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Predicting EGFR mutation, ALK rearrangement, and uncommon EGFR mutation in NSCLC patients by driverless artificial intelligence: a cohort study

BACKGROUND: Timely identification of epidermal growth factor receptor (EGFR) mutation and anaplastic lymphoma kinase (ALK) rearrangement status in patients with non-small cell lung cancer (NSCLC) is essential for tyrosine kinase inhibitors (TKIs) administration. We aimed to use artificial intelligen...

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Autores principales: Tan, Xueyun, Li, Yuan, Wang, Sufei, Xia, Hui, Meng, Rui, Xu, Juanjuan, Duan, Yanran, Li, Yan, Yang, Guanghai, Ma, Yanling, Jin, Yang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9145462/
https://www.ncbi.nlm.nih.gov/pubmed/35624472
http://dx.doi.org/10.1186/s12931-022-02053-2
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author Tan, Xueyun
Li, Yuan
Wang, Sufei
Xia, Hui
Meng, Rui
Xu, Juanjuan
Duan, Yanran
Li, Yan
Yang, Guanghai
Ma, Yanling
Jin, Yang
author_facet Tan, Xueyun
Li, Yuan
Wang, Sufei
Xia, Hui
Meng, Rui
Xu, Juanjuan
Duan, Yanran
Li, Yan
Yang, Guanghai
Ma, Yanling
Jin, Yang
author_sort Tan, Xueyun
collection PubMed
description BACKGROUND: Timely identification of epidermal growth factor receptor (EGFR) mutation and anaplastic lymphoma kinase (ALK) rearrangement status in patients with non-small cell lung cancer (NSCLC) is essential for tyrosine kinase inhibitors (TKIs) administration. We aimed to use artificial intelligence (AI) models to predict EGFR mutations and ALK rearrangement status using common demographic features, pathology and serum tumor markers (STMs). METHODS: In this single-center study, demographic features, pathology, EGFR mutation status, ALK rearrangement, and levels of STMs were collected from Wuhan Union Hospital. One retrospective set (N = 1089) was used to train diagnostic performance using one deep learning model and five machine learning models, as well as the stacked ensemble model for predicting EGFR mutations, uncommon EGFR mutations, and ALK rearrangement status. A consecutive testing cohort (n = 1464) was used to validate the predictive models. RESULTS: The final AI model using the stacked ensemble yielded optimal diagnostic performance with areas under the curve (AUC) of 0.897 and 0.883 for predicting EGFR mutation status and 0.995 and 0.921 for predicting ALK rearrangement in the training and testing cohorts, respectively. Furthermore, an overall accuracy of 0.93 and 0.83 in the training and testing cohorts, respectively, were achieved in distinguishing common and uncommon EGFR mutations, which were key evidence in guiding TKI selection. CONCLUSIONS: In this study, driverless AI based on robust variables could help clinicians identify EGFR mutations and ALK rearrangement status and provide vital guidance in TKI selection for targeted therapy in NSCLC patients. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12931-022-02053-2.
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spelling pubmed-91454622022-05-29 Predicting EGFR mutation, ALK rearrangement, and uncommon EGFR mutation in NSCLC patients by driverless artificial intelligence: a cohort study Tan, Xueyun Li, Yuan Wang, Sufei Xia, Hui Meng, Rui Xu, Juanjuan Duan, Yanran Li, Yan Yang, Guanghai Ma, Yanling Jin, Yang Respir Res Research BACKGROUND: Timely identification of epidermal growth factor receptor (EGFR) mutation and anaplastic lymphoma kinase (ALK) rearrangement status in patients with non-small cell lung cancer (NSCLC) is essential for tyrosine kinase inhibitors (TKIs) administration. We aimed to use artificial intelligence (AI) models to predict EGFR mutations and ALK rearrangement status using common demographic features, pathology and serum tumor markers (STMs). METHODS: In this single-center study, demographic features, pathology, EGFR mutation status, ALK rearrangement, and levels of STMs were collected from Wuhan Union Hospital. One retrospective set (N = 1089) was used to train diagnostic performance using one deep learning model and five machine learning models, as well as the stacked ensemble model for predicting EGFR mutations, uncommon EGFR mutations, and ALK rearrangement status. A consecutive testing cohort (n = 1464) was used to validate the predictive models. RESULTS: The final AI model using the stacked ensemble yielded optimal diagnostic performance with areas under the curve (AUC) of 0.897 and 0.883 for predicting EGFR mutation status and 0.995 and 0.921 for predicting ALK rearrangement in the training and testing cohorts, respectively. Furthermore, an overall accuracy of 0.93 and 0.83 in the training and testing cohorts, respectively, were achieved in distinguishing common and uncommon EGFR mutations, which were key evidence in guiding TKI selection. CONCLUSIONS: In this study, driverless AI based on robust variables could help clinicians identify EGFR mutations and ALK rearrangement status and provide vital guidance in TKI selection for targeted therapy in NSCLC patients. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12931-022-02053-2. BioMed Central 2022-05-27 2022 /pmc/articles/PMC9145462/ /pubmed/35624472 http://dx.doi.org/10.1186/s12931-022-02053-2 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Tan, Xueyun
Li, Yuan
Wang, Sufei
Xia, Hui
Meng, Rui
Xu, Juanjuan
Duan, Yanran
Li, Yan
Yang, Guanghai
Ma, Yanling
Jin, Yang
Predicting EGFR mutation, ALK rearrangement, and uncommon EGFR mutation in NSCLC patients by driverless artificial intelligence: a cohort study
title Predicting EGFR mutation, ALK rearrangement, and uncommon EGFR mutation in NSCLC patients by driverless artificial intelligence: a cohort study
title_full Predicting EGFR mutation, ALK rearrangement, and uncommon EGFR mutation in NSCLC patients by driverless artificial intelligence: a cohort study
title_fullStr Predicting EGFR mutation, ALK rearrangement, and uncommon EGFR mutation in NSCLC patients by driverless artificial intelligence: a cohort study
title_full_unstemmed Predicting EGFR mutation, ALK rearrangement, and uncommon EGFR mutation in NSCLC patients by driverless artificial intelligence: a cohort study
title_short Predicting EGFR mutation, ALK rearrangement, and uncommon EGFR mutation in NSCLC patients by driverless artificial intelligence: a cohort study
title_sort predicting egfr mutation, alk rearrangement, and uncommon egfr mutation in nsclc patients by driverless artificial intelligence: a cohort study
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9145462/
https://www.ncbi.nlm.nih.gov/pubmed/35624472
http://dx.doi.org/10.1186/s12931-022-02053-2
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