Quantificational and statistical analysis of the differences in centrosomal features of untreated lung cancer cells and normal cells.

Abstract

Objective: To distinguish untreated lung cancer cells from normal cells through quantitative analysis and statistical inference of centrosomal features extracted from cell images.

Study design: Recent research indicates that human cancer cell development is accompanied by centrosomal abnormalities. For quantitative analysis of centrosome abnormalities, high-resolution images of normal and untreated cancer lung cells were acquired. After the images were preprocessed and segmented, 11 features were extracted. Correlations among the features were calculated to remove redundant features. Ten nonredundant features were selected for further analysis. The mean values of 10 centrosome features were compared between cancer and normal cells by the two-sample t-test; distributions of the 10 features of cancer and normal centrosomes were compared by the two-sample Kolmogorov-Smirnov test.

Results: Both tests reject the null hypothesis; the means and distributions of features coincide for normal and cancer cells. The 10 centrosome features separate normal from cancer cells at the 5% significance level and show strong evidence that all 10 features exhibit major differences between normal and cancer cells.

Conclusion: Centrosomes from untreated cancer and normal bronchial epithelial cells can be distinguished through objective measurement and quantitative analysis, suggesting a new approach for lung cancer detection, early diagnosis and prognosis.