Breast cancer detection using neutron stimulated emission computed tomography: prominent elements and dose requirements.
Neutron stimulated emission computed tomography (NSECT) is being developed to noninvasively determine concentrations of trace elements in biological tissue. Studies have shown prominent differences in the trace element concentration of normal and malignant breast tissue. NSECT has the potential to detect these differences and diagnose malignancy with high accuracy with dose comparable to that of a single mammogram. In this study, NSECT imaging was simulated for normal and malignant human breast tissue samples to determine the significance of individual elements in determining malignancy. The normal and malignant models were designed with different elemental compositions, and each was scanned spectroscopically using a simulated 2.5 MeV neutron beam. The number of incident neutrons was varied from 0.5 million to 10 million neutrons. The resulting gamma spectra were evaluated through receiver operating characteristic (ROC) analysis to determine which trace elements were prominent enough to be considered markers for breast cancer detection. Four elemental isotopes (133Cs, 81Br, 79Br, and 87Rb) at five energy levels were shown to be promising features for breast cancer detection with an area under the ROC curve (A(Z)) above 0.85. One of these elements--87Rb at 1338 keV--achieved perfect classification at 10 million incident neutrons and could be detected with as low as 3 million incident neutrons. Patient dose was calculated for each gamma spectrum obtained and was found to range from between 0.05 and 0.112 mSv depending on the number of neutrons. This simulation demonstrates that NSECT has the potential to noninvasively detect breast cancer through five prominent trace element energy levels, at dose levels comparable to other breast cancer screening techniques.
Bender
Janelle E
JE
Department of Biomedical Engineering and Duke Advanced Imaging Laboratories, Duke University, Durham, North Carolina 27708, USA. jeb9@duke.edu
Kapadia
Anuj J
AJ
Sharma
Amy C
AC
Tourassi
Georgia D
GD
Harrawood
Brian P
BP
Floyd
Carey E
CE
D000465:Algorithms; D001943:Breast Neoplasms / Q000175:diagnosis* / Q000473:pathology*; D003198:Computer Simulation; D005720:Gamma Rays; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods; D009010:Monte Carlo Method; D009502:Neutrons; D012372:ROC Curve; D011874:Radiometry / Q000379:methods; D012984:Software; D013057:Spectrum Analysis / Q000379:methods; D014055:Tomography, Emission-Computed / Q000379:methods*