A Radiomics-Based Machine Learning Model for Prediction of Tumor Mutational Burden in Lower-Grade Gliomas
Glioma is a Center Nervous System (CNS) neoplasm that arises from the glial cells. In a new scheme category of the World Health Organization 2016, lower-grade gliomas (LGGs) are grade II and III gliomas. Following the discovery of suppression of negative immune regulation, immunotherapy is a promisi...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-07-01
|
| Series: | Cancers |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-6694/14/14/3492 |
| _version_ | 1797433741849657344 |
|---|---|
| author | Luu Ho Thanh Lam Ngan Thy Chu Thi-Oanh Tran Duyen Thi Do Nguyen Quoc Khanh Le |
| author_facet | Luu Ho Thanh Lam Ngan Thy Chu Thi-Oanh Tran Duyen Thi Do Nguyen Quoc Khanh Le |
| author_sort | Luu Ho Thanh Lam |
| collection | DOAJ |
| description | Glioma is a Center Nervous System (CNS) neoplasm that arises from the glial cells. In a new scheme category of the World Health Organization 2016, lower-grade gliomas (LGGs) are grade II and III gliomas. Following the discovery of suppression of negative immune regulation, immunotherapy is a promising effective treatment method for lower-grade glioma patients. However, the therapy is not effective for all types of LGGs, and tumor mutational burden (TMB) has been shown to be a potential biomarker for the susceptibility and prognosis of immunotherapy in lower-grade glioma patients. Hence, predicting TMB benefits brain cancer patients. In this study, we investigated the correlation between MRI (magnetic resonance imaging)-based radiomic features and TMB in LGG by applying machine learning methods. Six machine learning classifiers were examined on the features extracted from the genetic algorithm. Subsequently, a light gradient boosting machine (LightGBM) succeeded in selecting 11 radiomics signatures for TMB classification. Our LightGBM model resulted in high accuracy of 0.7936, and reached a balance between sensitivity and specificity, achieving 0.76 and 0.8107, respectively. To our knowledge, our study represents the best model for classification of TMB in LGG patients at present. |
| first_indexed | 2024-03-09T10:21:20Z |
| format | Article |
| id | doaj.art-8b3c3dadb1b647ee9cf99186a3be5431 |
| institution | Directory Open Access Journal |
| issn | 2072-6694 |
| language | English |
| last_indexed | 2024-03-09T10:21:20Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Cancers |
| spelling | doaj.art-8b3c3dadb1b647ee9cf99186a3be54312023-12-01T21:59:42ZengMDPI AGCancers2072-66942022-07-011414349210.3390/cancers14143492A Radiomics-Based Machine Learning Model for Prediction of Tumor Mutational Burden in Lower-Grade GliomasLuu Ho Thanh Lam0Ngan Thy Chu1Thi-Oanh Tran2Duyen Thi Do3Nguyen Quoc Khanh Le4International Master/Ph.D. Program in Medicine, College of Medicine, Taipei Medical University, Taipei 110, TaiwanCity Children’s Hospital, Ho Chi Minh City 70000, VietnamInternational Ph.D. Program for Cell Therapy and Regeneration Medicine, College of Medicine, Taipei Medical University, Taipei 110, TaiwanGraduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 106, TaiwanProfessional Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei 106, TaiwanGlioma is a Center Nervous System (CNS) neoplasm that arises from the glial cells. In a new scheme category of the World Health Organization 2016, lower-grade gliomas (LGGs) are grade II and III gliomas. Following the discovery of suppression of negative immune regulation, immunotherapy is a promising effective treatment method for lower-grade glioma patients. However, the therapy is not effective for all types of LGGs, and tumor mutational burden (TMB) has been shown to be a potential biomarker for the susceptibility and prognosis of immunotherapy in lower-grade glioma patients. Hence, predicting TMB benefits brain cancer patients. In this study, we investigated the correlation between MRI (magnetic resonance imaging)-based radiomic features and TMB in LGG by applying machine learning methods. Six machine learning classifiers were examined on the features extracted from the genetic algorithm. Subsequently, a light gradient boosting machine (LightGBM) succeeded in selecting 11 radiomics signatures for TMB classification. Our LightGBM model resulted in high accuracy of 0.7936, and reached a balance between sensitivity and specificity, achieving 0.76 and 0.8107, respectively. To our knowledge, our study represents the best model for classification of TMB in LGG patients at present.https://www.mdpi.com/2072-6694/14/14/3492lower-grade gliomatumor mutational burdengenetic algorithmradiomics signature |
| spellingShingle | Luu Ho Thanh Lam Ngan Thy Chu Thi-Oanh Tran Duyen Thi Do Nguyen Quoc Khanh Le A Radiomics-Based Machine Learning Model for Prediction of Tumor Mutational Burden in Lower-Grade Gliomas Cancers lower-grade glioma tumor mutational burden genetic algorithm radiomics signature |
| title | A Radiomics-Based Machine Learning Model for Prediction of Tumor Mutational Burden in Lower-Grade Gliomas |
| title_full | A Radiomics-Based Machine Learning Model for Prediction of Tumor Mutational Burden in Lower-Grade Gliomas |
| title_fullStr | A Radiomics-Based Machine Learning Model for Prediction of Tumor Mutational Burden in Lower-Grade Gliomas |
| title_full_unstemmed | A Radiomics-Based Machine Learning Model for Prediction of Tumor Mutational Burden in Lower-Grade Gliomas |
| title_short | A Radiomics-Based Machine Learning Model for Prediction of Tumor Mutational Burden in Lower-Grade Gliomas |
| title_sort | radiomics based machine learning model for prediction of tumor mutational burden in lower grade gliomas |
| topic | lower-grade glioma tumor mutational burden genetic algorithm radiomics signature |
| url | https://www.mdpi.com/2072-6694/14/14/3492 |
| work_keys_str_mv | AT luuhothanhlam aradiomicsbasedmachinelearningmodelforpredictionoftumormutationalburdeninlowergradegliomas AT nganthychu aradiomicsbasedmachinelearningmodelforpredictionoftumormutationalburdeninlowergradegliomas AT thioanhtran aradiomicsbasedmachinelearningmodelforpredictionoftumormutationalburdeninlowergradegliomas AT duyenthido aradiomicsbasedmachinelearningmodelforpredictionoftumormutationalburdeninlowergradegliomas AT nguyenquockhanhle aradiomicsbasedmachinelearningmodelforpredictionoftumormutationalburdeninlowergradegliomas AT luuhothanhlam radiomicsbasedmachinelearningmodelforpredictionoftumormutationalburdeninlowergradegliomas AT nganthychu radiomicsbasedmachinelearningmodelforpredictionoftumormutationalburdeninlowergradegliomas AT thioanhtran radiomicsbasedmachinelearningmodelforpredictionoftumormutationalburdeninlowergradegliomas AT duyenthido radiomicsbasedmachinelearningmodelforpredictionoftumormutationalburdeninlowergradegliomas AT nguyenquockhanhle radiomicsbasedmachinelearningmodelforpredictionoftumormutationalburdeninlowergradegliomas |