Automated Cytopathology of Fine Needle Aspiration for the Detection of Malignancy in Thyroid Cells
Keywords:
machine learning; cellprofiler; image cytometry; cytopathology; papillary thyroid carcinoma
Abstract
Cytopathology of thyroid cells is an established method to detect malignancies in the thyroid. It is of advantage because an anesthesia and a diagnostic laparotomy is not necessary. There are however not yet many studies about automated cytopathology in thyroid cells. To this end, the aim of the present study was to establish an automated diagnosis of malignancy using image analysis and subsequent machine learning and Artificial intelligence. Light microscopy images of 52 patients were analyzed and the results were compared to those of pathology. The results of the automated analysis yielded a sensitivity of 0.94 and a specificity of 0.91 when compared to those of the pathologic diagnoses. The process of machine learning yielded an under curve area of 0.91 as calculated by a ROC-curve. The software used for image analysis, machine learning and classification (diagnosis) are open-source software, respectively.
Downloads
How to Cite
References
M Tiford (2006) A Short History of Histopathology Technique. 29, 99-110.
M Den Bakker (2017) Histopathologisch onderzoek als gouden standaard? [Is histopathology still the gold standard?. 160, D981.
J Van Den Tweel, C Taylor (2010) Introduction to the History of Pathology series. 457(1), 1-2.
Stefano Crinò, Serena Ammendola, Anna Meneghetti, Laura Bernardoni, Maria Conti Bellocchi, Armando Gabbrielli, Luca Landoni, Salvatore Paiella, Federico Pin, Alice Parisi, Maria Mastrosimini, Antonio Amodio, Luca Frulloni, Antonio Facciorusso, Alberto Larghi, Erminia Manfrin (2021) Comparison between EUS-guided fine-needle aspiration cytology and EUS-guided fine-needle biopsy histology for the evaluation of pancreatic neuroendocrine tumors. 21(2), 443-450.
Andreas Gerstner, Attila Tàrnok (2002) Analysis of Fine‐Needle Aspirate Biopsies from Solid Tumors by Laser Scanning Cytometry (LSC). 22(1).
A De Ruiter, P Carter, D Katz, G Kocjan, C Whatrup, J Northover, A Mindel (1994) A comparison between cytology and histology to detect anal intraepithelial neoplasia.. 70(1), 22-25.
Andreas Gerstner, Wiebke Laffers, Attila Tárnok (2009) Clinical applications of slide‐based cytometry - an update. 2(8-9), 463-469.
Anja Mittag, Dominik Lenz, József Bocsi, Ulrich Sack, Andreas Gerstner, Attila Tárnok (2006) Sequential photobleaching of fluorochromes for polychromatic slide‐based cytometry. 69A(3), 139-141.
Aline Buzin, Fernanda Pinto, Kathleen Nieschke, Anja Mittag, Tadeu De Andrade, Denise Endringer, Attila Tarnok, Dominik Lenz (2015) Replacement of specific markers for apoptosis and necrosis by nuclear morphology for affordable cytometry. 420, 24-30.
Wiebke Laffers, Anja Mittag, Dominik Lenz, Attila Tárnok, Andreas Gerstner (2006) Iterative restaining as a pivotal tool for n‐color immunophenotyping by slide‐based cytometry. 69A(3), 127-130.
Susanne Melzer, Celio Nunes, Denise Endringer, Tadeu De Andrade, Attila Tarnok, Dominik Lenz (2016) Trypan blue as an affordable marker for automated live‐dead cell analysis in image cytometry. 38(6), 857-863.
J Rosai (1996) Ackerman's surgical pathology. 2, 493-567.
D Segev, D Clark, M Zeiger (2003) Beyond the suspicious thyroid fine needle aspirate: a review. 47, 709-722.
Manuel Aguilar-Diosdado, Ascensión Contreras, Inmaculada Gavilán, Luis Escobar-Jiménez, José Girón, Juan Escribano, Manuel Beltrán, Antonio García-Curiel, José Vázquez (1997) Thyroid Nodules. 41(3), 677-682.
L Koss (1992) Diagnostic cytology and its histopathologic basis. 2665-2708.
L Deka, S Gupta, R Gupta (2017) Nuclear morphometry and texture analysis on cytological smears of thyroid neoplasms: a study of 50 cases. 39(1), 33-37.
Young Park, Byung-Joo Lee (2021) Machine learning-based prediction model using clinico-pathologic factors for papillary thyroid carcinoma recurrence. 11(1), 4948.
Senerath Jayatilake, Gamage Ganegoda (2021) Involvement of Machine Learning Tools in Healthcare Decision Making. 2021, 1-20.
I Sarker (2021) Machine Learning: Algorithms, Real-World Applications and Research Directions. 2(3), 160.
Wei Wang, John Ozolek, Gustavo Rohde (2010) Detection and classification of thyroid follicular lesions based on nuclear structure from histopathology images. 77A(5), 485-494.
Y Wang, Q Guan, I Lao (2019) Using deep convolutional neural networks for multi-classification of thyroid tumor by histopathology: a large-scale pilot study. 7(18), 468.
Andreas Gerstner, Alexander Thiele, Attila Tárnok, Andrea Tannapfel, Anette Weber, Friedrich Bootz (2006) Prediction of upper aerodigestive tract cancer by slide‐based cytometry. 69A(7), 582-587.
M Athelogou, R Huss (2018) Tissue Phenomics: Profiling Cancer Patients for Treatment Decisions. 175-184.
Gabrielly Ribeiro, Denise Endringer, Tadeu De Andrade, Dominik Lenz (2019) Comparison between two programs for image analysis, machine learning and subsequent classification. 58, 12-16.
Gabrielly Ribeiro, Rafaela Valotto, Jairo De Oliveira, Marco Guimarães, Dominik Lenz (2020) An inexpensive, automated and reproducible method to conduct quality control in nanoparticles. 74(9), 2821-2824.
A Lucas, P Ryder, B Li (2021) Open-source deeplearning software for bioimage segmentation. 32(9), 823-829.
Published
2023-06-06
Issue
Section
License
Copyright (c) 2023 Authors and Global Journals Private Limited
This work is licensed under a Creative Commons Attribution 4.0 International License.