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Ancheta K, Le Calvez S, Williams J. The digital revolution in veterinary pathology. J Comp Pathol 2024; 214:19-31. [PMID: 39241697 DOI: 10.1016/j.jcpa.2024.08.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2024] [Revised: 06/14/2024] [Accepted: 08/01/2024] [Indexed: 09/09/2024]
Abstract
For the past two centuries, the use of traditional light microscopy to examine tissues to make diagnoses has remained relatively unchanged. While the fundamental concept of tissue slide analysis has stayed the same, our interaction with the microscope is undergoing significant changes. Digital pathology (DP) has gained momentum in veterinary science and is on the verge of becoming a vital tool in diagnostics, research and education. Many diagnostic laboratories have incorporated DP as a critical part of their workflows. Innovations in DP and whole slide image technology have made telediagnosis (the process of transmitting digital clinical data using telecommunication networks for distant diagnosis) more accessible, leading to improved patient care through streamlining of workflows and greater accessibility of second opinions. The integration of machine learning and artificial intelligence and human-in-the-loop protocols for DP workflows will further the development of computer-aided diagnosis and prognostic tools. Despite its present weaknesses, DP will progressively aid veterinary clinicians and pathologists in delivering more accurate and reliable diagnoses. Consistent incorporation of DP frontline advancements into routine veterinary diagnostic pipelines will assist in improving current tools and help prepare pathologists for the progression of digitalization in the field.
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Affiliation(s)
- Kenneth Ancheta
- The Royal Veterinary College, Hawkshead Campus, Hawkshead Lane, Hatfield, Hertfordshire AL9 7TA, UK
| | - Sophie Le Calvez
- IDEXX Laboratories Ltd, Grange House, Sandbeck Way, Wetherby, Yorkshire LS22 7DN, UK
| | - Jonathan Williams
- The Royal Veterinary College, Hawkshead Campus, Hawkshead Lane, Hatfield, Hertfordshire AL9 7TA, UK.
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2
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Turley J, Chenchiah IV, Martin P, Liverpool TB, Weavers H. Deep learning for rapid analysis of cell divisions in vivo during epithelial morphogenesis and repair. eLife 2024; 12:RP87949. [PMID: 39312468 PMCID: PMC11419669 DOI: 10.7554/elife.87949] [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] [Indexed: 09/25/2024] Open
Abstract
Cell division is fundamental to all healthy tissue growth, as well as being rate-limiting in the tissue repair response to wounding and during cancer progression. However, the role that cell divisions play in tissue growth is a collective one, requiring the integration of many individual cell division events. It is particularly difficult to accurately detect and quantify multiple features of large numbers of cell divisions (including their spatio-temporal synchronicity and orientation) over extended periods of time. It would thus be advantageous to perform such analyses in an automated fashion, which can naturally be enabled using deep learning. Hence, we develop a pipeline of deep learning models that accurately identify dividing cells in time-lapse movies of epithelial tissues in vivo. Our pipeline also determines their axis of division orientation, as well as their shape changes before and after division. This strategy enables us to analyse the dynamic profile of cell divisions within the Drosophila pupal wing epithelium, both as it undergoes developmental morphogenesis and as it repairs following laser wounding. We show that the division axis is biased according to lines of tissue tension and that wounding triggers a synchronised (but not oriented) burst of cell divisions back from the leading edge.
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Affiliation(s)
- Jake Turley
- School of Mathematics, University of BristolBristolUnited Kingdom
- School of Biochemistry, University of BristolBristolUnited Kingdom
- Mechanobiology Institute, National University of SingaporeSingaporeSingapore
| | | | - Paul Martin
- School of Biochemistry, University of BristolBristolUnited Kingdom
| | | | - Helen Weavers
- School of Biochemistry, University of BristolBristolUnited Kingdom
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3
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Meuten DJ, Moore FM, Kass PH, London CA, Reilly CM, Romansik EM, R Sueiro FA. "Errata" does anyone read them? Revisiting mitotic counts in mast cell tumors. Vet Pathol 2024; 61:675-677. [PMID: 38742653 DOI: 10.1177/03009858241246987] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
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4
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Kittichai V, Sompong W, Kaewthamasorn M, Sasisaowapak T, Naing KM, Tongloy T, Chuwongin S, Thanee S, Boonsang S. A novel approach for identification of zoonotic trypanosome utilizing deep metric learning and vector database-based image retrieval system. Heliyon 2024; 10:e30643. [PMID: 38774068 PMCID: PMC11107104 DOI: 10.1016/j.heliyon.2024.e30643] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2023] [Revised: 04/28/2024] [Accepted: 05/01/2024] [Indexed: 05/24/2024] Open
Abstract
Trypanosomiasis, a significant health concern in South America, South Asia, and Southeast Asia, requires active surveys to effectively control the disease. To address this, we have developed a hybrid model that combines deep metric learning (DML) and image retrieval. This model is proficient at identifying Trypanosoma species in microscopic images of thin-blood film examinations. Utilizing the ResNet50 backbone neural network, a trained-model has demonstrated outstanding performance, achieving an accuracy exceeding 99.71 % and up to 96 % in recall. Acknowledging the necessity for automated tools in field scenarios, we demonstrated the potential of our model as an autonomous screening approach. This was achieved by using prevailing convolutional neural network (CNN) applications, and vector database based-images returned by the KNN algorithm. This achievement is primarily attributed to the implementation of the Triplet Margin Loss function as 98 % of precision. The robustness of the model demonstrated in five-fold cross-validation highlights the ResNet50 neural network, based on DML, as a state-of-the-art CNN model as AUC >98 %. The adoption of DML significantly improves the performance of the model, remaining unaffected by variations in the dataset and rendering it a useful tool for fieldwork studies. DML offers several advantages over conventional classification model to manage large-scale datasets with a high volume of classes, enhancing scalability. The model has the capacity to generalize to novel classes that were not encountered during training, proving particularly advantageous in scenarios where new classes may consistently emerge. It is also well suited for applications requiring precise recognition, especially in discriminating between closely related classes. Furthermore, the DML exhibits greater resilience to issues related to class imbalance, as it concentrates on learning distances or similarities, which are more tolerant to such imbalances. These contributions significantly make the effectiveness and practicality of DML model, particularly in in fieldwork research.
