Outcome prediction in resectable lung adenocarcinoma patients: value of CT radiomics.

objectivesLung adenocarcinoma shows broad spectrum of prognosis and histologic heterogeneity. This study was to investigate the prognostic value of CT radiomics in resectable lung adenocarcinoma patients and assess its incremental value over clinical-pathologic risk factors.

methodsThis retrospective analysis evaluated 1058 patients who underwent curative surgery for lung adenocarcinoma (training cohort: N = 754; temporal validation cohort: N = 304). Radiomics features were extracted from preoperative contrast-enhanced CT. Radiomics signature to predict disease-free survival (DFS) and overall survival (OS) was generated. Association between the radiomics signature and prognosis were evaluated using univariable and multivariable Cox proportional hazards regression analyses. Incremental value of the radiomics signature beyond clinical-pathologic risk factors was assessed using concordance index (C-index).

resultsThe radiomics signatures were independently associated with DFS (hazard ratio [HR], 1.920; p < 0.001) and OS (HR, 2.079; p < 0.001). The radiomics signature showed performance comparable to stage in estimation of DFS (C-index, 0.724 vs 0.685) and OS (0.735 vs 0.703). The radiomics added prognostic value to clinical-pathologic models (stage and histologic subtype) in predicting DFS (C-index, 0.764 vs 0.713; p < 0.001), which was also shown in the validation cohort (0.782 vs 0.734; p = 0.016). In terms of OS, including radiomics led to significant improvement in prognostic performance of the clinical-pathologic model (stage and age) in the training cohort (0.784 vs 0.737; p < 0.001), but the improvement was not significant in the validation cohort (0.805 vs 0.734; p = 0.149).

conclusionsCT radiomics was effective in predicting prognosis in lung adenocarcinoma patients, providing additional prognostic information beyond clinical-pathologic risk factors. KEY POINTS: • CT radiomics signature was an independent prognostic factor predicting disease-free and overall survival along with clinical risk factors of lung adenocarcinoma (stage, histologic subtype, and age). • CT radiomics added prognostic value to clinical-pathologic models (stage and subtype) in predicting disease-free survival (C-index for integrated model and clinical-pathologic model, 0.764 vs 0.713; p < 0.001), which was also proven in the validation cohort (0.782 vs 0.734; p = 0.016). • Integrated model incorporating radiomics signature can successfully stratify patients into high-risk, intermediate-, or low-risk groups in patients with resectable lung adenocarcinoma.