| 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) |
| Abstrak/Abstract |
Background Gliomas present a signifcant economic burden and patient management challenge. The 2021 WHO
classifcation incorporates molecular parameters, which guide treatment decisions. However, acquiring these molecular data involves invasive biopsies, prompting a need for non-invasive diagnostic methods. This study aims to assess
the potential of Visually AcceSAble Rembrandt Images (VASARI) MRI features to predict glioma characteristics such
as grade, IDH mutation, and MGMT methylation status.
Methods This study enrolled 107 glioma patients treated between 2017 and 2022, meeting specifc criteria including the absence of prior chemotherapy/radiation therapy, and the presence of molecular and MRI data. Images were
assessed using the 27 VASARI MRI features by two blinded radiologists. Pathological and molecular assessments were
conducted according to WHO 2021 CNS Tumor classifcation. Cross-validation Least Absolute Shrinkage and Selection Operator (CV-LASSO) logistic regression was applied for statistical analysis to identify signifcant VASARI features
in determining glioma grade, IDH mutation, and MGMT methylation status.
Results The study demonstrated substantial observer agreement in VASARI feature evaluation (inter- and intraobserver κ=0.714 - 0.831 and 0.910, respectively). Patient imaging characteristics varied signifcantly with glioma
grade, IDH mutation, and MGMT methylation. A predictive model was established using VASARI features for glioma
grade prediction, exhibiting an AUC of 0.995 (95% CI=0.986 – 0.998), 100% sensitivity, and 92.86% specifcity. IDH
mutation status was predicted with AUC 0.930 (95% CI=0.882 - 0.977), and improved slightly to 0.933 with ’ageat-diagnosis’ added. A model predicting MGMT methylation had a satisfactory performance (AUC 0.757, 95%
CI=0.645 - 0.868), improving to 0.791 when ’age-at-diagnosis’ was added.
Conclusions The T1/FLAIR ratio, enhancement quality, hemorrhage, and proportion enhancing predict glioma grade
with excellent accuracy. The proportion enhancing, thickness of enhancing margin, and T1/FLAIR ratio are signifcant predictors for IDH mutation status. Lastly, MGMT methylation is related to the longest diameter of the lesion,
edema crossing the midline, and the proportion of the non-enhancing lesion. VASARI MRI features ofer non-invasive
and accurate predictive models for glioma grade, IDH mutation, and MGMT methylation status, enhancing glioma
patient management. |
