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Yücel Z, Akal F, Oltulu P. Automated AI-based grading of neuroendocrine tumors using Ki-67 proliferation index: comparative evaluation and performance analysis. Med Biol Eng Comput 2024; 62:1899-1909. [PMID: 38409645 DOI: 10.1007/s11517-024-03045-8] [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: 08/22/2023] [Accepted: 02/03/2024] [Indexed: 02/28/2024]
Abstract
Early detection is critical for successfully diagnosing cancer, and timely analysis of diagnostic tests is increasingly important. In the context of neuroendocrine tumors, the Ki-67 proliferation index serves as a fundamental biomarker, aiding pathologists in grading and diagnosing these tumors based on histopathological images. The appropriate treatment plan for the patient is determined based on the tumor grade. An artificial intelligence-based method is proposed to aid pathologists in the automated calculation and grading of the Ki-67 proliferation index. The proposed system first performs preprocessing to enhance image quality. Then, segmentation process is performed using the U-Net architecture, which is a deep learning algorithm, to separate the nuclei from the background. The identified nuclei are then evaluated as Ki-67 positive or negative based on basic color space information and other features. The Ki-67 proliferation index is then calculated, and the neuroendocrine tumor is graded accordingly. The proposed system's performance was evaluated on a dataset obtained from the Department of Pathology at Meram Faculty of Medicine Hospital, Necmettin Erbakan University. The results of the pathologist and the proposed system were compared, and the proposed system was found to have an accuracy of 95% in tumor grading when compared to the pathologist's report.
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Affiliation(s)
- Zehra Yücel
- Necmettin Erbakan University, Department of Computer Technologies, Konya, Turkey.
- Hacettepe University, Graduate School of Science and Engineering, Ankara, Turkey.
| | - Fuat Akal
- Hacettepe University, Faculty of Engineering, Department of Computer Engineering, Ankara, Turkey
| | - Pembe Oltulu
- Necmettin Erbakan University, Faculty of Medicine, Department of Pathology, Konya, Turkey
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Werid GM, Miller D, Hemmatzadeh F, Messele YE, Petrovski K. An overview of the detection of bovine respiratory disease complex pathogens using immunohistochemistry: emerging trends and opportunities. J Vet Diagn Invest 2024; 36:12-23. [PMID: 37982437 PMCID: PMC10734592 DOI: 10.1177/10406387231210489] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2023] Open
Abstract
The bovine respiratory disease complex (BRDC) is caused by a variety of pathogens, as well as contributing environmental and host-related risk factors. BRDC is the costliest disease for feedlot cattle globally. Immunohistochemistry (IHC) is a valuable tool for enhancing our understanding of BRDC given its specificity, sensitivity, cost-effectiveness, and capacity to provide information on antigen localization and immune response. Emerging trends in IHC include the use of multiplex IHC for the detection of coinfections, the use of digital imaging and automation, improved detection systems using enhanced fluorescent dyes, and the integration of IHC with spatial transcriptomics. Overall, identifying biomarkers for early detection, utilizing high-throughput IHC for large-scale studies, developing standardized protocols and reagents, and integrating IHC with other technologies are some of the opportunities to enhance the accuracy and applicability of IHC. We summarize here the various techniques and protocols used in IHC and highlight their current and potential role in BRDC research.
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Affiliation(s)
- Gebremeskel Mamu Werid
- Davies Livestock Research Centre, School of Animal and Veterinary Sciences, The University of Adelaide, Roseworthy, SA, Australia
| | - Darren Miller
- Davies Livestock Research Centre, School of Animal and Veterinary Sciences, The University of Adelaide, Roseworthy, SA, Australia
| | - Farhid Hemmatzadeh
- Australian Centre for Antimicrobial Resistance Ecology, School of Animal and Veterinary Sciences, The University of Adelaide, Roseworthy, SA, Australia
| | - Yohannes E. Messele
- Davies Livestock Research Centre, School of Animal and Veterinary Sciences, The University of Adelaide, Roseworthy, SA, Australia
| | - Kiro Petrovski
- Australian Centre for Antimicrobial Resistance Ecology, School of Animal and Veterinary Sciences, The University of Adelaide, Roseworthy, SA, Australia
- Davies Livestock Research Centre, School of Animal and Veterinary Sciences, The University of Adelaide, Roseworthy, SA, Australia
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Dent A, Faust K, Lam K, Alhangari N, Leon AJ, Tsang Q, Kamil ZS, Gao A, Pal P, Lheureux S, Oza A, Diamandis P. HAVOC: Small-scale histomic mapping of cancer biodiversity across large tissue distances using deep neural networks. SCIENCE ADVANCES 2023; 9:eadg1894. [PMID: 37774029 PMCID: PMC10541015 DOI: 10.1126/sciadv.adg1894] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Accepted: 08/28/2023] [Indexed: 10/01/2023]
Abstract
Intratumoral heterogeneity can wreak havoc on current precision medicine strategies because of challenges in sufficient sampling of geographically separated areas of biodiversity distributed across centimeter-scale tumor distances. To address this gap, we developed a deep learning pipeline that leverages histomorphologic fingerprints of tissue to create "Histomic Atlases of Variation Of Cancers" (HAVOC). Using a number of objective molecular readouts, we demonstrate that HAVOC can define regional cancer boundaries with distinct biology. Using larger tumor specimens, we show that HAVOC can map biodiversity even across multiple tissue sections. By guiding profiling of 19 partitions across six high-grade gliomas, HAVOC revealed that distinct differentiation states can often coexist and be regionally distributed within these tumors. Last, to highlight generalizability, we benchmark HAVOC on additional tumor types. Together, we establish HAVOC as a versatile tool to generate small-scale maps of tissue heterogeneity and guide regional deployment of molecular resources to relevant biodiverse niches.
