An emerging era of computational cytology.
artificial neural network
computational cytology
digital imaging
Journal
Diagnostic cytopathology
ISSN: 1097-0339
Titre abrégé: Diagn Cytopathol
Pays: United States
ID NLM: 8506895
Informations de publication
Date de publication:
Apr 2023
Apr 2023
Historique:
revised:
31
10
2022
received:
08
10
2022
accepted:
02
01
2023
pubmed:
13
1
2023
medline:
3
3
2023
entrez:
12
1
2023
Statut:
ppublish
Résumé
The significant advancement in digital imaging, data management, advanced computational power, and artificial neural network have an immense impact on the field of cytology. The amalgamation of these areas has generated a newer discipline known as computational cytology. In To discuss the various important aspects of computational cytology. We reviewed the different studies published in English during the last few years on computational cytology. Computational cytology is a newer and emerging discipline in pathology that deals with the patient's meta-data and digital image data to make a mathematical model to produce diagnostic interpretations and predictions. The role of the cytologist is now changing from a simple observational scientist and slide interpreter to a dynamic and integrated multi-parametric prediction-based scientist. In the current stage, the cytologist must understand the situation and should have a vision of the complete scenario on computational cytology.
Sections du résumé
BACKGROUND
BACKGROUND
The significant advancement in digital imaging, data management, advanced computational power, and artificial neural network have an immense impact on the field of cytology. The amalgamation of these areas has generated a newer discipline known as computational cytology.
AIMS AND OBJECTIVE
OBJECTIVE
In To discuss the various important aspects of computational cytology.
MATERIALS AND METHODS
METHODS
We reviewed the different studies published in English during the last few years on computational cytology.
RESULT
RESULTS
Computational cytology is a newer and emerging discipline in pathology that deals with the patient's meta-data and digital image data to make a mathematical model to produce diagnostic interpretations and predictions. The role of the cytologist is now changing from a simple observational scientist and slide interpreter to a dynamic and integrated multi-parametric prediction-based scientist.
CONCLUSION
CONCLUSIONS
In the current stage, the cytologist must understand the situation and should have a vision of the complete scenario on computational cytology.
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
270-275Informations de copyright
© 2023 Wiley Periodicals LLC.
Références
Louis DN, Feldman M, Carter AB, et al. Computational pathology: a path ahead. Arch Pathol Lab Med. 2016;140(1):41-50.
Abels E, Pantanowitz L, Aeffner F, et al. Computational pathology definitions, best practices, and recommendations for regulatory guidance: a white paper from the Digital Pathology Association. J Pathol. 2019;249(3):286-294.
Cui M, Zhang DY. Artificial intelligence and computational pathology. Lab Invest. 2021;101(4):412-422. doi:10.1038/s41374-020-00514-0
Louis DN, Gerber GK, Baron JM, et al. Computational pathology: an emerging definition. Arch Pathol Lab Med. 2014;138(9):1133-1138.
McAlpine ED, Michelow P. The cytopathologist's role in developing and evaluating artificial intelligence in cytopathology practice. Cytopathology. 2020;31(5):385-392.
Marée R. The need for careful data collection for pattern recognition in digital pathology. J Pathol Inform. 2017;8:19.
Dey P. The emerging role of deep learning in cytology. Cytopathology. 2021;32(2):154-160.
Dey P. Artificial neural network in diagnostic cytology. Cytojournal. 2022;19:27.
Dey P, Dey R. Artificial neural network: mechanism and application in pathology. Indian J Pathol Microbiol. 2002;45(3):371-374.
Hanna MG, Parwani A, Sirintrapun SJ. Whole slide imaging: technology and applications. Adv Anat Pathol. 2020;27(4):251-259. doi:10.1097/PAP.0000000000000273
Stålhammar G, Fuentes Martinez N, Lippert M, et al. Digital image analysis outperforms manual biomarker assessment in breast cancer. Mod Pathol. 2016;29(4):318-329.
Bankhead P, Fernández JA, McArt DG, et al. Integrated tumor identification and automated scoring minimizes pathologist involvement and provides new insights to key biomarkers in breast cancer. Lab Invest. 2018;98(1):15-26.
Stalhammar G, Robertson S, Wedlund L, et al. Digital image analysis of Ki67 in hot spots is superior to both manual Ki67 and mitotic counts in breast cancer. Histopathology. 2018;72:974-989.
