Value of explainable artificial intelligence ultrasound characteristic risk model in predicting cervical lymph node metastasis of papillary thyroid carcinoma
10.3760/cma.j.cn131148-20230818-00069
- VernacularTitle:可解释性人工智能超声影像特征风险模型预测甲状腺乳头状癌颈部淋巴结转移的价值
- Author:
Aqian CHEN
1
;
Ru CAO
;
Na LI
;
Xin YUAN
;
Lirong WANG
;
Jue JIANG
;
Qi ZHOU
;
Juan WANG
Author Information
1. 西安交通大学第二附属医院超声科，西安 710004
- Keywords:
Papillary thyroid carcinoma;
Cervical lymph node metastasis;
Traditional machine learning;
Artificial intelligence model of risk characteristic;
Nomogram
- From:
Chinese Journal of Ultrasonography
2024;33(1):14-20
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To construct an explainable artificial intelligence(AI) model of risk characteristics of papillary thyroid carcinoma(PTC), and to explore its value of it combined with clinical features in predicting cervical lymph node metastasis(CLNM) in PTC patients.Methods:From January 2021 to September 2022, 422 patients(422 nodules) with pathologically confirmed PTC underwent thyroidectomy and neck lymph node dissection in the Second Affiliated Hospital of Xi′an Jiaotong University were retrospectively collected, the patients were randomly divided into training set and test set according to the ratio of 7∶3. Ultrasonographic features highly correlated with PTC risk characteristics were extracted by traditional machine learning method, and an intelligent prediction model with optimal probability of risk characteristics was established. Then, a risk model for predicting CLNM of PTC patients was constructed in combination with clinical features. The diagnostic effectiveness of the model was evaluated by drawing a ROC curve and calculating the area under curve (AUC).Results:In the AI explaineable model of PTC risk characteristics in the test set, the intelligent diagnosis model of calcification based on logistic regression classification showed the highest diagnostic efficiency, with an AUC of 0.87 ( P<0.05). Compared with the probability model of risk characteristic of PTC alone, the comprehensive model combined with clinical characteristics showed higher diagnostic efficiency in predicting CLNM of PTC patients, with AUC of 0.97, diagnostic critical value of 0.15, corresponding accuracy, sensitivity and specificity of 92.65%, 92.76% and 92.54%, respectively (all P<0.05). Conclusions:The explaineble risk characteristics of PTC AI model combined with clinical features can effectively predict the cervical lymph node metastasis of PTC, and then provide effective information for clinical decision-making of PTC patients.
