Radiomics features of ground glass nodules tailored different pathological grades of lung adenocarcinoma.
e23127 Background: Different pathological subtypes as well as different grades of adenocarcinoma based on the IASLC/ATS/ERS classification had been proven to be stage-independent predictor of survival. Radiomics features, as a novel analytic method, has been increasingly applied in variety cancer research and may be a potential predictor for preoperatively differentiating pathological grades of adenocarcinoma. Methods: Patients (pts) with radiological proved as solitary ground glass nodule were eligible in this study. Radiomics features derived from computed tomography (CT) images were extracted by Chinese Academy of Science. All pts will be categorized into three groups with lepidic predominance as low-grade, acinar and papillary predominance as intermediate-grade, micropapillary and solid predominance as high-grade. We used L1 penalized constrained continuation ratio model to select relevant radiomics features, and corresponding radiomics signature was constructed. Association between the radiomics signature and pathological grades of adenocarcinoma was explored using the Kruskal-Wallis test and C-index was performed to test the efficacy of differentiating. Results: 82 pts were included in this study. Low-grade, intermediate-grade and high-grade contained 15 (18.3%), 53 (64.6%), 14 (17.1%) pts respectively. 475 radiomics features were extracted from thin section CT image and 10 of them selected through L1 penalized constrained continuation ratio model composed radiomics signature which significantly associated with pathological grades (P < 0.0001). C-index for radiomics signature were 0.813 (95%CI 0.793-0.833). Since clinical characters including gender, age, smoking status, NSE, CEA and CYFRA21-1 were not associated with different grades of adenocarcinoma, we could not establish nomogram based on the radiomics signature and correlated clinical characters. Conclusions: Radiomics features only can be a potential predictor for preoperatively differentiating pathological grades of adenocarcinoma, which may be a more applicable clinical predictor for patients’ survival. Yet large sample sizes are warranted to confirm the results.