Cancer prognosis using support vector regression in imaging modality

Abstract

The proposed techniques investigate the strength of support vector regression (SVR) in cancer prognosis using imaging features. Cancer image features were extracted from patients and recorded into censored data. To employ censored data for prognosis, SVR methods are needed to be adapted to uncertain targets. The effectiveness of two principle breast features, tumor size and lymph node status, was demonstrated by the combination of sampling and feature selection methods. In sampling, breast data were stratified according to tumor size and lymph node status. Three types of feature selection methods comprised of no selection, individual feature selection, and feature subset forward selection, were employed. The prognosis results were evaluated by comparative study using the following performance metrics: concordance index (CI) and Brier score (BS). Cox regression was employed to compare the results. The support vector regression method (SVCR) performs similarly to Cox regression in three feature selection methods and better than Cox regression in non-feature selection methods measured by CI and BS. Feature selection methods can improve the performance of Cox regression measured by CI. Among all cross validation results, stratified sampling of tumor size achieves the best regression results for both feature selection and non-feature selection methods. The SVCR regression results, perform better than Cox regression when the techniques are used with either CI or BS. The best CI value in the validation results is 0.6845. The best CI value corresponds to the best BS value 0.2065, which were obtained in the combination of SVCR, individual feature selection, and stratified sampling of the number of positive lymph nodes. In addition, we also observe that SVCR performs more consistently than Cox regression in all prognosis studies. The feature selection method does not have a significant impact on the metric values, especially on CI. We conclude that the combinational methods of SVCR, feature selection, and sampling can improve cancer prognosis, but more significant features may further enhance cancer prognosis accuracy.