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Affiliation(s)
- Veerayuth Kittichai
- Faculty of Medicine, King Mongkut's Institute of Technology Ladkrabang, Thailand
| | - Weerachat Sompong
- Faculty of Medicine, King Mongkut's Institute of Technology Ladkrabang, Thailand
| | - Morakot Kaewthamasorn
- Veterinary Parasitology Research Unit, Faculty of Veterinary Science, Chulalongkorn University, Bangkok, Thailand
| | - Thanyathep Sasisaowapak
- College of Advanced Manufacturing Innovation, King Mongkut's Institute of Technology Ladkrabang, Thailand
| | - Kaung Myat Naing
- College of Advanced Manufacturing Innovation, King Mongkut's Institute of Technology Ladkrabang, Thailand
| | - Teerawat Tongloy
- College of Advanced Manufacturing Innovation, King Mongkut's Institute of Technology Ladkrabang, Thailand
| | - Santhad Chuwongin
- College of Advanced Manufacturing Innovation, King Mongkut's Institute of Technology Ladkrabang, Thailand
| | - Suchansa Thanee
- Veterinary Parasitology Research Unit, Faculty of Veterinary Science, Chulalongkorn University, Bangkok, Thailand
| | - Siridech Boonsang
- Department of Electrical Engineering, Faculty of Engineering, King Mongkut's Institute of Technology Ladkrabang, Thailand
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5
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Scharre A, Scholler D, Gesell-May S, Müller T, Zablotski Y, Ertel W, May A. Comparison of veterinarians and a deep learning tool in the diagnosis of equine ophthalmic diseases. Equine Vet J 2024. [PMID: 38567426 DOI: 10.1111/evj.14087] [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: 08/14/2023] [Accepted: 02/25/2024] [Indexed: 04/04/2024]
Abstract
BACKGROUND/OBJECTIVES The aim was to compare ophthalmic diagnoses made by veterinarians to a deep learning (artificial intelligence) software tool which was developed to aid in the diagnosis of equine ophthalmic diseases. As equine ophthalmology is a very specialised field in equine medicine, the tool may be able to help in diagnosing equine ophthalmic emergencies such as uveitis. STUDY DESIGN In silico tool development and assessment of diagnostic performance. METHODS A deep learning tool which was developed and trained for classification of equine ophthalmic diseases was tested with 40 photographs displaying various equine ophthalmic diseases. The same data set was shown to different groups of veterinarians (equine, small animal, mixed practice, other) using an opinion poll to compare the results and evaluate the performance of the programme. Convolutional Neural Networks (CNN) were trained on 2346 photographs of equine eyes, which were augmented to 9384 images. Two hundred and sixty-one separate unmodified images were used to evaluate the trained network. The trained deep learning tool was used on 40 photographs of equine eyes (10 healthy, 12 uveitis, 18 other diseases). An opinion poll was used to evaluate the diagnostic performance of 148 veterinarians in comparison to the software tool. RESULTS The probability for the correct answer was 93% for the AI programme. Equine veterinarians answered correctly in 76%, whereas other veterinarians reached 67% probability for the correct diagnosis. MAIN LIMITATIONS Diagnosis was solely based on images of equine eyes without the possibility to evaluate the inner eye. CONCLUSIONS The deep learning tool proved to be at least equivalent to veterinarians in assessing ophthalmic diseases in photographs. We therefore conclude that the software tool may be useful in detecting potential emergency cases. In this context, blindness in horses may be prevented as the horse can receive accurate treatment or can be sent to an equine hospital. Furthermore, the tool gives less experienced veterinarians the opportunity to differentiate between uveitis and other ocular anterior segment disease and to support them in their decision-making regarding treatment.
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Affiliation(s)
- Annabel Scharre
- Equine Clinic, Ludwig Maximilians University, Oberschleissheim, Germany
| | - Dominik Scholler
- Equine Clinic, Ludwig Maximilians University, Oberschleissheim, Germany
| | | | | | - Yury Zablotski
- Clinic for Ruminants, Ludwig Maximilians University, Oberschleissheim, Germany
| | - Wolfgang Ertel
- Institute for Artificial Intelligence, Ravensburg-Weingarten University, Weingarten, Germany
| | - Anna May
- Equine Clinic, Ludwig Maximilians University, Oberschleissheim, Germany
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Smith A, Carroll PW, Aravamuthan S, Walleser E, Lin H, Anklam K, Döpfer D, Apostolopoulos N. Computer vision model for the detection of canine pododermatitis and neoplasia of the paw. Vet Dermatol 2024; 35:138-147. [PMID: 38057947 DOI: 10.1111/vde.13221] [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: 04/03/2023] [Revised: 09/01/2023] [Accepted: 11/20/2023] [Indexed: 12/08/2023]
Abstract
BACKGROUND Artificial intelligence (AI) has been used successfully in human dermatology. AI utilises convolutional neural networks (CNN) to accomplish tasks such as image classification, object detection and segmentation, facilitating early diagnosis. Computer vision (CV), a field of AI, has shown great results in detecting signs of human skin diseases. Canine paw skin diseases are a common problem in general veterinary practice, and computer vision tools could facilitate the detection and monitoring of disease processes. Currently, no such tool is available in veterinary dermatology. ANIMALS Digital images of paws from healthy dogs and paws with pododermatitis or neoplasia were used. OBJECTIVES We tested the novel object detection model Pawgnosis, a Tiny YOLOv4 image analysis model deployed on a microcomputer with a camera for the rapid detection of canine pododermatitis and neoplasia. MATERIALS AND METHODS The prediction performance metrics used to evaluate the models included mean average precision (mAP), precision, recall, average precision (AP) for accuracy and frames per second (FPS) for speed. RESULTS A large dataset labelled by a single individual (Dataset A) used to train a Tiny YOLOv4 model provided the best results with a mean mAP of 0.95, precision of 0.86, recall of 0.93 and 20 FPS. CONCLUSIONS AND CLINICAL RELEVANCE This novel object detection model has the potential for application in the field of veterinary dermatology.
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Affiliation(s)
- Andrew Smith
- School of Veterinary Medicine, Department of Medical Sciences, University of Wisconsin in Madison, Madison, Wisconsin, USA
| | - Patrick W Carroll
- School of Veterinary Medicine, Department of Medical Sciences, University of Wisconsin in Madison, Madison, Wisconsin, USA
| | - Srikanth Aravamuthan
- School of Veterinary Medicine, Department of Medical Sciences, University of Wisconsin in Madison, Madison, Wisconsin, USA
| | - Emil Walleser
- School of Veterinary Medicine, Department of Medical Sciences, University of Wisconsin in Madison, Madison, Wisconsin, USA
| | - Haley Lin
- School of Veterinary Medicine, Department of Medical Sciences, University of Wisconsin in Madison, Madison, Wisconsin, USA
| | - Kelly Anklam
- School of Veterinary Medicine, Department of Medical Sciences, University of Wisconsin in Madison, Madison, Wisconsin, USA
| | - Dörte Döpfer
- School of Veterinary Medicine, Department of Medical Sciences, University of Wisconsin in Madison, Madison, Wisconsin, USA
| | - Neoklis Apostolopoulos
- School of Veterinary Medicine, Department of Medical Sciences, University of Wisconsin in Madison, Madison, Wisconsin, USA
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Rai T, Morisi A, Bacci B, Bacon NJ, Dark MJ, Aboellail T, Thomas SA, La Ragione RM, Wells K. Keeping Pathologists in the Loop and an Adaptive F1-Score Threshold Method for Mitosis Detection in Canine Perivascular Wall Tumours. Cancers (Basel) 2024; 16:644. [PMID: 38339394 PMCID: PMC10854568 DOI: 10.3390/cancers16030644] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 01/17/2024] [Accepted: 01/29/2024] [Indexed: 02/12/2024] Open
Abstract
Performing a mitosis count (MC) is the diagnostic task of histologically grading canine Soft Tissue Sarcoma (cSTS). However, mitosis count is subject to inter- and intra-observer variability. Deep learning models can offer a standardisation in the process of MC used to histologically grade canine Soft Tissue Sarcomas. Subsequently, the focus of this study was mitosis detection in canine Perivascular Wall Tumours (cPWTs). Generating mitosis annotations is a long and arduous process open to inter-observer variability. Therefore, by keeping pathologists in the loop, a two-step annotation process was performed where a pre-trained Faster R-CNN model was trained on initial annotations provided by veterinary pathologists. The pathologists reviewed the output false positive mitosis candidates and determined whether these were overlooked candidates, thus updating the dataset. Faster R-CNN was then trained on this updated dataset. An optimal decision threshold was applied to maximise the F1-score predetermined using the validation set and produced our best F1-score of 0.75, which is competitive with the state of the art in the canine mitosis domain.
