"Two-Dimensional Quantitative Profiling of Serous Effusion Cells by Unsupervised Machine Learning Analysis" by Safaa Al-Qaysi Ph.D., Ding Dai MD Ph.D. et al.
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Abstract

Cytological evaluation of serous effusion specimens is an important part of cancer diagnosis. In this study we performed two-dimensional (2D) morphometric features and clustering analysis for development of useful techniques for identification and differentiation of malignant and begin cells in serous effusion specimens extracted from ten patients with clinical symptoms of pleural and peritoneal effusion. Our findings show that the two-dimensional (2D) morphometric features and clustering analysis are useful techniques for identification and differentiation of malignant and begin cells in serous effusion specimens, which can lead to development of new methods for rapid cells profiling in clinical application.

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

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