Details

Autor(en) / Beteiligte

• Lam, Luu Ho Thanh
• Do, Duyen Thi
• Diep, Doan Thi Ngoc
• Nguyet, Dang Le Nhu
• Truong, Quang Dinh
• Tri, Tran Thanh
• Thanh, Huynh Ngoc
• Le, Nguyen Quoc Khanh

Titel

Molecular subtype classification of low‐grade gliomas using magnetic resonance imaging‐based radiomics and machine learning

Ist Teil von

• NMR in biomedicine, 2022-11, Vol.35 (11), p.e4792-n/a

Ort / Verlag

Oxford: Wiley Subscription Services, Inc

Erscheinungsjahr

2022

Link zum Volltext

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• In 2016, the World Health Organization (WHO) updated the glioma classification by incorporating molecular biology parameters, including low‐grade glioma (LGG). In the new scheme, LGGs have three molecular subtypes: isocitrate dehydrogenase (IDH)‐mutated 1p/19q‐codeleted, IDH‐mutated 1p/19q‐noncodeleted, and IDH‐wild type 1p/19q‐noncodeleted entities. This work proposes a model prediction of LGG molecular subtypes using magnetic resonance imaging (MRI). MR images were segmented and converted into radiomics features, thereby providing predictive information about the brain tumor classification. With 726 raw features obtained from the feature extraction procedure, we developed a hybrid machine learning‐based radiomics by incorporating a genetic algorithm and eXtreme Gradient Boosting (XGBoost) classifier, to ascertain 12 optimal features for tumor classification. To resolve imbalanced data, the synthetic minority oversampling technique (SMOTE) was applied in our study. The XGBoost algorithm outperformed the other algorithms on the training dataset by an accuracy value of 0.885. We continued evaluating the XGBoost model, then achieved an overall accuracy of 0.6905 for the three‐subtype classification of LGGs on an external validation dataset. Our model is among just a few to have resolved the three‐subtype LGG classification challenge with high accuracy compared with previous studies performing similar work. This work proposes a prediction model for low‐grade glioma molecular subtypes using MRI. It is a hybrid machine learning‐based radiomics model developed by incorporating XGBoost and genetic algorithm. It is among just a few studies that have resolves the three‐subtype LGG classification challenge with high accuracy compared with previous studies performing similar work.