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Preoperative computed tomography-based tumoral radiomic features prediction for overall survival in resectable non-small cell lung cancer
OBJECTIVES: The purpose of this study was to evaluate whether preoperative radiomics features could meliorate risk stratification for the overall survival (OS) of non-small cell lung cancer (NSCLC) patients. METHODS: After rigorous screening, the 208 NSCLC patients without any pre-operative adjuvant...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10189057/ https://www.ncbi.nlm.nih.gov/pubmed/37207163 http://dx.doi.org/10.3389/fonc.2023.1131816 |
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author | Peng, Bo Wang, Kaiyu Xu, Ran Guo, Congying Lu, Tong Li, Yongchao Wang, Yiqiao Wang, Chenghao Chang, Xiaoyan Shen, Zhiping Shi, Jiaxin Xu, Chengyu Zhang, Linyou |
author_facet | Peng, Bo Wang, Kaiyu Xu, Ran Guo, Congying Lu, Tong Li, Yongchao Wang, Yiqiao Wang, Chenghao Chang, Xiaoyan Shen, Zhiping Shi, Jiaxin Xu, Chengyu Zhang, Linyou |
author_sort | Peng, Bo |
collection | PubMed |
description | OBJECTIVES: The purpose of this study was to evaluate whether preoperative radiomics features could meliorate risk stratification for the overall survival (OS) of non-small cell lung cancer (NSCLC) patients. METHODS: After rigorous screening, the 208 NSCLC patients without any pre-operative adjuvant therapy were eventually enrolled. We segmented the 3D volume of interest (VOI) based on malignant lesion of computed tomography (CT) imaging and extracted 1542 radiomics features. Interclass correlation coefficients (ICC) and LASSO Cox regression analysis were utilized to perform feature selection and radiomics model building. In the model evaluation phase, we carried out stratified analysis, receiver operating characteristic (ROC) curve, concordance index (C-index), and decision curve analysis (DCA). In addition, integrating the clinicopathological trait and radiomics score, we developed a nomogram to predict the OS at 1 year, 2 years, and 3 years, respectively. RESULTS: Six radiomics features, including gradient_glcm_InverseVariance, logarithm_firstorder_Median, logarithm_firstorder_RobustMeanAbsoluteDeviation, square_gldm_LargeDependenceEmphasis, wavelet_HLL_firstorder_Kurtosis, and wavelet_LLL_firstorder_Maximum, were selected to construct the radiomics signature, whose areas under the curve (AUCs) for 3-year prediction reached 0.857 in the training set (n=146) and 0.871 in the testing set (n=62). The results of multivariate analysis revealed that the radiomics score, radiological sign, and N stage were independent prognostic factors in NSCLC. Moreover, compared with clinical factors and the separate radiomics model, the established nomogram exhibited a better performance in predicting 3-year OS. CONCLUSIONS: Our radiomics model may provide a promising non-invasive approach for preoperative risk stratification and personalized postoperative surveillance for resectable NSCLC patients. |
format | Online Article Text |
id | pubmed-10189057 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-101890572023-05-18 Preoperative computed tomography-based tumoral radiomic features prediction for overall survival in resectable non-small cell lung cancer Peng, Bo Wang, Kaiyu Xu, Ran Guo, Congying Lu, Tong Li, Yongchao Wang, Yiqiao Wang, Chenghao Chang, Xiaoyan Shen, Zhiping Shi, Jiaxin Xu, Chengyu Zhang, Linyou Front Oncol Oncology OBJECTIVES: The purpose of this study was to evaluate whether preoperative radiomics features could meliorate risk stratification for the overall survival (OS) of non-small cell lung cancer (NSCLC) patients. METHODS: After rigorous screening, the 208 NSCLC patients without any pre-operative adjuvant therapy were eventually enrolled. We segmented the 3D volume of interest (VOI) based on malignant lesion of computed tomography (CT) imaging and extracted 1542 radiomics features. Interclass correlation coefficients (ICC) and LASSO Cox regression analysis were utilized to perform feature selection and radiomics model building. In the model evaluation phase, we carried out stratified analysis, receiver operating characteristic (ROC) curve, concordance index (C-index), and decision curve analysis (DCA). In addition, integrating the clinicopathological trait and radiomics score, we developed a nomogram to predict the OS at 1 year, 2 years, and 3 years, respectively. RESULTS: Six radiomics features, including gradient_glcm_InverseVariance, logarithm_firstorder_Median, logarithm_firstorder_RobustMeanAbsoluteDeviation, square_gldm_LargeDependenceEmphasis, wavelet_HLL_firstorder_Kurtosis, and wavelet_LLL_firstorder_Maximum, were selected to construct the radiomics signature, whose areas under the curve (AUCs) for 3-year prediction reached 0.857 in the training set (n=146) and 0.871 in the testing set (n=62). The results of multivariate analysis revealed that the radiomics score, radiological sign, and N stage were independent prognostic factors in NSCLC. Moreover, compared with clinical factors and the separate radiomics model, the established nomogram exhibited a better performance in predicting 3-year OS. CONCLUSIONS: Our radiomics model may provide a promising non-invasive approach for preoperative risk stratification and personalized postoperative surveillance for resectable NSCLC patients. Frontiers Media S.A. 2023-05-03 /pmc/articles/PMC10189057/ /pubmed/37207163 http://dx.doi.org/10.3389/fonc.2023.1131816 Text en Copyright © 2023 Peng, Wang, Xu, Guo, Lu, Li, Wang, Wang, Chang, Shen, Shi, Xu and Zhang 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 Peng, Bo Wang, Kaiyu Xu, Ran Guo, Congying Lu, Tong Li, Yongchao Wang, Yiqiao Wang, Chenghao Chang, Xiaoyan Shen, Zhiping Shi, Jiaxin Xu, Chengyu Zhang, Linyou Preoperative computed tomography-based tumoral radiomic features prediction for overall survival in resectable non-small cell lung cancer |
title | Preoperative computed tomography-based tumoral radiomic features prediction for overall survival in resectable non-small cell lung cancer |
title_full | Preoperative computed tomography-based tumoral radiomic features prediction for overall survival in resectable non-small cell lung cancer |
title_fullStr | Preoperative computed tomography-based tumoral radiomic features prediction for overall survival in resectable non-small cell lung cancer |
title_full_unstemmed | Preoperative computed tomography-based tumoral radiomic features prediction for overall survival in resectable non-small cell lung cancer |
title_short | Preoperative computed tomography-based tumoral radiomic features prediction for overall survival in resectable non-small cell lung cancer |
title_sort | preoperative computed tomography-based tumoral radiomic features prediction for overall survival in resectable non-small cell lung cancer |
topic | Oncology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10189057/ https://www.ncbi.nlm.nih.gov/pubmed/37207163 http://dx.doi.org/10.3389/fonc.2023.1131816 |
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