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Pathologic stratification of operable lung adenocarcinoma using radiomics features extracted from dual energy CT images

PURPOSE: To evaluate the usefulness of surrogate biomarkers as predictors of histopathologic tumor grade and aggressiveness using radiomics data from dual-energy computed tomography (DECT), with the ultimate goal of accomplishing stratification of early-stage lung adenocarcinoma for optimal treatmen...

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Autores principales: Bae, Jung Min, Jeong, Ji Yun, Lee, Ho Yun, Sohn, Insuk, Kim, Hye Seung, Son, Ji Ye, Kwon, O Jung, Choi, Joon Young, Lee, Kyung Soo, Shim, Young Mog
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Impact Journals LLC 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5352175/
https://www.ncbi.nlm.nih.gov/pubmed/27880938
http://dx.doi.org/10.18632/oncotarget.13476
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author Bae, Jung Min
Jeong, Ji Yun
Lee, Ho Yun
Sohn, Insuk
Kim, Hye Seung
Son, Ji Ye
Kwon, O Jung
Choi, Joon Young
Lee, Kyung Soo
Shim, Young Mog
author_facet Bae, Jung Min
Jeong, Ji Yun
Lee, Ho Yun
Sohn, Insuk
Kim, Hye Seung
Son, Ji Ye
Kwon, O Jung
Choi, Joon Young
Lee, Kyung Soo
Shim, Young Mog
author_sort Bae, Jung Min
collection PubMed
description PURPOSE: To evaluate the usefulness of surrogate biomarkers as predictors of histopathologic tumor grade and aggressiveness using radiomics data from dual-energy computed tomography (DECT), with the ultimate goal of accomplishing stratification of early-stage lung adenocarcinoma for optimal treatment. RESULTS: Pathologic grade was divided into grades 1, 2, and 3. Multinomial logistic regression analysis revealed i-uniformity and 97.5th percentile CT attenuation value as independent significant factors to stratify grade 2 or 3 from grade 1. The AUC value calculated from leave-one-out cross-validation procedure for discriminating grades 1, 2, and 3 was 0.9307 (95% CI: 0.8514–1), 0.8610 (95% CI: 0.7547–0.9672), and 0.8394 (95% CI: 0.7045–0.9743), respectively. MATERIALS AND METHODS: A total of 80 patients with 91 clinically and radiologically suspected stage I or II lung adenocarcinoma were prospectively enrolled. All patients underwent DECT and F-18-fluorodeoxyglucose (FDG) positron emission tomography (PET)/CT, followed by surgery. Quantitative CT and PET imaging characteristics were evaluated using a radiomics approach. Significant features for a tumor aggressiveness prediction model were extracted and used to calculate diagnostic performance for predicting all pathologic grades. CONCLUSIONS: Quantitative radiomics values from DECT imaging metrics can help predict pathologic aggressiveness of lung adenocarcinoma.
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spelling pubmed-53521752017-04-13 Pathologic stratification of operable lung adenocarcinoma using radiomics features extracted from dual energy CT images Bae, Jung Min Jeong, Ji Yun Lee, Ho Yun Sohn, Insuk Kim, Hye Seung Son, Ji Ye Kwon, O Jung Choi, Joon Young Lee, Kyung Soo Shim, Young Mog Oncotarget Research Paper PURPOSE: To evaluate the usefulness of surrogate biomarkers as predictors of histopathologic tumor grade and aggressiveness using radiomics data from dual-energy computed tomography (DECT), with the ultimate goal of accomplishing stratification of early-stage lung adenocarcinoma for optimal treatment. RESULTS: Pathologic grade was divided into grades 1, 2, and 3. Multinomial logistic regression analysis revealed i-uniformity and 97.5th percentile CT attenuation value as independent significant factors to stratify grade 2 or 3 from grade 1. The AUC value calculated from leave-one-out cross-validation procedure for discriminating grades 1, 2, and 3 was 0.9307 (95% CI: 0.8514–1), 0.8610 (95% CI: 0.7547–0.9672), and 0.8394 (95% CI: 0.7045–0.9743), respectively. MATERIALS AND METHODS: A total of 80 patients with 91 clinically and radiologically suspected stage I or II lung adenocarcinoma were prospectively enrolled. All patients underwent DECT and F-18-fluorodeoxyglucose (FDG) positron emission tomography (PET)/CT, followed by surgery. Quantitative CT and PET imaging characteristics were evaluated using a radiomics approach. Significant features for a tumor aggressiveness prediction model were extracted and used to calculate diagnostic performance for predicting all pathologic grades. CONCLUSIONS: Quantitative radiomics values from DECT imaging metrics can help predict pathologic aggressiveness of lung adenocarcinoma. Impact Journals LLC 2016-11-21 /pmc/articles/PMC5352175/ /pubmed/27880938 http://dx.doi.org/10.18632/oncotarget.13476 Text en Copyright: © 2017 Bae et al. http://creativecommons.org/licenses/by/3.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Paper
Bae, Jung Min
Jeong, Ji Yun
Lee, Ho Yun
Sohn, Insuk
Kim, Hye Seung
Son, Ji Ye
Kwon, O Jung
Choi, Joon Young
Lee, Kyung Soo
Shim, Young Mog
Pathologic stratification of operable lung adenocarcinoma using radiomics features extracted from dual energy CT images
title Pathologic stratification of operable lung adenocarcinoma using radiomics features extracted from dual energy CT images
title_full Pathologic stratification of operable lung adenocarcinoma using radiomics features extracted from dual energy CT images
title_fullStr Pathologic stratification of operable lung adenocarcinoma using radiomics features extracted from dual energy CT images
title_full_unstemmed Pathologic stratification of operable lung adenocarcinoma using radiomics features extracted from dual energy CT images
title_short Pathologic stratification of operable lung adenocarcinoma using radiomics features extracted from dual energy CT images
title_sort pathologic stratification of operable lung adenocarcinoma using radiomics features extracted from dual energy ct images
topic Research Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5352175/
https://www.ncbi.nlm.nih.gov/pubmed/27880938
http://dx.doi.org/10.18632/oncotarget.13476
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