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Forecasting new development of tumor areas using spatial and temporal distribution profiles of hemoglobin saturation in a mouse model

Forecasting new development of tumor areas using spatial and temporal distribution profiles of... Abstract.Features of the tumor microenvironment (TME), such as hemoglobin saturation (HbSat), can provide valuable information on early development and progression of tumors. HbSat correlates with high metabolism and precedes the formation of angiogenic tumors; therefore, changes in HbSat profile can be used as a biomarker for early cancer detection. In this project, we develop a methodology to evaluate HbSat for forecasting early tumor development in a mouse model. We built a delta (δ) cumulative feature that includes spatial and temporal distribution of HbSat for classifying tumor/normal areas. Using a two-class (normal and tumor) logistic regression, the δ feature successfully forecasts tumor areas in two window chamber mice (AUC=0.90 and 0.85). To assess the performance of the logistic regression-based classifier utilizing the δ feature of each region, we conduct a 10-fold cross-validation analysis (AUC of the ROC=0.87). These results show that the TME features based on HbSat can be used to evaluate tumor progression and forecast new occurrences of tumor areas. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Medical Imaging SPIE

Forecasting new development of tumor areas using spatial and temporal distribution profiles of hemoglobin saturation in a mouse model

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References (8)

Publisher
SPIE
Copyright
© 2014 Society of Photo-Optical Instrumentation Engineers (SPIE)
Subject
Computer-Aided Diagnosis; Paper
ISSN
2329-4302
eISSN
2329-4310
DOI
10.1117/1.JMI.1.1.014503
pmid
26158025
Publisher site
See Article on Publisher Site

Abstract

Abstract.Features of the tumor microenvironment (TME), such as hemoglobin saturation (HbSat), can provide valuable information on early development and progression of tumors. HbSat correlates with high metabolism and precedes the formation of angiogenic tumors; therefore, changes in HbSat profile can be used as a biomarker for early cancer detection. In this project, we develop a methodology to evaluate HbSat for forecasting early tumor development in a mouse model. We built a delta (δ) cumulative feature that includes spatial and temporal distribution of HbSat for classifying tumor/normal areas. Using a two-class (normal and tumor) logistic regression, the δ feature successfully forecasts tumor areas in two window chamber mice (AUC=0.90 and 0.85). To assess the performance of the logistic regression-based classifier utilizing the δ feature of each region, we conduct a 10-fold cross-validation analysis (AUC of the ROC=0.87). These results show that the TME features based on HbSat can be used to evaluate tumor progression and forecast new occurrences of tumor areas.

Journal

Journal of Medical ImagingSPIE

Published: Apr 1, 2014

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