Radiomics for Estimating Recurrence Risk of Patients with Lung Cancer by Using Survival Analysis

Abstract

Because of the promotion of cancer screening, the number of patients with lung cancer detected at the early stage has increased. However, it was reported that 30-40% of the lung cancer patients at stage I relapsed. If the recurrence risk can be accurately predicted, it is possible to give medical care for improving the prognosis of lung cancer patients. The purpose of this study was to develop a method for the prediction of recurrence risk of patients with lung cancer by using survival analysis of radiomics approach. A public database was used in this study. Fifty patients (25 recurrences and 25 censored cases) classified as stage I or II were selected and their pretreatment computed tomography (CT) images were obtained. First, we selected one slice containing the largest tumor area and manually segmented the tumor regions. We subsequently calculated 367 radiomic features such as tumor size, shape, CT values, and texture. Radiomic features were selected by using least absolute shrinkage and selection (Lasso). Cox regression model and random survival forest (RSF) with the selected radiomic features were used for estimating the recurrence functions of fifty patients. The experimental result showed that average area under the curve (AUC) values of Cox regression model and RSF for the prediction accuracy were 0.81 and 0.93, respectively. Since our scheme can predict recurrence risk of patients with lung cancer by using non-invasive image examinations, it would be useful for the selection of treatment and the follow-up after the treatment.