Abstract

This study was to develop a rapid and accurate NIR prediction method. According to statistical study, optimal score maps and occurrence rates (Frequency %) data were found in selectional zones B (1720–1796 nm) and C (2012–2058 nm), as a prognostic tool to distinguish the cancer from normal tissues of unknown human breast samples. In zone B, among 42 normal cases, there is just a single score mixed with cancer while some cancer sections were mixed with normal tissues which all came from lower aggressiveness. Moreover, in zone C, among 56 cancer cases, there is not a single score mixed with normal, but just a few control normal sections were mixed with cancer tissues which all came from higher aggressiveness. In the prediction set, the difficulty in identification was not only found from lower or middle aggressiveness but also from lower cancer and control normal tissue which was taken in the higher cancer case. Evident relationships between NIR data, histopathological diagnosis and glycolytic enzyme activity were found by using radiorespirometry and were expressed as a ratio of initial velocities (V) between cancer (Vc) and normal (Vn) tissues. The lowest Vc/Vn shows the lowest cancer aggressivenes as 1.45 to 1.51, 1.69, 2.35 to 2.86 and 4.82 to 10.38 in SBR I, lobular, SBR II and SBR III, respectively. This value corresponded exactly with the histopathological diagnosis and was in reverse order to NIR data, therefore the accuracy of the NIR prediction can be ensured.

© 1998 NIR Publications

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