المؤلفون: Parikshit Sanyal, Tanushri Mukherjee, Sanghita Barui, Avinash Das, Prabaha Gangopadhyay

المصدر: Journal of Pathology Informatics, Vol 9, Iss 1, Pp 43-43 (2018)

مصطلحات موضوعية: Artificial intelligence, cytology, fine-needle aspiration cytology, image classification, neural network, thyroid, Computer applications to medicine. Medical informatics, R858-859.7, Pathology, RB1-214

الوصف: Introduction: Fine-needle aspiration cytology (FNAC) for identification of papillary carcinoma thyroid is a moderately sensitive and specific modality. The present machine learning tools can correctly classify images into broad categories. Training software for recognition of papillary thyroid carcinoma on FNAC smears will be a decisive step toward automation of cytopathology. Aim: The aim of this study is to develop an artificial neural network (ANN) for the purpose of distinguishing papillary carcinoma thyroid and nonpapillary carcinoma thyroid on microphotographs from thyroid FNAC smears. Subjects and Methods: An ANN was developed in the Python programming language. In the training phase, 186 microphotographs from Romanowsky/Pap-stained smears of papillary carcinoma and 184 microphotographs from smears of other thyroid lesions (at ×10 and ×40 magnification) were used for training the ANN. After completion of training, performance was evaluated with a set of 174 microphotographs (66 – nonpapillary carcinoma and 21 – papillary carcinoma, each photographed at two magnifications ×10 and ×40). Results: The performance characteristics and limitations of the neural network were assessed, assuming FNAC diagnosis as gold standard. Combined results from two magnifications showed good sensitivity (90.48%), moderate specificity (83.33%), and a very high negative predictive value (96.49%) and 85.06% diagnostic accuracy. However, vague papillary formations by benign follicular cells identified wrongly as papillary carcinoma remain a drawback. Conclusion: With further training with a diverse dataset and in conjunction with automated microscopy, the ANN has the potential to develop into an accurate image classifier for thyroid FNACs.

وصف الملف: electronic resource

Relation: http://www.jpathinformatics.org/article.asp?issn=2153-3539;year=2018;volume=9;issue=1;spage=43;epage=43;aulast=Sanyal; https://doaj.org/toc/2153-3539

URL الوصول: https://doaj.org/article/3e53c18c8e064ae699e4a0ed6fbaa964

المؤلفون: Sanghita Barui, Prabaha Gangopadhyay, Avinash Das, Tanushri Mukherjee, Parikshit Sanyal

المصدر: Journal of Pathology Informatics, Vol 9, Iss 1, Pp 43-43 (2018)
Journal of Pathology Informatics

مصطلحات موضوعية: medicine.medical_specialty, Artificial intelligence, neural network, Magnification, 030209 endocrinology & metabolism, Health Informatics, lcsh:Computer applications to medicine. Medical informatics, Pathology and Forensic Medicine, thyroid, Thyroid carcinoma, 03 medical and health sciences, 0302 clinical medicine, Cytology, Carcinoma, lcsh:Pathology, Medicine, Artificial neural network, business.industry, Thyroid, Gold standard (test), medicine.disease, Computer Science Applications, body regions, fine-needle aspiration cytology, medicine.anatomical_structure, Cytopathology, 030220 oncology & carcinogenesis, cytology, lcsh:R858-859.7, Original Article, Radiology, business, image classification, lcsh:RB1-214

