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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MDPI AG
2021-10-01
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| Series: | Cancers |
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| Online Access: | https://www.mdpi.com/2072-6694/13/21/5398 |
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| 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 |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-10T06:05:36Z |
| format | Article |
| id | doaj.art-7966f887deb047f0b8e1f2fea1c3d200 |
| institution | Directory Open Access Journal |
| issn | 2072-6694 |
| language | English |
| last_indexed | 2024-03-10T06:05:36Z |
| publishDate | 2021-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Cancers |
| spelling | doaj.art-7966f887deb047f0b8e1f2fea1c3d2002023-11-22T20:34:40ZengMDPI AGCancers2072-66942021-10-011321539810.3390/cancers13215398Development and Validation of an Efficient MRI Radiomics Signature for Improving the Predictive Performance of 1p/19q Co-Deletion in Lower-Grade GliomasQuang-Hien Kha0Viet-Huan Le1Truong Nguyen Khanh Hung2Nguyen Quoc Khanh Le3International Master/Ph.D. Program in Medicine, College of Medicine, Taipei Medical University, Taipei 110, TaiwanInternational Master/Ph.D. Program in Medicine, College of Medicine, Taipei Medical University, Taipei 110, TaiwanInternational Master/Ph.D. Program in Medicine, College of Medicine, Taipei Medical University, Taipei 110, TaiwanInternational Master/Ph.D. Program in Medicine, College of Medicine, Taipei Medical University, Taipei 110, TaiwanThe 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.https://www.mdpi.com/2072-6694/13/21/5398low-grade gliomasradiogenomicsmachine learningchromosome 1p/19q codeletionmolecular subtypewavelet transform |
| spellingShingle | 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 Cancers low-grade gliomas radiogenomics machine learning chromosome 1p/19q codeletion molecular subtype wavelet transform |
| 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_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_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_sort | development and validation of an efficient mri radiomics signature for improving the predictive performance of 1p 19q co deletion in lower grade gliomas |
| topic | low-grade gliomas radiogenomics machine learning chromosome 1p/19q codeletion molecular subtype wavelet transform |
| url | https://www.mdpi.com/2072-6694/13/21/5398 |
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