Development and Validation of an Efficient MRI Radiomics Signature for Improving the Predictive Performance of 1p/19q Co-Deletion in Lower-Grade Gliomas

The prognosis and treatment plans for patients diagnosed with low-grade gliomas (LGGs) may significantly be improved if there is evidence of chromosome 1p/19q co-deletion mutation. Many studies proved that the codeletion status of 1p/19q enhances the sensitivity of the tumor to different types of th...

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Autores principales:	Quang-Hien Kha, Viet-Huan Le, Truong Nguyen Khanh Hung, Nguyen Quoc Khanh Le
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	low-grade gliomas radiogenomics machine learning chromosome 1p/19q codeletion molecular subtype wavelet transform Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282
Acceso en línea:	https://doaj.org/article/7966f887deb047f0b8e1f2fea1c3d200
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spelling	oai:doaj.org-article:7966f887deb047f0b8e1f2fea1c3d2002021-11-11T15:30:04ZDevelopment and Validation of an Efficient MRI Radiomics Signature for Improving the Predictive Performance of 1p/19q Co-Deletion in Lower-Grade Gliomas10.3390/cancers132153982072-6694https://doaj.org/article/7966f887deb047f0b8e1f2fea1c3d2002021-10-01T00:00:00Zhttps://www.mdpi.com/2072-6694/13/21/5398https://doaj.org/toc/2072-6694The prognosis and treatment plans for patients diagnosed with low-grade gliomas (LGGs) may significantly be improved if there is evidence of chromosome 1p/19q co-deletion mutation. Many studies proved that the codeletion status of 1p/19q enhances the sensitivity of the tumor to different types of therapeutics. However, the current clinical gold standard of detecting this chromosomal mutation remains invasive and poses implicit risks to patients. Radiomics features derived from medical images have been used as a new approach for non-invasive diagnosis and clinical decisions. This study proposed an eXtreme Gradient Boosting (XGBoost)-based model to predict the 1p/19q codeletion status in a binary classification task. We trained our model on the public database extracted from The Cancer Imaging Archive (TCIA), including 159 LGG patients with 1p/19q co-deletion mutation status. The XGBoost was the baseline algorithm, and we combined the SHapley Additive exPlanations (SHAP) analysis to select the seven most optimal radiomics features to build the final predictive model. Our final model achieved an accuracy of 87% and 82.8% on the training set and external test set, respectively. With seven wavelet radiomics features, our XGBoost-based model can identify the 1p/19q codeletion status in LGG-diagnosed patients for better management and address the drawbacks of invasive gold-standard tests in clinical practice.Quang-Hien KhaViet-Huan LeTruong Nguyen Khanh HungNguyen Quoc Khanh LeMDPI AGarticlelow-grade gliomasradiogenomicsmachine learningchromosome 1p/19q codeletionmolecular subtypewavelet transformNeoplasms. Tumors. Oncology. Including cancer and carcinogensRC254-282ENCancers, Vol 13, Iss 5398, p 5398 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	low-grade gliomas radiogenomics machine learning chromosome 1p/19q codeletion molecular subtype wavelet transform Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282
spellingShingle	low-grade gliomas radiogenomics machine learning chromosome 1p/19q codeletion molecular subtype wavelet transform Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282 Quang-Hien Kha Viet-Huan Le Truong Nguyen Khanh Hung Nguyen Quoc Khanh Le Development and Validation of an Efficient MRI Radiomics Signature for Improving the Predictive Performance of 1p/19q Co-Deletion in Lower-Grade Gliomas
description	The prognosis and treatment plans for patients diagnosed with low-grade gliomas (LGGs) may significantly be improved if there is evidence of chromosome 1p/19q co-deletion mutation. Many studies proved that the codeletion status of 1p/19q enhances the sensitivity of the tumor to different types of therapeutics. However, the current clinical gold standard of detecting this chromosomal mutation remains invasive and poses implicit risks to patients. Radiomics features derived from medical images have been used as a new approach for non-invasive diagnosis and clinical decisions. This study proposed an eXtreme Gradient Boosting (XGBoost)-based model to predict the 1p/19q codeletion status in a binary classification task. We trained our model on the public database extracted from The Cancer Imaging Archive (TCIA), including 159 LGG patients with 1p/19q co-deletion mutation status. The XGBoost was the baseline algorithm, and we combined the SHapley Additive exPlanations (SHAP) analysis to select the seven most optimal radiomics features to build the final predictive model. Our final model achieved an accuracy of 87% and 82.8% on the training set and external test set, respectively. With seven wavelet radiomics features, our XGBoost-based model can identify the 1p/19q codeletion status in LGG-diagnosed patients for better management and address the drawbacks of invasive gold-standard tests in clinical practice.
format	article
author	Quang-Hien Kha Viet-Huan Le Truong Nguyen Khanh Hung Nguyen Quoc Khanh Le
author_facet	Quang-Hien Kha Viet-Huan Le Truong Nguyen Khanh Hung Nguyen Quoc Khanh Le
author_sort	Quang-Hien Kha
title	Development and Validation of an Efficient MRI Radiomics Signature for Improving the Predictive Performance of 1p/19q Co-Deletion in Lower-Grade Gliomas
title_short	Development and Validation of an Efficient MRI Radiomics Signature for Improving the Predictive Performance of 1p/19q Co-Deletion in Lower-Grade Gliomas
title_full	Development and Validation of an Efficient MRI Radiomics Signature for Improving the Predictive Performance of 1p/19q Co-Deletion in Lower-Grade Gliomas
title_fullStr	Development and Validation of an Efficient MRI Radiomics Signature for Improving the Predictive Performance of 1p/19q Co-Deletion in Lower-Grade Gliomas
title_full_unstemmed	Development and Validation of an Efficient MRI Radiomics Signature for Improving the Predictive Performance of 1p/19q Co-Deletion in Lower-Grade Gliomas
title_sort	development and validation of an efficient mri radiomics signature for improving the predictive performance of 1p/19q co-deletion in lower-grade gliomas
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/7966f887deb047f0b8e1f2fea1c3d200
work_keys_str_mv	AT quanghienkha developmentandvalidationofanefficientmriradiomicssignatureforimprovingthepredictiveperformanceof1p19qcodeletioninlowergradegliomas AT viethuanle developmentandvalidationofanefficientmriradiomicssignatureforimprovingthepredictiveperformanceof1p19qcodeletioninlowergradegliomas AT truongnguyenkhanhhung developmentandvalidationofanefficientmriradiomicssignatureforimprovingthepredictiveperformanceof1p19qcodeletioninlowergradegliomas AT nguyenquockhanhle developmentandvalidationofanefficientmriradiomicssignatureforimprovingthepredictiveperformanceof1p19qcodeletioninlowergradegliomas
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Development and Validation of an Efficient MRI Radiomics Signature for Improving the Predictive Performance of 1p/19q Co-Deletion in Lower-Grade Gliomas

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