ABSTRACT
In order to improve the diagnostic rate of earlier stage colonic cancer with laser-induced 5-ALA-Pp IX fluorescence spectra, a novel method of extraction of fluorescence spectral feature using wavelet analysis and classification using artificial neural network trained with resilient back-propagation algorithm (R-BPNN) was developed. 504 spectra were collected from 8 normal SD rats, and 20 1,2-DMH-induced SD colon cancer models and 12 second generation rats of induced rats. 150 min later trail intravenous injections of 5-ALA dose of 25 mg x kg(-1) body weight (BW), and fluorescence spectra excited with 370 nm Ti-laser were collected in vivo. After preprocessing, 12 feature variants were extracted with wavelet analysis. With R-BPNN, all spectra were classified into two categories: normal or abnormal, which included dysplasia, early carcinoma (EC) and advanced carcinoma (AC). The sensitivity and specificity were 98.91% and 97.2% respectively. The accuracy of discriminating dysplasia, early carcinoma, and advanced carcinoma from normal tissue were 91.3%, 98.9% and 98.8 respectively. The result indicated that this method could effectively and easily diagnoses earlier stage colonic carcinomas.
Subject(s)
Colonic Neoplasms/diagnosis , Spectrometry, Fluorescence/methods , Animals , Disease Models, Animal , Neural Networks, Computer , RatsABSTRACT
In order to diagnosis colon early cancer with laser-induced 5-ALA-PpIX fluorescence spectra, a multivariate statistical method to distinguish these fluorescence spectra acquired in vivo was developed. 343 spectra were collected from 8 normal SD rats, and 20 1,2-DMH-induced SD colon cancer models, and 12 second generation rats of induced rats. 150 min after trail intravenous injections of 5-ALA at a dose of 25 mg x kg(-1) BW, fluorescence spectra excited with 370 nm Ti-laser were collected in vivo. All spectra were divided into a calibration group and a prediction group. After preprocessing, 4 principal components were extracted with PCA. And then, discrimination models were built by stepwise multivariate logistic regression (SMLR) on calibration group. 3 pathological styles were combined each other, and then 3 SMLR models were derived. Normal tissues were classified from early cancers and advanced cancers with sensitivity of 100% and 98.4%, and specificity of 96% and 100%, and accuracy of 98% and 99.2% on prediction group, respectively. The multivariate statistical discrimination method of PCA and SMLR together can effectively distinguish normal tissues from early cancers and advanced cancers with high sensitivity and specificity by means of systemic 5-ALA at low dose. Laser induced fluorescence 5-ALA-based technique is promising for the detection of colonic early cancer.