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Affiliation(s)
- Taranpreet Rai
- Centre for Vision, Speech and Signal Processing, University of Surrey, Guildford GU2 7XH, UK;
- Surrey DataHub, University of Surrey, Guildford GU2 7AL, UK
| | - Ambra Morisi
- School of Veterinary Medicine, University of Surrey, Guildford GU2 7AL, UK; (A.M.); (R.M.L.R.)
| | - Barbara Bacci
- Department of Veterinary Medical Sciences, University of Bologna, 40126 Bologna, Italy;
| | | | - Michael J. Dark
- Department of Comparative, Diagnostic and Population Medicine, College of Veterinary Medicine, University of Florida, Gainesville, FL 32611, USA;
| | - Tawfik Aboellail
- Department of Diagnostic Pathology and Pathobiology, Kansas State University, Manhattan, KS 66506, USA;
| | - Spencer A. Thomas
- Department of Computer Science, University of Surrey, Guildford GU2 7XH, UK;
- National Physical Laboratory, London TW11 0LW, UK
| | - Roberto M. La Ragione
- School of Veterinary Medicine, University of Surrey, Guildford GU2 7AL, UK; (A.M.); (R.M.L.R.)
- School of Biosciences, University of Surrey, Guildford GU2 7XH, UK
| | - Kevin Wells
- Centre for Vision, Speech and Signal Processing, University of Surrey, Guildford GU2 7XH, UK;
- Surrey DataHub, University of Surrey, Guildford GU2 7AL, UK
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Gu H, Yang C, Al-Kharouf I, Magaki S, Lakis N, Williams CK, Alrosan SM, Onstott EK, Yan W, Khanlou N, Cobos I, Zhang XR, Zarrin-Khameh N, Vinters HV, Chen XA, Haeri M. Enhancing mitosis quantification and detection in meningiomas with computational digital pathology. Acta Neuropathol Commun 2024; 12:7. [PMID: 38212848 PMCID: PMC10782692 DOI: 10.1186/s40478-023-01707-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Accepted: 12/10/2023] [Indexed: 01/13/2024] Open
Abstract
Mitosis is a critical criterion for meningioma grading. However, pathologists' assessment of mitoses is subject to significant inter-observer variation due to challenges in locating mitosis hotspots and accurately detecting mitotic figures. To address this issue, we leverage digital pathology and propose a computational strategy to enhance pathologists' mitosis assessment. The strategy has two components: (1) A depth-first search algorithm that quantifies the mathematically maximum mitotic count in 10 consecutive high-power fields, which can enhance the preciseness, especially in cases with borderline mitotic count. (2) Implementing a collaborative sphere to group a set of pathologists to detect mitoses under each high-power field, which can mitigate subjective random errors in mitosis detection originating from individual detection errors. By depth-first search algorithm (1) , we analyzed 19 meningioma slides and discovered that the proposed algorithm upgraded two borderline cases verified at consensus conferences. This improvement is attributed to the algorithm's ability to quantify the mitotic count more comprehensively compared to other conventional methods of counting mitoses. In implementing a collaborative sphere (2) , we evaluated the correctness of mitosis detection from grouped pathologists and/or pathology residents, where each member of the group annotated a set of 48 high-power field images for mitotic figures independently. We report that groups with sizes of three can achieve an average precision of 0.897 and sensitivity of 0.699 in mitosis detection, which is higher than an average pathologist in this study (precision: 0.750, sensitivity: 0.667). The proposed computational strategy can be integrated with artificial intelligence workflow, which envisions the future of achieving a rapid and robust mitosis assessment by interactive assisting algorithms that can ultimately benefit patient management.
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Affiliation(s)
- Hongyan Gu
- Electrical and Computer Engineering, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Chunxu Yang
- Electrical and Computer Engineering, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Issa Al-Kharouf
- Pathology and Laboratory Medicine, The University of Kansas Medical Center, Kansas City, KS, 66160, USA
| | - Shino Magaki
- Pathology and Laboratory Medicine, UCLA David Geffen School of Medicine, Los Angeles, CA, 90095, USA
| | - Nelli Lakis
- Pathology and Laboratory Medicine, The University of Kansas Medical Center, Kansas City, KS, 66160, USA
| | - Christopher Kazu Williams
- Pathology and Laboratory Medicine, UCLA David Geffen School of Medicine, Los Angeles, CA, 90095, USA
| | - Sallam Mohammad Alrosan
- Pathology and Laboratory Medicine, The University of Kansas Medical Center, Kansas City, KS, 66160, USA
| | - Ellie Kate Onstott
- Pathology and Laboratory Medicine, The University of Kansas Medical Center, Kansas City, KS, 66160, USA
| | - Wenzhong Yan
- Electrical and Computer Engineering, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Negar Khanlou
- Pathology and Laboratory Medicine, UCLA David Geffen School of Medicine, Los Angeles, CA, 90095, USA
| | - Inma Cobos
- Department of Pathology, Stanford Medical School, Stanford, CA, 94305, USA
| | | | | | - Harry V Vinters
- Pathology and Laboratory Medicine, UCLA David Geffen School of Medicine, Los Angeles, CA, 90095, USA
| | - Xiang Anthony Chen
- Electrical and Computer Engineering, University of California, Los Angeles, Los Angeles, CA, 90095, USA.
| | - Mohammad Haeri
- Pathology and Laboratory Medicine, The University of Kansas Medical Center, Kansas City, KS, 66160, USA.
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Bencosme-Cuevas E, Kim TK, Nguyen TT, Berry J, Li J, Adams LG, Smith LA, Batool SA, Swale DR, Kaufmann SHE, Jones-Hall Y, Mulenga A. Ixodes scapularis nymph saliva protein blocks host inflammation and complement-mediated killing of Lyme disease agent, Borrelia burgdorferi. Front Cell Infect Microbiol 2023; 13:1253670. [PMID: 37965264 PMCID: PMC10641286 DOI: 10.3389/fcimb.2023.1253670] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Accepted: 09/14/2023] [Indexed: 11/16/2023] Open
Abstract
Tick serine protease inhibitors (serpins) play crucial roles in tick feeding and pathogen transmission. We demonstrate that Ixodes scapularis (Ixs) nymph tick saliva serpin (S) 41 (IxsS41), secreted by Borrelia burgdorferi (Bb)-infected ticks at high abundance, is involved in regulating tick evasion of host innate immunity and promoting host colonization by Bb. Recombinant (r) proteins were expressed in Pichia pastoris, and substrate hydrolysis assays were used to determine. Ex vivo (complement and hemostasis function related) and in vivo (paw edema and effect on Bb colonization of C3H/HeN mice organs) assays were conducted to validate function. We demonstrate that rIxsS41 inhibits chymase and cathepsin G, pro-inflammatory proteases that are released by mast cells and neutrophils, the first immune cells at the tick feeding site. Importantly, stoichiometry of inhibition analysis revealed that 2.2 and 2.8 molecules of rIxsS41 are needed to 100% inhibit 1 molecule of chymase and cathepsin G, respectively, suggesting that findings here are likely events at the tick feeding site. Furthermore, chymase-mediated paw edema, induced by the mast cell degranulator, compound 48/80 (C48/80), was blocked by rIxsS41. Likewise, rIxsS41 reduced membrane attack complex (MAC) deposition via the alternative and lectin complement activation pathways and dose-dependently protected Bb from complement killing. Additionally, co-inoculating C3H/HeN mice with Bb together with rIxsS41 or with a mixture (rIxsS41 and C48/80). Findings in this study suggest that IxsS41 markedly contributes to tick feeding and host colonization by Bb. Therefore, we conclude that IxsS41 is a potential candidate for an anti-tick vaccine to prevent transmission of the Lyme disease agent.
