| Penulis/Author |
dr. Nurhuda Hendra Setyawan., Sp.Rad (1) ; Prof. Dr. dr. Lina Choridah, Sp.Rad(K). (2); Prof. Ir. Hanung Adi Nugroho, S.T., M.Eng., Ph.D., IPM., SMIEEE. (3); dr. Rusdy Ghazali Malueka, Ph.D., Sp.S(K) (4); dr. Ery Kus Dwianingsih, Ph.D., Sp.PA(K) (5); dr. Yana Supriatna, Ph.D., Sp.Rad.(K) RI (6); dr. Bambang Supriyadi, Sp.Rad., MM (7); dr. Rachmat Andi Hartanto, Sp.BS(K). (8) |
| Abstrak/Abstract |
Objectives: This study aimed to leverage Visually AcceSAble Rembrandt Images (VASARI) radiological
features, extracted from magnetic resonance imaging (MRI) scans, and machine-learning techniques to
predict glioma grade, isocitrate dehydrogenase (IDH) mutation status, and O6-methylguanine-DNA
methyltransferase (MGMT) methylation.
Methodology: A retrospective evaluation was undertaken, analyzing MRI and molecular data from 107
glioma patients treated at a tertiary hospital. Patients underwent MRI scans using established protocols and
were evaluated based on VASARI criteria. Tissue samples were assessed for glioma grade and underwent
molecular testing for IDH mutations and MGMT methylation. Four machine learning models, namely,
Random Forest, Elastic-Net, multivariate adaptive regression spline (MARS), and eXtreme Gradient
Boosting (XGBoost), were trained on 27 VASARI features using fivefold internal cross-validation. The
models' predictive performances were assessed using the area under the curve (AUC), sensitivity, and
specificity.
Results: For glioma grade prediction, XGBoost exhibited the highest AUC (0.978), sensitivity (0.879), and
specificity (0.964), with f6 (proportion of non-enhancing) and f12 (definition of enhancing margin) as the
most important predictors. In predicting IDH mutation status, XGBoost achieved an AUC of 0.806,
sensitivity of 0.364, and specificity of 0.880, with f1 (tumor location), f12, and f30 (perpendicular diameter
to f29) as primary predictors. For MGMT methylation, XGBoost displayed an AUC of 0.580, sensitivity of
0.372, and specificity of 0.759, highlighting f29 (longest diameter) as the key predictor.
Conclusions: This study underscores the robust potential of combining VASARI radiological features with
machine learning models in predicting glioma grade, IDH mutation status, and MGMT methylation. The best
and most balanced performance was achieved using the XGBoost model. While the prediction of glioma
grade showed promising results, the sensitivity in discerning IDH mutations and MGMT methylation still
leaves room for improvement. Follow-up studies with larger datasets and more advanced artificial
intelligence techniques can further refine our understanding and management of gliomas. |
