Prediction of the Growth Rate of Early-Stage Lung Adenocarcinoma by Radiomics

OBJECTIVES: To investigate the value of imaging in predicting the growth rate of early lung adenocarcinoma. METHODS: From January 2012 to June 2018, 402 patients with pathology-confirmed lung adenocarcinoma who had two or more thin-layer CT follow-up images were retrospectively analyzed, involving 4...

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Autores principales: Tan, Mingyu, Ma, Weiling, Sun, Yingli, Gao, Pan, Huang, Xuemei, Lu, Jinjuan, Chen, Wufei, Wu, Yue, Jin, Liang, Tang, Lin, Kuang, Kaiming, Li, Ming
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8082461/
https://www.ncbi.nlm.nih.gov/pubmed/33937070
http://dx.doi.org/10.3389/fonc.2021.658138
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author Tan, Mingyu
Ma, Weiling
Sun, Yingli
Gao, Pan
Huang, Xuemei
Lu, Jinjuan
Chen, Wufei
Wu, Yue
Jin, Liang
Tang, Lin
Kuang, Kaiming
Li, Ming
author_facet Tan, Mingyu
Ma, Weiling
Sun, Yingli
Gao, Pan
Huang, Xuemei
Lu, Jinjuan
Chen, Wufei
Wu, Yue
Jin, Liang
Tang, Lin
Kuang, Kaiming
Li, Ming
author_sort Tan, Mingyu
collection PubMed
description OBJECTIVES: To investigate the value of imaging in predicting the growth rate of early lung adenocarcinoma. METHODS: From January 2012 to June 2018, 402 patients with pathology-confirmed lung adenocarcinoma who had two or more thin-layer CT follow-up images were retrospectively analyzed, involving 407 nodules. Two complete preoperative CT images and complete clinical data were evaluated. Training and validation sets were randomly assigned according to an 8:2 ratio. All cases were divided into fast-growing and slow-growing groups. Researchers extracted 1218 radiomics features from each volumetric region of interest (VOI). Then, radiomics features were selected by repeatability analysis and Analysis of Variance (ANOVA); Based on the Univariate and multivariate analyses, the significant radiographic features is selected in training set. A decision tree algorithm was conducted to establish the radiographic model, radiomics model and the combined radiographic-radiomics model. Model performance was assessed by the area under the curve (AUC) obtained by receiver operating characteristic (ROC) analysis. RESULTS: Sixty-two radiomics features and one radiographic features were selected for predicting the growth rate of pulmonary nodules. The combined radiographic-radiomics model (AUC 0.78) performed better than the radiographic model (0.727) and the radiomics model (0.710) in the validation set. CONCLUSIONS: The model has good clinical application value and development prospects to predict the growth rate of early lung adenocarcinoma through the combined radiographic-radiomics model.
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spelling pubmed-80824612021-04-30 Prediction of the Growth Rate of Early-Stage Lung Adenocarcinoma by Radiomics Tan, Mingyu Ma, Weiling Sun, Yingli Gao, Pan Huang, Xuemei Lu, Jinjuan Chen, Wufei Wu, Yue Jin, Liang Tang, Lin Kuang, Kaiming Li, Ming Front Oncol Oncology OBJECTIVES: To investigate the value of imaging in predicting the growth rate of early lung adenocarcinoma. METHODS: From January 2012 to June 2018, 402 patients with pathology-confirmed lung adenocarcinoma who had two or more thin-layer CT follow-up images were retrospectively analyzed, involving 407 nodules. Two complete preoperative CT images and complete clinical data were evaluated. Training and validation sets were randomly assigned according to an 8:2 ratio. All cases were divided into fast-growing and slow-growing groups. Researchers extracted 1218 radiomics features from each volumetric region of interest (VOI). Then, radiomics features were selected by repeatability analysis and Analysis of Variance (ANOVA); Based on the Univariate and multivariate analyses, the significant radiographic features is selected in training set. A decision tree algorithm was conducted to establish the radiographic model, radiomics model and the combined radiographic-radiomics model. Model performance was assessed by the area under the curve (AUC) obtained by receiver operating characteristic (ROC) analysis. RESULTS: Sixty-two radiomics features and one radiographic features were selected for predicting the growth rate of pulmonary nodules. The combined radiographic-radiomics model (AUC 0.78) performed better than the radiographic model (0.727) and the radiomics model (0.710) in the validation set. CONCLUSIONS: The model has good clinical application value and development prospects to predict the growth rate of early lung adenocarcinoma through the combined radiographic-radiomics model. Frontiers Media S.A. 2021-04-15 /pmc/articles/PMC8082461/ /pubmed/33937070 http://dx.doi.org/10.3389/fonc.2021.658138 Text en Copyright © 2021 Tan, Ma, Sun, Gao, Huang, Lu, Chen, Wu, Jin, Tang, Kuang and Li 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 Oncology
Tan, Mingyu
Ma, Weiling
Sun, Yingli
Gao, Pan
Huang, Xuemei
Lu, Jinjuan
Chen, Wufei
Wu, Yue
Jin, Liang
Tang, Lin
Kuang, Kaiming
Li, Ming
Prediction of the Growth Rate of Early-Stage Lung Adenocarcinoma by Radiomics
title Prediction of the Growth Rate of Early-Stage Lung Adenocarcinoma by Radiomics
title_full Prediction of the Growth Rate of Early-Stage Lung Adenocarcinoma by Radiomics
title_fullStr Prediction of the Growth Rate of Early-Stage Lung Adenocarcinoma by Radiomics
title_full_unstemmed Prediction of the Growth Rate of Early-Stage Lung Adenocarcinoma by Radiomics
title_short Prediction of the Growth Rate of Early-Stage Lung Adenocarcinoma by Radiomics
title_sort prediction of the growth rate of early-stage lung adenocarcinoma by radiomics
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8082461/
https://www.ncbi.nlm.nih.gov/pubmed/33937070
http://dx.doi.org/10.3389/fonc.2021.658138
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