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Affiliation(s)
- Emily Bencosme-Cuevas
- Department of Veterinary Pathobiology, School of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX, United States
| | - Tae Kwon Kim
- Department of Veterinary Pathobiology, School of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX, United States
- Department of Diagnostic Medicine/Pathobiology, College of Veterinary Medicine, Kansas State University, Manhattan, KS, United States
| | - Thu-Thuy Nguyen
- Department of Veterinary Pathobiology, School of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX, United States
| | - Jacquie Berry
- Department of Veterinary Pathobiology, School of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX, United States
| | - Jianrong Li
- Department of Veterinary Integrative Biosciences, School of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX, United States
| | - Leslie Garry Adams
- Department of Veterinary Physiology and Pharmacology, School of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX, United States
| | | | | | - Daniel R. Swale
- Emerging Pathogens Institute, University of Florida, Gainesville, FL, United States
| | - Stefan H. E. Kaufmann
- Department of Veterinary Pathobiology, School of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX, United States
- Hagler Institute for Advanced Study, Texas A&M University, College Station, TX, United States
- Max Planck Institute for Infection Biology, Berlin, Germany
- Max Planck Institute for Multidisciplinary Sciences, Göttingen, Germany
| | - Yava Jones-Hall
- Department of Veterinary Pathobiology, School of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX, United States
| | - Albert Mulenga
- Department of Veterinary Pathobiology, School of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX, United States
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10
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Rudmann DG, Bertrand L, Zuraw A, Deiters J, Staup M, Rivenson Y, Kuklyte J. Building a nonclinical pathology laboratory of the future for pharmaceutical research excellence. Drug Discov Today 2023; 28:103747. [PMID: 37598916 DOI: 10.1016/j.drudis.2023.103747] [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: 07/12/2023] [Revised: 08/02/2023] [Accepted: 08/14/2023] [Indexed: 08/22/2023]
Abstract
We describe a roadmap for a fully digital artificial intelligence (AI)-augmented nonclinical pathology laboratory across three continents. Underpinning the design are Good Laboratory Practice (GLP)-validated laboratory information management systems (LIMS), whole slide-scanners (WSS), image management systems (IMS), and a digital microscope intended for use by the nonclinical pathologist. Digital diagnostics are supported by tools that include AI-based virtual staining and deep learning-based decision support. Implemented during the COVID-19 pandemic, the initial digitized workflow largely mitigated disruption of pivotal nonclinical studies required to support pharmaceutical clinical testing. We believe that this digital transformation of our nonclinical pathology laboratories will promote efficiency and innovation in the future and enhance the quality and speed of drug development decision making.
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Affiliation(s)
- D G Rudmann
- Charles River Laboratories, Digital Toxicologic Pathology, Discovery and Safety Assessment, Wilmington, DE, USA.
| | - L Bertrand
- Charles River Laboratories, Digital Toxicologic Pathology, Discovery and Safety Assessment, Wilmington, DE, USA
| | - A Zuraw
- Charles River Laboratories, Digital Toxicologic Pathology, Discovery and Safety Assessment, Wilmington, DE, USA
| | - J Deiters
- Charles River Laboratories, Digital Toxicologic Pathology, Discovery and Safety Assessment, Wilmington, DE, USA
| | - M Staup
- Charles River Laboratories, Digital Toxicologic Pathology, Discovery and Safety Assessment, Wilmington, DE, USA
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11
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Hwang JH, Lim M, Han G, Park H, Kim YB, Park J, Jun SY, Lee J, Cho JW. A comparative study on the implementation of deep learning algorithms for detection of hepatic necrosis in toxicity studies. Toxicol Res 2023; 39:399-408. [PMID: 37398569 PMCID: PMC10313597 DOI: 10.1007/s43188-023-00173-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2022] [Revised: 02/16/2023] [Accepted: 02/20/2023] [Indexed: 07/04/2023] Open
Abstract
Deep learning has recently become one of the most popular methods of image analysis. In non-clinical studies, several tissue slides are generated to investigate the toxicity of a test compound. These are converted into digital image data using a slide scanner, which is then studied by researchers to investigate abnormalities, and the deep learning method has been started to adopt in this study. However, comparative studies evaluating different deep learning algorithms for analyzing abnormal lesions are scarce. In this study, we applied three algorithms, SSD, Mask R-CNN, and DeepLabV3+, to detect hepatic necrosis in slide images and determine the best deep learning algorithm for analyzing abnormal lesions. We trained each algorithm on 5750 images and 5835 annotations of hepatic necrosis including validation and test, augmented with 500 image tiles of 448 × 448 pixels. Precision, recall, and accuracy were calculated for each algorithm based on the prediction results of 60 test images of 2688 × 2688 pixels. The two segmentation algorithms, DeepLabV3+ and Mask R-CNN, showed over 90% of accuracy (0.94 and 0.92, respectively), whereas SSD, an object detection algorithm, showed lower accuracy. The trained DeepLabV3+ outperformed all others in recall while also successfully separating hepatic necrosis from other features in the test images. It is important to localize and separate the abnormal lesion of interest from other features to investigate it on a slide level. Therefore, we suggest that segmentation algorithms are more appropriate than object detection algorithms for use in the pathological analysis of images in non-clinical studies. Supplementary Information The online version contains supplementary material available at 10.1007/s43188-023-00173-5.
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Affiliation(s)
- Ji-Hee Hwang
- Toxicologic Pathology Research Group, Department of Advanced Toxicology Research, Korea Institute of Toxicology, Daejeon, 34114 Republic of Korea
| | - Minyoung Lim
- Toxicologic Pathology Research Group, Department of Advanced Toxicology Research, Korea Institute of Toxicology, Daejeon, 34114 Republic of Korea
| | - Gyeongjin Han
- Toxicologic Pathology Research Group, Department of Advanced Toxicology Research, Korea Institute of Toxicology, Daejeon, 34114 Republic of Korea
| | - Heejin Park
- Toxicologic Pathology Research Group, Department of Advanced Toxicology Research, Korea Institute of Toxicology, Daejeon, 34114 Republic of Korea
| | - Yong-Bum Kim
- Department of Advanced Toxicology Research, Korea Institute of Toxicology, Daejeon, 34114 Republic of Korea
| | - Jinseok Park
- Research & Development Team, LAC Inc, Seoul, 07807 Republic of Korea
| | - Sang-Yeop Jun
- Research & Development Team, LAC Inc, Seoul, 07807 Republic of Korea
| | - Jaeku Lee
- Research & Development Team, LAC Inc, Seoul, 07807 Republic of Korea
| | - Jae-Woo Cho
- Toxicologic Pathology Research Group, Department of Advanced Toxicology Research, Korea Institute of Toxicology, Daejeon, 34114 Republic of Korea
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12
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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: 2] [Impact Index Per Article: 2.0] [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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13
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AbdulJabbar K, Castillo SP, Hughes K, Davidson H, Boddy AM, Abegglen LM, Minoli L, Iussich S, Murchison EP, Graham TA, Spiro S, Maley CC, Aresu L, Palmieri C, Yuan Y. Bridging clinic and wildlife care with AI-powered pan-species computational pathology. Nat Commun 2023; 14:2408. [PMID: 37100774 PMCID: PMC10133243 DOI: 10.1038/s41467-023-37879-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Accepted: 04/04/2023] [Indexed: 04/28/2023] Open
Abstract
Cancers occur across species. Understanding what is consistent and varies across species can provide new insights into cancer initiation and evolution, with significant implications for animal welfare and wildlife conservation. We build a pan-species cancer digital pathology atlas (panspecies.ai) and conduct a pan-species study of computational comparative pathology using a supervised convolutional neural network algorithm trained on human samples. The artificial intelligence algorithm achieves high accuracy in measuring immune response through single-cell classification for two transmissible cancers (canine transmissible venereal tumour, 0.94; Tasmanian devil facial tumour disease, 0.88). In 18 other vertebrate species (mammalia = 11, reptilia = 4, aves = 2, and amphibia = 1), accuracy (range 0.57-0.94) is influenced by cell morphological similarity preserved across different taxonomic groups, tumour sites, and variations in the immune compartment. Furthermore, a spatial immune score based on artificial intelligence and spatial statistics is associated with prognosis in canine melanoma and prostate tumours. A metric, named morphospace overlap, is developed to guide veterinary pathologists towards rational deployment of this technology on new samples. This study provides the foundation and guidelines for transferring artificial intelligence technologies to veterinary pathology based on understanding of morphological conservation, which could vastly accelerate developments in veterinary medicine and comparative oncology.
