Nagma Vohra1, Tanny Chavez1, Joel R Troncoso2, Narasimhan Rajaram2, Jingxian Wu1, Patricia N Coan3, Todd A Jackson3, Keith Bailey4, Magda El-Shenawee1. 1. University of Arkansas, Bell Engineering Center, Department of Electrical Engineering, Fayetteville, Arkansas, United States. 2. University of Arkansas, Bell Engineering Center, Department of Biomedical Engineering, Fayetteville, Arkansas, United States. 3. Oklahoma State University, Animal Resources Unit, Stillwater, Oklahoma, United States. 4. University of Illinois, Urbana-Champaign, Veterinary Diagnostic Laboratory, Urbana, Illinois, United States.
Abstract
Purpose: The objective of this study is to quantitatively evaluate terahertz (THz) imaging for differentiating cancerous from non-cancerous tissues in mammary tumors developed in response to injection of N-ethyl-N-nitrosourea (ENU) in Sprague Dawley rats. Approach: While previous studies have investigated the biology of mammary tumors of this model, the current work is the first study to employ an imaging modality to visualize these tumors. A pulsed THz imaging system is utilized to experimentally collect the time-domain reflection signals from each pixel of the rat's excised tumor. A statistical segmentation algorithm based on the expectation-maximization (EM) classification method is implemented to quantitatively assess the obtained THz images. The model classification of cancer is reported in terms of the receiver operating characteristic (ROC) curves and the areas under the curves. Results: The obtained low-power microscopic images of 17 ENU-rat tumor sections exhibited the presence of healthy connective tissue adjacent to cancerous tissue. The results also demonstrated that high reflection THz signals were received from cancerous compared with non-cancerous tissues. Decent tumor classification was achieved using the EM method with values ranging from 83% to 96% in fresh tissues and 89% to 96% in formalin-fixed paraffin-embedded tissues. Conclusions: The proposed ENU breast tumor model of Sprague Dawley rats showed a potential to obtain cancerous tissues, such as human breast tumors, adjacent to healthy tissues. The implemented EM classification algorithm quantitatively demonstrated the ability of THz imaging in differentiating cancerous from non-cancerous tissues.
Purpose: The objective of this study is to quantitatively evaluate terahertz (THz) imaging for differentiating cancerous from non-cancerous tissues in mammary tumors developed in response to injection of N-ethyl-N-nitrosourea (ENU) in Sprague Dawley rats. Approach: While previous studies have investigated the biology of mammary tumors of this model, the current work is the first study to employ an imaging modality to visualize these tumors. A pulsed THz imaging system is utilized to experimentally collect the time-domain reflection signals from each pixel of the rat's excised tumor. A statistical segmentation algorithm based on the expectation-maximization (EM) classification method is implemented to quantitatively assess the obtained THz images. The model classification of cancer is reported in terms of the receiver operating characteristic (ROC) curves and the areas under the curves. Results: The obtained low-power microscopic images of 17 ENU-rat tumor sections exhibited the presence of healthy connective tissue adjacent to cancerous tissue. The results also demonstrated that high reflection THz signals were received from cancerous compared with non-cancerous tissues. Decent tumor classification was achieved using the EM method with values ranging from 83% to 96% in fresh tissues and 89% to 96% in formalin-fixed paraffin-embedded tissues. Conclusions: The proposed ENU breast tumor model of Sprague Dawley rats showed a potential to obtain cancerous tissues, such as human breast tumors, adjacent to healthy tissues. The implemented EM classification algorithm quantitatively demonstrated the ability of THz imaging in differentiating cancerous from non-cancerous tissues.
Authors: K Turetschek; T P Roberts; E Floyd; A Preda; V Novikov; D M Shames; W O Carter; R C Brasch Journal: J Magn Reson Imaging Date: 2001-06 Impact factor: 4.813
Authors: Benjamin St Peter; Sigfrid Yngvesson; Paul Siqueira; Patrick Kelly; Ashraf Khan; Stephen Glick; Andrew Karellas Journal: IEEE J Biomed Health Inform Date: 2013-07 Impact factor: 5.772