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A machine learning-based radiomics model for prediction of tumor mutation burden in gastric cancer
Purpose: To evaluate the potential of machine learning (ML)-based radiomics approach for predicting tumor mutation burden (TMB) in gastric cancer (GC). Methods: The contrast enhanced CT (CECT) images with corresponding clinical information of 256 GC patients were retrospectively collected. Patients...
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/PMC10657897/ https://www.ncbi.nlm.nih.gov/pubmed/38028587 http://dx.doi.org/10.3389/fgene.2023.1283090 |
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author | Ma, Tingting Zhang, Yuwei Zhao, Mengran Wang, Lingwei Wang, Hua Ye, Zhaoxiang |
author_facet | Ma, Tingting Zhang, Yuwei Zhao, Mengran Wang, Lingwei Wang, Hua Ye, Zhaoxiang |
author_sort | Ma, Tingting |
collection | PubMed |
description | Purpose: To evaluate the potential of machine learning (ML)-based radiomics approach for predicting tumor mutation burden (TMB) in gastric cancer (GC). Methods: The contrast enhanced CT (CECT) images with corresponding clinical information of 256 GC patients were retrospectively collected. Patients were separated into training set (n = 180) and validation set (n = 76). A total of 3,390 radiomics features were extracted from three phases images of CECT. The least absolute shrinkage and selection operator (LASSO) model was used for feature screening. Seven machine learning (ML) algorithms were employed to find the optimal classifier. The predictive ability of radiomics model (RM) was evaluated with receiver operating characteristic. The correlation between RM and TMB values was evaluated using Spearman’s correlation coefficient. The explainability of RM was assessed by the Shapley Additive explanations (SHAP) method. Results: Logistic regression algorithm was chosen for model construction. The RM showed good predictive ability of TMB status with AUCs of 0.89 [95% confidence interval (CI): 0.85–0.94] and 0.86 (95% CI: 0.74–0.98) in the training and validation sets. The correlation analysis revealed a good correlation between RM and TMB levels (correlation coefficient: 0.62, p < 0.001). The RM also showed favorable and stable predictive accuracy within the cutoff value range 6–16 mut/Mb in both sets. Conclusion: The ML-based RM offered a promising image biomarker for predicting TMB status in GC patients. |
format | Online Article Text |
id | pubmed-10657897 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-106578972023-11-06 A machine learning-based radiomics model for prediction of tumor mutation burden in gastric cancer Ma, Tingting Zhang, Yuwei Zhao, Mengran Wang, Lingwei Wang, Hua Ye, Zhaoxiang Front Genet Genetics Purpose: To evaluate the potential of machine learning (ML)-based radiomics approach for predicting tumor mutation burden (TMB) in gastric cancer (GC). Methods: The contrast enhanced CT (CECT) images with corresponding clinical information of 256 GC patients were retrospectively collected. Patients were separated into training set (n = 180) and validation set (n = 76). A total of 3,390 radiomics features were extracted from three phases images of CECT. The least absolute shrinkage and selection operator (LASSO) model was used for feature screening. Seven machine learning (ML) algorithms were employed to find the optimal classifier. The predictive ability of radiomics model (RM) was evaluated with receiver operating characteristic. The correlation between RM and TMB values was evaluated using Spearman’s correlation coefficient. The explainability of RM was assessed by the Shapley Additive explanations (SHAP) method. Results: Logistic regression algorithm was chosen for model construction. The RM showed good predictive ability of TMB status with AUCs of 0.89 [95% confidence interval (CI): 0.85–0.94] and 0.86 (95% CI: 0.74–0.98) in the training and validation sets. The correlation analysis revealed a good correlation between RM and TMB levels (correlation coefficient: 0.62, p < 0.001). The RM also showed favorable and stable predictive accuracy within the cutoff value range 6–16 mut/Mb in both sets. Conclusion: The ML-based RM offered a promising image biomarker for predicting TMB status in GC patients. Frontiers Media S.A. 2023-11-06 /pmc/articles/PMC10657897/ /pubmed/38028587 http://dx.doi.org/10.3389/fgene.2023.1283090 Text en Copyright © 2023 Ma, Zhang, Zhao, Wang, Wang and Ye. 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 | Genetics Ma, Tingting Zhang, Yuwei Zhao, Mengran Wang, Lingwei Wang, Hua Ye, Zhaoxiang A machine learning-based radiomics model for prediction of tumor mutation burden in gastric cancer |
title | A machine learning-based radiomics model for prediction of tumor mutation burden in gastric cancer |
title_full | A machine learning-based radiomics model for prediction of tumor mutation burden in gastric cancer |
title_fullStr | A machine learning-based radiomics model for prediction of tumor mutation burden in gastric cancer |
title_full_unstemmed | A machine learning-based radiomics model for prediction of tumor mutation burden in gastric cancer |
title_short | A machine learning-based radiomics model for prediction of tumor mutation burden in gastric cancer |
title_sort | machine learning-based radiomics model for prediction of tumor mutation burden in gastric cancer |
topic | Genetics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10657897/ https://www.ncbi.nlm.nih.gov/pubmed/38028587 http://dx.doi.org/10.3389/fgene.2023.1283090 |
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