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Kalra N, Verma P, Verma S. Advancements in AI based healthcare techniques with FOCUS ON diagnostic techniques. Comput Biol Med 2024; 179:108917. [PMID: 39059212 DOI: 10.1016/j.compbiomed.2024.108917] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2024] [Revised: 07/15/2024] [Accepted: 07/15/2024] [Indexed: 07/28/2024]
Abstract
Since the past decade, the interest towards more precise and efficient healthcare techniques with special emphasis on diagnostic techniques has increased. Artificial Intelligence has proved to be instrumental in development of various such techniques. The various types of AI like ML, NLP, RPA etc. are being used, which have streamlined and organised the Electronic Health Records (EHR) along with aiding the healthcare provider with decision making and sample and data analysis. This article also deals with the 3 major categories of diagnostic techniques - Imaging based, Pathology based and Preventive diagnostic techniques and what all changes and modifications were brought upon them, due to use of AI. Due to such a high demand, the investment in AI based healthcare techniques has increased substantially, with predicted market size of almost 188 billon USD by 2030. In India itself, AI in healthcare is expected to raise the GDP by 25 billion USD by 2028. But there are also several challenges associated with this like unavailability of quality data, black box issue etc. One of the major challenges is the ethical considerations and issues during use of medical records as it is a very sensitive document. Due to this, there is several trust issues associated with adoption of AI by many organizations. These challenges have also been discussed in this article. Need for further development in the AI based diagnostic techniques is also done in the article. Alongside, the production of such techniques and devices which are easy to use and simple to incorporate into the daily workflows have immense scope in the upcoming times. The increasing scope of Clinical Decision Support System, Telemedicine etc. make AI a promising field in the healthcare and diagnostics arena. Concluding the article, it can be said that despite the presence of various challenges to the implementation and usage, the future prospects for AI in healthcare is immense and work needs to be done in order to ensure the availability of resources for same so that high level of accuracy can be achieved and better health outcomes can be provided to patients. Ethical concerns need to be addressed for smooth implementation and to reduce the burden of the developers, which has been discussed in this narrative review article.
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Affiliation(s)
- Nishita Kalra
- Department of Pharmaceutical Chemistry/Analysis, Delhi Pharmaceutical Sciences & Research University, Pushp Vihar, Sector 3, New Delhi, 110017, India
| | - Prachi Verma
- Department of Pharmaceutical Chemistry/Analysis, Delhi Pharmaceutical Sciences & Research University, Pushp Vihar, Sector 3, New Delhi, 110017, India
| | - Surajpal Verma
- Department of Pharmaceutical Chemistry/Analysis, Delhi Pharmaceutical Sciences & Research University, Pushp Vihar, Sector 3, New Delhi, 110017, India.
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Marini N, Marchesin S, Wodzinski M, Caputo A, Podareanu D, Guevara BC, Boytcheva S, Vatrano S, Fraggetta F, Ciompi F, Silvello G, Müller H, Atzori M. Multimodal representations of biomedical knowledge from limited training whole slide images and reports using deep learning. Med Image Anal 2024; 97:103303. [PMID: 39154617 DOI: 10.1016/j.media.2024.103303] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Revised: 08/08/2024] [Accepted: 08/09/2024] [Indexed: 08/20/2024]
Abstract
The increasing availability of biomedical data creates valuable resources for developing new deep learning algorithms to support experts, especially in domains where collecting large volumes of annotated data is not trivial. Biomedical data include several modalities containing complementary information, such as medical images and reports: images are often large and encode low-level information, while reports include a summarized high-level description of the findings identified within data and often only concerning a small part of the image. However, only a few methods allow to effectively link the visual content of images with the textual content of reports, preventing medical specialists from properly benefitting from the recent opportunities offered by deep learning models. This paper introduces a multimodal architecture creating a robust biomedical data representation encoding fine-grained text representations within image embeddings. The architecture aims to tackle data scarcity (combining supervised and self-supervised learning) and to create multimodal biomedical ontologies. The architecture is trained on over 6,000 colon whole slide Images (WSI), paired with the corresponding report, collected from two digital pathology workflows. The evaluation of the multimodal architecture involves three tasks: WSI classification (on data from pathology workflow and from public repositories), multimodal data retrieval, and linking between textual and visual concepts. Noticeably, the latter two tasks are available by architectural design without further training, showing that the multimodal architecture that can be adopted as a backbone to solve peculiar tasks. The multimodal data representation outperforms the unimodal one on the classification of colon WSIs and allows to halve the data needed to reach accurate performance, reducing the computational power required and thus the carbon footprint. The combination of images and reports exploiting self-supervised algorithms allows to mine databases without needing new annotations provided by experts, extracting new information. In particular, the multimodal visual ontology, linking semantic concepts to images, may pave the way to advancements in medicine and biomedical analysis domains, not limited to histopathology.
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Affiliation(s)
- Niccolò Marini
- Information Systems Institute, University of Applied Sciences Western Switzerland (HES-SO Valais), Sierre, Switzerland.
| | - Stefano Marchesin
- Department of Information Engineering, University of Padua, Padua, Italy.
| | - Marek Wodzinski
- Information Systems Institute, University of Applied Sciences Western Switzerland (HES-SO Valais), Sierre, Switzerland; Department of Measurement and Electronics, AGH University of Kraków, Krakow, Poland
| | - Alessandro Caputo
- Department of Pathology, Ruggi University Hospital, Salerno, Italy; Pathology Unit, Gravina Hospital Caltagirone ASP, Catania, Italy
| | | | | | - Svetla Boytcheva
- Ontotext, Sofia, Bulgaria; Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, Sofia, Bulgaria
| | - Simona Vatrano
- Pathology Unit, Gravina Hospital Caltagirone ASP, Catania, Italy
| | - Filippo Fraggetta
- Pathology Unit, Gravina Hospital Caltagirone ASP, Catania, Italy; Department of Pathology, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Francesco Ciompi
- Department of Pathology, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Gianmaria Silvello
- Department of Information Engineering, University of Padua, Padua, Italy
| | - Henning Müller
- Information Systems Institute, University of Applied Sciences Western Switzerland (HES-SO Valais), Sierre, Switzerland; Medical faculty, University of Geneva, 1211 Geneva, Switzerland
| | - Manfredo Atzori
- Information Systems Institute, University of Applied Sciences Western Switzerland (HES-SO Valais), Sierre, Switzerland; Department of Neurosciences, University of Padua, Padua, Italy
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Dominguez-Morales JP, Duran-Lopez L, Marini N, Vicente-Diaz S, Linares-Barranco A, Atzori M, Müller H. A systematic comparison of deep learning methods for Gleason grading and scoring. Med Image Anal 2024; 95:103191. [PMID: 38728903 DOI: 10.1016/j.media.2024.103191] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Revised: 01/16/2024] [Accepted: 05/02/2024] [Indexed: 05/12/2024]
Abstract
Prostate cancer is the second most frequent cancer in men worldwide after lung cancer. Its diagnosis is based on the identification of the Gleason score that evaluates the abnormality of cells in glands through the analysis of the different Gleason patterns within tissue samples. The recent advancements in computational pathology, a domain aiming at developing algorithms to automatically analyze digitized histopathology images, lead to a large variety and availability of datasets and algorithms for Gleason grading and scoring. However, there is no clear consensus on which methods are best suited for each problem in relation to the characteristics of data and labels. This paper provides a systematic comparison on nine datasets with state-of-the-art training approaches for deep neural networks (including fully-supervised learning, weakly-supervised learning, semi-supervised learning, Additive-MIL, Attention-Based MIL, Dual-Stream MIL, TransMIL and CLAM) applied to Gleason grading and scoring tasks. The nine datasets are collected from pathology institutes and openly accessible repositories. The results show that the best methods for Gleason grading and Gleason scoring tasks are fully supervised learning and CLAM, respectively, guiding researchers to the best practice to adopt depending on the task to solve and the labels that are available.
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Affiliation(s)
- Juan P Dominguez-Morales
- Robotics and Technology of Computers Lab., ETSII-EPS, Universidad de Sevilla, Sevilla 41012, Spain; SCORE Lab, I3US. Universidad de Sevilla, Spain.
| | - Lourdes Duran-Lopez
- Robotics and Technology of Computers Lab., ETSII-EPS, Universidad de Sevilla, Sevilla 41012, Spain; SCORE Lab, I3US. Universidad de Sevilla, Spain
| | - Niccolò Marini
- Information Systems Institute, University of Applied Sciences Western Switzerland (HES-SO Valais), Technopôle 3, Sierre 3960, Switzerland; Centre Universitaire d'Informatique, University of Geneva, Carouge 1227, Switzerland
| | - Saturnino Vicente-Diaz
- Robotics and Technology of Computers Lab., ETSII-EPS, Universidad de Sevilla, Sevilla 41012, Spain; SCORE Lab, I3US. Universidad de Sevilla, Spain
| | - Alejandro Linares-Barranco
- Robotics and Technology of Computers Lab., ETSII-EPS, Universidad de Sevilla, Sevilla 41012, Spain; SCORE Lab, I3US. Universidad de Sevilla, Spain
| | - Manfredo Atzori
- Information Systems Institute, University of Applied Sciences Western Switzerland (HES-SO Valais), Technopôle 3, Sierre 3960, Switzerland; Department of Neuroscience, University of Padua, Via Giustiniani 2, Padua, 35128, Italy
| | - Henning Müller
- Information Systems Institute, University of Applied Sciences Western Switzerland (HES-SO Valais), Technopôle 3, Sierre 3960, Switzerland; Medical faculty, University of Geneva, Geneva 1211, Switzerland
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Cazzato G, Rongioletti F. Artificial intelligence in dermatopathology: Updates, strengths, and challenges. Clin Dermatol 2024:S0738-081X(24)00094-4. [PMID: 38909860 DOI: 10.1016/j.clindermatol.2024.06.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/25/2024]
Abstract
Artificial intelligence (AI) has evolved to become a significant force in various domains, including medicine. We explore the role of AI in pathology, with a specific focus on dermatopathology and neoplastic dermatopathology. AI, encompassing machine learning and deep learning, has demonstrated its potential in tasks ranging from diagnostic applications on whole slide imaging to predictive and prognostic functions in skin pathology. In dermatopathology, studies have assessed AI's ability to identify skin lesions, classify melanomas, and improve diagnostic accuracy. Results indicate that AI, particularly convolutional neural networks, can outperform human pathologists in terms of sensitivity and specificity. AI aids in predicting disease outcomes, identifying aggressive tumors, and differentiating between various skin conditions. Neoplastic dermatopathology showcases AI's prowess in classifying melanocytic lesions, discriminating between melanomas and nevi, and aids dermatopathologists in making accurate diagnoses. Studies emphasize the reproducibility and diagnostic aid that AI provides, especially in challenging cases. In inflammatory and lymphoproliferative dermatopathology, limited research exists, but studies show attempts to use AI to differentiate conditions such as mycosis fungoides and eczema. Although some results are promising, further exploration is needed in these areas. We highlight the extraordinary interest AI has garnered in the scientific community and its potential to assist clinicians and pathologists. Despite the advancements, we have stressed the importance of collaboration between medical professionals, computer scientists, bioinformaticians, and engineers to harness AI's benefits and acknowledging its limitations and risks. The integration of AI into dermatopathology holds great promise, positioning it as a valuable tool rather than as a replacement for human expertise.
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Affiliation(s)
- Gerardo Cazzato
- Section of Molecular Pathology, Department of Precision and Regenerative Medicine and Ionian Area (DiMePRe-J), University of Bari Aldo Moro, Bari, Italy.
| | - Franco Rongioletti
- Vita-Salute San Raffaele University, IRCCS San Raffaele Hospital, Milan, Italy
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Dantas de Oliveira A, Rubio Maturana C, Zarzuela Serrat F, Carvalho BM, Sulleiro E, Prats C, Veiga A, Bosch M, Zulueta J, Abelló A, Sayrol E, Joseph-Munné J, López-Codina D. Development of a low-cost robotized 3D-prototype for automated optical microscopy diagnosis: An open-source system. PLoS One 2024; 19:e0304085. [PMID: 38905190 PMCID: PMC11192333 DOI: 10.1371/journal.pone.0304085] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Accepted: 05/07/2024] [Indexed: 06/23/2024] Open
Abstract
In a clinical context, conventional optical microscopy is commonly used for the visualization of biological samples for diagnosis. However, the availability of molecular techniques and rapid diagnostic tests are reducing the use of conventional microscopy, and consequently the number of experienced professionals starts to decrease. Moreover, the continuous visualization during long periods of time through an optical microscope could affect the final diagnosis results due to induced human errors and fatigue. Therefore, microscopy automation is a challenge to be achieved and address this problem. The aim of the study is to develop a low-cost automated system for the visualization of microbiological/parasitological samples by using a conventional optical microscope, and specially designed for its implementation in resource-poor settings laboratories. A 3D-prototype to automate the majority of conventional optical microscopes was designed. Pieces were built with 3D-printing technology and polylactic acid biodegradable material with Tinkercad/Ultimaker Cura 5.1 slicing softwares. The system's components were divided into three subgroups: microscope stage pieces, storage/autofocus-pieces, and smartphone pieces. The prototype is based on servo motors, controlled by Arduino open-source electronic platform, to emulate the X-Y and auto-focus (Z) movements of the microscope. An average time of 27.00 ± 2.58 seconds is required to auto-focus a single FoV. Auto-focus evaluation demonstrates a mean average maximum Laplacian value of 11.83 with tested images. The whole automation process is controlled by a smartphone device, which is responsible for acquiring images for further diagnosis via convolutional neural networks. The prototype is specially designed for resource-poor settings, where microscopy diagnosis is still a routine process. The coalescence between convolutional neural network predictive models and the automation of the movements of a conventional optical microscope confer the system a wide range of image-based diagnosis applications. The accessibility of the system could help improve diagnostics and provide new tools to laboratories worldwide.
