational modalities. While virtual pathology applications have risen dramatically, resident utilization of digital libraries, slide scanner availability, and comfort with virtual slides are not well-known. This study aims to assess the current landscape of educational resources used by dermatology residents. METHODS A 17-question survey was sent to dermatology residents through a national email database. The survey was a self-assessment of their experience in dermatopathology education and the use of departmental slide scanners. RESULTS The use of digital dermatopathology is high among trainees, despite only half of respondents reporting slide scanner access. Residents report using virtual images more often in non-clinical dermatopathology didactics and independent studies compared to clinical dermatopathology rotations. Public slide set use was common, while professional society and departmental slide sets may be underutilized. Over half of respondents report being extremely or very comfortable navigating interactive scanned slides. CONCLUSIONS Survey data suggests digital slides are currently predominantly used in non-clinical dermatopathology rotations and independent studies. Incorporation of slide scanners into departments may benefit resident education through the development of high-quality, curated departmental slide sets.
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Affiliation(s)
- Danielle Rinck
- Department of Pathology, Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
| | - Martin Dittmer
- Department of Pathology, University of Washington, Seattle, Washington, USA
| | - Daniel Tinker
- Department of Dermatology, Saint Louis University School of Medicine, Saint Louis, Missouri, USA
| | - Kristin Smith
- Department of Dermatology, Saint Louis University School of Medicine, Saint Louis, Missouri, USA
| | - Gillian Heinecke
- Department of Dermatology, Saint Louis University School of Medicine, Saint Louis, Missouri, USA
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Cazzaniga G, Eccher A, Munari E, Marletta S, Bonoldi E, Della Mea V, Cadei M, Sbaraglia M, Guerriero A, Dei Tos AP, Pagni F, L’Imperio V. Natural Language Processing to extract SNOMED-CT codes from pathological reports. Pathologica 2023; 115:318-324. [PMID: 38180139 PMCID: PMC10767798 DOI: 10.32074/1591-951x-952] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Accepted: 11/17/2023] [Indexed: 01/06/2024] Open
Abstract
Objective The use of standardized structured reports (SSR) and suitable terminologies like SNOMED-CT can enhance data retrieval and analysis, fostering large-scale studies and collaboration. However, the still large prevalence of narrative reports in our laboratories warrants alternative and automated labeling approaches. In this project, natural language processing (NLP) methods were used to associate SNOMED-CT codes to structured and unstructured reports from an Italian Digital Pathology Department. Methods Two NLP-based automatic coding systems (support vector machine, SVM, and long-short term memory, LSTM) were trained and applied to a series of narrative reports. Results The 1163 cases were tested with both algorithms, showing good performances in terms of accuracy, precision, recall, and F1 score, with SVM showing slightly better performances as compared to LSTM (0.84, 0.87, 0.83, 0.82 vs 0.83, 0.85, 0.83, 0.82, respectively). The integration of an explainability allowed identification of terms and groups of words of importance, enabling fine-tuning, balancing semantic meaning and model performance. Conclusions AI tools allow the automatic SNOMED-CT labeling of the pathology archives, providing a retrospective fix to the large lack of organization of narrative reports.
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Affiliation(s)
- Giorgio Cazzaniga
- Department of Medicine and Surgery, Pathology, IRCCS Fondazione San Gerardo dei Tintori, University of Milano-Bicocca, Italy
| | - Albino Eccher
- Section of Pathology, Department of Medical and Surgical Sciences for Children and Adults, University of Modena and Reggio Emilia, University Hospital of Modena, Modena, Italy
| | - Enrico Munari
- Department of Diagnostic and Public Health, Section of Pathology, University of Verona, Verona, Italy
| | - Stefano Marletta
- Department of Diagnostic and Public Health, Section of Pathology, University of Verona, Verona, Italy
| | - Emanuela Bonoldi
- Unit of Surgical Pathology and Cytogenetics, ASST Grande Ospedale Metropolitano Niguarda, Milan, Italy
| | - Vincenzo Della Mea
- Department of Mathematics, Computer Science and Physics, University of Udine, Udine, Italy
| | - Moris Cadei
- Pathology Unit, ASST Spedali Civili di Brescia, Brescia, Italy
| | - Marta Sbaraglia
- Surgical Pathology and Cytopathology Unit, Department of Medicine-DIMED, University of Padua School of Medicine, Padua, Italy
| | - Angela Guerriero
- Surgical Pathology and Cytopathology Unit, Department of Medicine-DIMED, University of Padua School of Medicine, Padua, Italy
| | - Angelo Paolo Dei Tos
- Surgical Pathology and Cytopathology Unit, Department of Medicine-DIMED, University of Padua School of Medicine, Padua, Italy
| | - Fabio Pagni
- Department of Medicine and Surgery, Pathology, IRCCS Fondazione San Gerardo dei Tintori, University of Milano-Bicocca, Italy
| | - Vincenzo L’Imperio
- Department of Medicine and Surgery, Pathology, IRCCS Fondazione San Gerardo dei Tintori, University of Milano-Bicocca, Italy
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