The feasibility of early diagnosis of endometrial carcinoma was studied by high dimensional data analysis (HDDA) that classified near infrared (NIR) spectra of tissues. NIR spectra of 77 specimens of endometrium were collected. The spectra were pretreated by principal component orthogonal signal correction (PC-OSC) and emphatic orthogonal signal correction (EOSC) methods to improve the signal-to-noise ratio (SNR) and remove the influences of background and baseline. The effects of modeling parameters were investigated using bootstrapped Latin-partition methods. The optimal HDDA model of the PC-OSC pretreatment method successfully classified the samples with prediction accuracies of 95.2 ± 1.9%. The proposed procedure proved to be rapid and convenient, which is suitable to be developed as a non-invasive diagnosis method for cancer tissue.
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