A prognostic analysis method for non-small cell lung cancer based on the computed tomography radiomics.

In order to assist doctors in arranging the postoperative treatments and re-examinations for non-small cell lung cancer (NSCLC) patients, this study was initiated to explore a prognostic analysis method for NSCLC based on computed tomography (CT) radiomics. The data of 173 NSCLC patients were collected retrospectively and the clinically meaningful 3-year survival was used as the predictive limit to predict the patient's prognosis survival time range. Firstly, lung tumors were segmented and the radiomics features were extracted. Secondly, the feature weighting algorithm was used to screen and optimize the extracted original feature data. Then, the selected feature data combining with the prognosis survival of patients were used to train machine learning classification models. Finally, a prognostic survival prediction model and radiomics prognostic factors were obtained to predict the prognosis survival time range of NSCLC patients. The classification accuracy rate under cross-validation was up to 88.7% in the prognosis survival analysis model. When verifying on an independent data set, the model also yielded a high prediction accuracy which is up to 79.6%. Inverse different moment, lobulation sign and angular second moment were NSCLC prognostic factors based on radiomics. This study proved that CT radiomics features could effectively assist doctors to make more accurate prognosis survival prediction for NSCLC patients, so as to help doctors to optimize treatment and re-examination for NSCLC patients to extend their survival time.