Interpretable deep fuzzy network-aided detection of central lymph node metastasis status in papillary thyroid carcinoma.
Authors
Affiliations (9)
Affiliations (9)
- School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, Guangdong Province, China.
- Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, 510515, Guangdong Province, China.
- School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, Guangdong Province, China. [email protected].
- Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, 510515, Guangdong Province, China. [email protected].
- Shanghai Key Laboratory of Molecular Imaging, Shanghai University of Medicine and Health Sciences, Shanghai, 201318, China. [email protected].
- Department of Ultrasonic Medicine, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, Guangdong Province, China.
- School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, Guangdong Province, China. [email protected].
- Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, 510515, Guangdong Province, China. [email protected].
- Department of Ultrasonic Medicine, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, Guangdong Province, China. [email protected].
Abstract
The non-invasive assessment of central lymph node metastasis (CLNM) in patients with papillary thyroid carcinoma (PTC) plays a crucial role in assisting treatment decision and prognosis planning. This study aims to use an interpretable deep fuzzy network guided by expert knowledge to predict the CLNM status of patients with PTC from ultrasound images. A total of 1019 PTC patients were enrolled in this study, comprising 465 CLNM patients and 554 non-CLNM patients. Pathological diagnosis served as the gold standard to determine metastasis status. Clinical and morphological features of thyroid were collected as expert knowledge to guide the deep fuzzy network in predicting CLNM status. The network consisted of a region of interest (ROI) segmentation module, a knowledge-aware feature extraction module, and a fuzzy prediction module. The network was trained on 652 patients, validated on 163 patients and tested on 204 patients. The model exhibited promising performance in predicting CLNM status, achieving the area under the receiver operating characteristic curve (AUC), accuracy, precision, sensitivity and specificity of 0.786 (95% CI 0.720-0.846), 0.745 (95% CI 0.681-0.799), 0.727 (95% CI 0.636-0.819), 0.696 (95% CI 0.594-0.789), and 0.786 (95% CI 0.712-0.864), respectively. In addition, the rules of the fuzzy system in the model are easy to understand and explain, and have good interpretability. The deep fuzzy network guided by expert knowledge predicted CLNM status of PTC patients with high accuracy and good interpretability, and may be considered as an effective tool to guide preoperative clinical decision-making.