Random Survival Forest を用いた肺がん患者の予後予測

抄録

The treatment plan of lung cancer patient is determined based on TNM classification. However, this treatment plan is not necessarily based on prognosis. The ability to predict patient's prognosis by image examination would yield new information in formulating a treatment plan. The purpose of this study is to develop a method for the prognostic prediction among lung cancer patients. The public database NSCLC-Radiomics was used in this study. Sixty seven patients classified as stage I were selected and their pretreatment computed tomography（CT）images and survival times were obtained. First,we selected one slice containing the largest tumor area and manually segmented the tumor regions. We subsequently determined 294 radiomic features such as tumor size, shape, CT values, texture, and so on. Four radiomic features were selected by using least absolute shrinkage and selection（Lasso）. Cox regression model and random survival forest（RSF）with the selected 4 radiomic features were employed for estimating the survivor functions of 67 patients. Time-dependent receiver operating characteristic（ROC）analysis was used for evaluating the estimation accuracy. Average area under the curve（AUC）values of Cox regression model and RSF were 0.741 and 0.826, respectively. Therefore, it revealed that RSF had higher accuracy in prognostic prediction. Our proposed method for the prognostic prediction of lung cancer patients can provide useful information in formulating patients' treatment plans.