Background: To study the role of computed tomography (CT)-derived radiomics features and clinical characteristics on the prognosis of "driver gene-negative" lung adenocarcinoma (LUAD) and to explore the potential molecular biological which may be helpful for patients' individual postoperative care.
Methods: A total of 180 patients with stage I-III "driver gene-negative" LUAD in the First Affiliated Hospital of Sun Yat-Sen University from September 2003 to June 2015 were retrospectively collected. The Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression model was used to screen radiomics features and calculated the Rad-score. The prediction performance of the nomogram model based on radiomics features and clinical characteristics was validated and then assessed with respect to calibration. Gene set enrichment analysis (GSEA) was used to explore the relevant biological pathways.
Results: The radiomics and the clinicopathological characteristics were combined to construct a nomogram resulted in better performance for the estimation of OS (C-index: 0.815; 95% confidence interval [CI]: 0.756-0.874) than the clinicopathological nomogram (C-index: 0.765; 95% CI: 0.692-0.837). Decision curve analysis demonstrated that in terms of clinical usefulness, the radiomics nomogram outperformed the traditional staging system and the clinicopathological nomogram. The clinical prognostic risk score of each patient was calculated based on the radiomics nomogram and divided by X-tile into high-risk (> 65.28) and low-risk (≤ 65.28) groups. GSEA results showed that the low-risk score group was directly related to amino acid metabolism, and the high-risk score group was related to immune and metabolism pathways.
Conclusions: The radiomics nomogram was promising to predict the prognosis of patients with "driver gene-negative" LUAD. The metabolism and immune-related pathways may provide new treatment orientation for this genetically unique subset of patients, which may serve as a potential tool to guide individual postoperative care for those patients.
Keywords: Biological pathways; Driver gene-negative; Lung adenocarcinoma; Radiomics; X-ray computed tomography.
© 2023. The Author(s).