Rizzardi AE, Johnson AT, Vogel RI, et al. Quantitative comparison of immunohistochemical staining measured by digital image analysis versus pathologist visual scoring. Diagn Pathol. 2012;7:42.
Chea V, Pleiner V, Schweizer V, Herzog B, Bode B, Tinguely M. Optimized workflow for digitalized FISH analysis in pathology. Diagn Pathol. 2021;16(1):42.
Wadapurkar RM, Vyas R. Computational analysis of next generation sequencing data and its applications in clinical oncology. Inform Med Unlocked. 2018;11:75-82.
Zhao W, Chen JJ, Perkins R, et al. A novel procedure on next generation sequencing data analysis using text mining algorithm. BMC Bioinform. 2016;17(1):213.
Dander A, Baldauf M, Sperk M, Pabinger S, Hiltpolt B, Trajanoski Z. Personalized oncology suite: integrating next-generation sequencing data and whole-slide bioimages. BMC Bioinform. 2014;15(1):306.
Carrillo-Perez F, Morales JC, Castillo-Secilla D, et al. Non-small-cell lung cancer classification via RNA-Seq and histology imaging probability fusion. BMC Bioinform. 2021;22(1):454.
Jiang H, Zhou Y, Lin Y, Chan RC, Liu J, Chen H. Deep learning for computational cytology: a survey. arXiv preprint, arXiv:2202.05126, 2022.
Liang Y, Pan C, Sun W, Liu Q, Du Y. Global context-aware cervical cell detection with soft scale anchor matching. Comput Methods Programs Biomed. 2021;204:106061.
Xie X, Fu CC, Lv L, et al. Deep convolutional neural network-based classification of cancer cells on cytological pleural effusion images. Mod Pathol. 2022;35(5):609-614.
Zhang Z, Fu X, Liu J, et al. Developing a machine learning algorithm for identifying abnormal urothelial cells: a feasibility study. Acta Cytol. 2021;65(4):335-341. doi:10.1159/000510474
Garud H, Karri SP, Sheet D, Chatterjee J, Mahadevappa M, Ray AK, Ghosh A, Maity AK. High-magnification multi-views based classification of breast fine needle aspiration cytology cell samples using fusion of decisions from deep convolutional networks. Paper presented at: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops; 2017:76-81.
Teramoto A, Tsukamoto T, Yamada A, et al. Deep learning approach to classification of lung cytological images: two-step training using actual and synthesized images by progressive growing of generative adversarial networks. PLoS One. 2020;15(3):e0229951.
Guan Q, Wang Y, Ping B, et al. Deep convolutional neural network VGG-16 model for differential diagnosing of papillary thyroid carcinomas in cytological images: a pilot study. J Cancer. 2019;10(20):4876-4882.
Sanghvi AB, Allen EZ, Callenberg KM, Pantanowitz L. Performance of an artificial intelligence algorithm for reporting urine cytopathology. Cancer Cytopathol. 2019;127(10):658-666.
Wu M, Yan C, Liu H, Liu Q. Automatic classification of ovarian cancer types from cytological images using deep convolutional neural networks. Biosci Rep. 2018;38(3):BSR20180289.
Wu M, Yan C, Liu H, Liu Q, Yin Y. Automatic classification of cervical cancer from cytological images by using convolutional neural network. Biosci Rep. 2018;38(6):BSR20181769.
Teramoto A, Tsukamoto T, Kiriyama Y, Fujita H. Automated classification of lung cancer types from cytological images using deep convolutional neural networks. Biomed Res Int. 2017;2017:4067832.
Arvaniti E, Fricker KS, Moret M, et al. Automated Gleason grading of prostate cancer tissue microarrays via deep learning. Sci Rep. 2018;8(1):12054.
Awan R, Sirinukunwattana K, Epstein D, et al. Glandular morphometrics for objective grading of colorectal adenocarcinoma histology images. Sci Rep. 2017;7(1):16852.
Bychkov D, Linder N, Turkki R, et al. Deep learning based tissue analysis predicts outcome in colorectal cancer. Sci Rep. 2018;8(1):3395.
Sharma S, Gupta N, Singh N, Chaturvedi R, Behera D, Rajwanshi A. Cytomorphological features as predictors of epidermal growth factor receptor mutation status in lung adenocarcinoma. Cytojournal. 2018;15:11.
Chico V. The impact of the general data protection regulation on health research. Br Med Bull. 2018;128(1):109-118.