Measurement and classification of dielectric properties in human brain tissues: differentiating glioma from normal tissues using machine learning.

Glioma is primarily treated through surgical resection, but accurately identifying tumor boundaries remains challenging. Traditional intraoperative diagnostic techniques, such as frozen section pathological examination and intraoperative magnetic resonance imaging, suffer from issues such as long duration, high cost, and complex operation. A rapid and accurate intraoperative auxiliary diagnostic method for glioma based on the differences in dielectric properties combined with machine learning is proposed in this study. Using an open-ended coaxial probe technique, the dielectric properties of 81 glioma tissue samples and 47 normal brain tissue samples from 14 patients were measured over a frequency range of 1 MHz-4 GHz. After feature selection and dimensionality reduction using the Lasso method, four machine learning models-Naive Bayes (NB), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Artificial Neural Network (ANN)-were used to classify the samples. Model performance was evaluated using accuracy, precision, recall, F1 score, and the area under the Receiver Operating Characteristic curve (AUC value). The experimental results demonstrated that the dielectric properties of glioma tissues are higher than those of normal brain tissues (with an average increase of 22% in conductivity and 18% in relative permittivity). On the test set, the KNN model exhibited the highest classification accuracy (90%), while the ANN model showed the best AUC value (0.95). This study confirms that the rapid identification of glioma can be achieved based on dielectric properties combined with machine learning techniques, providing neurosurgeons with a novel auxiliary diagnostic technology for precise intraoperative margin detection of glioma.