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Establishment and Optimization of Radiomics Algorithms for Prediction of KRAS Gene Mutation by Integration of NSCLC Gene Mutation Mutual Exclusion Information
Purpose: To assess the significance of mutation mutual exclusion information in the optimization of radiomics algorithms for predicting gene mutation. Methods: We retrospectively analyzed 258 non-small cell lung cancer (NSCLC) patients. Patients were randomly divided into training (n = 180) and vali...
Autores principales: | , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9010886/ https://www.ncbi.nlm.nih.gov/pubmed/35431943 http://dx.doi.org/10.3389/fphar.2022.862581 |
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author | Wang, Jingyi Lv, Xing Huang, Weicheng Quan, Zhiyong Li, Guiyu Wu, Shuo Wang, Yirong Xie, Zhaojuan Yan, Yuhao Li, Xiang Ma, Wenhui Yang, Weidong Cao, Xin Kang, Fei Wang, Jing |
author_facet | Wang, Jingyi Lv, Xing Huang, Weicheng Quan, Zhiyong Li, Guiyu Wu, Shuo Wang, Yirong Xie, Zhaojuan Yan, Yuhao Li, Xiang Ma, Wenhui Yang, Weidong Cao, Xin Kang, Fei Wang, Jing |
author_sort | Wang, Jingyi |
collection | PubMed |
description | Purpose: To assess the significance of mutation mutual exclusion information in the optimization of radiomics algorithms for predicting gene mutation. Methods: We retrospectively analyzed 258 non-small cell lung cancer (NSCLC) patients. Patients were randomly divided into training (n = 180) and validation (n = 78) cohorts. Based on radiomics features, radiomics score (RS) models were developed for predicting KRAS proto-oncogene mutations. Furthermore, a composite model combining mixedRS and epidermal growth factor receptor (EGFR) mutation status was developed. Results: Compared with CT model, the PET/CT radiomics score model exhibited higher AUC for predicting KRAS mutations (0.834 vs. 0.770). By integrating EGFR mutation information into the PET/CT RS model, the AUC, sensitivity, specificity, and accuracy for predicting KRAS mutations were all elevated in the validation cohort (0.921, 0.949, 0.872, 0.910 vs. 0.834, 0.923, 0.641, 0.782). By adding EGFR exclusive mutation information, the composite model corrected 64.3% false positive cases produced by the PET/CT RS model in the validation cohort. Conclusion: Integrating EGFR mutation status has potential utility for the optimization of radiomics models for prediction of KRAS gene mutations. This method may be used when repeated biopsies would carry unacceptable risks for the patient. |
format | Online Article Text |
id | pubmed-9010886 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-90108862022-04-16 Establishment and Optimization of Radiomics Algorithms for Prediction of KRAS Gene Mutation by Integration of NSCLC Gene Mutation Mutual Exclusion Information Wang, Jingyi Lv, Xing Huang, Weicheng Quan, Zhiyong Li, Guiyu Wu, Shuo Wang, Yirong Xie, Zhaojuan Yan, Yuhao Li, Xiang Ma, Wenhui Yang, Weidong Cao, Xin Kang, Fei Wang, Jing Front Pharmacol Pharmacology Purpose: To assess the significance of mutation mutual exclusion information in the optimization of radiomics algorithms for predicting gene mutation. Methods: We retrospectively analyzed 258 non-small cell lung cancer (NSCLC) patients. Patients were randomly divided into training (n = 180) and validation (n = 78) cohorts. Based on radiomics features, radiomics score (RS) models were developed for predicting KRAS proto-oncogene mutations. Furthermore, a composite model combining mixedRS and epidermal growth factor receptor (EGFR) mutation status was developed. Results: Compared with CT model, the PET/CT radiomics score model exhibited higher AUC for predicting KRAS mutations (0.834 vs. 0.770). By integrating EGFR mutation information into the PET/CT RS model, the AUC, sensitivity, specificity, and accuracy for predicting KRAS mutations were all elevated in the validation cohort (0.921, 0.949, 0.872, 0.910 vs. 0.834, 0.923, 0.641, 0.782). By adding EGFR exclusive mutation information, the composite model corrected 64.3% false positive cases produced by the PET/CT RS model in the validation cohort. Conclusion: Integrating EGFR mutation status has potential utility for the optimization of radiomics models for prediction of KRAS gene mutations. This method may be used when repeated biopsies would carry unacceptable risks for the patient. Frontiers Media S.A. 2022-04-01 /pmc/articles/PMC9010886/ /pubmed/35431943 http://dx.doi.org/10.3389/fphar.2022.862581 Text en Copyright © 2022 Wang, Lv, Huang, Quan, Li, Wu, Wang, Xie, Yan, Li, Ma, Yang, Cao, Kang and Wang. 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 | Pharmacology Wang, Jingyi Lv, Xing Huang, Weicheng Quan, Zhiyong Li, Guiyu Wu, Shuo Wang, Yirong Xie, Zhaojuan Yan, Yuhao Li, Xiang Ma, Wenhui Yang, Weidong Cao, Xin Kang, Fei Wang, Jing Establishment and Optimization of Radiomics Algorithms for Prediction of KRAS Gene Mutation by Integration of NSCLC Gene Mutation Mutual Exclusion Information |
title | Establishment and Optimization of Radiomics Algorithms for Prediction of KRAS Gene Mutation by Integration of NSCLC Gene Mutation Mutual Exclusion Information |
title_full | Establishment and Optimization of Radiomics Algorithms for Prediction of KRAS Gene Mutation by Integration of NSCLC Gene Mutation Mutual Exclusion Information |
title_fullStr | Establishment and Optimization of Radiomics Algorithms for Prediction of KRAS Gene Mutation by Integration of NSCLC Gene Mutation Mutual Exclusion Information |
title_full_unstemmed | Establishment and Optimization of Radiomics Algorithms for Prediction of KRAS Gene Mutation by Integration of NSCLC Gene Mutation Mutual Exclusion Information |
title_short | Establishment and Optimization of Radiomics Algorithms for Prediction of KRAS Gene Mutation by Integration of NSCLC Gene Mutation Mutual Exclusion Information |
title_sort | establishment and optimization of radiomics algorithms for prediction of kras gene mutation by integration of nsclc gene mutation mutual exclusion information |
topic | Pharmacology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9010886/ https://www.ncbi.nlm.nih.gov/pubmed/35431943 http://dx.doi.org/10.3389/fphar.2022.862581 |
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