Prediction of invasiveness in follicular variant of papillary thyroid carcinoma using nomograms based on ultrasonic features
10.3760/cma.j.cn131148-20240418-00233
- VernacularTitle:构建基于超声特征列线图预测甲状腺乳头状癌滤泡亚型的侵袭性
- Author:
YuXin ZHENG
1
;
Yajiao ZHANG
;
Liyu CHEN
;
Kefeng LU
;
Jiangyan LOU
;
Junping LIU
;
Dong XU
Author Information
1. 浙江中医药大学第二临床医学院，杭州 310022
- Keywords:
Ultrasonography;
Follicular variant of papillary thyroid carcinoma;
Nomogram;
Predictive model
- From:
Chinese Journal of Ultrasonography
2024;33(9):800-806
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To explore the relationship between ultrasound characteristics and invasiveness in the follicular variant of papillary thyroid carcinoma (FVPTC), and to integrate multiple ultrasound parameters for visual assessment of predictive outcomes by using Nomogram.Methods:A total of 312 FVPTC patients who were pathologically confirmed through surgery in Zhejiang Cancer Hospital and Zhejiang Provincial People′s Hospital from January 2013 to December 2023 were retrospectively collected.Based on defined criteria, FVPTC patients were categorized into high-invasion and low-invasion groups. The dataset was divided into a training set and a validation set in a ratio of 7 to 3. Clinical information and ultrasound feature parameters were collected. Univariate and multivariate Logistic regression analyses were performed on the training set. A predictive model for FVPTC invasiveness was constructed based on ultrasound features. The model′s discriminative ability and calibration were evaluated in the validation set, and a nomogram was generated.Results:The training set included a total of 218 patients with FVPTC, among which 131 were classified as high invasive.The validation set consisted of 94 patients, with 53 cases of high invasive FVPTC patients. Multivariate logistic regression analysis on the training set revealed that tumor multifocality ( OR=6.505, P=0.016), hypoechoic ( OR=3.235, P=0.103), shape ( OR=0.521, P=0.049), and microcalcifications ( OR=2.479, P=0.004) were independent influencing factors for predicting invasiveness in FVPTC. In the training set, the area under the curve (AUC) of the ultrasound predictive model was 0.704 (95% CI=0.634-0.771), and in the validation set, the AUC was 0.650 (95% CI=0.531-0.770), indicated good discriminative ability.The calibration curve showed good alignment with the ideal curve, demonstrating favorable calibration performance. Conclusions:Ultrasound features provide valuable information for assessing the invasiveness of FVPTC, and the model constructed by combining ultrasound features demonstrates good predictive efficacy for the invasiveness of FVPTC.
