Cheng Sheng, Zeng Xian-Tao, Liang Xia, Hong Zhi-Liang, Yang Jian-Chuan, You Zi-Ling, Wu Song-Song
Department of Ultrasound, Fuzhou University Affiliated Provincial Hospital, Fujian Provincial Hospital, Shengli Clinical Medical College of Fujian Medical University, Fuzhou, Fujian, China.
Department of Ultrasound, First Affiliated Hospital of Fujian Medical University, National Regional Medical Center, Fujian Medical University, Fuzhou, Fujian, China.
Front Oncol. 2025 May 15;15:1549866. doi: 10.3389/fonc.2025.1549866. eCollection 2025.
Thyroid Imaging Reporting and Data System (TIRADS) does not perform well in thyroid adenomatoid nodules on ultrasound (TANU). Therefore, we aimed to generate and validate a nomogram based on radiomics features and clinical information to predict the nature of TANU.
A total of 200 TANU in 200 patients were enrolled. Firstly, radiomics nomograms (R_Nomogram) and clinical nomograms (C_Nomogram) were constructed using eight machine-learning algorithms. The best R_Nomogram and C_Nomogram generated the Radiomics-clinical nomogram (R-C_nomogram). We compared the Area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis (DCA) of different nomograms. The unnecessary intervention rates were compared between nomograms and the 2017 ACR TI-RADS recommendations.
The R-C_Nomogram had a higher AUC than other nomograms [training cohort: R-C_Nomogram (AUC: 0.922) Vs. C_Nomogram (AUC: 0.825): <0.001, R-C_Nomogram Vs. R_ Nomogram (AUC:0.836), =0.007); validation cohort: R-C_Nomogram (AUC: 0.868) Vs. C_Nomogram (AUC: 0.850): =0.778, R-C_Nomogram Vs. R_Nomogram (AUC:0.684), =0.005). The R-C_Nomogram has the lowest unnecessary intervention rate among all approaches.
The R-C_Nomogram exhibited excellent diagnostic performances for predicting the nature of TANU. By incorporating clinical and radiomics features, the R-C Nomogram can reduce unnecessary biopsies and guide treatment decisions such as ultrasound-guided thermal ablation, improving patient management and reducing healthcare resource burden.
甲状腺影像报告和数据系统（TIRADS）在超声检查甲状腺腺瘤样结节（TANU）方面表现不佳。因此，我们旨在生成并验证一种基于影像组学特征和临床信息的列线图，以预测TANU的性质。
纳入200例患者的200个TANU。首先，使用八种机器学习算法构建影像组学列线图（R_Nomogram）和临床列线图（C_Nomogram）。最佳的R_Nomogram和C_Nomogram生成了影像组学-临床列线图（R-C_nomogram）。我们比较了不同列线图的受试者操作特征曲线下面积（AUC）、校准曲线和决策曲线分析（DCA）。比较了列线图与2017年美国放射学会（ACR）TI-RADS建议之间的不必要干预率。
R-C_Nomogram的AUC高于其他列线图[训练队列：R-C_Nomogram（AUC：0.922）对比C_Nomogram（AUC：0.825）：<0.001，R-C_Nomogram对比R_Nomogram（AUC：0.836），=0.007]；验证队列：R-C_Nomogram（AUC：0.868）对比C_Nomogram（AUC：0.850）：=0.778，R-C_Nomogram对比R_Nomogram（AUC：0.684），=0.005]。R-C_Nomogram在所有方法中不必要干预率最低。
R-C_Nomogram在预测TANU性质方面表现出优异的诊断性能。通过纳入临床和影像组学特征，R-C列线图可以减少不必要的活检，并指导超声引导下热消融等治疗决策，改善患者管理并减轻医疗资源负担。