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Affiliation(s)
- Khalid AbdulJabbar
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
- Division of Molecular Pathology, The Institute of Cancer Research, London, UK
| | - Simon P Castillo
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
- Division of Molecular Pathology, The Institute of Cancer Research, London, UK
| | - Katherine Hughes
- Department of Veterinary Medicine, University of Cambridge, Madingley Road, Cambridge, UK
| | - Hannah Davidson
- Zoological Society of London, London, UK
- Centre for Genomics and Computational Biology, Barts Cancer Institute, Queen Mary University of London, Charterhouse Sq, London, UK
| | - Amy M Boddy
- Department of Anthropology, University of California Santa Barbara, Santa Barbara, CA, USA
| | - Lisa M Abegglen
- Department of Pediatrics and Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA
- PEEL Therapeutics, Inc., Salt Lake City, UT, USA
| | - Lucia Minoli
- Department of Veterinary Sciences, University of Turin, 10095, Grugliasco, Italy
| | - Selina Iussich
- Department of Veterinary Sciences, University of Turin, 10095, Grugliasco, Italy
| | - Elizabeth P Murchison
- Department of Veterinary Medicine, University of Cambridge, Madingley Road, Cambridge, UK
| | - Trevor A Graham
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
- Centre for Genomics and Computational Biology, Barts Cancer Institute, Queen Mary University of London, Charterhouse Sq, London, UK
| | | | - Carlo C Maley
- Arizona Cancer Evolution Center, Biodesign Institute and School of Life Sciences, Arizona State University, Tempe, AZ, USA
| | - Luca Aresu
- Department of Veterinary Sciences, University of Turin, 10095, Grugliasco, Italy
| | - Chiara Palmieri
- School of Veterinary Science, The University of Queensland, 4343, Gatton, QLD, Australia
| | - Yinyin Yuan
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK.
- Division of Molecular Pathology, The Institute of Cancer Research, London, UK.
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
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14
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Piansaddhayanaon C, Santisukwongchote S, Shuangshoti S, Tao Q, Sriswasdi S, Chuangsuwanich E. ReCasNet: Improving consistency within the two-stage mitosis detection framework. Artif Intell Med 2023; 135:102462. [PMID: 36628784 DOI: 10.1016/j.artmed.2022.102462] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Revised: 10/11/2022] [Accepted: 11/23/2022] [Indexed: 11/26/2022]
Abstract
Mitotic count (MC) is an important histological parameter for cancer diagnosis and grading, but the manual process for obtaining MC from whole-slide histopathological images is very time-consuming and prone to error. Therefore, deep learning models have been proposed to facilitate this process. Existing approaches utilize a two-stage pipeline: the detection stage for identifying the locations of potential mitotic cells and the classification stage for refining prediction confidences. However, this pipeline formulation can lead to inconsistencies in the classification stage due to the poor prediction quality of the detection stage and the mismatches in training data distributions between the two stages. In this study, we propose a Refine Cascade Network (ReCasNet), an enhanced deep learning pipeline that mitigates the aforementioned problems with three improvements. First, window relocation was used to reduce the number of poor quality false positives generated during the detection stage. Second, object re-cropping was performed with another deep learning model to adjust poorly centered objects. Third, improved data selection strategies were introduced during the classification stage to reduce the mismatches in training data distributions. ReCasNet was evaluated on two large-scale mitotic figure recognition datasets, canine cutaneous mast cell tumor (CCMCT) and canine mammary carcinoma (CMC), which resulted in up to 4.8% percentage point improvements in the F1 scores for mitotic cell detection and 44.1% reductions in mean absolute percentage error (MAPE) for MC prediction. Techniques that underlie ReCasNet can be generalized to other two-stage object detection pipeline and should contribute to improving the performances of deep learning models in broad digital pathology applications.
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Affiliation(s)
- Chawan Piansaddhayanaon
- Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, Thailand; Chula Intelligent and Complex Systems, Faculty of Science, Chulalongkorn University, Bangkok, Thailand
| | - Sakun Santisukwongchote
- Department of Pathology, King Chulalongkorn Memorial Hospital and Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Shanop Shuangshoti
- Department of Pathology, King Chulalongkorn Memorial Hospital and Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | | | - Sira Sriswasdi
- Chula Intelligent and Complex Systems, Faculty of Science, Chulalongkorn University, Bangkok, Thailand; Center for Artificial Intelligence in Medicine, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand; Center of Excellence in Computational Molecular Biology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand.
| | - Ekapol Chuangsuwanich
- Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, Thailand; Chula Intelligent and Complex Systems, Faculty of Science, Chulalongkorn University, Bangkok, Thailand; Center of Excellence in Computational Molecular Biology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand.
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15
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Bertram CA, Marzahl C, Bartel A, Stayt J, Bonsembiante F, Beeler-Marfisi J, Barton AK, Brocca G, Gelain ME, Gläsel A, du Preez K, Weiler K, Weissenbacher-Lang C, Breininger K, Aubreville M, Maier A, Klopfleisch R, Hill J. Cytologic scoring of equine exercise-induced pulmonary hemorrhage: Performance of human experts and a deep learning-based algorithm. Vet Pathol 2023; 60:75-85. [PMID: 36384369 PMCID: PMC9827485 DOI: 10.1177/03009858221137582] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Exercise-induced pulmonary hemorrhage (EIPH) is a relevant respiratory disease in sport horses, which can be diagnosed by examination of bronchoalveolar lavage fluid (BALF) cells using the total hemosiderin score (THS). The aim of this study was to evaluate the diagnostic accuracy and reproducibility of annotators and to validate a deep learning-based algorithm for the THS. Digitized cytological specimens stained for iron were prepared from 52 equine BALF samples. Ten annotators produced a THS for each slide according to published methods. The reference methods for comparing annotator's and algorithmic performance included a ground truth dataset, the mean annotators' THSs, and chemical iron measurements. Results of the study showed that annotators had marked interobserver variability of the THS, which was mostly due to a systematic error between annotators in grading the intracytoplasmatic hemosiderin content of individual macrophages. Regarding overall measurement error between the annotators, 87.7% of the variance could be reduced by using standardized grades based on the ground truth. The algorithm was highly consistent with the ground truth in assigning hemosiderin grades. Compared with the ground truth THS, annotators had an accuracy of diagnosing EIPH (THS of < or ≥ 75) of 75.7%, whereas, the algorithm had an accuracy of 92.3% with no relevant differences in correlation with chemical iron measurements. The results show that deep learning-based algorithms are useful for improving reproducibility and routine applicability of the THS. For THS by experts, a diagnostic uncertainty interval of 40 to 110 is proposed. THSs within this interval have insufficient reproducibility regarding the EIPH diagnosis.