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Affiliation(s)
- Allisson Dantas de Oliveira
- Computational Biology and Complex Systems Group, Physics Department, Universitat Politècnica de Catalunya (UPC), Castelldefels, Spain
- Microbiology Department, Vall d’Hebron Research Institute (VHIR), Vall d’Hebron University Hospital, Barcelona, Spain
| | - Carles Rubio Maturana
- Microbiology Department, Vall d’Hebron Research Institute (VHIR), Vall d’Hebron University Hospital, Barcelona, Spain
- Department of Microbiology and Genetics, Universitat Autònoma de Barcelona (UAB), Barcelona, Spain
| | - Francesc Zarzuela Serrat
- Microbiology Department, Vall d’Hebron Research Institute (VHIR), Vall d’Hebron University Hospital, Barcelona, Spain
| | - Bruno Motta Carvalho
- Department of Informatics and Applied Mathematics, Federal University of Rio Grande do Norte, Natal, Brazil
| | - Elena Sulleiro
- Microbiology Department, Vall d’Hebron Research Institute (VHIR), Vall d’Hebron University Hospital, Barcelona, Spain
- Department of Microbiology and Genetics, Universitat Autònoma de Barcelona (UAB), Barcelona, Spain
- CIBERINFEC, ISCIII- CIBER de Enfermedades Infecciosas, Instituto de Salud Carlos III, Madrid, Spain
| | - Clara Prats
- Computational Biology and Complex Systems Group, Physics Department, Universitat Politècnica de Catalunya (UPC), Castelldefels, Spain
| | | | | | | | - Alberto Abelló
- Database Technologies and Information Group, Service and Information Systems Engineering Department, Universitat Politècnica de Catalunya (UPC), Barcelona, Spain
| | - Elisa Sayrol
- Tecnocampus, Universitat Pompeu Fabra, Mataró, Spain
| | - Joan Joseph-Munné
- Microbiology Department, Vall d’Hebron Research Institute (VHIR), Vall d’Hebron University Hospital, Barcelona, Spain
| | - Daniel López-Codina
- Computational Biology and Complex Systems Group, Physics Department, Universitat Politècnica de Catalunya (UPC), Castelldefels, Spain
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6
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Li M, Xiong X, Xu B, Dickson C. Chinese Oncologists' Perspectives on Integrating AI into Clinical Practice: Cross-Sectional Survey Study. JMIR Form Res 2024; 8:e53918. [PMID: 38838307 PMCID: PMC11187515 DOI: 10.2196/53918] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Revised: 02/21/2024] [Accepted: 04/03/2024] [Indexed: 06/07/2024] Open
Abstract
BACKGROUND The rapid development of artificial intelligence (AI) has brought significant interest to its potential applications in oncology. Although AI-powered tools are already being implemented in some Chinese hospitals, their integration into clinical practice raises several concerns for Chinese oncologists. OBJECTIVE This study aims to explore the concerns of Chinese oncologists regarding the integration of AI into clinical practice and to identify the factors influencing these concerns. METHODS A total of 228 Chinese oncologists participated in a cross-sectional web-based survey from April to June in 2023 in mainland China. The survey gauged their worries about AI with multiple-choice questions. The survey evaluated their views on the statements of "The impact of AI on the doctor-patient relationship" and "AI will replace doctors." The data were analyzed using descriptive statistics, and variate analyses were used to find correlations between the oncologists' backgrounds and their concerns. RESULTS The study revealed that the most prominent concerns were the potential for AI to mislead diagnosis and treatment (163/228, 71.5%); an overreliance on AI (162/228, 71%); data and algorithm bias (123/228, 54%); issues with data security and patient privacy (123/228, 54%); and a lag in the adaptation of laws, regulations, and policies in keeping up with AI's development (115/228, 50.4%). Oncologists with a bachelor's degree expressed heightened concerns related to data and algorithm bias (34/49, 69%; P=.03) and the lagging nature of legal, regulatory, and policy issues (32/49, 65%; P=.046). Regarding AI's impact on doctor-patient relationships, 53.1% (121/228) saw a positive impact, whereas 35.5% (81/228) found it difficult to judge, 9.2% (21/228) feared increased disputes, and 2.2% (5/228) believed that there is no impact. Although sex differences were not significant (P=.08), perceptions varied-male oncologists tended to be more positive than female oncologists (74/135, 54.8% vs 47/93, 50%). Oncologists with a bachelor's degree (26/49, 53%; P=.03) and experienced clinicians (≥21 years; 28/56, 50%; P=.054). found it the hardest to judge. Those with IT experience were significantly more positive (25/35, 71%) than those without (96/193, 49.7%; P=.02). Opinions regarding the possibility of AI replacing doctors were diverse, with 23.2% (53/228) strongly disagreeing, 14% (32/228) disagreeing, 29.8% (68/228) being neutral, 16.2% (37/228) agreeing, and 16.7% (38/228) strongly agreeing. There were no significant correlations with demographic and professional factors (all P>.05). CONCLUSIONS Addressing oncologists' concerns about AI requires collaborative efforts from policy makers, developers, health care professionals, and legal experts. Emphasizing transparency, human-centered design, bias mitigation, and education about AI's potential and limitations is crucial. Through close collaboration and a multidisciplinary strategy, AI can be effectively integrated into oncology, balancing benefits with ethical considerations and enhancing patient care.
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Affiliation(s)
- Ming Li
- Department of Health Policy Management, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, United States
| | - XiaoMin Xiong
- Chongqing Key Laboratory of Intelligent Oncology for Breast Cancer, Chongqing University Cancer Hospital, Chongqing University School of Medicine, Chongqing, China
| | - Bo Xu
- Chongqing Key Laboratory of Intelligent Oncology for Breast Cancer, Chongqing University Cancer Hospital, Chongqing University School of Medicine, Chongqing, China
- Department of Biochemistry and Molecular Biology, Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, National Cancer Research Center, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
| | - Conan Dickson
- Department of Health Policy Management, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, United States
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Galaz-Montoya JG. The advent of preventive high-resolution structural histopathology by artificial-intelligence-powered cryogenic electron tomography. Front Mol Biosci 2024; 11:1390858. [PMID: 38868297 PMCID: PMC11167099 DOI: 10.3389/fmolb.2024.1390858] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2024] [Accepted: 05/08/2024] [Indexed: 06/14/2024] Open
Abstract
Advances in cryogenic electron microscopy (cryoEM) single particle analysis have revolutionized structural biology by facilitating the in vitro determination of atomic- and near-atomic-resolution structures for fully hydrated macromolecular complexes exhibiting compositional and conformational heterogeneity across a wide range of sizes. Cryogenic electron tomography (cryoET) and subtomogram averaging are rapidly progressing toward delivering similar insights for macromolecular complexes in situ, without requiring tags or harsh biochemical purification. Furthermore, cryoET enables the visualization of cellular and tissue phenotypes directly at molecular, nanometric resolution without chemical fixation or staining artifacts. This forward-looking review covers recent developments in cryoEM/ET and related technologies such as cryogenic focused ion beam milling scanning electron microscopy and correlative light microscopy, increasingly enhanced and supported by artificial intelligence algorithms. Their potential application to emerging concepts is discussed, primarily the prospect of complementing medical histopathology analysis. Machine learning solutions are poised to address current challenges posed by "big data" in cryoET of tissues, cells, and macromolecules, offering the promise of enabling novel, quantitative insights into disease processes, which may translate into the clinic and lead to improved diagnostics and targeted therapeutics.
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Affiliation(s)
- Jesús G. Galaz-Montoya
- Department of Bioengineering, James H. Clark Center, Stanford University, Stanford, CA, United States
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8
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Kveton M, Hudec L, Vykopal I, Halinkovic M, Laco M, Felsoova A, Benesova W, Fabian O. Digital pathology in cardiac transplant diagnostics: from biopsies to algorithms. Cardiovasc Pathol 2024; 68:107587. [PMID: 37926351 DOI: 10.1016/j.carpath.2023.107587] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Revised: 10/03/2023] [Accepted: 10/30/2023] [Indexed: 11/07/2023] Open
Abstract
In the field of heart transplantation, the ability to accurately and promptly diagnose cardiac allograft rejection is crucial. This comprehensive review explores the transformative role of digital pathology and computational pathology, especially through machine learning, in this critical domain. These methodologies harness large datasets to extract subtle patterns and valuable information that extend beyond human perceptual capabilities, potentially enhancing diagnostic outcomes. Current research indicates that these computer-based systems could offer accuracy and performance matching, or even exceeding, that of expert pathologists, thereby introducing more objectivity and reducing observer variability. Despite promising results, several challenges such as limited sample sizes, diverse data sources, and the absence of standardized protocols pose significant barriers to the widespread adoption of these techniques. The future of digital pathology in heart transplantation diagnostics depends on utilizing larger, more diverse patient cohorts, standardizing data collection, processing, and evaluation protocols, and fostering collaborative research efforts. The integration of various data types, including clinical, demographic, and imaging information, could further refine diagnostic precision. As researchers address these challenges and promote collaborative efforts, digital pathology has the potential to become an integral part of clinical practice, ultimately improving patient care in heart transplantation.
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Affiliation(s)
- Martin Kveton
- Third Faculty of Medicine, Charles University, Prague, Czech Republic; Clinical and Transplant Pathology Centre, Institute for Clinical and Experimental Medicine, Prague, Czech Republic.
| | - Lukas Hudec
- Faculty of Informatics and Information Technologies, Slovak University of Technology, Bratislava, Slovakia
| | - Ivan Vykopal
- Faculty of Informatics and Information Technologies, Slovak University of Technology, Bratislava, Slovakia
| | - Matej Halinkovic
- Faculty of Informatics and Information Technologies, Slovak University of Technology, Bratislava, Slovakia
| | - Miroslav Laco
- Faculty of Informatics and Information Technologies, Slovak University of Technology, Bratislava, Slovakia
| | - Andrea Felsoova
- Clinical and Transplant Pathology Centre, Institute for Clinical and Experimental Medicine, Prague, Czech Republic; Department of Histology and Embryology, Second Faculty of Medicine, Charles University, Prague, Czech Republic
| | - Wanda Benesova
- Faculty of Informatics and Information Technologies, Slovak University of Technology, Bratislava, Slovakia
| | - Ondrej Fabian
- Clinical and Transplant Pathology Centre, Institute for Clinical and Experimental Medicine, Prague, Czech Republic; Department of Pathology and Molecular Medicine, Third Faculty of Medicine, Charles University and Thomayer Hospital, Prague, Czech Republic
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9
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Vizcarra JC, Pearce TM, Dugger BN, Keiser MJ, Gearing M, Crary JF, Kiely EJ, Morris M, White B, Glass JD, Farrell K, Gutman DA. Toward a generalizable machine learning workflow for neurodegenerative disease staging with focus on neurofibrillary tangles. Acta Neuropathol Commun 2023; 11:202. [PMID: 38110981 PMCID: PMC10726581 DOI: 10.1186/s40478-023-01691-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Accepted: 11/19/2023] [Indexed: 12/20/2023] Open
Abstract
Machine learning (ML) has increasingly been used to assist and expand current practices in neuropathology. However, generating large imaging datasets with quality labels is challenging in fields which demand high levels of expertise. Further complicating matters is the often seen disagreement between experts in neuropathology-related tasks, both at the case level and at a more granular level. Neurofibrillary tangles (NFTs) are a hallmark pathological feature of Alzheimer disease, and are associated with disease progression which warrants further investigation and granular quantification at a scale not currently accessible in routine human assessment. In this work, we first provide a baseline of annotator/rater agreement for the tasks of Braak NFT staging between experts and NFT detection using both experts and novices in neuropathology. We use a whole-slide-image (WSI) cohort of neuropathology cases from Emory University Hospital immunohistochemically stained for Tau. We develop a workflow for gathering annotations of the early stage formation of NFTs (Pre-NFTs) and mature intracellular (iNFTs) and show ML models can be trained to learn annotator nuances for the task of NFT detection in WSIs. We utilize a model-assisted-labeling approach and demonstrate ML models can be used to aid in labeling large datasets efficiently. We also show these models can be used to extract case-level features, which predict Braak NFT stages comparable to expert human raters, and do so at scale. This study provides a generalizable workflow for various pathology and related fields, and also provides a technique for accomplishing a high-level neuropathology task with limited human annotations.
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Affiliation(s)
- Juan C Vizcarra
- The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, 313 Ferst Dr NW, Atlanta, GA, 30332, USA
| | - Thomas M Pearce
- Department of Pathology, Division of Neuropathology, University of Pittsburgh Medical Center, Room S701 Scaife Hall 3550 Terrace Street, Pittsburgh, PA, 15261, USA
| | - Brittany N Dugger
- Department of Pathology and Laboratory Medicine, University of California-Davis School of Medicine, 3400A Research Building III Sacramento, Davis, CA, 95817, USA
| | - Michael J Keiser
- Department of Pharmaceutical Chemistry, Department of Bioengineering and Therapeutic Sciences, Institute for Neurodegenerative Diseases, Kavli Institute for Fundamental Neuroscience, and Bakar Computational Health Sciences Institute, University of California, 675 Nelson Rising Ln, Box 0518, San Francisco, CA, 94143, USA
| | - Marla Gearing
- Department of Neurology, Emory University School of Medicine, 12 Executive Park Dr NE, Atlanta, GA, 30322, USA
- Department of Pathology and Laboratory Medicine, Emory University School of Medicine, 1364 Clifton Rd, Atlanta, GA, 30322, USA
| | - John F Crary
- Departments of Pathology, Neuroscience, and Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Neuropathology Brain Bank and Research Core, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Ronald M. Loeb Center for Alzheimer's Disease, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Pathology, Icahn School of Medicine at Mount Sinai, Icahn Building 9th Floor, Room 20A, 1425 Madison Avenue, New York, NY, 10029, USA
| | - Evan J Kiely
- Department of Pathology and Laboratory Medicine, Emory University School of Medicine, 1364 Clifton Rd, Atlanta, GA, 30322, USA
| | - Meaghan Morris
- Department of Pathology, Johns Hopkins School of Medicine, Baltimore, MD, 21218, USA
| | - Bartholomew White
- Department of Pathology, Beth Israel Deaconess Medical Center, 330 Brookline Avenue, Boston, MA, 02215, USA
| | - Jonathan D Glass
- Department of Neurology, Emory University School of Medicine, 12 Executive Park Dr NE, Atlanta, GA, 30322, USA
- Department of Pathology and Laboratory Medicine, Emory University School of Medicine, 1364 Clifton Rd, Atlanta, GA, 30322, USA
- Center for Neurodegenerative Disease, Emory University School of Medicine, Whitehead Biomedical Research Building, 615 Michael Street, 5th Floor, Suite 500, Atlanta, GA, 30322, USA
| | - Kurt Farrell
- Departments of Pathology, Neuroscience, and Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Neuropathology Brain Bank and Research Core, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Ronald M. Loeb Center for Alzheimer's Disease, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Pathology, Icahn School of Medicine at Mount Sinai, Icahn Building 9th Floor, L9-02C, 1425 Madison, Avenue, New York, NY, USA
| | - David A Gutman
- Department of Pathology and Laboratory Medicine, Emory University School of Medicine, 1364 Clifton Rd, Atlanta, GA, 30322, USA.
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10
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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] [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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11
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Kohane IS, Churchill S, Tan ALM, Vella M, Perry CL. The digital-physical divide for pathology research. Lancet Digit Health 2023; 5:e859-e861. [PMID: 38000870 DOI: 10.1016/s2589-7500(23)00184-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 09/01/2023] [Accepted: 09/13/2023] [Indexed: 11/26/2023]
Affiliation(s)
- Isaac S Kohane
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA.
| | - Susanne Churchill
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA
| | - Amelia Li Min Tan
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA
| | - Margaret Vella
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA
| | - Cassandra L Perry
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA
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12
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Shaker N, Shilo K, Esnakula AK, Shafi S, Challa B, Patel A, Kellough DA, Hammond S, Javaid S, Satturwar S, Yearsley MM, Li Z, Limbach AL, Lujan G, Parwani AV. Comparison of four different displays for identification of select pathologic features extracted from whole slide images of surgical pathology cases. Pathol Res Pract 2023; 251:154843. [PMID: 37826873 DOI: 10.1016/j.prp.2023.154843] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Accepted: 09/30/2023] [Indexed: 10/14/2023]
Abstract
BACKGROUND The establishment of minimum standards for display selection for the whole slide image (WSI) interpretation has not been fully defined. Recently, pathologists have increasingly preferred using remote displays for clinical diagnostics. Our study aims to assess and compare the performance of three fixed work displays and one remote personal display in accurately identifying ten selected pathologic features integrated into WSIs. DESIGN Hematoxylin and eosin-stained glass slides were digitized using Philips scanners. Seven practicing pathologists and three residents reviewed ninety WSIs to identify ten pathologic features using the LG, Dell, and Samsung and an optional consumer-grade display. Ten pathologic features included eosinophils, neutrophils, plasma cells, granulomas, necrosis, mucin, hemosiderin, crystals, nucleoli, and mitoses. RESULTS The accuracy of the identification of ten features on different types of displays did not significantly differ among the three types of "fixed" workplace displays. The highest accuracy was observed for the identification of neutrophils, eosinophils, plasma cells, granuloma, and mucin. On the other hand, a lower accuracy was observed for the identification of crystals, mitoses, necrosis, hemosiderin, and nucleoli. Participant pathologists and residents preferred the use of larger displays (>30″) with a higher pixel count, resolution, and luminance. CONCLUSION Most features can be identified using any display. However, certain features posed more challenges across the three fixed display types. Furthermore, the use of a remote personal consumer-grade display chosen according to the pathologists' preference showed similar feature identification accuracy. Several factors of display characteristics seemed to influence pathologists' display preferences such as the display size, color, contrast ratio, pixel count, and luminance calibration. This study supports the use of standard "unlocked" vendor-agnostic displays for clinical digital pathology workflow rather than purchasing "locked" and more expensive displays that are part of a digital pathology system.
