Integrating dielectric properties analysis and machine learning for accurate liver cancer identification and infiltration depth prediction.

Abstract

The study of dielectric properties (DPs) reveals significant differences between normal and liver cancer tissues. Although the open-ended coaxial probe (OCP) method is widely used for measuring DPs, tumor infiltration depth affects the measurements, blurring dielectric thresholds and posing challenges for tissue identification based on DPs. This study combines DPs analysis with machine learning (ML) to achieve two key goals: (1) accurately distinguish tissue types, (2) reliably predict tumor infiltration depth. We simulated the DPs of liver cancer tissues at different infiltration depths, using a total of 90,000 samples with 181 frequency-point features. We evaluated the performance of common ML models, including artificial neural networks (ANN), support vector machines (SVM), and Bagging tree ensembles, and validated them using real tissue and phantom measurements. Additionally, the probe's detection depth was experimentally validated. Experimental results showed that all three ML models performed well in tissue identification and tumor infiltration depth prediction. SVM achieved the highest classification accuracy of 98.91%. For depth prediction, SVM and ANN yielded MAPE/RMSE of 0.1742/0.0673 and 0.1658/0.0730, respectively. The probe's effective detection range was 0.1-0.6 mm, essential for accurate measurement and prediction. The models also demonstrated strong performance in real tissue and phantom validations, with the Bagging ensemble achieving 100% classification accuracy and MAPE/RMSE of 0.1434/0.0614 for prediction. These findings confirm the method's reliability for precise tissue identification and infiltration depth estimation, supporting accurate tumor resection and improved patient outcomes.