首页|South China University of Technology Reports Findings in Lung Cancer (Radiomics-based Support Vector Machine Distinguishes Molecular Events Driving Progression of Lung Adenocarcinoma)
South China University of Technology Reports Findings in Lung Cancer (Radiomics-based Support Vector Machine Distinguishes Molecular Events Driving Progression of Lung Adenocarcinoma)
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New research on Oncology -Lung Cancer is the subject of a report. According to news reporting originating in Guangzho u, People's Republic of China, by NewsRx journalists, research stated, "An incre asing number of early-stage lung adenocarcinoma (LUAD) are detected as lung nodu les. The radiological features related to LUAD progression remain further invest igation." The news reporters obtained a quote from the research from the South China Unive rsity of Technology, "Exploration is required to bridge the gap between radiomic s features and molecular characteristics of lung nodules. Consensus clustering w as applied to the radiomics features of 1,212 patients to establish stable clust ering. Clusters were illustrated using clinicopathological and next-generation s equencing (NGS). A classifier was constructed to further investigate the molecul ar characteristic in patients with paired CT and RNA-seq data. Patients were clu stered into 4 clusters. Cluster 1 was associated with a low consolidation-to-tum or ratio (CTR), pre-invasion, grade I disease and good prognosis. Clusters 2 and 3 showed increasing malignancy with higher CTR, higher pathological grade and p oor prognosis. Cluster 2 possessed more spread through air spaces (STAS) and clu ster 3 showed higher proportion of pleural invasion. Cluster 4 had similar clini copathological features with cluster 1 except higher proportion of grade II dise ase. RNA-seq indicated that cluster 1 represented nodules with indolent growth a nd good differentiation, whereas cluster 4 showed progression in cell developmen t but still had low proliferative activity. Nodules with high proliferation were classified into clusters 2 and 3. Additionally, the radiomics classifier distin guished cluster 2 as nodules harboring an activated immune environment, while cl uster 3 represented nodules with a suppressive immune environment. Furthermore, gene signatures associated with the prognosis of early-stage LUAD were validated in external datasets. Radiomics features can manifest molecular events driving progression of lung adenocarcinoma."
GuangzhouPeople's Republic of ChinaA siaAdenocarcinomaCancerEmerging TechnologiesHealth and MedicineLung Ca ncerLung Diseases and ConditionsMachine LearningOncologySupport Vector M achinesVector Machines
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