RESUMO
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.
RESUMO
Occupational exposure to pesticides leads to the development of cancer. Aberrant DNA methylation plays a crucial role in cancer. The manifestation of the carcinogenic effect of pesticides could be determined by the variation of genes encoding enzyme, including PON1 Q192R and GSTM1. The goal of this study was to find out polymorphism of PON1 Q192R and methylation of p16 gene promoter, and their correlation on Javanese farmers in the agricultural area of Ngablak Subdistrict, Magelang Regency, Central Java. Seventy-eight pesticide-exposed farmers enrolled in the study. Polymorphism of PON Q192R was determined using PCR-RFLP and variation of GSTM1 was examined using conventional PCR. The methylation of the p16 gene promoter was determined using methylation-specific PCR. The result revealed 94.9% polymorphism of PON1 Q192R, which was higher in the R/R (Arg/Arg) genotypes than Q/R (Gln/Arg) and lowest in Q/Q (Gln/Gln) genotypes. We also found 82.1% GSTM1 null genotype among the farmers enrolled in the study. As many as 26.9% methylations of p16 gene promoter were found among farmers. Genetic variation of PON1 Q192R and GSTM1 were not found to be correlated to the methylation status of p16 gene promoter in the Javanese population.