Early thyroid cancer detection and differentiation by using electrical impedance spectroscopy and deep learning: a preliminary study
10.3760/cma.j.cn115807-20240507-00152
- VernacularTitle:基于生物阻抗和深度学习技术的甲状腺组织分类模型研究
- Author:
Aoling HUANG
1
;
Wenwen HUANG
;
Pengwei DONG
;
Xianli JU
;
Dandan YAN
;
Jingping YUAN
Author Information
1. 武汉大学人民医院病理科，武汉 430060
- Keywords:
Thyroid nodules;
Thyroid carcinoma;
Bioelectrical impedance spectroscopy;
Electrodes
- From:
Chinese Journal of Endocrine Surgery
2024;18(4):484-488
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To aid in the detection of thyroid cancer by using deep learning to differentiate the unique bioimpedance parameter patterns of different thyroid tissues.Methods:An electrical impedance system was designed to measure 331 ex-vivo thyroid specimens from 321 patients during surgery. The impedance data was then analyzed with one dimensional convolution neural (1D-CNN) combining with long short-term memory (LSTM) network models of deep learning. In the process of analysis, we assigned 80% of the data to training set (1072/1340) and the remaining 20% data to the test set (268/1340). The performance of final model was assessed using receiver operating characteristic (ROC) curves. In addition, sensitivity, specificity, positive predictive value, negative predictive value, Youden index were applied to compare impedance model with ultrasound results.Results:The ROC curve of the two-classification (malignant /non-malignant tissue) model showed a good performance (area-under-the-curve AUC=0.94), with an overall accuracy of 91.4%. To better fit clinical practice, we further performed a three-classification (malignant/ benign/ normal tissue) model, of which the areas under ROC curve were 0.91, 0.85, 0.92 for normal, benign, and malignant group, respectively. The results indicated that the area under micro-average ROC curve and the macro-average ROC curve were 0.91 and 0.90, respectively. Moreover, compared with ultrasound, the impedance model exhibited higher specificity.Conclusions:A deep learning model (CNN-LSTM) trained by thyroid electrical impedance spectroscopy (EIS) parameters shows an excellent performance in distinguishing among different in-vitro thyroid tissues, which is promising for applications. In future clinical utility, our study does not replace existing tests, but rather complements others, thus contributing to therapeutic decision-making and management of thyroid disease.
