Han Haiyang, Sun Heng, Zhou Chang, Wei Li, Xu Liang, Shen Dian, Hu Wenshu
Department of Ultrasound, The First College of Clinical Medical Science, China Three Gorges University & Yichang Central People's Hospital, Yichang, 443003, China.
BMC Med Imaging. 2025 Jul 1;25(1):228. doi: 10.1186/s12880-025-01757-3.
Papillary thyroid microcarcinoma (PTMC) is the most common malignant subtype of thyroid cancer. Preoperative assessment of the risk of central compartment lymph node metastasis (CCLNM) can provide scientific support for personalized treatment decisions prior to microwave ablation of thyroid nodules. The objective of this study was to develop a predictive model for CCLNM in patients with solitary PTMC on the basis of a combination of ultrasound radiomics and clinical parameters.
We retrospectively analyzed data from 480 patients diagnosed with PTMC via postoperative pathological examination. The patients were randomly divided into a training set (n = 336) and a validation set (n = 144) at a 7:3 ratio. The cohort was stratified into a metastasis group and a nonmetastasis group on the basis of postoperative pathological results. Ultrasound radiomic features were extracted from routine thyroid ultrasound images, and multiple feature selection methods were applied to construct radiomic models for each group. Independent risk factors, along with radiomics features identified through multivariate logistic regression analysis, were subsequently refined through additional feature selection techniques to develop combined predictive models. The performance of each model was then evaluated.
The combined model, which incorporates age, the presence of Hashimoto's thyroiditis (HT), and radiomics features selected via an optimal feature selection approach (percentage-based), exhibited superior predictive efficacy, with AUC values of 0.767 (95% CI: 0.716-0.818) in the training set and 0.729 (95% CI: 0.648-0.810) in the validation set.
A machine learning-based model combining ultrasound radiomics and clinical variables shows promise for the preoperative risk stratification of CCLNM in patients with PTMC. However, further validation in larger, more diverse cohorts is needed before clinical application.
Not applicable.
甲状腺微小乳头状癌（PTMC）是甲状腺癌最常见的恶性亚型。术前评估中央区淋巴结转移（CCLNM）风险可为甲状腺结节微波消融术前的个体化治疗决策提供科学依据。本研究的目的是基于超声影像组学和临床参数的组合，开发一种针对孤立性PTMC患者CCLNM的预测模型。
我们回顾性分析了480例经术后病理检查确诊为PTMC的患者的数据。患者按7:3的比例随机分为训练集（n = 336）和验证集（n = 144）。根据术后病理结果将队列分为转移组和非转移组。从常规甲状腺超声图像中提取超声影像组学特征，并应用多种特征选择方法为每组构建影像组学模型。通过多因素逻辑回归分析确定的独立危险因素以及影像组学特征，随后通过额外的特征选择技术进行优化，以开发联合预测模型。然后评估每个模型的性能。
结合年龄、桥本甲状腺炎（HT）的存在以及通过最佳特征选择方法（基于百分比）选择的影像组学特征的联合模型显示出卓越的预测效能，训练集中的AUC值为0.767（95%CI：0.716 - 0.818），验证集中的AUC值为0.729（95%CI：0.648 - 0.810）。
基于机器学习的模型结合超声影像组学和临床变量，在PTMC患者CCLNM的术前风险分层方面显示出前景。然而，在临床应用之前，需要在更大、更多样化的队列中进行进一步验证。
不适用。
