Gao Lei, Wen Xiuli, Yue Guanghui, Wang Hui, Lu Ziqing, Wu Beibei, Liu Zhihong, Wu Yuming, Lin Dongmei, Yi Shijian, Jiang Wei, Hao Yi
Department of Ultrasound, South China Hospital, Medical School, Shenzhen University, Shenzhen, China.
School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, China.
Ultrason Imaging. 2025 Mar;47(2):93-103. doi: 10.1177/01617346251313982. Epub 2025 Jan 27.
This study aims to establish and validate an ultrasound radiomics nomogram for preoperative prediction of central lymph node metastasis in papillary thyroid microcarcinoma (PTMC) before operation. A retrospective analysis conducted on ultrasonic images and clinical features derived from 288 PTMC patients, who were divided into training cohorts ( = 201) and validating cohorts ( = 87) in a ratio of 7:3 base on the principle of random allocation. Radiomics features were extracted from the PTMC patients after ultrasonic examination, followed by dimension reduction and characteristic selection to construct the radiomics score (Radscore) using LASSO regression analysis. Subsequently, the models, ultrasound features plus clinical features (US-Clin), radiomics score model, and combined model of clinical features plus ultrasound features and Radscore (Combined-model) were built through multi-factor logistic regression analysis. After that, the nomograms were developed for visualization and presentation of these models. The discriminative power, calibration and clinical utility of the nomogram models were evaluated in the training and validating cohorts. The Radscore model comprised 12 carefully selected features. The independent risk factors for conventional ultrasound features and clinical features of PTMC in predicting CLNM included age <45 years, tumor envelope invasion, male gender and presence of microcalcifications, while the enhanced ultrasound features risk factor was extrathyroidal expansion. The combined model showed good performance in predicting PTMC CLNM, with AUCs of 0.921 and 0.889 in the training and validating cohorts, respectively. And DCA based on the prediction model showed good clinical utility. The nomogram developed based on preoperative clinical data, ultrasound features, and Radscore of PTMC patients can more accurately predict central lymph node metastasis (CLNM) in PTMC patients. However, it needs to be validated for clinical applicability in multicenter studies with larger sample sizes and combined with genomic mutation analyses of the tumors.
本研究旨在建立并验证一种超声影像组学列线图，用于术前预测甲状腺微小乳头状癌（PTMC）的中央区淋巴结转移。对288例PTMC患者的超声图像和临床特征进行回顾性分析，根据随机分配原则，以7:3的比例将患者分为训练队列（n = 201）和验证队列（n = 87）。超声检查后从PTMC患者中提取影像组学特征，随后进行降维和特征选择，使用LASSO回归分析构建影像组学评分（Radscore）。随后，通过多因素逻辑回归分析建立模型，即超声特征加临床特征（US-Clin）模型、影像组学评分模型以及临床特征加超声特征和Radscore的联合模型（Combined-model）。之后，绘制列线图以直观展示这些模型。在训练队列和验证队列中评估列线图模型的鉴别能力、校准度和临床实用性。Radscore模型包含12个精心挑选的特征。PTMC常规超声特征和临床特征预测中央区淋巴结转移的独立危险因素包括年龄<45岁、肿瘤包膜侵犯、男性以及存在微钙化，而超声造影特征的危险因素是甲状腺外扩展。联合模型在预测PTMC中央区淋巴结转移方面表现良好，训练队列和验证队列中的AUC分别为0.921和0.889。基于预测模型的DCA显示出良好的临床实用性。基于PTMC患者术前临床数据、超声特征和Radscore开发的列线图能够更准确地预测PTMC患者的中央区淋巴结转移（CLNM）。然而，其临床适用性需要在更大样本量的多中心研究中结合肿瘤的基因组突变分析进行验证。
