doi: 10.7763/IJET.2016.V8.916
Modeling Tumor Growth for Kidney Cancer Based on Nuclei Clusters of Pathology Slides
- 1 Dept. of Electrical Eng., The City College of the City University of New York, USA
- 2 Memorial Sloan Kettering Cancer Center, New York, USA
Abstract
Descriptive features of nuclei positions from tumor tissue samples are studied to construct mathematical models fortumor growth. Extracted from kidney cancer patients, tumorous tissue pieces are implanted in the flanks of mice tomeasure the course of tumor mass, which are then sampled onglass slides. H&E slides are digitized under light microscope and analyzed to identify the structure of nuclei positions. Using k-means clustering method, the nuclei locations of each H&Eslide are evaluated. The cluster features are used as inputs to our artificial intelligence based personalized tumor growth parameter computation method, called PReP-C. Theexponential linear tumor growth model parameters and thecorresponding growth curves computed by PReP-C arecompared to the preclinical tumor volume measurements. Thecorrelation between the computed results and themeasurements from 14 H&E pathology slides is encouraging tobuild personalized mathematical models for tumor growth.
Keywords
- H&E slide
- tumor growth models
- kidneycancer
- exponential linear model
- k-means clustering
How to Cite
Aydin Saribudak, Yiyu Dong, James Hsieh, and M. Umit Uyar, "Modeling Tumor Growth for Kidney Cancer Based on Nuclei Clusters of Pathology Slides," International Journal of Engineering and Technology, vol. 8, no. 5, pp. 375-379, 2016. https://doi.org/ 10.7763/IJET.2016.V8.916
Copyright & License
Copyright © 2016 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).