Computational Predictions of MGMT Promoter Methylation in Gliomas: A Mathematical Radiogenomics Approach

In the treatment of glioblastomas, non-invasive methods for determining MGMT gene promoter methylation status are crucial due to their implications for chemotherapy responsiveness. This study utilizes a mathematical and computational framework to extract and analyze radiogenomic data from MRI images to predict the methylation status. The first step in our framework involves extracting radiogenomic data from MRI images. This process requires sophisticated image processing techniques to convert MRI scans into a format suitable for machine learning analysis. The features extracted include textural patterns, intensity distributions, and other relevant radiomic characteristics.To identify the most significant features, we employ a Random Forest (RF) algorithm. Mathematically, RF is an ensemble learning method that operates by constructing multiple decision trees during training and outputting the mode of the classes for classification tasks. The importance of each feature is evaluated based on its contribution to the accuracy of the model, quantified by metrics such as Gini impurity or information gain. Employing advanced machine learning models like VGG19, ResNet50, and Sequential FCNet Artificial Neural Networks, alongside traditional classifiers such as Naive Bayes and Logistic Regression, we analyze features identified by a Random Forest algorithm. Our mathematical approach ensures rigorous evaluation of model accuracy through sensitivity, specificity, and other performance metrics, presenting the Sequential FCNet ANN combined with ResNet50 as the superior model. This research contributes to the field of precision healthcare by enhancing the mathematical methods used in the non-invasive diagnosis of glioblastomas.