Abstract
IntroductionAn increasing number of early-stage lung adenocarcinomas (LUAD) are detected as lung nodules. The radiological features related to LUAD progression warrant further investigation. Exploration is required to bridge the gap between radiomics-based features and molecular characteristics of lung nodules. MethodsConsensus clustering was applied to the radiomic features of 1212 patients to establish stable clustering. Clusters were illustrated using clinicopathological and next-generation sequencing. A classifier was constructed to further investigate the molecular characteristics in patients with paired computed tomography and RNA sequencing data. ResultsPatients were clustered into four clusters. Cluster 1 was associated with a low consolidation-to-tumor ratio, preinvasion, grade I disease, and good prognosis. Clusters 2 and 3 reported increasing malignancy with a higher consolidation-to-tumor ratio, higher pathologic grade, and poor prognosis. Cluster 2 possessed more spread through air spaces and cluster 3 reported a higher proportion of pleural invasion. Cluster 4 had similar clinicopathological features as cluster 1 except but a proportion of grade II disease. RNA sequencing indicated that cluster 1 represented nodules with indolent growth and good differentiation, whereas cluster 4 reported progression in cell development but still had low proliferative activity. Nodules with high proliferation were classified into clusters 2 and 3. In addition, the radiomics classifier distinguished cluster 2 as nodules harboring an activated immune environment, whereas cluster 3 represented nodules with a suppressive immune environment. Furthermore, signatures associated with the prognosis of early-stage LUAD were validated in external datasets. ConclusionsRadiomics features can manifest molecular events driving the progression of LUAD. Our study provides molecular insight into radiomics features and assists in the diagnosis and treatment of early-stage LUAD.
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