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Affiliation(s)
- Christof A. Bertram
- University of Veterinary Medicine
Vienna, Vienna, Austria
- Freie Universität Berlin, Berlin,
Germany
| | - Christian Marzahl
- Friedrich-Alexander-Universität
Erlangen-Nürnberg, Erlangen, Germany
- EUROIMMUN Medizinische Labordiagnostika
AG, Lübeck, Germany
| | - Alexander Bartel
- Freie Universität Berlin, Berlin,
Germany
- Alexander Bartel, Department of Veterinary
Medicine, Institute for Veterinary Epidemiology and Biostatistics, Freie
Universität Berlin, Koenigsweg 67, Berlin, 14163 Berlin, Germany.
| | - Jason Stayt
- Novavet Diagnostics, Bayswater, Western
Australia
| | | | | | | | | | | | - Agnes Gläsel
- Justus-Liebig-Universität Giessen,
Giessen, Germany
| | | | | | | | | | | | - Andreas Maier
- Friedrich-Alexander-Universität
Erlangen-Nürnberg, Erlangen, Germany
| | | | - Jenny Hill
- Novavet Diagnostics, Bayswater, Western
Australia
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16
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Li Y, Xue Y, Li L, Zhang X, Qian X. Domain Adaptive Box-Supervised Instance Segmentation Network for Mitosis Detection. IEEE TRANSACTIONS ON MEDICAL IMAGING 2022; 41:2469-2485. [PMID: 35389862 DOI: 10.1109/tmi.2022.3165518] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
The number of mitotic cells present in histopathological slides is an important predictor of tumor proliferation in the diagnosis of breast cancer. However, the current approaches can hardly perform precise pixel-level prediction for mitosis datasets with only weak labels (i.e., only provide the centroid location of mitotic cells), and take no account of the large domain gap across histopathological slides from different pathology laboratories. In this work, we propose a Domain adaptive Box-supervised Instance segmentation Network (DBIN) to address the above issues. In DBIN, we propose a high-performance Box-supervised Instance-Aware (BIA) head with the core idea of redesigning three box-supervised mask loss terms. Furthermore, we add a Pseudo-Mask-supervised Semantic (PMS) head for enriching characteristics extracted from underlying feature maps. Besides, we align the pixel-level feature distributions between source and target domains by a Cross-Domain Adaptive Module (CDAM), so as to adapt the detector learned from one lab can work well on unlabeled data from another lab. The proposed method achieves state-of-the-art performance across four mainstream datasets. A series of analysis and experiments show that our proposed BIA and PMS head can accomplish mitosis pixel-wise localization under weak supervision, and we can boost the generalization ability of our model by CDAM.
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17
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An Online Pattern Recognition-Oriented Workshop to Promote Interest among Undergraduate Students in How Mathematical Principles Could Be Applied within Veterinary Science. SUSTAINABILITY 2022. [DOI: 10.3390/su14116768] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Knowing the importance of mathematics and its relationship with veterinary medicine plays an important role for students. To promote interest in this relationship, we developed the workshop “Math in Nature” that utilizes the surrounding environment for stimulating pattern-recognition and observational skills. It consisted of four sections: A talk by a professional researcher, a question-and-answer section, a mathematical pattern identification session, and a discussion of the ideas proposed by students. The effectiveness of the program to raise interest in mathematics was evaluated using a questionnaire applied before and after the workshop. Following the course, a higher number of students agreed with the fact that biological phenomena can be explained and predicted by applying mathematics, and that it is possible to identify mathematical patterns in living beings. However, the students’ perspectives regarding the importance of mathematics in their careers, as well as their interest in deepening their mathematical knowledge, did not change. Arguably, “Math in Nature” could have exerted a positive effect on the students’ interest in mathematics. We thus recommend the application of similar workshops to improve interests and skills in relevant subjects among undergraduate students.
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18
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Basran PS, Appleby RB. The unmet potential of artificial intelligence in veterinary medicine. Am J Vet Res 2022; 83:385-392. [PMID: 35353711 DOI: 10.2460/ajvr.22.03.0038] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
Abstract
Veterinary medicine is a broad and growing discipline that includes topics such as companion animal health, population medicine and zoonotic diseases, and agriculture. In this article, we provide insight on how artificial intelligence works and how it is currently applied in veterinary medicine. We also discuss its potential in veterinary medicine. Given the rapid pace of research and commercial product developments in this area, the next several years will pose challenges to understanding, interpreting, and adopting this powerful and evolving technology. Artificial intelligence has the potential to enable veterinarians to perform tasks more efficiently while providing new insights for the management and treatment of disorders. It is our hope that this will translate to better quality of life for animals and those who care for them.
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Affiliation(s)
- Parminder S Basran
- Department of Clinical Sciences, College of Veterinary Medicine, Cornell University, Ithaca, NY
| | - Ryan B Appleby
- Department of Clinical Studies, Ontario Veterinary College, University of Guelph, Guelph, ON, Canada
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19
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Bertram CA, Stathonikos N, Donovan TA, Bartel A, Fuchs-Baumgartinger A, Lipnik K, van Diest PJ, Bonsembiante F, Klopfleisch R. Validation of digital microscopy: Review of validation methods and sources of bias. Vet Pathol 2022; 59:26-38. [PMID: 34433345 PMCID: PMC8761960 DOI: 10.1177/03009858211040476] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Digital microscopy (DM) is increasingly replacing traditional light microscopy (LM) for performing routine diagnostic and research work in human and veterinary pathology. The DM workflow encompasses specimen preparation, whole-slide image acquisition, slide retrieval, and the workstation, each of which has the potential (depending on the technical parameters) to introduce limitations and artifacts into microscopic examination by pathologists. Performing validation studies according to guidelines established in human pathology ensures that the best-practice approaches for patient care are not deteriorated by implementing DM. Whereas current publications on validation studies suggest an overall high reliability of DM, each laboratory is encouraged to perform an individual validation study to ensure that the DM workflow performs as expected in the respective clinical or research environment. With the exception of validation guidelines developed by the College of American Pathologists in 2013 and its update in 2021, there is no current review of the application of methods fundamental to validation. We highlight that there is high methodological variation between published validation studies, each having advantages and limitations. The diagnostic concordance rate between DM and LM is the most relevant outcome measure, which is influenced (regardless of the viewing modality used) by different sources of bias including complexity of the cases examined, diagnostic experience of the study pathologists, and case recall. Here, we review 3 general study designs used for previous publications on DM validation as well as different approaches for avoiding bias.
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Affiliation(s)
- Christof A. Bertram
- University of Veterinary Medicine, Vienna, Austria
- Freie Universität Berlin, Berlin, Germany
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20
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Bertram CA, Aubreville M, Donovan TA, Bartel A, Wilm F, Marzahl C, Assenmacher CA, Becker K, Bennett M, Corner S, Cossic B, Denk D, Dettwiler M, Gonzalez BG, Gurtner C, Haverkamp AK, Heier A, Lehmbecker A, Merz S, Noland EL, Plog S, Schmidt A, Sebastian F, Sledge DG, Smedley RC, Tecilla M, Thaiwong T, Fuchs-Baumgartinger A, Meuten DJ, Breininger K, Kiupel M, Maier A, Klopfleisch R. Computer-assisted mitotic count using a deep learning–based algorithm improves interobserver reproducibility and accuracy. Vet Pathol 2021; 59:211-226. [PMID: 34965805 PMCID: PMC8928234 DOI: 10.1177/03009858211067478] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
The mitotic count (MC) is an important histological parameter for prognostication of malignant neoplasms. However, it has inter- and intraobserver discrepancies due to difficulties in selecting the region of interest (MC-ROI) and in identifying or classifying mitotic figures (MFs). Recent progress in the field of artificial intelligence has allowed the development of high-performance algorithms that may improve standardization of the MC. As algorithmic predictions are not flawless, computer-assisted review by pathologists may ensure reliability. In the present study, we compared partial (MC-ROI preselection) and full (additional visualization of MF candidates and display of algorithmic confidence values) computer-assisted MC analysis to the routine (unaided) MC analysis by 23 pathologists for whole-slide images of 50 canine cutaneous mast cell tumors (ccMCTs). Algorithmic predictions aimed to assist pathologists in detecting mitotic hotspot locations, reducing omission of MFs, and improving classification against imposters. The interobserver consistency for the MC significantly increased with computer assistance (interobserver correlation coefficient, ICC = 0.92) compared to the unaided approach (ICC = 0.70). Classification into prognostic stratifications had a higher accuracy with computer assistance. The algorithmically preselected hotspot MC-ROIs had a consistently higher MCs than the manually selected MC-ROIs. Compared to a ground truth (developed with immunohistochemistry for phosphohistone H3), pathologist performance in detecting individual MF was augmented when using computer assistance (F1-score of 0.68 increased to 0.79) with a reduction in false negatives by 38%. The results of this study demonstrate that computer assistance may lead to more reproducible and accurate MCs in ccMCTs.