الوصف: Introduction: Fine-needle aspiration cytology (FNAC) for identification of papillary carcinoma thyroid is a moderately sensitive and specific modality. The present machine learning tools can correctly classify images into broad categories. Training software for recognition of papillary thyroid carcinoma on FNAC smears will be a decisive step toward automation of cytopathology. Aim: The aim of this study is to develop an artificial neural network (ANN) for the purpose of distinguishing papillary carcinoma thyroid and nonpapillary carcinoma thyroid on microphotographs from thyroid FNAC smears. Subjects and Methods: An ANN was developed in the Python programming language. In the training phase, 186 microphotographs from Romanowsky/Pap-stained smears of papillary carcinoma and 184 microphotographs from smears of other thyroid lesions (at ×10 and ×40 magnification) were used for training the ANN. After completion of training, performance was evaluated with a set of 174 microphotographs (66 – nonpapillary carcinoma and 21 – papillary carcinoma, each photographed at two magnifications ×10 and ×40). Results: The performance characteristics and limitations of the neural network were assessed, assuming FNAC diagnosis as gold standard. Combined results from two magnifications showed good sensitivity (90.48%), moderate specificity (83.33%), and a very high negative predictive value (96.49%) and 85.06% diagnostic accuracy. However, vague papillary formations by benign follicular cells identified wrongly as papillary carcinoma remain a drawback. Conclusion: With further training with a diverse dataset and in conjunction with automated microscopy, the ANN has the potential to develop into an accurate image classifier for thyroid FNACs.

URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::cb9f4cdbb77820514b4d8f18365b32de
http://www.jpathinformatics.org/article.asp?issn=2153-3539;year=2018;volume=9;issue=1;spage=43;epage=43;aulast=Sanyal

لا يتم عرض هذه النتيجة على الضيوف.
تسجيل الدخول للوصول الكامل.

المؤلفون: Maheswari Mukherjee, John J. Baker, Amber D Donnelly, Stanley J. Radio, Mary F McGaughey, Elizabeth Lyden, Whitney Wedel

المصدر: Journal of Pathology Informatics, Vol 6, Iss 1, Pp 43-43 (2015)
Journal of Pathology Informatics

مصطلحات موضوعية: Pathology, medicine.medical_specialty, scanning parameters, Concordance, thyroid fine needle aspiration, Health Informatics, lcsh:Computer applications to medicine. Medical informatics, virtual microscopy, Pathology and Forensic Medicine, Fine needle aspiration cytology, Cytology, medicine, lcsh:Pathology, Thyroid.FNA, medicine.diagnostic_test, business.industry, Thyroid, Computer Science Applications, Digital imaging, scanning parameters, thyroid fine needle aspiration, virtual microscopy, Fine-needle aspiration, medicine.anatomical_structure, Digital imaging, lcsh:R858-859.7, Original Article, business, Nuclear medicine, Virtual microscopy, lcsh:RB1-214

الوصف: Background: Interest in developing more feasible and affordable applications of virtual microscopy in the field of cytology continues to grow. Aims: The aim of this study was to investigate the scanning parameters for the thyroid fine needle aspiration (FNA) cytology specimens. Subjects and Methods: A total of twelve glass slides from thyroid FNA cytology specimens were digitized at ×40 with 1 micron (μ) interval using seven focal plane (FP) levels (Group 1), five FP levels (Group 2), and three FP levels (Group 3) using iScan Coreo Au scanner (Ventana, AZ, USA) producing 36 virtual images (VI). With an average wash out period of 2 days, three participants diagnosed the preannotated cells of Groups 1, 2, and 3 using BioImagene's Image Viewer (version 3.1) (Ventana, Inc., Tucson, AZ, USA), and the corresponding 12 glass slides (Group 4) using conventional light microscopy. Results: All three raters correctly identified and showed complete agreement on the glass and VI for: 86% of the cases at FP Level 3, 83% of the cases at both the FP Levels 5 and 7. The intra-observer concordance between the glass slides and VI for all three raters was highest (97%) for Level 3 and glass, same (94%) for Level 5 and glass; and Level 7 and glass. The inter-rater reliability was found to be highest for the glass slides, and three FP levels (77%), followed by five FP levels (69.5%), and seven FP levels (69.1%). Conclusions: This pilot study found that among the three different FP levels, the VI digitized using three FP levels had slightly higher concordance, intra-observer concordance, and inter-rater reliability. Scanning additional levels above three FP levels did not improve concordance. We believe that there is no added benefit of acquiring five FP levels or more especially when considering the file size, and storage costs. Hence, this study reports that FP level three and 1 μ could be the potential scanning parameters for the thyroid FNA cytology specimens.

URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::41f8647f139bf8fa5096414482953230
http://www.jpathinformatics.org/article.asp?issn=2153-3539;year=2015;volume=6;issue=1;spage=43;epage=43;aulast=Mukherjee