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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...
Autores principales: | , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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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. |
format | Online Article Text |
id | pubmed-9865229 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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