MRI Texture Analysis for the Prediction of Stereotactic Radiosurgery Outcomes in Brain Metastases from Lung Cancer

This study aims to evaluate the utility of texture analysis in predicting the outcome of stereotactic radiosurgery (SRS) for brain metastases from lung cancer. From 83 patients with lung cancer who underwent SRS for brain metastasis, a total of 118 metastatic lesions were included. Two neuroradiolog...

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Autores principales: Park, Jung Hyun, Choi, Byung Se, Han, Jung Ho, Kim, Chae-Yong, Cho, Jungheum, Bae, Yun Jung, Sunwoo, Leonard, Kim, Jae Hyoung
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7827024/
https://www.ncbi.nlm.nih.gov/pubmed/33440723
http://dx.doi.org/10.3390/jcm10020237
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author Park, Jung Hyun
Choi, Byung Se
Han, Jung Ho
Kim, Chae-Yong
Cho, Jungheum
Bae, Yun Jung
Sunwoo, Leonard
Kim, Jae Hyoung
author_facet Park, Jung Hyun
Choi, Byung Se
Han, Jung Ho
Kim, Chae-Yong
Cho, Jungheum
Bae, Yun Jung
Sunwoo, Leonard
Kim, Jae Hyoung
author_sort Park, Jung Hyun
collection PubMed
description This study aims to evaluate the utility of texture analysis in predicting the outcome of stereotactic radiosurgery (SRS) for brain metastases from lung cancer. From 83 patients with lung cancer who underwent SRS for brain metastasis, a total of 118 metastatic lesions were included. Two neuroradiologists independently performed magnetic resonance imaging (MRI)-based texture analysis using the Imaging Biomarker Explorer software. Inter-reader reliability as well as univariable and multivariable analyses were performed for texture features and clinical parameters to determine independent predictors for local progression-free survival (PFS) and overall survival (OS). Furthermore, Harrell’s concordance index (C-index) was used to assess the performance of the independent texture features. The primary tumor histology of small cell lung cancer (SCLC) was the only clinical parameter significantly associated with local PFS in multivariable analysis. Run-length non-uniformity (RLN) and short-run emphasis were the independent texture features associated with local PFS. In the non-SCLC (NSCLC) subgroup analysis, RLN and local range mean were associated with local PFS. The C-index of independent texture features was 0.79 for the all-patients group and 0.73 for the NSCLC subgroup. In conclusion, texture analysis on pre-treatment MRI of lung cancer patients with brain metastases may have a role in predicting SRS response.
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spelling pubmed-78270242021-01-25 MRI Texture Analysis for the Prediction of Stereotactic Radiosurgery Outcomes in Brain Metastases from Lung Cancer Park, Jung Hyun Choi, Byung Se Han, Jung Ho Kim, Chae-Yong Cho, Jungheum Bae, Yun Jung Sunwoo, Leonard Kim, Jae Hyoung J Clin Med Article This study aims to evaluate the utility of texture analysis in predicting the outcome of stereotactic radiosurgery (SRS) for brain metastases from lung cancer. From 83 patients with lung cancer who underwent SRS for brain metastasis, a total of 118 metastatic lesions were included. Two neuroradiologists independently performed magnetic resonance imaging (MRI)-based texture analysis using the Imaging Biomarker Explorer software. Inter-reader reliability as well as univariable and multivariable analyses were performed for texture features and clinical parameters to determine independent predictors for local progression-free survival (PFS) and overall survival (OS). Furthermore, Harrell’s concordance index (C-index) was used to assess the performance of the independent texture features. The primary tumor histology of small cell lung cancer (SCLC) was the only clinical parameter significantly associated with local PFS in multivariable analysis. Run-length non-uniformity (RLN) and short-run emphasis were the independent texture features associated with local PFS. In the non-SCLC (NSCLC) subgroup analysis, RLN and local range mean were associated with local PFS. The C-index of independent texture features was 0.79 for the all-patients group and 0.73 for the NSCLC subgroup. In conclusion, texture analysis on pre-treatment MRI of lung cancer patients with brain metastases may have a role in predicting SRS response. MDPI 2021-01-11 /pmc/articles/PMC7827024/ /pubmed/33440723 http://dx.doi.org/10.3390/jcm10020237 Text en © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Park, Jung Hyun
Choi, Byung Se
Han, Jung Ho
Kim, Chae-Yong
Cho, Jungheum
Bae, Yun Jung
Sunwoo, Leonard
Kim, Jae Hyoung
MRI Texture Analysis for the Prediction of Stereotactic Radiosurgery Outcomes in Brain Metastases from Lung Cancer
title MRI Texture Analysis for the Prediction of Stereotactic Radiosurgery Outcomes in Brain Metastases from Lung Cancer
title_full MRI Texture Analysis for the Prediction of Stereotactic Radiosurgery Outcomes in Brain Metastases from Lung Cancer
title_fullStr MRI Texture Analysis for the Prediction of Stereotactic Radiosurgery Outcomes in Brain Metastases from Lung Cancer
title_full_unstemmed MRI Texture Analysis for the Prediction of Stereotactic Radiosurgery Outcomes in Brain Metastases from Lung Cancer
title_short MRI Texture Analysis for the Prediction of Stereotactic Radiosurgery Outcomes in Brain Metastases from Lung Cancer
title_sort mri texture analysis for the prediction of stereotactic radiosurgery outcomes in brain metastases from lung cancer
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7827024/
https://www.ncbi.nlm.nih.gov/pubmed/33440723
http://dx.doi.org/10.3390/jcm10020237
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