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Predicting Tumor Mutation Burden and EGFR Mutation Using Clinical and Radiomic Features in Patients with Malignant Pulmonary Nodules

Pulmonary nodules (PNs) shown as persistent or growing ground-glass opacities (GGOs) are usually lung adenocarcinomas or their preinvasive lesions. Tumor mutation burden (TMB) and somatic mutations are important determinants for the choice of strategy in patients with lung cancer during therapy. A t...

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Autores principales: Yin, Wenda, Wang, Wei, Zou, Chong, Li, Ming, Chen, Hao, Meng, Fanchen, Dong, Guozhang, Wang, Jie, Yu, Qian, Sun, Mengting, Xu, Lin, Lv, Yang, Wang, Xiaoxiao, Yin, Rong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9865229/
https://www.ncbi.nlm.nih.gov/pubmed/36675677
http://dx.doi.org/10.3390/jpm13010016
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author Yin, Wenda
Wang, Wei
Zou, Chong
Li, Ming
Chen, Hao
Meng, Fanchen
Dong, Guozhang
Wang, Jie
Yu, Qian
Sun, Mengting
Xu, Lin
Lv, Yang
Wang, Xiaoxiao
Yin, Rong
author_facet Yin, Wenda
Wang, Wei
Zou, Chong
Li, Ming
Chen, Hao
Meng, Fanchen
Dong, Guozhang
Wang, Jie
Yu, Qian
Sun, Mengting
Xu, Lin
Lv, Yang
Wang, Xiaoxiao
Yin, Rong
author_sort Yin, Wenda
collection PubMed
description Pulmonary nodules (PNs) shown as persistent or growing ground-glass opacities (GGOs) are usually lung adenocarcinomas or their preinvasive lesions. Tumor mutation burden (TMB) and somatic mutations are important determinants for the choice of strategy in patients with lung cancer during therapy. A total of 93 post-operative patients with 108 malignant PNs were enrolled for analysis (75 cases in the training cohort and 33 cases in the validation cohort). Radiomics features were extracted from preoperative non-contrast computed tomography (CT) images of the entire tumor. Using commercial next generation sequencing, we detected TMB status and somatic mutations of all FFPE samples. Here, 870 quantitative radiomics features were extracted from the segmentations of PNs, and pathological and clinical characteristics were collected from medical records. The LASSO (least absolute shrinkage and selection operator) regression and stepwise logistic regressions were performed to establish the predictive model. For the epidermal growth factor receptor (EGFR) mutation, the AUCs of the clinical model and the integrative model validated by the validation set were 0.6726 (0.4755–0.8697) and 0.7421 (0.5698–0.9144). For the TMB status, the ROCs showed that AUCs of the clinical model and the integrative model validated by the validation set were 0.7808 (0.6231–0.9384) and 0.8462 (0.7132–0.9791). The quantitative radiomics signatures showed potential value in predicting the EGFR mutant and TMB status in GGOs. Moreover, the integrative model provided sufficient information for the selection of therapy and deserves further analysis.
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spelling pubmed-98652292023-01-22 Predicting Tumor Mutation Burden and EGFR Mutation Using Clinical and Radiomic Features in Patients with Malignant Pulmonary Nodules Yin, Wenda Wang, Wei Zou, Chong Li, Ming Chen, Hao Meng, Fanchen Dong, Guozhang Wang, Jie Yu, Qian Sun, Mengting Xu, Lin Lv, Yang Wang, Xiaoxiao Yin, Rong J Pers Med Article Pulmonary nodules (PNs) shown as persistent or growing ground-glass opacities (GGOs) are usually lung adenocarcinomas or their preinvasive lesions. Tumor mutation burden (TMB) and somatic mutations are important determinants for the choice of strategy in patients with lung cancer during therapy. A total of 93 post-operative patients with 108 malignant PNs were enrolled for analysis (75 cases in the training cohort and 33 cases in the validation cohort). Radiomics features were extracted from preoperative non-contrast computed tomography (CT) images of the entire tumor. Using commercial next generation sequencing, we detected TMB status and somatic mutations of all FFPE samples. Here, 870 quantitative radiomics features were extracted from the segmentations of PNs, and pathological and clinical characteristics were collected from medical records. The LASSO (least absolute shrinkage and selection operator) regression and stepwise logistic regressions were performed to establish the predictive model. For the epidermal growth factor receptor (EGFR) mutation, the AUCs of the clinical model and the integrative model validated by the validation set were 0.6726 (0.4755–0.8697) and 0.7421 (0.5698–0.9144). For the TMB status, the ROCs showed that AUCs of the clinical model and the integrative model validated by the validation set were 0.7808 (0.6231–0.9384) and 0.8462 (0.7132–0.9791). The quantitative radiomics signatures showed potential value in predicting the EGFR mutant and TMB status in GGOs. Moreover, the integrative model provided sufficient information for the selection of therapy and deserves further analysis. MDPI 2022-12-22 /pmc/articles/PMC9865229/ /pubmed/36675677 http://dx.doi.org/10.3390/jpm13010016 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Yin, Wenda
Wang, Wei
Zou, Chong
Li, Ming
Chen, Hao
Meng, Fanchen
Dong, Guozhang
Wang, Jie
Yu, Qian
Sun, Mengting
Xu, Lin
Lv, Yang
Wang, Xiaoxiao
Yin, Rong
Predicting Tumor Mutation Burden and EGFR Mutation Using Clinical and Radiomic Features in Patients with Malignant Pulmonary Nodules
title Predicting Tumor Mutation Burden and EGFR Mutation Using Clinical and Radiomic Features in Patients with Malignant Pulmonary Nodules
title_full Predicting Tumor Mutation Burden and EGFR Mutation Using Clinical and Radiomic Features in Patients with Malignant Pulmonary Nodules
title_fullStr Predicting Tumor Mutation Burden and EGFR Mutation Using Clinical and Radiomic Features in Patients with Malignant Pulmonary Nodules
title_full_unstemmed Predicting Tumor Mutation Burden and EGFR Mutation Using Clinical and Radiomic Features in Patients with Malignant Pulmonary Nodules
title_short Predicting Tumor Mutation Burden and EGFR Mutation Using Clinical and Radiomic Features in Patients with Malignant Pulmonary Nodules
title_sort predicting tumor mutation burden and egfr mutation using clinical and radiomic features in patients with malignant pulmonary nodules
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9865229/
https://www.ncbi.nlm.nih.gov/pubmed/36675677
http://dx.doi.org/10.3390/jpm13010016
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