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Pretreatment Thoracic CT Radiomic Features to Predict Brain Metastases in Patients With ALK-Rearranged Non-Small Cell Lung Cancer
Objective: To identify CT imaging biomarkers based on radiomic features for predicting brain metastases (BM) in patients with ALK-rearranged non-small cell lung cancer (NSCLC). Methods: NSCLC patients with pathologically confirmed ALK rearrangement from January 2014 to December 2020 in our hospital...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8914538/ https://www.ncbi.nlm.nih.gov/pubmed/35281837 http://dx.doi.org/10.3389/fgene.2022.772090 |
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author | Wang, Hua Chen, Yong-Zi Li, Wan-Hu Han, Ying Li, Qi Ye, Zhaoxiang |
author_facet | Wang, Hua Chen, Yong-Zi Li, Wan-Hu Han, Ying Li, Qi Ye, Zhaoxiang |
author_sort | Wang, Hua |
collection | PubMed |
description | Objective: To identify CT imaging biomarkers based on radiomic features for predicting brain metastases (BM) in patients with ALK-rearranged non-small cell lung cancer (NSCLC). Methods: NSCLC patients with pathologically confirmed ALK rearrangement from January 2014 to December 2020 in our hospital were enrolled retrospectively in this study. Finally, 77 patients were included according to the inclusion and exclusion criteria. Patients were divided into two groups: BM+ were those patients who were diagnosed with BM at baseline examination (n = 16) or within 1 year’s follow-up (n = 14), and BM− were those without BM followed up for at least 1 year (n = 47). Radiomic features were extracted from the pretreatment thoracic CT images. Sequential univariate logistic regression, LASSO regression, and backward stepwise logistic regression were used to select radiomic features and develop a BM-predicting model. Results: Five robust radiomic features were found to be independent predictors of BM. AUC for radiomics model was 0.828 (95% CI: 0.736–0.921), and when combined with clinical features, the AUC was increased (p = 0.017) to 0.909 (95% CI: 0.845–0.972). The individualized BM-predicting model incorporated with clinical features was visualized by the nomogram. Conclusion: Radiomic features extracted from pretreatment thoracic CT images have the potential to predict BM within 1 year after detection of the primary tumor in patients with ALK-rearranged NSCLC. The radiomics model incorporated with clinical features shows improved risk stratification for such patients. |
format | Online Article Text |
id | pubmed-8914538 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-89145382022-03-12 Pretreatment Thoracic CT Radiomic Features to Predict Brain Metastases in Patients With ALK-Rearranged Non-Small Cell Lung Cancer Wang, Hua Chen, Yong-Zi Li, Wan-Hu Han, Ying Li, Qi Ye, Zhaoxiang Front Genet Genetics Objective: To identify CT imaging biomarkers based on radiomic features for predicting brain metastases (BM) in patients with ALK-rearranged non-small cell lung cancer (NSCLC). Methods: NSCLC patients with pathologically confirmed ALK rearrangement from January 2014 to December 2020 in our hospital were enrolled retrospectively in this study. Finally, 77 patients were included according to the inclusion and exclusion criteria. Patients were divided into two groups: BM+ were those patients who were diagnosed with BM at baseline examination (n = 16) or within 1 year’s follow-up (n = 14), and BM− were those without BM followed up for at least 1 year (n = 47). Radiomic features were extracted from the pretreatment thoracic CT images. Sequential univariate logistic regression, LASSO regression, and backward stepwise logistic regression were used to select radiomic features and develop a BM-predicting model. Results: Five robust radiomic features were found to be independent predictors of BM. AUC for radiomics model was 0.828 (95% CI: 0.736–0.921), and when combined with clinical features, the AUC was increased (p = 0.017) to 0.909 (95% CI: 0.845–0.972). The individualized BM-predicting model incorporated with clinical features was visualized by the nomogram. Conclusion: Radiomic features extracted from pretreatment thoracic CT images have the potential to predict BM within 1 year after detection of the primary tumor in patients with ALK-rearranged NSCLC. The radiomics model incorporated with clinical features shows improved risk stratification for such patients. Frontiers Media S.A. 2022-02-25 /pmc/articles/PMC8914538/ /pubmed/35281837 http://dx.doi.org/10.3389/fgene.2022.772090 Text en Copyright © 2022 Wang, Chen, Li, Han, Li and Ye. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Genetics Wang, Hua Chen, Yong-Zi Li, Wan-Hu Han, Ying Li, Qi Ye, Zhaoxiang Pretreatment Thoracic CT Radiomic Features to Predict Brain Metastases in Patients With ALK-Rearranged Non-Small Cell Lung Cancer |
title | Pretreatment Thoracic CT Radiomic Features to Predict Brain Metastases in Patients With ALK-Rearranged Non-Small Cell Lung Cancer |
title_full | Pretreatment Thoracic CT Radiomic Features to Predict Brain Metastases in Patients With ALK-Rearranged Non-Small Cell Lung Cancer |
title_fullStr | Pretreatment Thoracic CT Radiomic Features to Predict Brain Metastases in Patients With ALK-Rearranged Non-Small Cell Lung Cancer |
title_full_unstemmed | Pretreatment Thoracic CT Radiomic Features to Predict Brain Metastases in Patients With ALK-Rearranged Non-Small Cell Lung Cancer |
title_short | Pretreatment Thoracic CT Radiomic Features to Predict Brain Metastases in Patients With ALK-Rearranged Non-Small Cell Lung Cancer |
title_sort | pretreatment thoracic ct radiomic features to predict brain metastases in patients with alk-rearranged non-small cell lung cancer |
topic | Genetics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8914538/ https://www.ncbi.nlm.nih.gov/pubmed/35281837 http://dx.doi.org/10.3389/fgene.2022.772090 |
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