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Affiliation(s)
- Anglin Dent
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON M5S 1A8, Canada
| | - Kevin Faust
- Department of Computer Science, University of Toronto, 40 St. George Street, Toronto, ON M5S 2E4, Canada
- Princess Margaret Cancer Centre, 101 College Street, Toronto, ON M5G 1L7, Canada
| | - K. H. Brian Lam
- Princess Margaret Cancer Centre, 101 College Street, Toronto, ON M5G 1L7, Canada
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Narges Alhangari
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON M5S 1A8, Canada
| | - Alberto J. Leon
- Princess Margaret Cancer Centre, 101 College Street, Toronto, ON M5G 1L7, Canada
| | - Queenie Tsang
- Princess Margaret Cancer Centre, 101 College Street, Toronto, ON M5G 1L7, Canada
| | - Zaid Saeed Kamil
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON M5S 1A8, Canada
- Laboratory Medicine Program, Department of Pathology, University Health Network, 200 Elizabeth Street, Toronto, ON M5G 2C4, Canada
| | - Andrew Gao
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON M5S 1A8, Canada
- Laboratory Medicine Program, Department of Pathology, University Health Network, 200 Elizabeth Street, Toronto, ON M5G 2C4, Canada
| | - Prodipto Pal
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON M5S 1A8, Canada
- Laboratory Medicine Program, Department of Pathology, University Health Network, 200 Elizabeth Street, Toronto, ON M5G 2C4, Canada
| | - Stephanie Lheureux
- Princess Margaret Cancer Centre, 101 College Street, Toronto, ON M5G 1L7, Canada
| | - Amit Oza
- Princess Margaret Cancer Centre, 101 College Street, Toronto, ON M5G 1L7, Canada
| | - Phedias Diamandis
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON M5S 1A8, Canada
- Princess Margaret Cancer Centre, 101 College Street, Toronto, ON M5G 1L7, Canada
- Laboratory Medicine Program, Department of Pathology, University Health Network, 200 Elizabeth Street, Toronto, ON M5G 2C4, Canada
- Department of Medical Biophysics, University of Toronto, 101 College St, Toronto, ON M5G 1L7, Canada
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Zhang Z, Wang W, An A, Qin Y, Yang F. A human activity recognition method using wearable sensors based on convtransformer model. EVOLVING SYSTEMS 2023. [DOI: 10.1007/s12530-022-09480-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/29/2023]
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Zheng T, Zheng S, Wang K, Quan H, Bai Q, Li S, Qi R, Zhao Y, Cui X, Gao X. Automatic CD30 scoring method for whole slide images of primary cutaneous CD30 + lymphoproliferative diseases. J Clin Pathol 2022; 76:jclinpath-2022-208344. [PMID: 35863885 DOI: 10.1136/jcp-2022-208344] [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: 04/18/2022] [Accepted: 07/07/2022] [Indexed: 11/03/2022]
Abstract
AIMS Deep-learning methods for scoring biomarkers are an active research topic. However, the superior performance of many studies relies on large datasets collected from clinical samples. In addition, there are fewer studies on immunohistochemical marker assessment for dermatological diseases. Accordingly, we developed a method for scoring CD30 based on convolutional neural networks for a few primary cutaneous CD30+ lymphoproliferative disorders and used this method to evaluate other biomarkers. METHODS A multipatch spatial attention mechanism and conditional random field algorithm were used to fully fuse tumour tissue characteristics on immunohistochemical slides and alleviate the few sample feature deficits. We trained and tested 28 CD30+ immunohistochemical whole slide images (WSIs), evaluated them with a performance index, and compared them with the diagnoses of senior dermatologists. Finally, the model's performance was further demonstrated on the publicly available Yale HER2 cohort. RESULTS Compared with the diagnoses by senior dermatologists, this method can better locate the tumour area and reduce the misdiagnosis rate. The prediction of CD3 and Ki-67 validated the model's ability to identify other biomarkers. CONCLUSIONS In this study, using a few immunohistochemical WSIs, our model can accurately identify CD30, CD3 and Ki-67 markers. In addition, the model could be applied to additional tumour identification tasks to aid pathologists in diagnosis and benefit clinical evaluation.
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Affiliation(s)
- Tingting Zheng
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning, China
| | - Song Zheng
- Department of Dermatology, The First Hospital of China Medical University, Shenyang, Liaoning, China
- National and Local Joint Engineering Research Center of Immunodermatological Theranostics No, Heping District, Liaoning Province, China
- NHC Key Laboratory of Immunodermatology, Heping District, Liaoning Province, China
| | - Ke Wang
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning, China
| | - Hao Quan
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning, China
| | - Qun Bai
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning, China
| | - Shuqin Li
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning, China
| | - Ruiqun Qi
- Department of Dermatology, The First Hospital of China Medical University, Shenyang, Liaoning, China
- National and Local Joint Engineering Research Center of Immunodermatological Theranostics No, Heping District, Liaoning Province, China
- NHC Key Laboratory of Immunodermatology, Heping District, Liaoning Province, China
| | - Yue Zhao
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning, China
- National and Local Joint Engineering Research Center of Immunodermatological Theranostics No, Heping District, Liaoning Province, China
| | - Xiaoyu Cui
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning, China
| | - Xinghua Gao
- Department of Dermatology, The First Hospital of China Medical University, Shenyang, Liaoning, China
- National and Local Joint Engineering Research Center of Immunodermatological Theranostics No, Heping District, Liaoning Province, China
- NHC Key Laboratory of Immunodermatology, Heping District, Liaoning Province, China
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