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Parallel comparison and combining effect of radiomic and emerging genomic data for prognostic stratification of non-small cell lung carcinoma patients

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Title
Parallel comparison and combining effect of radiomic and emerging genomic data for prognostic stratification of non-small cell lung carcinoma patients
Author(s)
Ki Hwan Kim; Jinho Kim; Hyunjin Park; Hankyul Kim; Seung-hak Le; Insuk Sohn; Ho Yun Lee; Woong-Yang Park
Subject
CT, ; lung adenocarcinoma, ; quantitative imaging, ; survival stratification, ; texture analysis
Publication Date
2020-09
Journal
Thoracic Cancer, v.11, no.9, pp.2542 - 2551
Publisher
WILEY-BLACKWELL
Abstract
© 2020 The Authors. Thoracic Cancer published by China Lung Oncology Group and John Wiley & Sons Australia, Ltd. Background: A 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. Methods: Using 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. Results: On 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. Conclusions: The 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
URI
https://pr.ibs.re.kr/handle/8788114/8486
DOI
10.1111/1759-7714.13568
ISSN
1759-7706
Appears in Collections:
Center for Neuroscience Imaging Research (뇌과학 이미징 연구단) > 1. Journal Papers (저널논문)
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