Research on prediction model for high-volume lymph node metastasis in multifocal papillary thyroid carcinoma
10.3969/j.issn.1673-9701.2024.29.013
- VernacularTitle:多灶性甲状腺乳头状癌高容量淋巴结转移预测模型研究
- Author:
Sha LYU
1
,
2
;
Zhigang TAO
;
Zhijiang HAN
;
Chunfeng HU
;
Huijun CAO
;
Tong ZHANG
Author Information
1. 浙江中医药大学第四临床医学院,浙江 杭州 310053
2. 杭州市余杭区第三人民医院放射科,浙江 杭州 311115
- Keywords:
Thyroid tumors;
Lymph node metastasis;
Risk factors;
Nomogram
- From:
China Modern Doctor
2024;62(29):54-57
- CountryChina
- Language:Chinese
-
Abstract:
Objective To construct and validate of a nomogram predictive model for high-volume lymph node metastasis(HVM)in multifocal papillary thyroid carcinoma(MPTC).Methods Between January 2010 to January 2024,a total of 1146 and 234 patients with multifocal papillary thyroid carcinoma(MPTC)were diagnosed at Hangzhou First People's Hospital(Center A)and Hangzhou Cancer Hospital(Center B),respectively.Patients from Center A were randomly allocated to training set(n=803)and testing set(n=343)in a 7:3 ratio,while those from Center B(n=234)comprised an external validation set.Independent risk factors for HVM in MPTC patients were identified through univariate and multivariate Logistic regression analysis in training set,leading to the development of a nomogram predictive model.The generalizability of this model was subsequently assessed using both testing set and external validation set.The area under the curve(AUC)of receiver operating characteristic curve,sensitivity,and specificity evaluate the discriminative ability of the model.Results The incidence of HVM was 13.3％at center A and 12.8％at center B.Logistic regression identified male gender(OR=2.91,95％CI:1.835-4.599),maximum lesion diameter(OR=1.05,95％CI:1.021-1.070),and age(OR=0.95,95％CI:0.936-0.972)as independent risk factors for HVM.Anomogram based on these factors showed an AUC of 0.767 with 72.6％sensitivity and 70.2％specificity in training set,and 0.838 with 94.9％sensitivity and 68.4％specificity in testing set,and 0.769 with 63.3％sensitivity and 84.3％specificity in external validation set.The calibration curve demonstrated good agreement with the ideal curve.Conclusion The prediction model constructed based on clinical risk factors can effectively predict the probability of HVM in MPTC patients.