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Affiliation(s)
- Nada Shaker
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, OH, USA.
| | - Konstantin Shilo
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, OH, USA
| | - Ashwini K Esnakula
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, OH, USA
| | - Saba Shafi
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, OH, USA
| | - Bindu Challa
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, OH, USA
| | - Ankush Patel
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, OH, USA
| | - David A Kellough
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, OH, USA
| | - Scott Hammond
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, OH, USA
| | - Sehrish Javaid
- Woody L. Hunt School of Dental Medicine, Texas Tech University Health Science Center, El Paso, TX, USA
| | - Swati Satturwar
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, OH, USA
| | - Martha M Yearsley
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, OH, USA
| | - Zaibo Li
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, OH, USA
| | - Abberly Lott Limbach
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, OH, USA
| | - Giovanni Lujan
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, OH, USA
| | - Anil V Parwani
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, OH, USA
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13
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Qin X, Ran T, Chen Y, Zhang Y, Wang D, Zhou C, Zou D. Artificial Intelligence in Endoscopic Ultrasonography-Guided Fine-Needle Aspiration/Biopsy (EUS-FNA/B) for Solid Pancreatic Lesions: Opportunities and Challenges. Diagnostics (Basel) 2023; 13:3054. [PMID: 37835797 PMCID: PMC10572518 DOI: 10.3390/diagnostics13193054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 09/06/2023] [Accepted: 09/06/2023] [Indexed: 10/15/2023] Open
Abstract
Solid pancreatic lesions (SPLs) encompass a variety of benign and malignant diseases and accurate diagnosis is crucial for guiding appropriate treatment decisions. Endoscopic ultrasonography-guided fine-needle aspiration/biopsy (EUS-FNA/B) serves as a front-line diagnostic tool for pancreatic mass lesions and is widely used in clinical practice. Artificial intelligence (AI) is a mathematical technique that automates the learning and recognition of data patterns. Its strong self-learning ability and unbiased nature have led to its gradual adoption in the medical field. In this paper, we describe the fundamentals of AI and provide a summary of reports on AI in EUS-FNA/B to help endoscopists understand and realize its potential in improving pathological diagnosis and guiding targeted EUS-FNA/B. However, AI models have limitations and shortages that need to be addressed before clinical use. Furthermore, as most AI studies are retrospective, large-scale prospective clinical trials are necessary to evaluate their clinical usefulness accurately. Although AI in EUS-FNA/B is still in its infancy, the constant input of clinical data and the advancements in computer technology are expected to make computer-aided diagnosis and treatment more feasible.
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Affiliation(s)
| | | | | | | | | | - Chunhua Zhou
- Department of Gastroenterology, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200025, China; (X.Q.); (T.R.); (Y.C.); (Y.Z.); (D.W.)
| | - Duowu Zou
- Department of Gastroenterology, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200025, China; (X.Q.); (T.R.); (Y.C.); (Y.Z.); (D.W.)
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14
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Hale O, Cooper HJ, Marty MT. High-Throughput Deconvolution of Native Protein Mass Spectrometry Imaging Data Sets for Mass Domain Analysis. Anal Chem 2023; 95:14009-14015. [PMID: 37672655 PMCID: PMC10515104 DOI: 10.1021/acs.analchem.3c02616] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Accepted: 08/22/2023] [Indexed: 09/08/2023]
Abstract
Protein mass spectrometry imaging (MSI) with electrospray-based ambient ionization techniques, such as nanospray desorption electrospray ionization (nano-DESI), generates data sets in which each pixel corresponds to a mass spectrum populated by peaks corresponding to multiply charged protein ions. Importantly, the signal associated with each protein is split among multiple charge states. These peaks can be transformed into the mass domain by spectral deconvolution. When proteins are imaged under native/non-denaturing conditions to retain non-covalent interactions, deconvolution is particularly valuable in helping interpret the data. To improve the acquisition speed, signal-to-noise ratio, and sensitivity, native MSI is usually performed using mass resolving powers that do not provide isotopic resolution, and conventional algorithms for deconvolution of lower-resolution data are not suitable for these large data sets. UniDec was originally developed to enable rapid deconvolution of complex protein mass spectra. Here, we developed an updated feature set harnessing the high-throughput module, MetaUniDec, to deconvolve each pixel of native MSI data sets and transform m/z-domain image files to the mass domain. New tools enable the reading, processing, and output of open format .imzML files for downstream analysis. Transformation of data into the mass domain also provides greater accessibility, with mass information readily interpretable by users of established protein biology tools such as sodium dodecyl sulfate polyacrylamide gel electrophoresis.
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Affiliation(s)
- Oliver
J. Hale
- School
of Biosciences, University of Birmingham, Edgbaston, Birmingham B15 2TT, U.K.
| | - Helen J. Cooper
- School
of Biosciences, University of Birmingham, Edgbaston, Birmingham B15 2TT, U.K.
| | - Michael T. Marty
- Department
of Chemistry and Biochemistry and Bio5 Institute, University of Arizona, 1306 E University Blvd Tucson, Arizona 85721, United States
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15
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Frascarelli C, Bonizzi G, Musico CR, Mane E, Cassi C, Guerini Rocco E, Farina A, Scarpa A, Lawlor R, Reggiani Bonetti L, Caramaschi S, Eccher A, Marletta S, Fusco N. Revolutionizing Cancer Research: The Impact of Artificial Intelligence in Digital Biobanking. J Pers Med 2023; 13:1390. [PMID: 37763157 PMCID: PMC10532470 DOI: 10.3390/jpm13091390] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Revised: 09/05/2023] [Accepted: 09/12/2023] [Indexed: 09/29/2023] Open
Abstract
BACKGROUND Biobanks are vital research infrastructures aiming to collect, process, store, and distribute biological specimens along with associated data in an organized and governed manner. Exploiting diverse datasets produced by the biobanks and the downstream research from various sources and integrating bioinformatics and "omics" data has proven instrumental in advancing research such as cancer research. Biobanks offer different types of biological samples matched with rich datasets comprising clinicopathologic information. As digital pathology and artificial intelligence (AI) have entered the precision medicine arena, biobanks are progressively transitioning from mere biorepositories to integrated computational databanks. Consequently, the application of AI and machine learning on these biobank datasets holds huge potential to profoundly impact cancer research. METHODS In this paper, we explore how AI and machine learning can respond to the digital evolution of biobanks with flexibility, solutions, and effective services. We look at the different data that ranges from specimen-related data, including digital images, patient health records and downstream genetic/genomic data and resulting "Big Data" and the analytic approaches used for analysis. RESULTS These cutting-edge technologies can address the challenges faced by translational and clinical research, enhancing their capabilities in data management, analysis, and interpretation. By leveraging AI, biobanks can unlock valuable insights from their vast repositories, enabling the identification of novel biomarkers, prediction of treatment responses, and ultimately facilitating the development of personalized cancer therapies. CONCLUSIONS The integration of biobanking with AI has the potential not only to expand the current understanding of cancer biology but also to pave the way for more precise, patient-centric healthcare strategies.
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Affiliation(s)
- Chiara Frascarelli
- Division of Pathology, IEO, European Institute of Oncology IRCCS, 20139 Milan, Italy; (C.F.); (E.M.); (E.G.R.); (N.F.)
- Department of Oncology and Hemato-Oncology, University of Milan, 20122 Milan, Italy
| | - Giuseppina Bonizzi
- Biobank for Translational and Digital Medicine, IEO, European Institute of Oncology IRCCS, 20139 Milan, Italy; (G.B.); (C.R.M.); (C.C.)
| | - Camilla Rosella Musico
- Biobank for Translational and Digital Medicine, IEO, European Institute of Oncology IRCCS, 20139 Milan, Italy; (G.B.); (C.R.M.); (C.C.)
| | - Eltjona Mane
- Division of Pathology, IEO, European Institute of Oncology IRCCS, 20139 Milan, Italy; (C.F.); (E.M.); (E.G.R.); (N.F.)
| | - Cristina Cassi
- Biobank for Translational and Digital Medicine, IEO, European Institute of Oncology IRCCS, 20139 Milan, Italy; (G.B.); (C.R.M.); (C.C.)
| | - Elena Guerini Rocco
- Division of Pathology, IEO, European Institute of Oncology IRCCS, 20139 Milan, Italy; (C.F.); (E.M.); (E.G.R.); (N.F.)
- Department of Oncology and Hemato-Oncology, University of Milan, 20122 Milan, Italy
| | - Annarosa Farina
- Central Information Systems and Technology Directorate, IEO, European Institute of Oncology IRCCS, 20139 Milan, Italy;
| | - Aldo Scarpa
- Department of Diagnostics and Public Health, Section of Pathology, University of Verona, 37134 Verona, Italy; (A.S.); (S.M.)
| | - Rita Lawlor
- ARC-Net Research Centre and Department of Diagnostics and Public Health, University of Verona, 37134 Verona, Italy;
| | - Luca Reggiani Bonetti
- Section of Pathology, Department of Medical and Surgical Sciences for Children and Adults, University of Modena and Reggio Emilia, University Hospital of Modena, 41121 Modena, Italy; (L.R.B.); (S.C.)
| | - Stefania Caramaschi
- Section of Pathology, Department of Medical and Surgical Sciences for Children and Adults, University of Modena and Reggio Emilia, University Hospital of Modena, 41121 Modena, Italy; (L.R.B.); (S.C.)
| | - 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, 41121 Modena, Italy; (L.R.B.); (S.C.)
| | - Stefano Marletta
- Department of Diagnostics and Public Health, Section of Pathology, University of Verona, 37134 Verona, Italy; (A.S.); (S.M.)
- Division of Pathology, Humanitas Cancer Center, 95045 Catania, Italy
| | - Nicola Fusco
- Division of Pathology, IEO, European Institute of Oncology IRCCS, 20139 Milan, Italy; (C.F.); (E.M.); (E.G.R.); (N.F.)
- Department of Oncology and Hemato-Oncology, University of Milan, 20122 Milan, Italy
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16
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Stoks M, Vieco-Martí I, Noguera I, Sánchez-Sánchez M, Burgos-Panadero R, Navarro S, Noguera R. Digital image analysis workflows for evaluation of cell behavior and tumor microenvironment to aid therapeutic assessment in high-risk neuroblastoma. Comput Biol Med 2023; 164:107364. [PMID: 37598482 DOI: 10.1016/j.compbiomed.2023.107364] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Revised: 08/03/2023] [Accepted: 08/12/2023] [Indexed: 08/22/2023]
Abstract
Digital pathology and artificial intelligence are promising emerging tools in precision oncology as they provide more robust and reproducible analysis of histologic, morphologic and topologic characteristics of tumor cells and the surrounding microenvironment. This study aims to develop digital image analysis workflows for therapeutic assessment in preclinical in vivo models. For this purpose, we generated pipelines that enable automatic detection and quantification of vitronectin and αvβ3 in heterotopic high-risk neuroblastoma xenografts, demonstrating that digital analysis workflows can be used to provide robust detection of vitronectin secretion and αvβ3 expression by malignant neuroblasts and to evaluate the possibility of combining traditional chemotherapy (etoposide) with extracellular matrix-targeted therapies (cilengitide). Digital image analysis added evidence for the relevance of territorial vitronectin as a therapeutic target in neuroblastoma, since its expression is modified after treatment, with a mean percentage of 60.44% in combined therapy tumors vs 45.08% in control ones. In addition, the present study revealed the efficacy of cilengitide for reducing αvβ3 expression, with a mean αvβ3 positivity of 34.17% in cilengitide treated material vs 66.14% in control and with less tumor growth when combined with etoposide, with a final mean volume of 0.04 cm3 in combined therapy vs 1.45 cm3 in control. The results of this work highlight the importance of extracellular matrix-focused therapies in preclinical studies to improve therapeutic assessment for high-risk neuroblastoma patients.
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Affiliation(s)
- M Stoks
- CIBERONC, Carlos III Health Institute (Ministry of Economy and Competitiveness), 28029, Madrid, Spain
| | - I Vieco-Martí
- CIBERONC, Carlos III Health Institute (Ministry of Economy and Competitiveness), 28029, Madrid, Spain; Department of Pathology, Medical School, University of Valencia - INCLIVA Biomedical Health Research Institute, 46010, Valencia, Spain
| | - I Noguera
- CIBERONC, Carlos III Health Institute (Ministry of Economy and Competitiveness), 28029, Madrid, Spain; Central Support Service for Experimental Research (SCSIE), University of Valencia, Burjassot, Valencia, Spain
| | - M Sánchez-Sánchez
- Department of Pathology, Medical School, University of Valencia - INCLIVA Biomedical Health Research Institute, 46010, Valencia, Spain
| | - R Burgos-Panadero
- CIBERONC, Carlos III Health Institute (Ministry of Economy and Competitiveness), 28029, Madrid, Spain; Department of Pathology, Medical School, University of Valencia - INCLIVA Biomedical Health Research Institute, 46010, Valencia, Spain
| | - S Navarro
- CIBERONC, Carlos III Health Institute (Ministry of Economy and Competitiveness), 28029, Madrid, Spain; Department of Pathology, Medical School, University of Valencia - INCLIVA Biomedical Health Research Institute, 46010, Valencia, Spain
| | - R Noguera
- CIBERONC, Carlos III Health Institute (Ministry of Economy and Competitiveness), 28029, Madrid, Spain; Department of Pathology, Medical School, University of Valencia - INCLIVA Biomedical Health Research Institute, 46010, Valencia, Spain.
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17
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Caliò A, Barreca A, Marletta S, Achenza MIS, Alessi M, Angelico R, Apicella L, Argiolas D, Bossini N, Carrano R, Carriero C, Castellano G, Comai G, Di Bella C, D’Ignoto F, Gallico A, Gastaldon F, Merlotti G, Paloschi V, Panarese A, Parodi A, Perna F, Picciotto D, Regalia A, Rossini M, Russo E, Salerno MP, Toti L, Tulissi P, Vischini G, Zaza G, Eccher A. Histology for nephrology, from pre-implantation to post-transplant kidney biopsy. Lesson learned from ReBIrth (Renal BIopsy for Kidney Transplantation Therapy). Pathologica 2023; 115:199-204. [PMID: 37314869 PMCID: PMC10688244 DOI: 10.32074/1591-951x-858] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Accepted: 06/06/2023] [Indexed: 06/16/2023] Open
Abstract
A meeting entitled Renal BIopsy for Kidney Transplantation Therapy (ReBIrth) took place on May 31st, 2022 in Bologna, Italy. The meeting drew together nephrologists, surgeons, and pathologists and recognized as experts in the field of kidney transplantation in Italy. In this paper, we present our experience working with kidney transplants in the current era of immunosuppression therapy. The primary aim is to report the histopathological characteristics of failed kidney allografts after a consensus of experts reviewed the cases on a wholeslide imaging digital platform. Regardless of the cases discussed, digital pathology was reliable in identifying all the morphological and immunohistochemical features required to improve the correct use of immunosuppressive therapy to prevent graft failure and optimize patient management.