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Affiliation(s)
- Christof A. Bertram
- University of Veterinary Medicine, Vienna, Austria
- Freie Universität Berlin, Berlin, Germany
| | | | | | | | - Frauke Wilm
- Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Christian Marzahl
- Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | | | | | | | | | | | | | | | | | | | | | | | | | - Sophie Merz
- IDEXX Vet Med Labor GmbH, Kornwestheim, Germany
| | | | | | | | | | | | | | - Marco Tecilla
- Roche Pharmaceutical Research and Early Development (pRED), Basel, Switzerland
| | | | | | | | | | | | - Andreas Maier
- Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
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21
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Mehrvar S, Himmel LE, Babburi P, Goldberg AL, Guffroy M, Janardhan K, Krempley AL, Bawa B. Deep Learning Approaches and Applications in Toxicologic Histopathology: Current Status and Future Perspectives. J Pathol Inform 2021; 12:42. [PMID: 34881097 PMCID: PMC8609289 DOI: 10.4103/jpi.jpi_36_21] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Accepted: 07/18/2021] [Indexed: 12/13/2022] Open
Abstract
Whole slide imaging enables the use of a wide array of digital image analysis tools that are revolutionizing pathology. Recent advances in digital pathology and deep convolutional neural networks have created an enormous opportunity to improve workflow efficiency, provide more quantitative, objective, and consistent assessments of pathology datasets, and develop decision support systems. Such innovations are already making their way into clinical practice. However, the progress of machine learning - in particular, deep learning (DL) - has been rather slower in nonclinical toxicology studies. Histopathology data from toxicology studies are critical during the drug development process that is required by regulatory bodies to assess drug-related toxicity in laboratory animals and its impact on human safety in clinical trials. Due to the high volume of slides routinely evaluated, low-throughput, or narrowly performing DL methods that may work well in small-scale diagnostic studies or for the identification of a single abnormality are tedious and impractical for toxicologic pathology. Furthermore, regulatory requirements around good laboratory practice are a major hurdle for the adoption of DL in toxicologic pathology. This paper reviews the major DL concepts, emerging applications, and examples of DL in toxicologic pathology image analysis. We end with a discussion of specific challenges and directions for future research.
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Affiliation(s)
- Shima Mehrvar
- Preclinical Safety, AbbVie Inc., North Chicago, IL, USA
| | | | - Pradeep Babburi
- Business Technology Solutions, AbbVie Inc., North Chicago, IL, USA
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22
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Zuraw A, Aeffner F. Whole-slide imaging, tissue image analysis, and artificial intelligence in veterinary pathology: An updated introduction and review. Vet Pathol 2021; 59:6-25. [PMID: 34521285 DOI: 10.1177/03009858211040484] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Since whole-slide imaging has been commercially available for over 2 decades, digital pathology has become a constantly expanding aspect of the pathology profession that will continue to significantly impact how pathologists conduct their craft. While some aspects, such as whole-slide imaging for archiving, consulting, and teaching, have gained broader acceptance, other facets such as quantitative tissue image analysis and artificial intelligence-based assessments are still met with some reservations. While most vendors in this space have focused on diagnostic applications, that is, viewing one or few slides at a time, some are developing solutions tailored more specifically to the various aspects of veterinary pathology including updated diagnostic, discovery, and research applications. This has especially advanced the use of digital pathology in toxicologic pathology and drug development, for primary reads as well as peer reviews. It is crucial that pathologists gain a deeper understanding of digital pathology and tissue image analysis technology and their applications in order to fully use these tools in a way that enhances and improves the pathologist's assessment as well as work environment. This review focuses on an updated introduction to the basics of digital pathology and image analysis and introduces emerging topics around artificial intelligence and machine learning.
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Affiliation(s)
| | - Famke Aeffner
- Amgen Inc, Amgen Research, South San Francisco, CA, USA
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23
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Kittichai V, Kaewthamasorn M, Thanee S, Jomtarak R, Klanboot K, Naing KM, Tongloy T, Chuwongin S, Boonsang S. Classification for avian malaria parasite Plasmodium gallinaceum blood stages by using deep convolutional neural networks. Sci Rep 2021; 11:16919. [PMID: 34413434 PMCID: PMC8376898 DOI: 10.1038/s41598-021-96475-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Accepted: 08/11/2021] [Indexed: 12/21/2022] Open
Abstract
The infection of an avian malaria parasite (Plasmodium gallinaceum) in domestic chickens presents a major threat to the poultry industry because it causes economic loss in both the quality and quantity of meat and egg production. Computer-aided diagnosis has been developed to automatically identify avian malaria infections and classify the blood infection stage development. In this study, four types of deep convolutional neural networks, namely Darknet, Darknet19, Darknet19-448 and Densenet201 are used to classify P. gallinaceum blood stages. We randomly collected a dataset of 12,761 single-cell images consisting of three parasite stages from ten-infected blood films stained by Giemsa. All images were confirmed by three well-trained examiners. The study mainly compared several image classification models and used both qualitative and quantitative data for the evaluation of the proposed models. In the model-wise comparison, the four neural network models gave us high values with a mean average accuracy of at least 97%. The Darknet can reproduce a superior performance in the classification of the P. gallinaceum development stages across any other model architectures. Furthermore, the Darknet has the best performance in multiple class-wise classification, with average values of greater than 99% in accuracy, specificity, and sensitivity. It also has a low misclassification rate (< 1%) than the other three models. Therefore, the model is more suitable in the classification of P. gallinaceum blood stages. The findings could help us create a fast-screening method to help non-experts in field studies where there is a lack of specialized instruments for avian malaria diagnostics.
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Affiliation(s)
- Veerayuth Kittichai
- Faculty of Medicine, King Mongkut's Institute of Technology Ladkrabang, Bangkok, Thailand
| | - Morakot Kaewthamasorn
- Veterinary Parasitology Research Unit, Faculty of Veterinary Science, Chulalongkorn University, Bangkok, Thailand
| | - Suchansa Thanee
- Veterinary Parasitology Research Unit, Faculty of Veterinary Science, Chulalongkorn University, Bangkok, Thailand
| | - Rangsan Jomtarak
- Faculty of Science and Technology, Suan Dusit University, Bangkok, Thailand
| | - Kamonpob Klanboot
- College of Advanced Manufacturing Innovation, King Mongkut's Institute of Technology Ladkrabang, Bangkok, Thailand
| | - Kaung Myat Naing
- College of Advanced Manufacturing Innovation, King Mongkut's Institute of Technology Ladkrabang, Bangkok, Thailand
| | - Teerawat Tongloy
- College of Advanced Manufacturing Innovation, King Mongkut's Institute of Technology Ladkrabang, Bangkok, Thailand
| | - Santhad Chuwongin
- College of Advanced Manufacturing Innovation, King Mongkut's Institute of Technology Ladkrabang, Bangkok, Thailand
| | - Siridech Boonsang
- Department of Electrical Engineering, School of Engineering, King Mongkut's Institute of Technology Ladkrabang, Bangkok, Thailand.
