Access the full text.
Sign up today, get DeepDyve free for 14 days.
B. Chance, S. Nioka, Jun Zhang, E. Conant, Emily Hwang, S. Briest, S. Orel, M. Schnall, B. Czerniecki (2005)
Breast Cancer Detection Based on Incremental Biochemical and Physiological Properties of Breast CancersAcademic Radiology, 12
B. Sorg, B. Moeller, Owen Donovan, Yiting Cao, M. Dewhirst (2005)
Hyperspectral imaging of hemoglobin saturation in tumor microvasculature and tumor hypoxia development.Journal of biomedical optics, 10 4
G. Palmer, Changfang Zhu, T. Breslin, Fushen Xu, K. Gilchrist, N. Ramanujam (2006)
Monte Carlo-based inverse model for calculating tissue optical properties. Part II: Application to breast cancer diagnosis.Applied optics, 45 5
Xuefeng Cheng, J. Mao, R. Bush, D. Kopans, Richard Moore, M. Chorlton (2003)
Breast cancer detection by mapping hemoglobin concentration and oxygen saturation.Applied optics, 42 31
Austin Moy, Sean White, E. Indrawan, J. Lotfi, Matthew Nudelman, S. Costantini, N. Agarwal, W. Jia, K. Kelly, B. Sorg, Bernard Choi (2011)
Wide-field functional imaging of blood flow and hemoglobin oxygen saturation in the rodent dorsal window chamber.Microvascular research, 82 3
B. Moeller, Yiting Cao, Chuan-Yuan Li, M. Dewhirst (2004)
Radiation activates HIF-1 to regulate vascular radiosensitivity in tumors: role of reoxygenation, free radicals, and stress granules.Cancer cell, 5 5
C. Carpenter, B. Pogue, Shudong Jiang, H. Dehghani, Xin Wang, K. Paulsen, W. Wells, J. Forero, C. Kogel, J. Weaver, S. Poplack, P. Kaufman (2007)
Image-guided optical spectroscopy provides molecular-specific information in vivo: MRI-guided spectroscopy of breast cancer hemoglobin, water, and scatterer size.Optics letters, 32 8
B. Brooksby, B. Pogue, Shudong Jiang, H. Dehghani, S. Srinivasan, C. Kogel, T. Tosteson, J. Weaver, S. Poplack, K. Paulsen (2006)
Imaging breast adipose and fibroglandular tissue molecular signatures by using hybrid MRI-guided near-infrared spectral tomography.Proceedings of the National Academy of Sciences of the United States of America, 103 23
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 of Medical Imaging – SPIE
Published: Apr 1, 2014
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.