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Affiliation(s)
- Anna Caliò
- Department of Diagnostic and Public Health, Section of Pathology, University and Hospital Trust of Verona, Verona, Italy
| | - Antonella Barreca
- Pathology Unit, “Città della Salute e della Scienza di Torino” University Hospital, Turin, Italy
| | - Stefano Marletta
- Department of Diagnostic and Public Health, Section of Pathology, University and Hospital Trust of Verona, Verona, Italy
- Department of Pathology, Pederzoli Hospital, Peschiera del Garda, Italy
| | | | - Marianna Alessi
- Department of Medicine, Nephrology, University of Padova, Padova, Italy
| | - Roberta Angelico
- Department of Surgical Sciences, HPB and Transplant Unit, University of Rome Tor Vergata, Roma Italy
| | - Luca Apicella
- Division of Nephrology, Dialysis and Renal Transplantation, University Hospital “San Giovanni di Dio e Ruggi d’Aragona”, Salerno, Italy
| | - Davide Argiolas
- Renal Transplant Unit, Azienda Ospedaliera Brotzu, Cagliari, Italy
| | - Nicola Bossini
- Division of Nephrology and Dialysis, ASST Spedali Civili, Brescia, Italy
| | - Rosa Carrano
- UOSD Nefrologia e Trapianto Renale, Dipartimento di Chirurgia Generale e Chirurgie Specialistiche dei Trapianti di Rene, Nefrologia, Cure Intensive e del Dolore, Azienda Ospedaliera Universitaria Federico II, Napoli, Italy
| | | | - Giuseppe Castellano
- Nephrology, Dialysis and Transplantation, Fondazione IRCCS Ca’ Granda, Ospedale Maggiore Policlinico, Milan, Italy
- Department of Clinical Sciences and Community Health, University of Milan, Milan, Italy
| | - Giorgia Comai
- Nephrology, Dialysis and Renal Transplant Unit, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
| | - Caterina Di Bella
- Kidney and Pancreas Transplantation Unit, Department of Surgical, Oncological and Gastroenterological Sciences, University of Padua, Padua, Italy
| | | | - Agnese Gallico
- Division of Nephrology and Dialysis, ASST Spedali Civili, Brescia, Italy
| | - Fiorella Gastaldon
- Unità Operativa Complessa di Nefrologia, Dialisi e Trapianto Renale, AULSS8 Berica, Ospedale San Bortolo, Vicenza, Italy
| | - Guido Merlotti
- Nephrology and Kidney Transplantation Unit, Department of Translational Medicine, University of Piemonte Orientale (UPO), “Maggiore della Carità” University Hospital, Novara, Italy
| | - Vera Paloschi
- Transplant Medicine Unit, Department of Internal Medicine, IRCCS San Raffaele Hospital, Milano, Italy
| | - Alessandra Panarese
- Department of Biotechnological and Applied Clinical Sciences (DISCAB), University of L’Aquila, L’Aquila, Italy
| | - Angelica Parodi
- Unit of Nephrology Dialysis and Transplantation, Department of Internal Medicine, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Francesco Perna
- Fondazione Policlinico A. Gemelli – UOS Trapianti di Rene, Roma, Italy
| | - Daniela Picciotto
- Unit of Nephrology Dialysis and Transplantation, Department of Internal Medicine, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Anna Regalia
- Nephrology, Dialysis and Transplantation, Fondazione IRCCS Ca’ Granda, Ospedale Maggiore Policlinico, Milan, Italy
| | - Michele Rossini
- Nephrology, Dialysis and Transplantation Unit, Department of Emergency and Organ Transplantation, University of Bari, Bari, Italy
| | - Enrico Russo
- General Surgery and Kidney Transplantation Unit, “OO.RR. San Giovanni di Dio e Ruggi d’Aragona” University Hospital, Salerno, Italy
| | | | - Luca Toti
- Transplant and HPB Unit, Department of Surgical Sciences, University of Rome Tor Vergata, Rome, Italy
| | - Patrizia Tulissi
- Unità complessa di Nefrologia, Dialisi e Trapianto, Udine, Italy
| | - Gisella Vischini
- Nephrology, Dialysis and Renal Transplant Unit, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
| | - Gianluigi Zaza
- Nephrology, Dialysis and Transplantation Unit, Department of Medical and Surgical Sciences, University of Foggia, Foggia, Italy
| | - Albino Eccher
- Department of Diagnostic and Public Health, Section of Pathology, University and Hospital Trust of Verona, Verona, Italy
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Wu J, Yuan T, Zeng J, Gou F. A Medically Assisted Model for Precise Segmentation of Osteosarcoma Nuclei on Pathological Images. IEEE J Biomed Health Inform 2023; 27:3982-3993. [PMID: 37216252 DOI: 10.1109/jbhi.2023.3278303] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
Osteosarcoma is the most common malignant bone tumor with a high degree of malignancy and misdiagnosis rates. Pathological images are crucial for its diagnosis. However, underdeveloped regions currently lack sufficient high-level pathologists, leading to uncertain diagnostic accuracy and efficiency. Existing research on pathological image segmentation often neglects the differences in staining styles and lack of data, without considering medical backgrounds. To alleviate the difficulty in diagnosing osteosarcoma in underdeveloped areas, an intelligent assisted diagnosis and treatment scheme for osteosarcoma pathological images, ENMViT, is proposed. ENMViT utilizes KIN to achieve normalization of mismatched images with limited GPU resources and uses traditional data enhancement methods, such as cleaning, cropping, mosaic, Laplacian sharpening, and other techniques to alleviate the issue of insufficient data. A multi-path semantic segmentation network combining Transformer and CNN is used to segment images, and the degree of edge offset in the spatial domain is introduced into the loss function. Finally, noise is filtered according to the size of the connecting domain. This article experimented on more than 2000 osteosarcoma pathological images from Central South University. The experimental results demonstrate that this scheme performs well in each stage of the osteosarcoma pathological image processing, and the segmentation results' IoU index is 9.4% higher than the comparative models, demonstrating its significant value in the medical industry.
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Duenweg SR, Bobholz SA, Lowman AK, Stebbins MA, Winiarz A, Nath B, Kyereme F, Iczkowski KA, LaViolette PS. Whole slide imaging (WSI) scanner differences influence optical and computed properties of digitized prostate cancer histology. J Pathol Inform 2023; 14:100321. [PMID: 37496560 PMCID: PMC10365953 DOI: 10.1016/j.jpi.2023.100321] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 06/13/2023] [Accepted: 06/28/2023] [Indexed: 07/28/2023] Open
Abstract
Purpose Digital pathology is becoming an increasingly popular area of advancement in both research and clinically. Pathologists are now able to manage and interpret slides digitally, as well as collaborate with external pathologists with digital copies of slides. Differences in slide scanners include variation in resolution, image contrast, and optical properties, which may influence downstream image processing. This study tested the hypothesis that varying slide scanners would result in differences in computed pathomic features on prostate cancer whole mount slides. Design This study collected 192 unique tissue slides from 30 patients following prostatectomy. Tissue samples were paraffin-embedded, stained for hematoxylin and eosin (H&E), and digitized using 3 different scanning microscopes at the highest available magnification rate, for a total of 3 digitized slides per tissue slide. These scanners included a (S1) Nikon microscope equipped with an automated sliding stage, an (S2) Olympus VS120 slide scanner, and a (S3) Huron TissueScope LE scanner. A color deconvolution algorithm was then used to optimize contrast by projecting the RGB image into color channels representing optical stain density. The resulting intensity standardized images were then computationally processed to segment tissue and calculate pathomic features including lumen, stroma, epithelium, and epithelial cell density, as well as second-order features including lumen area and roundness; epithelial area, roundness, and wall thickness; and cell fraction. For each tested feature, mean values of that feature per digitized slide were collected and compared across slide scanners using mixed effect models, fit to compare differences in the tested feature associated with all slide scanners for each slide, including a random effect of subject with a nested random effect of slide to account for repeated measures. Similar models were also computed for tissue densities to examine how differences in scanner impact downstream processing. Results Each mean color channel intensity (i.e., Red, Green, Blue) differed between slide scanners (all P<.001). Of the color deconvolved images, only the hematoxylin channel was similar in all 3 scanners (all P>.05). Lumen and stroma densities between S3 and S1 slides, and epithelial cell density between S3 and S2 (P>.05) were comparable but all other comparisons were significantly different (P<.05). The second-order features were found to be comparable for all scanner comparisons, except for lumen area and epithelium area. Conclusion This study demonstrates that both optical and computed properties of digitized histological samples are impacted by slide scanner differences. Future research is warranted to better understand which scanner properties influence the tissue segmentation process and to develop harmonization techniques for comparing data across multiple slide scanners.
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Affiliation(s)
- Savannah R. Duenweg
- Departments of Biophysics, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA
| | - Samuel A. Bobholz
- Department of Radiology, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA
| | - Allison K. Lowman
- Department of Radiology, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA
| | - Margaret A. Stebbins
- Departments of Biophysics, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA
| | - Aleksandra Winiarz
- Departments of Biophysics, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA
| | - Biprojit Nath
- Departments of Biophysics, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA
| | - Fitzgerald Kyereme
- Department of Radiology, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA
| | - Kenneth A. Iczkowski
- Department of Pathology, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA
| | - Peter S. LaViolette
- Departments of Biophysics, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA
- Department of Radiology, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA
- Department of Biomedical Engineering, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA
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20
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Miguel R, Gregorio B, Santos C, Andriotti C, Valle L, Saieg M. Validation of cytopathology specimens for digital pathology. Cytopathology 2023; 34:302-307. [PMID: 36974500 DOI: 10.1111/cyt.13234] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2022] [Revised: 03/02/2023] [Accepted: 03/05/2023] [Indexed: 03/29/2023]
Abstract
INTRODUCTION Digital cytopathology is being progressively implemented in centres worldwide, but impediments such as the three-dimensionality of specimens and the size of scanned images have prevented its use from becoming widespread. This study aimed to validate the use of digital whole slide image scanning of cytopathology samples for routine sign-out. METHODS Specimens were scanned using the Leica Aperio GT 450 System. The following sample types were used: liquid-based cytology, direct conventional smears from fine needle aspirates and cytospins. Cases were validated by the same pathologist who originally rendered the conventional diagnosis, with a washout of at least 3 months. Final digital diagnoses were compared to the original analogical diagnoses, and cases were considered concordant up to a one-degree difference between the original and digital diagnoses. Reasons for the unsuccessful scanning of slides were also noted. The technical procedures followed the College of American Pathologists' guidelines for digital pathology validation. RESULTS A total of 730 slides from 383 cases (337 female, 51 male; median age 42) were successfully scanned. These cases consisted of the following sample types: 81 (21.1%) conventional smears, 240 (62.7%) liquid-based cytology samples and 62 (16.2%) cytospins. There were only five discordant cases, with a 98.7% agreement between original and digital diagnoses using the difference rate of up to one degree. Seventy-seven slides (10.5%) had to be rescanned due to technical problems. The main reasons for unsuccessful scanning were paucicellular samples (44; 57.1%), the thickness of the smears (18; 23.4%) and issues with the coverslip (15; 19.5%). CONCLUSION Cytological specimens can be successfully scanned and used for digital pathology, with excellent agreement with the original diagnoses.
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Affiliation(s)
| | | | | | | | | | - Mauro Saieg
- Fleury Group, São Paulo, Brazil
- Santa Casa Medical School, São Paulo, Brazil
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21
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Urken ML, Yun J, Saturno MP, Greenberg LA, Chai RL, Sharif K, Brandwein-Weber M. Frozen Section Analysis in Head and Neck Surgical Pathology: A Narrative Review of the Past, Present, and Future of Intraoperative Pathologic Consultation. Oral Oncol 2023; 143:106445. [PMID: 37285683 DOI: 10.1016/j.oraloncology.2023.106445] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Revised: 05/13/2023] [Accepted: 05/30/2023] [Indexed: 06/09/2023]
Abstract
Frozen section has remained the diagnostic gold standard for intraoperative pathological evaluation of surgical margins for head and neck specimens. While achieving tumor-free margins is of utmost importance to all head and neck surgeons, in practice, there are numerous debates and a lack of standardization for the role and method of intraoperative pathologic consultation. This review serves as a summary guide to the historical and contemporary practice of frozen section analysis and margin mapping in head and neck cancer. In addition, this review discusses current challenges in head and neck surgical pathology, and introduces 3D scanning as a groundbreaking technology to bypass many of the pitfalls in the current frozen section workflow. The ultimate goal for all head and neck pathologists and surgeons should be to modernize practices and take advantage of new technology, such as virtual 3D specimen mapping techniques, that improves the workflow for intraoperative frozen section analysis.
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Affiliation(s)
- Mark L Urken
- THANC (Thyroid, Head & Neck Cancer) Foundation, New York, NY, USA; Dept. of Otolaryngology-Head and Neck Surgery, Icahn School of Medicine at Mount Sinai, NY, USA
| | - Jun Yun
- THANC (Thyroid, Head & Neck Cancer) Foundation, New York, NY, USA; Dept. of Otolaryngology-Head and Neck Surgery, Icahn School of Medicine at Mount Sinai, NY, USA
| | | | - Lily A Greenberg
- THANC (Thyroid, Head & Neck Cancer) Foundation, New York, NY, USA
| | - Raymond L Chai
- Dept. of Otolaryngology-Head and Neck Surgery, Icahn School of Medicine at Mount Sinai, NY, USA
| | - Kayvon Sharif
- Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Margaret Brandwein-Weber
- THANC (Thyroid, Head & Neck Cancer) Foundation, New York, NY, USA; Dept. of Pathology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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22
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Burrai GP, Gabrieli A, Polinas M, Murgia C, Becchere MP, Demontis P, Antuofermo E. Canine Mammary Tumor Histopathological Image Classification via Computer-Aided Pathology: An Available Dataset for Imaging Analysis. Animals (Basel) 2023; 13:ani13091563. [PMID: 37174600 PMCID: PMC10177203 DOI: 10.3390/ani13091563] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Revised: 04/27/2023] [Accepted: 05/04/2023] [Indexed: 05/15/2023] Open
Abstract
Histopathology, the gold-standard technique in classifying canine mammary tumors (CMTs), is a time-consuming process, affected by high inter-observer variability. Digital (DP) and Computer-aided pathology (CAD) are emergent fields that will improve overall classification accuracy. In this study, the ability of the CAD systems to distinguish benign from malignant CMTs has been explored on a dataset-namely CMTD-of 1056 hematoxylin and eosin JPEG images from 20 benign and 24 malignant CMTs, with three different CAD systems based on the combination of a convolutional neural network (VGG16, Inception v3, EfficientNet), which acts as a feature extractor, and a classifier (support vector machines (SVM) or stochastic gradient boosting (SGB)), placed on top of the neural net. Based on a human breast cancer dataset (i.e., BreakHis) (accuracy from 0.86 to 0.91), our models were applied to the CMT dataset, showing accuracy from 0.63 to 0.85 across all architectures. The EfficientNet framework coupled with SVM resulted in the best performances with an accuracy from 0.82 to 0.85. The encouraging results obtained by the use of DP and CAD systems in CMTs provide an interesting perspective on the integration of artificial intelligence and machine learning technologies in cancer-related research.
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Affiliation(s)
- Giovanni P Burrai
- Department of Veterinary Medicine, University of Sassari, Via Vienna 2, 07100 Sassari, Italy
- Mediterranean Center for Disease Control (MCDC), University of Sassari, Via Vienna 2, 07100 Sassari, Italy
| | - Andrea Gabrieli
- Department of Veterinary Medicine, University of Sassari, Via Vienna 2, 07100 Sassari, Italy
| | - Marta Polinas
- Department of Veterinary Medicine, University of Sassari, Via Vienna 2, 07100 Sassari, Italy
| | - Claudio Murgia
- Department of Veterinary Medicine, University of Sassari, Via Vienna 2, 07100 Sassari, Italy
| | | | - Pierfranco Demontis
- Department of Chemical, Physical, Mathematical and Natural Sciences, University of Sassari, Via Vienna 2, 07100 Sassari, Italy
| | - Elisabetta Antuofermo
- Department of Veterinary Medicine, University of Sassari, Via Vienna 2, 07100 Sassari, Italy
- Mediterranean Center for Disease Control (MCDC), University of Sassari, Via Vienna 2, 07100 Sassari, Italy
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23
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Scalco R, Hamsafar Y, White CL, Schneider JA, Reichard RR, Prokop S, Perrin RJ, Nelson PT, Mooney S, Lieberman AP, Kukull WA, Kofler J, Keene CD, Kapasi A, Irwin DJ, Gutman DA, Flanagan ME, Crary JF, Chan KC, Murray ME, Dugger BN. The status of digital pathology and associated infrastructure within Alzheimer's Disease Centers. J Neuropathol Exp Neurol 2023; 82:202-211. [PMID: 36692179 PMCID: PMC9941826 DOI: 10.1093/jnen/nlac127] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023] Open
Abstract
Digital pathology (DP) has transformative potential, especially for Alzheimer disease and related disorders. However, infrastructure barriers may limit adoption. To provide benchmarks and insights into implementation barriers, a survey was conducted in 2019 within National Institutes of Health's Alzheimer's Disease Centers (ADCs). Questions covered infrastructure, funding sources, and data management related to digital pathology. Of the 35 ADCs to which the survey was sent, 33 responded. Most respondents (81%) stated that their ADC had digital slide scanner access, with the most frequent brand being Aperio/Leica (62.9%). Approximately a third of respondents stated there were fees to utilize the scanner. For DP and machine learning (ML) resources, 41% of respondents stated none was supported by their ADC. For scanner purchasing and operations, 50% of respondents stated they received institutional support. Some were unsure of the file size of scanned digital images (37%) and total amount of storage space files occupied (50%). Most (76%) were aware of other departments at their institution working with ML; a similar (76%) percentage were unaware of multiuniversity or industry partnerships. These results demonstrate many ADCs have access to a digital slide scanner; additional investigations are needed to further understand hurdles to implement DP and ML workflows.