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24
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Meuten DJ, Moore FM, Donovan TA, Bertram CA, Klopfleisch R, Foster RA, Smedley RC, Dark MJ, Milovancev M, Stromberg P, Williams BH, Aubreville M, Avallone G, Bolfa P, Cullen J, Dennis MM, Goldschmidt M, Luong R, Miller AD, Miller MA, Munday JS, Roccabianca P, Salas EN, Schulman FY, Laufer-Amorim R, Asakawa MG, Craig L, Dervisis N, Esplin DG, George JW, Hauck M, Kagawa Y, Kiupel M, Linder K, Meichner K, Marconato L, Oblak ML, Santos RL, Simpson RM, Tvedten H, Whitley D. International Guidelines for Veterinary Tumor Pathology: A Call to Action. Vet Pathol 2021; 58:766-794. [PMID: 34282984 DOI: 10.1177/03009858211013712] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Standardization of tumor assessment lays the foundation for validation of grading systems, permits reproducibility of oncologic studies among investigators, and increases confidence in the significance of study results. Currently, there is minimal methodological standardization for assessing tumors in veterinary medicine, with few attempts to validate published protocols and grading schemes. The current article attempts to address these shortcomings by providing standard guidelines for tumor assessment parameters and protocols for evaluating specific tumor types. More detailed information is available in the Supplemental Files, the intention of which is 2-fold: publication as part of this commentary, but more importantly, these will be available as "living documents" on a website (www.vetcancerprotocols.org), which will be updated as new information is presented in the peer-reviewed literature. Our hope is that veterinary pathologists will agree that this initiative is needed, and will contribute to and utilize this information for routine diagnostic work and oncologic studies. Journal editors and reviewers can utilize checklists to ensure publications include sufficient detail and standardized methods of tumor assessment. To maintain the relevance of the guidelines and protocols, it is critical that the information is periodically updated and revised as new studies are published and validated with the intent of providing a repository of this information. Our hope is that this initiative (a continuation of efforts published in this journal in 2011) will facilitate collaboration and reproducibility between pathologists and institutions, increase case numbers, and strengthen clinical research findings, thus ensuring continued progress in veterinary oncologic pathology and improving patient care.
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Affiliation(s)
| | | | | | - Christof A Bertram
- Freie Universität Berlin, Berlin, Germany.,University of Veterinary Medicine, Vienna, Austria
| | | | | | | | | | | | | | | | | | | | - Pompei Bolfa
- Ross University, Basseterre, Saint Kitts and Nevis
| | - John Cullen
- North Carolina State University, Raleigh, NC, USA
| | | | | | | | | | | | | | | | | | | | | | | | | | - Nick Dervisis
- VA-MD College of Veterinary Medicine, Blacksburg, VA, USA
| | | | | | | | | | | | - Keith Linder
- North Carolina State University, Raleigh, NC, USA
| | | | | | | | - Renato L Santos
- Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil
| | - R Mark Simpson
- Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
| | - Harold Tvedten
- Swedish University of Agricultural Sciences, Uppsala, Sweden
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25
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Rütgen BC, Baumgartner D, Fuchs-Baumgartinger A, Rigillo A, Škor O, Hammer SE, Saalmüller A, Schwendenwein I. Flow Cytometric Assessment of Ki-67 Expression in Lymphocytes From Physiologic Lymph Nodes, Lymphoma Cell Populations and Remnant Normal Cell Populations From Lymphomatous Lymph Nodes. Front Vet Sci 2021; 8:663656. [PMID: 34268346 PMCID: PMC8276100 DOI: 10.3389/fvets.2021.663656] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Accepted: 05/31/2021] [Indexed: 12/18/2022] Open
Abstract
Recent literature suggests conventional flow cytometric (FCM) immunophenotyping complemented by Ki-67 FCM assessment as a reliable tool to classify canine lymphomas. Ki-67 expression assessed by FCM is rarely reported in canine lymphoma cases and reference data for normal canine lymph nodes are missing. Moreover, nothing is known about the Ki-67 expression within the occasionally observed remnant cell population within the gates of normal lymphocytes in lymphoma cases. Aim of this study was to compare flow cytometric Ki-67 expression of lymphocyte populations from normal canine lymph nodes, lymphoma cells from World-Health-Organisation (WHO) classified lymphoma patient samples and their neighboring normal remnant cell population. Cryopreserved lymphocyte cell suspensions from normal lymph nodes from eight dogs free of lymphoma served as reference material. Fourteen cases diagnosed by cytology, FCM, clonality testing, histopathology including immunohistochemistry consisting of 10 DLBCL, 1 MZL, 1 PTCL and 2 TZL showed a residual small lymphocyte population and were investigated. The Ki-67 expression in normal canine lymphoid tissue was 3.19 ± 2.17%. Mean Ki-67 expression in the malignant cell populations was 41 ± 24.36%. Ki-67 positivity was 12.34 ± 10.68% in the residual physiologic lymphocyte population, which otherwise exhibited a physiologic immunophenotype pattern. This ratio was equivalent (n = 3) or lower (n = 11) than the Ki-67 expression of the malignant cell population within the sample. This is the first report of FCM derived Ki-67 expression combined with immunophenotype patterns in normal canine lymph nodes, compared with lymphoma cell populations and residual normal cell populations of lymphoma cases diagnosed by state of the art technology.
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Affiliation(s)
- Barbara C. Rütgen
- Clinical Pathology, Department of Pathobiology, University of Veterinary Medicine Vienna, Vienna, Austria
| | - Daniel Baumgartner
- Clinical Pathology, Department of Pathobiology, University of Veterinary Medicine Vienna, Vienna, Austria
| | - Andrea Fuchs-Baumgartinger
- Institute of Pathology, Department of Pathobiology, University of Veterinary Medicine Vienna, Vienna, Austria
| | - Antonella Rigillo
- Department of Veterinary Medical Sciences, University of Bologna, Bologna, Italy
| | - Ondřej Škor
- Clinic for Internal Medicine, Department for Small Animals and Horses, University of Veterinary Medicine Vienna, Vienna, Austria
| | - Sabine E. Hammer
- Institute of Immunology, Department of Pathobiology, University of Veterinary Medicine Vienna, Vienna, Austria
| | - Armin Saalmüller
- Institute of Immunology, Department of Pathobiology, University of Veterinary Medicine Vienna, Vienna, Austria
| | - Ilse Schwendenwein
- Clinical Pathology, Department of Pathobiology, University of Veterinary Medicine Vienna, Vienna, Austria
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26
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Donovan TA, Moore FM, Bertram CA, Luong R, Bolfa P, Klopfleisch R, Tvedten H, Salas EN, Whitley DB, Aubreville M, Meuten DJ. Mitotic Figures-Normal, Atypical, and Imposters: A Guide to Identification. Vet Pathol 2020; 58:243-257. [PMID: 33371818 DOI: 10.1177/0300985820980049] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Counting mitotic figures (MF) in hematoxylin and eosin-stained histologic sections is an integral part of the diagnostic pathologist's tumor evaluation. The mitotic count (MC) is used alone or as part of a grading scheme for assessment of prognosis and clinical decisions. Determining MCs is subjective, somewhat laborious, and has interobserver variation. Proposals for standardizing this parameter in the veterinary field are limited to terminology (use of the term MC) and area (MC is counted in an area measuring 2.37 mm2). Digital imaging techniques are now commonplace and widely used among veterinary pathologists, and field of view area can be easily calculated with digital imaging software. In addition to standardizing the methods of counting MF, the morphologic characteristics of MF and distinguishing atypical mitotic figures (AMF) versus mitotic-like figures (MLF) need to be defined. This article provides morphologic criteria for MF identification and for distinguishing normal phases of MF from AMF and MLF. Pertinent features of digital microscopy and application of computational pathology (CPATH) methods are discussed. Correct identification of MF will improve MC consistency, reproducibility, and accuracy obtained from manual (glass slide or whole-slide imaging) and CPATH approaches.
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Affiliation(s)
| | | | | | | | - Pompei Bolfa
- 41635Ross University, Basseterre, Saint Kitts and Nevis
| | | | - Harold Tvedten
- 8095Swedish University of Agricultural Sciences, Uppsala, Sweden
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