ArticleThoracic cancer2020
Parallel comparison and combining effect of radiomic and emerging genomic data for prognostic stratification of non-small cell lung carcinoma patients.
Article in Thoracic cancer, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- From Images to Genes: Radiogenomics Based on Artificial Intelligence to Achieve Non-Invasive Precision Medicine in Cancer Patients.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- The prognostic value of radiogenomics using CT in patients with lung cancer: a systematic review.Insights into imaging · 2024Review
- Radiogenomics models for predicting prognosis in locally advanced non-small cell lung cancer patients undergoing definitive chemoradiotherapy.Translational lung cancer research · 2024Article
- Feature selection methods and predictive models in CT lung cancer radiomics.Journal of applied clinical medical physics · 2023Review
- Paired analysis of tumor mutation burden calculated by targeted deep sequencing panel and whole exome sequencing in non-small cell lung cancer.BMB reports · 2021Article
- Are radiomics features universally applicable to different organs?Cancer imaging : the official publication of the International Cancer Imaging Society · 2021Article
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Authors and funding
8 authors.
Funding
Abstract
backgroundA single institution retrospective analysis of 124 non-small cell lung carcinoma (NSCLC) patients was performed to identify whether disease-free survival (DFS) achieves incremental values when radiomic and genomic data are combined with clinical information.
methodsUsing the least absolute shrinkage and selection operator (LASSO) Cox regression method, radiomic and genetic features were reduced in number for selection of the most useful prognostic feature. We created four models using only baseline clinical data, clinical data with selected genetic features, clinical data with selected radiomic features, and clinical data with selected genetic and radiomic features together. Multivariate Cox proportional hazards analysis was performed to determine predictors of DFS. Receiver operating characteristic (ROC) calculation was made to compare the discriminative performance for DFS prediction by four constructed models at the five-year time point.
resultsOn precontrast scan, improved discrimination performance was obtained in a merging of selected radiomics and genetics (AUC = 0.8638), compared with clinical data only (AUC = 0.7990), selected genetic features (AUC = 0.8497), and selected radiomic features (AUC = 0.8355). On post-contrast scan, discrimination performance was improved (AUC = 0.8672) compared with the clinical variables (AUC = 0.7913), and selected genetic features (AUC = 0.8376) and selected radiomic features (AUC = 0.8399) were considered.
conclusionsThe combination of selected radiomic and genomic features improved stratification of NSCLC patients upon survival. Thus, integrating clinicopathologic model with radiomic and genomic features may lead to improved prognostic accuracy compared to conventional clinicopathological data alone. KEY POINTS: SIGNIFICANT FINDINGS OF THE STUDY: Receiver operating characteristic (ROC) calculation was made to compare the discriminative performance for disease-free survival (DFS). The discriminative performance for DFS was better when combining radiomic and genetic features compared to clinical data only, selected genetic features, and selected radiomic features. WHAT THIS STUDY ADDS: The combination of selected radiomic and genomic features improved stratification of NSCLC patients upon survival. Thus, integrating a clinicopathological model with radiomic and genomic features may lead to improved prognostic accuracy compared to conventional clinicopathological data alone.
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