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Affiliation(s)
- Rebeca Scalco
- Department of Pathology and Laboratory Medicine, University of California-Davis, Sacramento, California, USA
| | - Yamah Hamsafar
- Department of Pathology and Laboratory Medicine, University of California-Davis, Sacramento, California, USA
| | - Charles L White
- Department of Pathology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | | | | | - Stefan Prokop
- Department of Pathology, College of Medicine, University of Florida, Gainesville, Florida, USA
| | - Richard J Perrin
- Department of Pathology and Immunology, Washington University School of Medicine, Saint Louis, Missouri, USA
- Department of Neurology, Washington University School of Medicine, Saint Louis, Missouri, USA
- Knight Alzheimer’s Disease Research Center, Washington University School of Medicine, Saint Louis, Missouri, USA
| | | | - Sean Mooney
- Institute for Medical Data Science and Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, Washington, USA
| | - Andrew P Lieberman
- Department of Pathology, University of Michigan Medical School, Ann Arbor, Michigan, USA
| | - Walter A Kukull
- Institute for Medical Data Science and Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, Washington, USA
| | - Julia Kofler
- Department of Pathology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Christopher Dirk Keene
- Department Laboratory Medicine and Pathology, University of Washington, Seattle, Washington, USA
| | | | - David J Irwin
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - David A Gutman
- Departments of Neurology, Psychiatry, and Biomedical Informatics, Emory University School of Medicine, Atlanta, Georgia, USA
| | - Margaret E Flanagan
- Department of Pathology, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
- Mesulam Center for Cognitive Neurology and Alzheimer’s Disease, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | - John F Crary
- Department of Pathology, Ronald M. Loeb Center for Alzheimer’s Disease, Friedman Brain Institute, Neuropathology Brain Bank & Research CoRE, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Department of Neuroscience, Ronald M. Loeb Center for Alzheimer’s Disease, Friedman Brain Institute, Neuropathology Brain Bank & Research CoRE, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Department of Artificial Intelligence & Human Health, Ronald M. Loeb Center for Alzheimer’s Disease, Friedman Brain Institute, Neuropathology Brain Bank & Research CoRE, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Kwun C Chan
- Institute for Medical Data Science and Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, Washington, USA
| | - Melissa E Murray
- Department of Neuroscience, Mayo Clinic, Jacksonville, Florida, USA
| | - Brittany N Dugger
- Department of Pathology and Laboratory Medicine, University of California-Davis, Sacramento, California, USA
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24
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Ren W, Zhu Y, Wang Q, Jin H, Guo Y, Lin D. Deep Learning-Based Classification and Targeted Gene Alteration Prediction from Pleural Effusion Cell Block Whole-Slide Images. Cancers (Basel) 2023; 15:cancers15030752. [PMID: 36765710 PMCID: PMC9913862 DOI: 10.3390/cancers15030752] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Revised: 01/23/2023] [Accepted: 01/23/2023] [Indexed: 01/27/2023] Open
Abstract
Cytopathological examination is one of the main examinations for pleural effusion, and especially for many patients with advanced cancer, pleural effusion is the only accessible specimen for establishing a pathological diagnosis. The lack of cytopathologists and the high cost of gene detection present opportunities for the application of deep learning. In this retrospective analysis, data representing 1321 consecutive cases of pleural effusion were collected. We trained and evaluated our deep learning model based on several tasks, including the diagnosis of benign and malignant pleural effusion, the identification of the primary location of common metastatic cancer from pleural effusion, and the prediction of genetic alterations associated with targeted therapy. We achieved good results in identifying benign and malignant pleural effusions (0.932 AUC (area under the ROC curve)) and the primary location of common metastatic cancer (0.910 AUC). In addition, we analyzed ten genes related to targeted therapy in specimens and used them to train the model regarding four alteration statuses, which also yielded reasonable results (0.869 AUC for ALK fusion, 0.804 AUC for KRAS mutation, 0.644 AUC for EGFR mutation and 0.774 AUC for NONE alteration). Our research shows the feasibility and benefits of deep learning to assist in cytopathological diagnosis in clinical settings.
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25
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Antonini P, Santonicco N, Pantanowitz L, Girolami I, Rizzo PC, Brunelli M, Bellevicine C, Vigliar E, Negri G, Troncone G, Fadda G, Parwani A, Marletta S, Eccher A. Relevance of the College of American Pathologists guideline for validating whole slide imaging for diagnostic purposes to cytopathology. Cytopathology 2023; 34:5-14. [PMID: 36082410 PMCID: PMC10087327 DOI: 10.1111/cyt.13178] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Revised: 08/17/2022] [Accepted: 08/31/2022] [Indexed: 12/14/2022]
Abstract
Whole slide imaging (WSI) allows pathologists to view virtual versions of slides on computer monitors. With increasing adoption of digital pathology, laboratories have begun to validate their WSI systems for diagnostic purposes according to reference guidelines. Among these the College of American Pathologists (CAP) guideline includes three strong recommendations (SRs) and nine good practice statements (GPSs). To date, the application of WSI to cytopathology has been beyond the scope of the CAP guideline due to limited evidence. Herein we systematically reviewed the published literature on WSI validation studies in cytology. A systematic search was carried out in PubMed-MEDLINE and Embase databases up to November 2021 to identify all publications regarding validation of WSI in cytology. Each article was reviewed to determine if SRs and/or GPSs recommended by the CAP guideline were adequately satisfied. Of 3963 retrieved articles, 25 were included. Only 4/25 studies (16%) satisfied all three SRs, with only one publication (1/25, 4%) fulfilling all three SRs and nine GPSs. Lack of a suitable validation dataset was the main missing SR (16/25, 64%) and less than a third of the studies reported intra-observer variability data (7/25, 28%). Whilst the CAP guideline for WSI validation in clinical practice helped the widespread adoption of digital pathology, more evidence is required to routinely employ WSI for diagnostic purposes in cytopathology practice. More dedicated validation studies satisfying all SRs and/or GPSs recommended by the CAP are needed to help expedite the use of WSI for primary diagnosis in cytopathology.
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Affiliation(s)
- Pietro Antonini
- Section of Pathology, Department of Diagnostics and Public Health, University and Hospital Trust of Verona, Verona, Italy
| | - Nicola Santonicco
- Section of Pathology, Department of Diagnostics and Public Health, University and Hospital Trust of Verona, Verona, Italy
| | - Liron Pantanowitz
- Department of Pathology, University of Michigan, Ann Arbor, Michigan, USA
| | - Ilaria Girolami
- Department of Pathology, Provincial Hospital of Bolzano (SABES-ASDAA), Bolzano-Bozen, Italy
| | - Paola Chiara Rizzo
- Section of Pathology, Department of Diagnostics and Public Health, University and Hospital Trust of Verona, Verona, Italy
| | - Matteo Brunelli
- Section of Pathology, Department of Diagnostics and Public Health, University and Hospital Trust of Verona, Verona, Italy
| | | | - Elena Vigliar
- Public Health, University of Naples Federico II, Naples, Italy
| | - Giovanni Negri
- Department of Pathology, Provincial Hospital of Bolzano (SABES-ASDAA), Bolzano-Bozen, Italy
| | | | - Guido Fadda
- Section of Pathological Anatomy, Department of Human Pathology in Adulthood and Childhood "G. Barresi", University Hospital G. Martino, University of Messina, Messina, Italy
| | - Anil Parwani
- Department of Pathology, The Ohio State University, Columbus, Ohio, USA
| | - Stefano Marletta
- Section of Pathology, Department of Diagnostics and Public Health, University and Hospital Trust of Verona, Verona, Italy
| | - Albino Eccher
- Department of Pathology and Diagnostics, University and Hospital Trust of Verona, Verona, Italy
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26
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Johnson AC, Silva JAF, Kim SC, Larsen CP. Progress in kidney transplantation: The role for systems immunology. Front Med (Lausanne) 2022; 9:1070385. [PMID: 36590970 PMCID: PMC9800623 DOI: 10.3389/fmed.2022.1070385] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Accepted: 11/16/2022] [Indexed: 12/23/2022] Open
Abstract
The development of systems biology represents an immense breakthrough in our ability to perform translational research and deliver personalized and precision medicine. A multidisciplinary approach in combination with use of novel techniques allows for the extraction and analysis of vast quantities of data even from the volume and source limited samples that can be obtained from human subjects. Continued advances in microfluidics, scalability and affordability of sequencing technologies, and development of data analysis tools have made the application of a multi-omics, or systems, approach more accessible for use outside of specialized centers. The study of alloimmune and protective immune responses after solid organ transplant offers innumerable opportunities for a multi-omics approach, however, transplant immunology labs are only just beginning to adopt the systems methodology. In this review, we focus on advances in biological techniques and how they are improving our understanding of the immune system and its interactions, highlighting potential applications in transplant immunology. First, we describe the techniques that are available, with emphasis on major advances that allow for increased scalability. Then, we review initial applications in the field of transplantation with a focus on topics that are nearing clinical integration. Finally, we examine major barriers to adapting these methods and discuss potential future developments.
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27
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Marini N, Marchesin S, Otálora S, Wodzinski M, Caputo A, van Rijthoven M, Aswolinskiy W, Bokhorst JM, Podareanu D, Petters E, Boytcheva S, Buttafuoco G, Vatrano S, Fraggetta F, van der Laak J, Agosti M, Ciompi F, Silvello G, Muller H, Atzori M. Unleashing the potential of digital pathology data by training computer-aided diagnosis models without human annotations. NPJ Digit Med 2022; 5:102. [PMID: 35869179 PMCID: PMC9307641 DOI: 10.1038/s41746-022-00635-4] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Accepted: 06/24/2022] [Indexed: 01/02/2023] Open
Abstract
The digitalization of clinical workflows and the increasing performance of deep learning algorithms are paving the way towards new methods for tackling cancer diagnosis. However, the availability of medical specialists to annotate digitized images and free-text diagnostic reports does not scale with the need for large datasets required to train robust computer-aided diagnosis methods that can target the high variability of clinical cases and data produced. This work proposes and evaluates an approach to eliminate the need for manual annotations to train computer-aided diagnosis tools in digital pathology. The approach includes two components, to automatically extract semantically meaningful concepts from diagnostic reports and use them as weak labels to train convolutional neural networks (CNNs) for histopathology diagnosis. The approach is trained (through 10-fold cross-validation) on 3’769 clinical images and reports, provided by two hospitals and tested on over 11’000 images from private and publicly available datasets. The CNN, trained with automatically generated labels, is compared with the same architecture trained with manual labels. Results show that combining text analysis and end-to-end deep neural networks allows building computer-aided diagnosis tools that reach solid performance (micro-accuracy = 0.908 at image-level) based only on existing clinical data without the need for manual annotations.
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28
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Chung A, Nasralla D, Quaglia A. Understanding the Immunoenvironment of Primary Liver Cancer: A Histopathology Perspective. J Hepatocell Carcinoma 2022; 9:1149-1169. [PMID: 36349146 PMCID: PMC9637345 DOI: 10.2147/jhc.s382310] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Accepted: 09/01/2022] [Indexed: 11/26/2022] Open
Abstract
One of the most common cancers worldwide, primary liver cancer remains a major cause of cancer-related mortality. Hepatocellular carcinoma and cholangiocarcinoma represent the majority of primary liver cancer cases. Despite advances in the development of novel anti-cancer therapies that exploit targets within the immune system, survival rates from liver cancer remain poor. Furthermore, responses to immunotherapies, such as immune checkpoint inhibitors, have revealed limited and variable responses amongst patients with hepatocellular carcinoma, although combination immunotherapies have shown recent breakthroughs in clinical trials. This has shifted the focus towards improving our understanding of the underlying immune and molecular characteristics of liver tumours that may influence their response to immune-modulating treatments. In this review, we outline the complex interactions that occur in the tumour microenvironment of hepatocellular carcinoma and cholangiocarcinoma, respectively, from a histopathological perspective. We explore the potential role of a classification system based on immune-specific characteristics within each cancer type, the importance of understanding inter- and intra-tumoural heterogeneity and consider the future role of histopathology and novel technologies within this field.
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Affiliation(s)
- Annabelle Chung
- Department of Cellular Pathology, Royal Free Hospital, London, UK,Correspondence: Annabelle Chung, Department of Cellular Pathology, Royal Free Hospital, Pond Street, London, NW3 2QG, UK, Tel +44 20 7794 0500 ext. 35641, Email
| | - David Nasralla
- Department of Hepato-Pancreato-Biliary Surgery, Royal Free Hospital, London, UK
| | - Alberto Quaglia
- Department of Cellular Pathology, Royal Free Hospital, London, UK
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Ghaffari Laleh N, Truhn D, Veldhuizen GP, Han T, van Treeck M, Buelow RD, Langer R, Dislich B, Boor P, Schulz V, Kather JN. Adversarial attacks and adversarial robustness in computational pathology. Nat Commun 2022; 13:5711. [PMID: 36175413 PMCID: PMC9522657 DOI: 10.1038/s41467-022-33266-0] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2022] [Accepted: 09/09/2022] [Indexed: 11/09/2022] Open
Abstract
Artificial Intelligence (AI) can support diagnostic workflows in oncology by aiding diagnosis and providing biomarkers directly from routine pathology slides. However, AI applications are vulnerable to adversarial attacks. Hence, it is essential to quantify and mitigate this risk before widespread clinical use. Here, we show that convolutional neural networks (CNNs) are highly susceptible to white- and black-box adversarial attacks in clinically relevant weakly-supervised classification tasks. Adversarially robust training and dual batch normalization (DBN) are possible mitigation strategies but require precise knowledge of the type of attack used in the inference. We demonstrate that vision transformers (ViTs) perform equally well compared to CNNs at baseline, but are orders of magnitude more robust to white- and black-box attacks. At a mechanistic level, we show that this is associated with a more robust latent representation of clinically relevant categories in ViTs compared to CNNs. Our results are in line with previous theoretical studies and provide empirical evidence that ViTs are robust learners in computational pathology. This implies that large-scale rollout of AI models in computational pathology should rely on ViTs rather than CNN-based classifiers to provide inherent protection against perturbation of the input data, especially adversarial attacks. Artificial Intelligence can support diagnostic workflows in oncology, but they are vulnerable to adversarial attacks. Here, the authors show that convolutional neural networks are highly susceptible to white- and black-box adversarial attacks in clinically relevant classification tasks.
