Abstract
PurposeThe purpose of this study was to investigate the prognostic value of pre-treatment CT radiomics and clinical factors for the overall survival (OS) of advanced (IIIB–IV) lung adenocarcinoma patients.MethodsThis study involved 165 patients with advanced lung adenocarcinoma. The Lasso–Cox regression model was used for feature selection and radiomics signature building. Then a clinical model was built based on clinical factors; a combined model in the form of nomogram was constructed with both clinical factors and the radiomics signature. Harrell’s concordance index (C-Index) and Receiver operating characteristic (ROC) curves at cut-off time points of 1-, 2-, and 3- year were used to estimate and compare the predictive ability of all three models. Finally, the discriminatory ability and calibration of the nomogram were analyzed.ResultsThirteen significant features were selected to build the radiomics signature whose C-indexes were 0.746 (95% CI, 0.699 to 0.792) in the training cohort and 0.677 (95% CI, 0.597 to 0.766) in the validation cohort. The C-indexes of combined model achieved 0.799 (95% CI, 0.757 to 0.84) in the training cohort and 0.733 (95% CI, 0.656 to 0.81) in the validation cohort, which outperformed the clinical model and radiomics signature. Moreover, the areas under the curve (AUCs) of the radiomic signature for 2-year prediction was superior to that of the clinical model. The combined model had the best AUCs for 2- and 3-year predictions.ConclusionsRadiomic signatures and clinical factors have prognostic value for OS in advanced (IIIB–IV) lung adenocarcinoma patients. The optimal model should be selected according to different cut-off time points in clinical application.
Highlights
Lung cancer, as a leading cause of cancer-related mortality, is responsible for approximately 1.4 million deaths annually throughout the world (1)
There were no significant differences except Eastern Cooperative Oncology Group (ECOG) performance status in clinical factors between the two cohorts
The present study explored whether a radiomic approach could be used to generate prognostic biomarkers of overall survival (OS) for advanced lung adenocarcinoma patients
Summary
As a leading cause of cancer-related mortality, is responsible for approximately 1.4 million deaths annually throughout the world (1). As NSCLC has no specific early symptoms and signs, 57% of patients present with advanced stage disease at primary diagnosis (3), which may deny patients the opportunity to receive resection and result in a diminished survival time. Since the 1990s, emergence of chemotherapy with platinum doublets and tyrosine kinase inhibitors (TKIs) has made breakthroughs in the treatment for NSCLC (4); the 5-year overall survival (OS) rate is only 5% for those with metastatic disease (5). The ability to predict clinical outcomes accurately is crucial for clinicians to judge the most appropriate therapies for these patients to improve prognosis. To this end, biomarkers are needed (6)
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