Prediction of Cervical Cancer Staging via Diffusion-Weighted MR Radiomic Features Analysis

Contemporary technology has made it possible to diagnose diseases with greater precision, enabling doctors to determine tumor stages by measuring and analyzing features in medical images. This study aimed evaluate radiomic features of DWI-MRI cervical and to classify tumors based on their stages using several machine learning models. The study involved 60 patients from the National Cancer Institute in Putrajaya using which 852 radiomic were derived from each patient. The patients were divided into two groups based on the stage of cervical cancer: Group one is defined by stages I and II, while group two is labelled by stages III and IV. A semi-automatic process was used to segment the image data and extract various features such as shape, wavelength, and both first and second-order statistics (GLSZM) from the segmented images. In our study, we used three different model algorithms: Logistic Regression (LR), Support Vector Machine (SVM) and Decision Tree (DT). We conducted a detailed analysis to compare their performance. The SVM algorithm stood out as the top performer, achieving the highest accuracy among the models tested. We evaluated its effectiveness using multiple performance metrics, which confirmed its superiority in processing features extracted from enhanced images. Notably, the SVM classifier demonstrated excellent results, with an accuracy of 0.77, and precision of 0.63. It also showed a robust area under the ROC curve of 96%, highlighting its effectiveness in classification tasks. Furthermore, this research emphasizes the significance of image enhancement as the useful preprocessing step which increases the classification accuracy.