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...

Full description

Saved in:
Bibliographic Details
Published in:Cancers 2022-07, Vol.14 (14), p.3492
Main Authors: Lam, Luu Ho Thanh, Chu, Ngan Thy, Tran, Thi-Oanh, Do, Duyen Thi, Le, Nguyen Quoc Khanh
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Artificial intelligence
Biomarkers
Biopsy
Brain cancer
Brain tumors
Cancer
Central nervous system
Classification
Deep learning
Discriminant analysis
Epidemiology
Experiments
Gene mutations
Genetic algorithms
Genetic aspects
Genotypes
Glial cells
Glioma cells
Gliomas
Immunoregulation
Immunotherapy
Learning algorithms
Machine learning
Magnetic resonance imaging
Medical prognosis
Mutation
Neural networks
Neuroimaging
Oncology, Experimental
Patients
Prognosis
Radiomics
Support vector machines
Tumors
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2072-6694
2072-6694
DOI:10.3390/cancers14143492