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Affiliation(s)
- Narmin Ghaffari Laleh
- Department of Medicine III, University Hospital RWTH Aachen, RWTH Aachen university, Aachen, Germany
| | - Daniel Truhn
- Department of Diagnostic and Interventional Radiology, University Hospital Aachen, Aachen, Germany
| | - Gregory Patrick Veldhuizen
- Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, Technical University Dresden, Dresden, Germany
| | - Tianyu Han
- Department of Physics of Molecular Imaging Systems, Institute for Experimental Molecular Imaging, RWTH Aachen University, Aachen, Germany
| | - Marko van Treeck
- Department of Medicine III, University Hospital RWTH Aachen, RWTH Aachen university, Aachen, Germany
| | - Roman D Buelow
- Institute of Pathology, University Hospital RWTH Aachen, Aachen, Germany
| | - Rupert Langer
- Institute of Pathology, University of Bern, Bern, Switzerland.,Institute of Pathology and Molecular Pathology, Kepler University Hospital, Johannes Kepler University Linz, Linz, Austria
| | - Bastian Dislich
- Institute of Pathology, University of Bern, Bern, Switzerland
| | - Peter Boor
- Institute of Pathology, University Hospital RWTH Aachen, Aachen, Germany
| | - Volkmar Schulz
- Department of Physics of Molecular Imaging Systems, Institute for Experimental Molecular Imaging, RWTH Aachen University, Aachen, Germany.,Physics Institute III B, RWTH Aachen University, Aachen, Germany.,Fraunhofer Institute for Digital Medicine MEVIS, Aachen, Germany.,Hyperion Hybrid Imaging Systems GmbH, Aachen, Germany
| | - Jakob Nikolas Kather
- Department of Medicine III, University Hospital RWTH Aachen, RWTH Aachen university, Aachen, Germany. .,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. .,Division of Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK. .,Department of Medicine 1, University Hospital and Faculty of Medicine Carl Gustav Carus, Technical University Dresden, Dresden, Germany.
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Ali M, Evans H, Whitney P, Minhas F, Snead DRJ. Using Systemised Nomenclature of Medicine (SNOMED) codes to select digital pathology whole slide images for long-term archiving. J Clin Pathol 2022; 76:349-352. [PMID: 36109157 PMCID: PMC10176345 DOI: 10.1136/jcp-2022-208483] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Accepted: 08/20/2022] [Indexed: 11/04/2022]
Abstract
The archiving of whole slide images represents a hurdle to digital pathology implementation largely because of the amount of data generated. The retention of glass slides is currently recommended for a minimum of 10 years, but it is for individual departments to determine how digital images are archived and for how long. In a retrospective study, we examined the combination of Systemised Nomenclature of Medicine (SNOMED) codes allocated to cases reported between July 2011 and December 2015 and recalled more than 12 months after diagnosis in comparison to non-recalled cases.Our results show that 0.2% of cases are recalled after 12 months, and SNOMED code combinations can be used to identify which cases are likely to be recalled and which are not. This approach could reduce the number of cases archived by 62% and still ensure all cases likely to be recalled remain in the archive.
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Affiliation(s)
- Mahmoud Ali
- Histopathology Department, University Hospitals Coventry and Warwickshire NHS Trust, Coventry, UK
- Histopathology Department, Cambridge University Hospitals NHS Foundation Trust, Cambridge, Cambridgeshire, UK
| | - Harriet Evans
- Histopathology Department, University Hospitals Coventry and Warwickshire NHS Trust, Coventry, UK
| | - Peter Whitney
- Histopathology Department, University Hospitals Coventry and Warwickshire NHS Trust, Coventry, UK
| | - Fayyaz Minhas
- Department of Computer Science, University of Warwick, Coventry, West Midlands, UK
| | - David R J Snead
- Histopathology Department, University Hospitals Coventry and Warwickshire NHS Trust, Coventry, UK
- Warick Medical School, University of Warwick, Coventry, UK
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Bosisio FM, Van Herck Y, Messiaen J, Bolognesi MM, Marcelis L, Van Haele M, Cattoretti G, Antoranz A, De Smet F. Next-Generation Pathology Using Multiplexed Immunohistochemistry: Mapping Tissue Architecture at Single-Cell Level. Front Oncol 2022; 12:918900. [PMID: 35992810 PMCID: PMC9389457 DOI: 10.3389/fonc.2022.918900] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Accepted: 06/20/2022] [Indexed: 01/23/2023] Open
Abstract
Single-cell omics aim at charting the different types and properties of all cells in the human body in health and disease. Over the past years, myriads of cellular phenotypes have been defined by methods that mostly required cells to be dissociated and removed from their original microenvironment, thus destroying valuable information about their location and interactions. Growing insights, however, are showing that such information is crucial to understand complex disease states. For decades, pathologists have interpreted cells in the context of their tissue using low-plex antibody- and morphology-based methods. Novel technologies for multiplexed immunohistochemistry are now rendering it possible to perform extended single-cell expression profiling using dozens of protein markers in the spatial context of a single tissue section. The combination of these novel technologies with extended data analysis tools allows us now to study cell-cell interactions, define cellular sociology, and describe detailed aberrations in tissue architecture, as such gaining much deeper insights in disease states. In this review, we provide a comprehensive overview of the available technologies for multiplexed immunohistochemistry, their advantages and challenges. We also provide the principles on how to interpret high-dimensional data in a spatial context. Similar to the fact that no one can just “read” a genome, pathological assessments are in dire need of extended digital data repositories to bring diagnostics and tissue interpretation to the next level.
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Affiliation(s)
- Francesca Maria Bosisio
- Translational Cell and Tissue Research Unit, Department of Imaging and Pathology, KU Leuven, Leuven, Belgium
- *Correspondence: Frederik De Smet, ; Francesca Maria Bosisio,
| | | | - Julie Messiaen
- Translational Cell and Tissue Research Unit, Department of Imaging and Pathology, KU Leuven, Leuven, Belgium
- The Laboratory for Precision Cancer Medicine, Translational Cell and Tissue Research Unit, Department of Imaging and Pathology, KU Leuven, Leuven, Belgium
- Department of Pediatrics, University Hospitals Leuven, Leuven, Belgium
| | - Maddalena Maria Bolognesi
- Pathology, Department of Medicine and Surgery, Università di Milano-Bicocca, Monza, Italy
- Department of Pathology, Azienda Socio Sanitaria Territoriale (ASST) Monza, Ospedale San Gerardo, Monza, Italy
| | - Lukas Marcelis
- Translational Cell and Tissue Research Unit, Department of Imaging and Pathology, KU Leuven, Leuven, Belgium
| | - Matthias Van Haele
- Translational Cell and Tissue Research Unit, Department of Imaging and Pathology, KU Leuven, Leuven, Belgium
| | - Giorgio Cattoretti
- Pathology, Department of Medicine and Surgery, Università di Milano-Bicocca, Monza, Italy
- Department of Pathology, Azienda Socio Sanitaria Territoriale (ASST) Monza, Ospedale San Gerardo, Monza, Italy
| | - Asier Antoranz
- Translational Cell and Tissue Research Unit, Department of Imaging and Pathology, KU Leuven, Leuven, Belgium
- The Laboratory for Precision Cancer Medicine, Translational Cell and Tissue Research Unit, Department of Imaging and Pathology, KU Leuven, Leuven, Belgium
| | - Frederik De Smet
- Translational Cell and Tissue Research Unit, Department of Imaging and Pathology, KU Leuven, Leuven, Belgium
- The Laboratory for Precision Cancer Medicine, Translational Cell and Tissue Research Unit, Department of Imaging and Pathology, KU Leuven, Leuven, Belgium
- *Correspondence: Frederik De Smet, ; Francesca Maria Bosisio,
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Bright-Field Multiplex Immunohistochemistry Assay for Tumor Microenvironment Evaluation in Melanoma Tissues. Cancers (Basel) 2022; 14:cancers14153682. [PMID: 35954345 PMCID: PMC9367593 DOI: 10.3390/cancers14153682] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Revised: 07/20/2022] [Accepted: 07/26/2022] [Indexed: 02/05/2023] Open
Abstract
Simple Summary Bright-field (BF) immunohistochemistry (IHC) remains the gold standard for histopathological evaluations. The development of new BF multiplex IHC could be very useful for the study and characterization of the tumor microenvironment (TME) in melanoma samples. We herein compared different BF IHC multiplex protocols for the study of TME in primary cutaneous melanoma tissues and offered the best optimized protocol for visualization and evaluation. These methodologies are studied to maximize the quality of staining considering the tissue characteristics under examination, maintaining a high level of standardization and reproducibility. Abstract The tumor microenvironment (TME) plays a crucial role in melanoma development, progression and response to treatment. As many of the most relevant TME cell phenotypes are defined by the simultaneous detection of more than two markers, the bright-field (BF) multiplex immunohistochemistry (IHC) technique has been introduced for the quantitative assessment and evaluation of the relative spatial distances between immune cells and melanoma cells. In the current study, we aimed to validate BF multiplex IHC techniques in the Ventana Discovery Ultra Immunostainer to be applied to the evaluation of the TME in variably pigmented melanoma tissues. The BF multiplex IHC staining was performed using different combinations of six immune-cell markers—CD3, CD4, CD8, CD20, CD68 and CD163—and the melanoma cell marker SOX10. Our results show that the BF double IHC Yellow/Purple protocol guarantees the maximum contrast in all the cell populations tested and the combination SOX10 (Green), CD8 (Yellow) and CD163 (Purple) of the BF triple IHC protocol ensures the best contrast and discrimination between the three stained cell populations. Furthermore, the labeled cells were clearly distinct and easily identifiable using the image analysis software. Our standardized BF IHC multiplex protocols can be used to better assess the immune contexts of melanoma patients with potential applications to drive therapeutic decisions within clinical trials.
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Patel AU, Shaker N, Mohanty S, Sharma S, Gangal S, Eloy C, Parwani AV. Cultivating Clinical Clarity through Computer Vision: A Current Perspective on Whole Slide Imaging and Artificial Intelligence. Diagnostics (Basel) 2022; 12:diagnostics12081778. [PMID: 35892487 PMCID: PMC9332710 DOI: 10.3390/diagnostics12081778] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2022] [Revised: 07/10/2022] [Accepted: 07/11/2022] [Indexed: 11/17/2022] Open
Abstract
Diagnostic devices, methodological approaches, and traditional constructs of clinical pathology practice, cultivated throughout centuries, have transformed radically in the wake of explosive technological growth and other, e.g., environmental, catalysts of change. Ushered into the fray of modern laboratory medicine are digital imaging devices and machine-learning (ML) software fashioned to mitigate challenges, e.g., practitioner shortage while preparing clinicians for emerging interconnectivity of environments and diagnostic information in the era of big data. As computer vision shapes new constructs for the modern world and intertwines with clinical medicine, cultivating clarity of our new terrain through examining the trajectory and current scope of computational pathology and its pertinence to clinical practice is vital. Through review of numerous studies, we find developmental efforts for ML migrating from research to standardized clinical frameworks while overcoming obstacles that have formerly curtailed adoption of these tools, e.g., generalizability, data availability, and user-friendly accessibility. Groundbreaking validatory efforts have facilitated the clinical deployment of ML tools demonstrating the capacity to effectively aid in distinguishing tumor subtype and grade, classify early vs. advanced cancer stages, and assist in quality control and primary diagnosis applications. Case studies have demonstrated the benefits of streamlined, digitized workflows for practitioners alleviated by decreased burdens.
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Affiliation(s)
- Ankush U. Patel
- Mayo Clinic Department of Laboratory Medicine and Pathology, Rochester, MN 55905, USA
- Correspondence: ; Tel.: +1-206-451-3519
| | - Nada Shaker
- Department of Pathology, Wexner Medical Center, The Ohio State University, Columbus, OH 43210, USA; (N.S.); (S.G.); (A.V.P.)
| | - Sambit Mohanty
- CORE Diagnostics, Gurugram 122016, India; (S.M.); (S.S.)
- Advanced Medical Research Institute, Bareilly 243001, India
| | - Shivani Sharma
- CORE Diagnostics, Gurugram 122016, India; (S.M.); (S.S.)
| | - Shivam Gangal
- Department of Pathology, Wexner Medical Center, The Ohio State University, Columbus, OH 43210, USA; (N.S.); (S.G.); (A.V.P.)
- College of Engineering, Biomedical Engineering, The Ohio State University, Columbus, OH 43210, USA
| | - Catarina Eloy
- Institute of Molecular Pathology and Immunology of the University of Porto (IPATIMUP), Rua Júlio Amaral de Carvalho, 45, 4200-135 Porto, Portugal;
- Institute for Research and Innovation in Health (I3S Consortium), Rua Alfredo Allen, 208, 4200-135 Porto, Portugal
| | - Anil V. Parwani
- Department of Pathology, Wexner Medical Center, The Ohio State University, Columbus, OH 43210, USA; (N.S.); (S.G.); (A.V.P.)
- Cooperative Human Tissue Network (CHTN) Midwestern Division, Columbus, OH 43240, USA
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Conditional GANs based system for fibrosis detection and quantification in Hematoxylin and Eosin whole slide images. Med Image Anal 2022; 81:102537. [DOI: 10.1016/j.media.2022.102537] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Revised: 04/12/2022] [Accepted: 07/11/2022] [Indexed: 11/22/2022]
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Brunner A, Schmidt VM, Zelger B, Woess C, Arora R, Zelger P, Huck CW, Pallua J. Visible and Near-Infrared hyperspectral imaging (HSI) can reliably quantify CD3 and CD45 positive inflammatory cells in myocarditis: Pilot study on formalin-fixed paraffin-embedded specimens from myocard obtained during autopsy. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022; 274:121092. [PMID: 35257987 DOI: 10.1016/j.saa.2022.121092] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Revised: 02/17/2022] [Accepted: 02/25/2022] [Indexed: 06/14/2023]
Abstract
INTRODUCTION To implement Hyperspectral Imaging (HSI) as a tool for quantifying inflammatory cells in tissue specimens by the example of myocarditis in a collective of forensic patients. MATERIAL AND METHODS 44 consecutive patients with suspected myocardial inflammation at autopsy, diagnosed between 2013 and 2018 at the Institute of ForensicMedicine, Medical University of Innsbruck, were selected for this study. Using the IMEC SNAPSCAN camera, visible and near infrared hyperspectral images were collected from slides stained with CD3 and CD45 to assess quantity and spatial distribution of positive cells. Results were compared with visual assessment (VA) and conventional digital image analysis (DIA). RESULTS Finally, specimens of 40 patients were evaluated, of whom 36 patients (90%) suffered from myocarditis, two patients (5%) had suspected healing/healed myocarditis, and two did no have myocarditis (5%). The amount of CD3 and CD45 positive cells did not differ significantly between VA, HSI, and DIA (pVA/HSI/DIA = 0.46 for CD3 and 0.81 for CD45). Coheńs Kappa showed a very high correlation between VA versus HSI, VA versus DIA, and HSI versus DIA for CD3 (Coheńs Kappa = 0.91, 1.00, and 0.91, respectively). For CD45 an almost as high correlation was seen for VA versus HSI and HSI versus DIA (Coheńs Kappa = 0.75 and 0.70) and VA versus DIA (Coheńs Kappa = 0.89). CONCLUSION HSI is a reliable and objective method to count inflammatory cells in tissue slides of suspected myocarditis. Implementation of HSI in digital pathology might further expand the possibility of a sophisticated method.
