Classification of Metastatic and Non-Metastatic Thoracic Lymph Nodes in Lung Cancer Patients Based on Dielectric Properties Using Adaptive Probabilistic Neural Networks

Abstract

ObjectiveDielectric properties can be used in normal and malignant tissue identification, which requires an effective classifier because of the high throughput nature of the data. With easy training and fast convergence, probabilistic neural networks (PNNs) are widely applied in pattern classification problems. This study aims to propose a classifier to identify metastatic and non-metastatic thoracic lymph nodes in lung cancer patients based on dielectric properties.MethodsThe dielectric properties (permittivity and conductivity) of lymph nodes were measured using an open-ended coaxial probe. The Synthetic Minority Oversampling Technique method was adopted to modify the dataset. Feature parameters were scored to select the appropriate feature vector using a Statistical Dependency algorithm. The dataset was classified using adaptive PNNs with an optimized smooth factor using the simulated annealing PNN (SA-PNN). The results were compared with traditional Probabilistic, Support Vector Machines, k-Nearest Neighbor and the Classify functions in MATLAB.ResultsThe conductivity frequencies of 3959, 3958, 3960, 3978, 3510, 3889, 3888, and 3976 MHz were selected as the feature vectors for 219 lymph nodes (178 non-metastatic and 41 metastatic). Compared with the other methods, SA-PNN achieved the highest classification accuracy (92.92%) and the corresponding specificity and sensitivity were 94.72% and 91.11%, respectively.ConclusionsCompared with the other methods, the SA-PNN proposed in the present study achieved a higher classification accuracy, which provides a new scheme for classification of metastatic and non-metastatic thoracic lymph nodes in lung cancer patients based on dielectric properties.

Dielectric property measurements for the rapid differentiation of thoracic lymph nodes using XGBoost in patients with non-small cell lung cancer: a self-control clinical trial

“…Choi et al (7) measured the dielectric properties of breast cancer tissue from 0.5 to 30 GHz and demonstrated that both malignant LNs and breast cancer tissue clearly differ from benign tissues. Our previous retrospective study showed that the dielectric property measurements of malignant LNs were higher than those of benign LNs in the frequency range of 1-4,000 MHz (9,10).…”

“…Although the dielectric properties of a variety of physical structures and tissues, including malignant and benign tissues, have been reported by several studies (5)(6)(7)(8)(10)(11)(12)(13), there is still a paucity of data regarding the specific efficacy of these parameters in the classification of pulmonary LN metastasis. Therefore, to investigate the efficacy of using permittivity and conductivity to predict pulmonary LN metastasis, the dielectric properties of both benign and malignant pulmonary LNs were examined over a frequency range of 1-4,000 MHz (9,10).…”

Applications of Microwaves in Medicine Leveraging Artificial Intelligence: Future Perspectives

“…In China, Lu et al [41] developed an ML model (XG Boost) combining dielectric properties to assess whether thoracic lymph nodes were benign or malignant in patients with non-small cell lung cancer. Traditionally, surgeons use frozen sections to diagnose metastatic lymph nodes, which is time-consuming and expensive.…”