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Radiomic feature stability across 4D respiratory phases and its impact on lung tumor prognosis prediction

Radiomic analysis has recently demonstrated versatile uses in improving diagnostic and prognostic prediction accuracy for lung cancer. However, since lung tumors are subject to substantial motion due to respiration, the stability of radiomic features over the respiratory cycle of the patient needs t...

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Autores principales: Du, Qian, Baine, Michael, Bavitz, Kyle, McAllister, Josiah, Liang, Xiaoying, Yu, Hongfeng, Ryckman, Jeffrey, Yu, Lina, Jiang, Hengle, Zhou, Sumin, Zhang, Chi, Zheng, Dandan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6504105/
https://www.ncbi.nlm.nih.gov/pubmed/31063500
http://dx.doi.org/10.1371/journal.pone.0216480
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author Du, Qian
Baine, Michael
Bavitz, Kyle
McAllister, Josiah
Liang, Xiaoying
Yu, Hongfeng
Ryckman, Jeffrey
Yu, Lina
Jiang, Hengle
Zhou, Sumin
Zhang, Chi
Zheng, Dandan
author_facet Du, Qian
Baine, Michael
Bavitz, Kyle
McAllister, Josiah
Liang, Xiaoying
Yu, Hongfeng
Ryckman, Jeffrey
Yu, Lina
Jiang, Hengle
Zhou, Sumin
Zhang, Chi
Zheng, Dandan
author_sort Du, Qian
collection PubMed
description Radiomic analysis has recently demonstrated versatile uses in improving diagnostic and prognostic prediction accuracy for lung cancer. However, since lung tumors are subject to substantial motion due to respiration, the stability of radiomic features over the respiratory cycle of the patient needs to be investigated to better evaluate the robustness of the inter-patient feature variability for clinical applications, and its impact in such applications needs to be assessed. A full panel of 841 radiomic features, including tumor intensity, shape, texture, and wavelet features, were extracted from individual phases of a four-dimensional (4D) computed tomography on 20 early-stage non-small-cell lung cancer (NSCLC) patients. The stability of each radiomic feature was assessed across different phase images of the same patient using the coefficient of variation (COV). The relationship between individual COVs and tumor motion magnitude was inspected. Population COVs, the mean COVs of all 20 patients, were used to evaluate feature motion stability and categorize the radiomic features into 4 different groups. The two extremes, the Very Small group (COV≤5%) and the Large group (COV>20%), each accounted for about a quarter of the features. Shape features were the most stable, with COV≤10% for all features. A clinical study was subsequently conducted using 140 early-stage NSCLC patients. Radiomic features were employed to predict the overall survival with a 500-round bootstrapping. Identical multiple regression model development process was applied, and the model performance was compared between models with and without a feature pre-selection step based on 4D COV to pre-exclude unstable features. Among the systematically tested cutoff values, feature pre-selection with 4D COV≤5% achieved the optimal model performance. The resulting 3-feature radiomic model significantly outperformed its counterpart with no 4D COV pre-selection, with P = 2.16x10(-27) in the one-tailed t-test comparing the prediction performances of the two models.
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spelling pubmed-65041052019-05-09 Radiomic feature stability across 4D respiratory phases and its impact on lung tumor prognosis prediction Du, Qian Baine, Michael Bavitz, Kyle McAllister, Josiah Liang, Xiaoying Yu, Hongfeng Ryckman, Jeffrey Yu, Lina Jiang, Hengle Zhou, Sumin Zhang, Chi Zheng, Dandan PLoS One Research Article Radiomic analysis has recently demonstrated versatile uses in improving diagnostic and prognostic prediction accuracy for lung cancer. However, since lung tumors are subject to substantial motion due to respiration, the stability of radiomic features over the respiratory cycle of the patient needs to be investigated to better evaluate the robustness of the inter-patient feature variability for clinical applications, and its impact in such applications needs to be assessed. A full panel of 841 radiomic features, including tumor intensity, shape, texture, and wavelet features, were extracted from individual phases of a four-dimensional (4D) computed tomography on 20 early-stage non-small-cell lung cancer (NSCLC) patients. The stability of each radiomic feature was assessed across different phase images of the same patient using the coefficient of variation (COV). The relationship between individual COVs and tumor motion magnitude was inspected. Population COVs, the mean COVs of all 20 patients, were used to evaluate feature motion stability and categorize the radiomic features into 4 different groups. The two extremes, the Very Small group (COV≤5%) and the Large group (COV>20%), each accounted for about a quarter of the features. Shape features were the most stable, with COV≤10% for all features. A clinical study was subsequently conducted using 140 early-stage NSCLC patients. Radiomic features were employed to predict the overall survival with a 500-round bootstrapping. Identical multiple regression model development process was applied, and the model performance was compared between models with and without a feature pre-selection step based on 4D COV to pre-exclude unstable features. Among the systematically tested cutoff values, feature pre-selection with 4D COV≤5% achieved the optimal model performance. The resulting 3-feature radiomic model significantly outperformed its counterpart with no 4D COV pre-selection, with P = 2.16x10(-27) in the one-tailed t-test comparing the prediction performances of the two models. Public Library of Science 2019-05-07 /pmc/articles/PMC6504105/ /pubmed/31063500 http://dx.doi.org/10.1371/journal.pone.0216480 Text en © 2019 Du et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Du, Qian
Baine, Michael
Bavitz, Kyle
McAllister, Josiah
Liang, Xiaoying
Yu, Hongfeng
Ryckman, Jeffrey
Yu, Lina
Jiang, Hengle
Zhou, Sumin
Zhang, Chi
Zheng, Dandan
Radiomic feature stability across 4D respiratory phases and its impact on lung tumor prognosis prediction
title Radiomic feature stability across 4D respiratory phases and its impact on lung tumor prognosis prediction
title_full Radiomic feature stability across 4D respiratory phases and its impact on lung tumor prognosis prediction
title_fullStr Radiomic feature stability across 4D respiratory phases and its impact on lung tumor prognosis prediction
title_full_unstemmed Radiomic feature stability across 4D respiratory phases and its impact on lung tumor prognosis prediction
title_short Radiomic feature stability across 4D respiratory phases and its impact on lung tumor prognosis prediction
title_sort radiomic feature stability across 4d respiratory phases and its impact on lung tumor prognosis prediction
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6504105/
https://www.ncbi.nlm.nih.gov/pubmed/31063500
http://dx.doi.org/10.1371/journal.pone.0216480
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