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Affiliation(s)
- A Brunner
- Innsbruck Medical University, Institute of Pathology, Neuropathology, and Molecular Pathology, Muellerstrasse 44, 6020 Innsbruck, Austria
| | - V M Schmidt
- Institute of Forensic Medicine, Medical University of Innsbruck, Muellerstraße 44, 6020 Innsbruck, Austria
| | - B Zelger
- Innsbruck Medical University, Institute of Pathology, Neuropathology, and Molecular Pathology, Muellerstrasse 44, 6020 Innsbruck, Austria
| | - C Woess
- Institute of Forensic Medicine, Medical University of Innsbruck, Muellerstraße 44, 6020 Innsbruck, Austria.
| | - R Arora
- University Hospital for Orthopedics and Traumatology, Medical University of Innsbruck, Anichstraße 35, 6020 Innsbruck, Austria
| | - P Zelger
- University Clinic for Hearing, Voice and Speech Disorders, Medical University of Innsbruck, Anichstrasse 35, Innsbruck, Austria
| | - C W Huck
- Institute of Analytical Chemistry and Radiochemistry, University of Innsbruck, Innrain 80-82, Innsbruck, Austria
| | - J Pallua
- Innsbruck Medical University, Institute of Pathology, Neuropathology, and Molecular Pathology, Muellerstrasse 44, 6020 Innsbruck, Austria; Institute of Forensic Medicine, Medical University of Innsbruck, Muellerstraße 44, 6020 Innsbruck, Austria; University Hospital for Orthopedics and Traumatology, Medical University of Innsbruck, Anichstraße 35, 6020 Innsbruck, Austria
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A BERT model generates diagnostically relevant semantic embeddings from pathology synopses with active learning. COMMUNICATIONS MEDICINE 2022; 1:11. [PMID: 35602188 PMCID: PMC9053264 DOI: 10.1038/s43856-021-00008-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Accepted: 05/13/2021] [Indexed: 02/08/2023] Open
Abstract
Background Pathology synopses consist of semi-structured or unstructured text summarizing visual information by observing human tissue. Experts write and interpret these synopses with high domain-specific knowledge to extract tissue semantics and formulate a diagnosis in the context of ancillary testing and clinical information. The limited number of specialists available to interpret pathology synopses restricts the utility of the inherent information. Deep learning offers a tool for information extraction and automatic feature generation from complex datasets. Methods Using an active learning approach, we developed a set of semantic labels for bone marrow aspirate pathology synopses. We then trained a transformer-based deep-learning model to map these synopses to one or more semantic labels, and extracted learned embeddings (i.e., meaningful attributes) from the model's hidden layer. Results Here we demonstrate that with a small amount of training data, a transformer-based natural language model can extract embeddings from pathology synopses that capture diagnostically relevant information. On average, these embeddings can be used to generate semantic labels mapping patients to probable diagnostic groups with a micro-average F1 score of 0.779 Â ± 0.025. Conclusions We provide a generalizable deep learning model and approach to unlock the semantic information inherent in pathology synopses toward improved diagnostics, biodiscovery and AI-assisted computational pathology.
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Sun K, Gao Y, Xie T, Wang X, Yang Q, Chen L, Wang K, Yu G. A low-cost pathological image digitalization method based on 5 times magnification scanning. Quant Imaging Med Surg 2022; 12:2813-2829. [PMID: 35502389 PMCID: PMC9014144 DOI: 10.21037/qims-21-749] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2021] [Accepted: 01/06/2022] [Indexed: 10/10/2023]
Abstract
BACKGROUND Digital pathology has aroused widespread interest in modern pathology. The key to digitalization is to scan the whole slide image (WSI) at high magnification. The file size of each WSI at 40 times magnification (40×) may range from 1 gigabyte (GB) to 5 GB depending on the size of the specimen, which leads to huge storage capacity, very slow scanning and network exchange, seriously increasing time and storage costs for digital pathology. METHODS We design a strategy to scan slides with low resolution (LR) (5×), and a superresolution (SR) method is proposed to restore the image details during diagnosis. The method is based on a multiscale generative adversarial network, which can sequentially generate three high-resolution (HR) images: 10×, 20×, and 40×. A dataset consisting of 100,000 pathological images from 10 types of human body systems is used for training and testing. The differences between the generated images and the real images have been extensively evaluated using quantitative evaluation, visual inspection, medical scoring, and diagnosis. RESULTS The file size of each 5× WSI is approximately 15 Megabytes. The peak-signal-to-noise ratios (PSNRs) of 10× to 40× generated images are 24.167±3.734 dB, 22.272±4.272 dB, and 20.436±3.845 dB, and the structural similarity (SSIM) index values are 0.845±0.089, 0.680±0.150, and 0.559±0.179, which are better than those of other SR networks and conventional digital zoom methods. Visual inspections show that the generated images have details similar to the real images. Visual scoring average with 0.95 confidence interval from three pathologists are 3.630±1.024, 3.700±1.126, and 3.740±1.095, respectively, and the P value of analysis of variance is 0.367, indicating the pathologists confirm that generated images include sufficient information for diagnosis. The average value of the Kappa test of the diagnoses of paired generated and real images is 0.990, meaning the diagnosis of generated images is highly consistent with that of the real images. CONCLUSIONS The proposed method can generate high-quality 10×, 20×, 40× images from 5× images, which can effectively reduce the time and storage costs of digitalization up to 1/64 of the previous costs, which shows the potential for clinical applications and is expected to be an alternative digitalization method after large-scale evaluation.
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Affiliation(s)
- Kai Sun
- Department of Biomedical Engineering, School of Basic Medical Science, Central South University, Changsha, China
| | - Yanhua Gao
- Department of Ultrasound, Shaanxi Provincial People’s Hospital, Xi’an, China
| | - Ting Xie
- Department of Biomedical Engineering, School of Basic Medical Science, Central South University, Changsha, China
| | - Xun Wang
- Department of Biomedical Engineering, School of Basic Medical Science, Central South University, Changsha, China
| | - Qingqing Yang
- Department of Biomedical Engineering, School of Basic Medical Science, Central South University, Changsha, China
| | - Le Chen
- Department of Biomedical Engineering, School of Basic Medical Science, Central South University, Changsha, China
| | - Kuansong Wang
- Department of Pathology, School of Basic Medical Science, Central South University, Changsha, China
| | - Gang Yu
- Department of Biomedical Engineering, School of Basic Medical Science, Central South University, Changsha, China
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Jones H, Kalirai H, Taktak A, Chen K, Coupland SE. Vascular Lakes in Uveal Melanoma and Their Association With Outcome. Transl Vis Sci Technol 2022; 11:32. [PMID: 35348598 PMCID: PMC8976925 DOI: 10.1167/tvst.11.3.32] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Abstract
Purpose Prognostic predictors in uveal melanoma (UM) consist of clinical, histomorphologic, and genetic features. Vascular lakes (VLs) are immature blood vessels within UM with unknown significance for metastatic risk. Methods A clinically well-phenotyped cohort of 136 hematoxylin and eosin–stained slides of UM enucleation specimens were retrospectively analyzed on scanned whole-slide images. These were annotated for VL in QuPath, assessing VL number and area. Using SPSS (V27.0), the Mann–Whitney U test and Cox regression were applied to evaluate whether there was any correlation between VL number and area within the tumor (VL-TA) compared with other prognostic parameters and patient survival times. Results UMs with monosomy 3 (M3) have significant differences in their VL numbers (P = 0.008) and VL-TA ratios (P = 0.002) compared with disomy 3-UM. Nuclear BAP1-negative (nBAP1–) UMs have significant differences in their VL-TA ratio (P = 0.002) compared to nBAP1+ UMs. Survival times of patients with UM with epithelioid-celled tumors varied depending on their VL-TA ratio (P = 0.057). Similarly, in M3-UM, significant differences in survival (P = 0.009) were seen in patients, depending on VL number. Finally, patients with UM with shorter overall survival showed significant differences in their tumor VL-TA ratios (P = 0.043) and the number of VLs present (P = 0.002) than patients with UM who had longer survival. Conclusions Our pilot data suggest that VL-TA is an additional poor prognostic parameter in UM. Translational Relevance Digital analysis of UM can be easily performed to assess various prognostic parameters. Our pilot study demonstrates that UM-VL could be combined with other parameters to determine metastatic risk of patients with UM.
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Affiliation(s)
- Hayley Jones
- Department of Molecular and Clinical Cancer Medicine, Institute of Molecular, Systems and Integrative Biology, University of Liverpool, Liverpool, UK.,Department of Engineering, Institute of Risk and Uncertainty, University of Liverpool, Liverpool, UK
| | - Helen Kalirai
- Liverpool Ocular Oncology Research Group, University of Liverpool, Liverpool, UK.,Liverpool Clinical Laboratories, Liverpool University Hospitals NHS Foundation Trust, Liverpool, UK
| | - Azzam Taktak
- Department of Medical Physics and Clinical Engineering, Liverpool University Hospitals NHS Foundation Trust, Liverpool, UK
| | - Ke Chen
- Department of Mathematical Sciences, University of Liverpool, Liverpool, UK
| | - Sarah E Coupland
- Liverpool Ocular Oncology Research Group, University of Liverpool, Liverpool, UK.,Liverpool Clinical Laboratories, Liverpool University Hospitals NHS Foundation Trust, Liverpool, UK
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Digital Pathology Implementation in Private Practice: Specific Challenges and Opportunities. Diagnostics (Basel) 2022; 12:diagnostics12020529. [PMID: 35204617 PMCID: PMC8871027 DOI: 10.3390/diagnostics12020529] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Revised: 02/15/2022] [Accepted: 02/16/2022] [Indexed: 01/27/2023] Open
Abstract
Digital pathology (DP) is being deployed in many pathology laboratories, but most reported experiences refer to public health facilities. In this paper, we report our experience in DP transition at a high-volume private laboratory, addressing the main challenges in DP implementation in a private practice setting and how to overcome these issues. We started our implementation in 2020 and we are currently scanning 100% of our histology cases. Pre-existing sample tracking infrastructure facilitated this process. We are currently using two high-capacity scanners (Aperio GT450DX) to digitize all histology slides at 40×. Aperio eSlide Manager WebViewer viewing software is bidirectionally linked with the laboratory information system. Scanning error rate, during the test phase, was 2.1% (errors detected by the scanners) and 3.5% (manual quality control). Pre-scanning phase optimizations and vendor feedback and collaboration were crucial to improve WSI quality and are ongoing processes. Regarding pathologists' validation, we followed the Royal College of Pathologists recommendations for DP implementation (adapted to our practice). Although private sector implementation of DP is not without its challenges, it will ultimately benefit from DP safety and quality-associated features. Furthermore, DP deployment lays the foundation for artificial intelligence tools integration, which will ultimately contribute to improving patient care.
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Hanna MG, Ardon O, Reuter VE, Sirintrapun SJ, England C, Klimstra DS, Hameed MR. Integrating digital pathology into clinical practice. Mod Pathol 2022; 35:152-164. [PMID: 34599281 DOI: 10.1038/s41379-021-00929-0] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Revised: 09/03/2021] [Accepted: 09/12/2021] [Indexed: 11/09/2022]
Abstract
The field of anatomic pathology has been evolving in the last few decades and the advancements have been largely fostered by innovative technology. Immunohistochemistry enabled a paradigm shift in discovery and diagnostic evaluation, followed by booming genomic advancements which allowed for submicroscopic pathologic characterization, and now the field of digital pathology coupled with machine learning and big data acquisition is paving the way to revolutionize the pathology medical domain. Whole slide imaging (WSI) is a disruptive technology where glass slides are digitized to produce on-screen whole slide images. Specifically, in the past decade, there have been significant advances in digital pathology systems that have allowed this technology to promote integration into clinical practice. Whole slide images (WSI), or digital slides, can be viewed and navigated comparable to glass slides on a microscope, as digital files. Whole slide imaging has increased in adoption among pathologists, pathology departments, and scientists for clinical, educational, and research initiatives. Integration of digital pathology systems requires a coordinated effort with numerous stakeholders, not only within the pathology department, but across the entire enterprise. Each pathology department has distinct needs, use cases and blueprints, however the framework components and variables for successful clinical integration can be generalized across any organization seeking to undergo a digital transformation at any scale. This article will review those components and considerations for integrating digital pathology systems into clinical practice.
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Affiliation(s)
- Matthew G Hanna
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
| | - Orly Ardon
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Victor E Reuter
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | | | - Christine England
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - David S Klimstra
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Meera R Hameed
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
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Eloy C, Bychkov A, Pantanowitz L, Fraggetta F, Bui MM, Fukuoka J, Zerbe N, Hassell L, Parwani A. DPA-ESDIP-JSDP Task Force for Worldwide Adoption of Digital Pathology. J Pathol Inform 2022; 12:51. [PMID: 35070480 PMCID: PMC8721866 DOI: 10.4103/jpi.jpi_65_21] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2021] [Accepted: 11/15/2021] [Indexed: 12/11/2022] Open
Affiliation(s)
- Catarina Eloy
- Department of Pathology, Institute of Molecular Pathology and Immunology of University of Porto (IPATIMUP), Porto, Portugal.,Department of Pathology, Medical Faculty of Porto University, Porto, Portugal
| | - Andrey Bychkov
- Department of Pathology, Kameda Medical Center, Kamogawa, Japan
| | - Liron Pantanowitz
- Department of Pathology, University of Michigan, Ann Arbor, Michigan, USA
| | | | - Marilyn M Bui
- Department of Pathology, Moffitt Cancer Center & Research Institute, Tampa, Florida, USA
| | - Junya Fukuoka
- Department of Pathology, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki, Japan
| | - Norman Zerbe
- Charité - University Medicine Berlin & Research IT Services, Berlin Institute of Health & Institute of Pathology, Berlin, Germany
| | - Lewis Hassell
- Department of Pathology, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma, USA
| | - Anil Parwani
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, Ohio, USA
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42
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Shakir MN, Dugger BN. Advances in Deep Neuropathological Phenotyping of Alzheimer Disease: Past, Present, and Future. J Neuropathol Exp Neurol 2022; 81:2-15. [PMID: 34981115 PMCID: PMC8825756 DOI: 10.1093/jnen/nlab122] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
Alzheimer disease (AD) is a neurodegenerative disorder characterized pathologically by the presence of neurofibrillary tangles and amyloid beta (Aβ) plaques in the brain. The disease was first described in 1906 by Alois Alzheimer, and since then, there have been many advancements in technologies that have aided in unlocking the secrets of this devastating disease. Such advancements include improving microscopy and staining techniques, refining diagnostic criteria for the disease, and increased appreciation for disease heterogeneity both in neuroanatomic location of abnormalities as well as overlap with other brain diseases; for example, Lewy body disease and vascular dementia. Despite numerous advancements, there is still much to achieve as there is not a cure for AD and postmortem histological analyses is still the gold standard for appreciating AD neuropathologic changes. Recent technological advances such as in-vivo biomarkers and machine learning algorithms permit great strides in disease understanding, and pave the way for potential new therapies and precision medicine approaches. Here, we review the history of human AD neuropathology research to include the notable advancements in understanding common co-pathologies in the setting of AD, and microscopy and staining methods. We also discuss future approaches with a specific focus on deep phenotyping using machine learning.
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Affiliation(s)
- Mustafa N Shakir
- From the Department of Pathology and Laboratory Medicine, University of California, Davis, Sacramento, California, USA (MNS, BND)
| | - Brittany N Dugger
- From the Department of Pathology and Laboratory Medicine, University of California, Davis, Sacramento, California, USA (MNS, BND)
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Deep convolutional neural network-based classification of cancer cells on cytological pleural effusion images. Mod Pathol 2022; 35:609-614. [PMID: 35013527 PMCID: PMC9042694 DOI: 10.1038/s41379-021-00987-4] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Revised: 11/20/2021] [Accepted: 11/22/2021] [Indexed: 11/23/2022]
Abstract
Lung cancer is one of the leading causes of cancer-related death worldwide. Cytology plays an important role in the initial evaluation and diagnosis of patients with lung cancer. However, due to the subjectivity of cytopathologists and the region-dependent diagnostic levels, the low consistency of liquid-based cytological diagnosis results in certain proportions of misdiagnoses and missed diagnoses. In this study, we performed a weakly supervised deep learning method for the classification of benign and malignant cells in lung cytological images through a deep convolutional neural network (DCNN). A total of 404 cases of lung cancer cells in effusion cytology specimens from Shanghai Pulmonary Hospital were investigated, in which 266, 78, and 60 cases were used as the training, validation and test sets, respectively. The proposed method was evaluated on 60 whole-slide images (WSIs) of lung cancer pleural effusion specimens. This study showed that the method had an accuracy, sensitivity, and specificity respectively of 91.67%, 87.50% and 94.44% in classifying malignant and benign lesions (or normal). The area under the receiver operating characteristic (ROC) curve (AUC) was 0.9526 (95% confidence interval (CI): 0.9019-9.9909). In contrast, the average accuracies of senior and junior cytopathologists were 98.34% and 83.34%, respectively. The proposed deep learning method will be useful and may assist pathologists with different levels of experience in the diagnosis of cancer cells on cytological pleural effusion images in the future.
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Jessup J, Krueger R, Warchol S, Hoffer J, Muhlich J, Ritch CC, Gaglia G, Coy S, Chen YA, Lin JR, Santagata S, Sorger PK, Pfister H. Scope2Screen: Focus+Context Techniques for Pathology Tumor Assessment in Multivariate Image Data. IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2022; 28:259-269. [PMID: 34606456 PMCID: PMC8805697 DOI: 10.1109/tvcg.2021.3114786] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/20/2023]
Abstract
Inspection of tissues using a light microscope is the primary method of diagnosing many diseases, notably cancer. Highly multiplexed tissue imaging builds on this foundation, enabling the collection of up to 60 channels of molecular information plus cell and tissue morphology using antibody staining. This provides unique insight into disease biology and promises to help with the design of patient-specific therapies. However, a substantial gap remains with respect to visualizing the resulting multivariate image data and effectively supporting pathology workflows in digital environments on screen. We, therefore, developed Scope2Screen, a scalable software system for focus+context exploration and annotation of whole-slide, high-plex, tissue images. Our approach scales to analyzing 100GB images of 109 or more pixels per channel, containing millions of individual cells. A multidisciplinary team of visualization experts, microscopists, and pathologists identified key image exploration and annotation tasks involving finding, magnifying, quantifying, and organizing regions of interest (ROIs) in an intuitive and cohesive manner. Building on a scope-to-screen metaphor, we present interactive lensing techniques that operate at single-cell and tissue levels. Lenses are equipped with task-specific functionality and descriptive statistics, making it possible to analyze image features, cell types, and spatial arrangements (neighborhoods) across image channels and scales. A fast sliding-window search guides users to regions similar to those under the lens; these regions can be analyzed and considered either separately or as part of a larger image collection. A novel snapshot method enables linked lens configurations and image statistics to be saved, restored, and shared with these regions. We validate our designs with domain experts and apply Scope2Screen in two case studies involving lung and colorectal cancers to discover cancer-relevant image features.
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Patel A, Balis UGJ, Cheng J, Li Z, Lujan G, McClintock DS, Pantanowitz L, Parwani A. Contemporary Whole Slide Imaging Devices and Their Applications within the Modern Pathology Department: A Selected Hardware Review. J Pathol Inform 2021; 12:50. [PMID: 35070479 PMCID: PMC8721869 DOI: 10.4103/jpi.jpi_66_21] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Accepted: 09/22/2021] [Indexed: 12/21/2022] Open
Abstract
Digital pathology (DP) has disrupted the practice of traditional pathology, including applications in education, research, and clinical practice. Contemporary whole slide imaging (WSI) devices include technological advances that help address some of the challenges facing modern pathology, such as increasing workloads with fewer subspecialized pathologists, expanding integrated delivery networks with global reach, and greater customization when working up cases for precision medicine. This review focuses on integral hardware components of 43 market available and soon-to-be released digital WSI devices utilized throughout the world. Components such as objective lens type and magnification, scanning camera, illumination, and slide capacity were evaluated with respect to scan time, throughput, accuracy of scanning, and image quality. This analysis of assorted modern WSI devices offers essential, valuable information for successfully selecting and implementing a digital WSI solution for any given pathology practice.
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Affiliation(s)
- Ankush Patel
- Department of Pathology, The Ohio State University, Columbus, Ohio, USA
| | | | - Jerome Cheng
- Department of Pathology, University of Michigan, Ann Arbor, Michigan, USA
| | - Zaibo Li
- Department of Pathology, The Ohio State University, Columbus, Ohio, USA
| | - Giovanni Lujan
- Department of Pathology, The Ohio State University, Columbus, Ohio, USA
| | | | - Liron Pantanowitz
- Department of Pathology, University of Michigan, Ann Arbor, Michigan, USA
| | - Anil Parwani
- Department of Pathology, The Ohio State University, Columbus, Ohio, USA
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Santonicco N, Marletta S, Pantanowitz L, Fadda G, Troncone G, Brunelli M, Ghimenton C, Antonini P, Paolino G, Girolami I, Eccher A. Impact of mobile devices on cancer diagnosis in cytology. Diagn Cytopathol 2021; 50:34-45. [PMID: 34677909 DOI: 10.1002/dc.24890] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Revised: 10/06/2021] [Accepted: 10/14/2021] [Indexed: 11/05/2022]
Abstract
BACKGROUND Digital pathology has widened pathologists' opportunities to examine both surgical and cytological samples. Recently, portable mobile devices like tablets and smartphones have been tested for application with digital technologies including static, dynamic, and more recently whole slide imaging. This study aimed to review the published literature on the impact of mobile devices on cancer diagnoses in cytology. This analysis focused on their diagnostic potential, technical details, critical issues and pitfalls, and economical aspects. METHODS A systematic search was carried out in the electronic databases Embase and PubMed. Studies dealing with the application of mobile devices for diagnosing cancer on cytological specimens were included. The quality of studies was assessed with the QUADAS-2 tool. The main themes addressed were the comparison of manual examination with light microscopy and the use of mobile tools for primary diagnosis. The technical features of different models of smartphones and tablets, software, and adapters were also studied in terms of feasibility and costs-analysis. RESULTS Of 2458 retrieved articles, 18 were included. Concordance with light microscopy was good and diagnostic performance comparable with an expert pathologist's diagnosis. The mobile devices studied differed, sometimes significantly, in terms of speed and cost. The utility was improved by employing specifically designed adapters. Image acquisition and transmission represent the main critical points in almost all studies. CONCLUSION The use of mobile devices demonstrated promising results regarding the digital evaluation of cytological samples. Widespread adoption even in underserved areas is anticipated following validation studies, technology improvements, and reduction in the costs.
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Affiliation(s)
- Nicola Santonicco
- Department of Diagnostics and Public Health, Section of Pathology, University and Hospital Trust of Verona, Verona, Italy
| | - Stefano Marletta
- Department of Diagnostics and Public Health, Section of Pathology, University and Hospital Trust of Verona, Verona, Italy
| | - Liron Pantanowitz
- Department of Pathology & Clinical Labs, University of Michigan, Ann Arbor, Michigan, USA
| | - Guido Fadda
- Department of Human Pathology of the Adulthood and of the Developing Age "Gaetano Barresi", University of Messina Faculty of Medicine and Surgery, Messina, Italy
| | - Giancarlo Troncone
- Department of Public Health, University of Naples Federico II, Naples, Italy
| | - Matteo Brunelli
- Department of Diagnostics and Public Health, Section of Pathology, University and Hospital Trust of Verona, Verona, Italy
| | - Claudio Ghimenton
- Department of Pathology and Diagnostics, University and Hospital Trust of Verona, Verona, Italy
| | - Pietro Antonini
- Department of Diagnostics and Public Health, Section of Pathology, University and Hospital Trust of Verona, Verona, Italy
| | - Gaetano Paolino
- Department of Diagnostics and Public Health, Section of Pathology, University and Hospital Trust of Verona, Verona, Italy
| | - Ilaria Girolami
- Division of Pathology, Central Hospital Bolzano, Bolzano, Italy
| | - Albino Eccher
- Department of Pathology and Diagnostics, University and Hospital Trust of Verona, Verona, Italy
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Schie IW, Stiebing C, Popp J. Looking for a perfect match: multimodal combinations of Raman spectroscopy for biomedical applications. JOURNAL OF BIOMEDICAL OPTICS 2021; 26:JBO-210137VR. [PMID: 34387049 PMCID: PMC8358667 DOI: 10.1117/1.jbo.26.8.080601] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Accepted: 07/16/2021] [Indexed: 06/13/2023]
Abstract
Raman spectroscopy has shown very promising results in medical diagnostics by providing label-free and highly specific molecular information of pathological tissue ex vivo and in vivo. Nevertheless, the high specificity of Raman spectroscopy comes at a price, i.e., low acquisition rate, no direct access to depth information, and limited sampling areas. However, a similar case regarding advantages and disadvantages can also be made for other highly regarded optical modalities, such as optical coherence tomography, autofluorescence imaging and fluorescence spectroscopy, fluorescence lifetime microscopy, second-harmonic generation, and others. While in these modalities the acquisition speed is significantly higher, they have no or only limited molecular specificity and are only sensitive to a small group of molecules. It can be safely stated that a single modality provides only a limited view on a specific aspect of a biological specimen and cannot assess the entire complexity of a sample. To solve this issue, multimodal optical systems, which combine different optical modalities tailored to a particular need, become more and more common in translational research and will be indispensable diagnostic tools in clinical pathology in the near future. These systems can assess different and partially complementary aspects of a sample and provide a distinct set of independent biomarkers. Here, we want to give an overview on the development of multimodal systems that use RS in combination with other optical modalities to improve the diagnostic performance.
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Affiliation(s)
- Iwan W. Schie
- Leibniz Institute of Photonic Technology, Jena, Germany
- University of Applied Sciences—Jena, Department for Medical Engineering and Biotechnology, Jena, Germany
| | | | - Jürgen Popp
- Leibniz Institute of Photonic Technology, Jena, Germany
- Friedrich Schiller University Jena, Institute of Physical Chemistry and Abbe Center of Photonics, Jena, Germany
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48
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Cimadamore A, Scarpelli M, Cheng L, Lopez-Beltran A, Montorsi F, Montironi R. Digital whole mount sections of the prostate: heading towards new ways of communicating with clinicians and patients without microscope. Minerva Urol Nephrol 2021; 74:127-129. [PMID: 34263745 DOI: 10.23736/s2724-6051.21.04552-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Affiliation(s)
- Alessia Cimadamore
- Section of Pathological Anatomy, School of Medicine, United Hospitals, Polytechnic University of the Marche Region, Ancona, Italy
| | - Marina Scarpelli
- Section of Pathological Anatomy, School of Medicine, United Hospitals, Polytechnic University of the Marche Region, Ancona, Italy
| | - Liang Cheng
- Department of Pathology and Laboratory Medicine, Indiana University School of Medicine, Indianapolis, IN, USA
| | | | - Francesco Montorsi
- Unit of Urology, Division of Oncology, URI, IRCCS Ospedale San Raffaele, Milan, Italy
| | - Rodolfo Montironi
- Section of Pathological Anatomy, School of Medicine, United Hospitals, Polytechnic University of the Marche Region, Ancona, Italy -
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49
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Klamminger GG, Gérardy JJ, Jelke F, Mirizzi G, Slimani R, Klein K, Husch A, Hertel F, Mittelbronn M, Kleine-Borgmann FB. Application of Raman spectroscopy for detection of histologically distinct areas in formalin-fixed paraffin-embedded glioblastoma. Neurooncol Adv 2021; 3:vdab077. [PMID: 34355170 PMCID: PMC8331050 DOI: 10.1093/noajnl/vdab077] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
Abstract
BACKGROUND Although microscopic assessment is still the diagnostic gold standard in pathology, non-light microscopic methods such as new imaging methods and molecular pathology have considerably contributed to more precise diagnostics. As an upcoming method, Raman spectroscopy (RS) offers a "molecular fingerprint" that could be used to differentiate tissue heterogeneity or diagnostic entities. RS has been successfully applied on fresh and frozen tissue, however more aggressively, chemically treated tissue such as formalin-fixed, paraffin-embedded (FFPE) samples are challenging for RS. METHODS To address this issue, we examined FFPE samples of morphologically highly heterogeneous glioblastoma (GBM) using RS in order to classify histologically defined GBM areas according to RS spectral properties. We have set up an SVM (support vector machine)-based classifier in a training cohort and corroborated our findings in a validation cohort. RESULTS Our trained classifier identified distinct histological areas such as tumor core and necroses in GBM with an overall accuracy of 70.5% based on the spectral properties of RS. With an absolute misclassification of 21 out of 471 Raman measurements, our classifier has the property of precisely distinguishing between normal-appearing brain tissue and necrosis. When verifying the suitability of our classifier system in a second independent dataset, very little overlap between necrosis and normal-appearing brain tissue can be detected. CONCLUSION These findings show that histologically highly variable samples such as GBM can be reliably recognized by their spectral properties using RS. As conclusion, we propose that RS may serve useful as a future method in the pathological toolbox.
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Affiliation(s)
| | - Jean-Jacques Gérardy
- National Center of Pathology (NCP), Laboratoire national de santé (LNS), Dudelange, Luxembourg
- Luxembourg Center of Neuropathology (LCNP), Dudelange, Luxembourg
| | - Finn Jelke
- Saarland University Medical Center and Faculty of Medicine, Homburg, Germany
- National Center of Neurosurgery, Centre Hospitalier de Luxembourg (CHL), Luxembourg, Luxembourg
| | - Giulia Mirizzi
- Saarland University Medical Center and Faculty of Medicine, Homburg, Germany
- National Center of Neurosurgery, Centre Hospitalier de Luxembourg (CHL), Luxembourg, Luxembourg
| | - Rédouane Slimani
- Department of Oncology (DONC), Luxembourg Institute of Health (LIH), Luxembourg, Luxembourg
- Doctoral School in Science and Engineering (DSSE), University of Luxembourg (UL), Esch-sur-Alzette, Luxembourg
| | - Karoline Klein
- Saarland University Medical Center and Faculty of Medicine, Homburg, Germany
- National Center of Neurosurgery, Centre Hospitalier de Luxembourg (CHL), Luxembourg, Luxembourg
| | - Andreas Husch
- Luxembourg Centre of Systems Biomedicine (LCSB), University of Luxembourg (UL), Esch-sur-Alzette, Luxembourg
| | - Frank Hertel
- Saarland University Medical Center and Faculty of Medicine, Homburg, Germany
- National Center of Neurosurgery, Centre Hospitalier de Luxembourg (CHL), Luxembourg, Luxembourg
- Luxembourg Centre of Systems Biomedicine (LCSB), University of Luxembourg (UL), Esch-sur-Alzette, Luxembourg
| | - Michel Mittelbronn
- National Center of Pathology (NCP), Laboratoire national de santé (LNS), Dudelange, Luxembourg
- Luxembourg Center of Neuropathology (LCNP), Dudelange, Luxembourg
- Department of Oncology (DONC), Luxembourg Institute of Health (LIH), Luxembourg, Luxembourg
- Luxembourg Centre of Systems Biomedicine (LCSB), University of Luxembourg (UL), Esch-sur-Alzette, Luxembourg
| | - Felix B Kleine-Borgmann
- Saarland University Medical Center and Faculty of Medicine, Homburg, Germany
- Luxembourg Center of Neuropathology (LCNP), Dudelange, Luxembourg
- Department of Oncology (DONC), Luxembourg Institute of Health (LIH), Luxembourg, Luxembourg
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50
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Montironi R, Cimadamore A, Scarpelli M, Cheng L, Lopez-Beltran A, Mikuz G. Pathology without microscope: From a projection screen to a virtual slide. Pathol Res Pract 2020; 216:153196. [PMID: 32927307 PMCID: PMC7467085 DOI: 10.1016/j.prp.2020.153196] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/26/2020] [Accepted: 08/28/2020] [Indexed: 02/06/2023]
Abstract
We have witnessed successive stages since the Seventies in the advancements towards digital pathology. We agree with Dr Pallua et al on the tremendous changes that are taking place in pathology, all leading toward greater role of digitalization in the field of pathology, both in terms of consultation and teaching. In particular, distance teaching using digital pathology will grow into a mainstream mode of pathology teaching, something that has been reinforced by COVID-19.
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Affiliation(s)
- R Montironi
- Section of Pathological Anatomy, Polytechnic University of the Marche Region, School of Medicine, United Hospitals, Ancona, Italy.
| | - A Cimadamore
- Section of Pathological Anatomy, Polytechnic University of the Marche Region, School of Medicine, United Hospitals, Ancona, Italy
| | - M Scarpelli
- Section of Pathological Anatomy, Polytechnic University of the Marche Region, School of Medicine, United Hospitals, Ancona, Italy
| | - L Cheng
- Department of Pathology and Laboratory Medicine, Indiana University School of Medicine, Indianapolis, USA
| | - A Lopez-Beltran
- Department of Pathology and Surgery, Faculty of Medicine, Cordoba, Spain
| | - G Mikuz
- Institute of Pathology, Medical University Innsbruck, Innsbruck, Austria
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