Liu Chang, Yang Shangjie, Xue Tian, Zhang Qian, Zhang Yanjing, Zhao Yufang, Yin Guolin, Yan Xiaohui, Liang Ping, Liu Liping
Department of Interventional Ultrasound, First Hospital of Shanxi Medical University, Taiyuan, China.
Department of Ultrasound, Xi'an Central Hospital, Xi'an, China.
Front Oncol. 2025 Jan 17;14:1507953. doi: 10.3389/fonc.2024.1507953. eCollection 2024.
PTC (papillary thyroid cancer) is a lymphotropic malignancy associated with cervical lymph node metastasis (CLNM, including central and lateral LNM), which compromises the effect of treatment and prognosis of patients. Accurate preoperative identification will provide valuable reference information for the formulation of diagnostic and treatment strategies. The aim of this study was to develop and validate a clinical-multimodal ultrasound radiomics model for predicting CLNM of PTC.
One hundred sixty-four patients with PTC who underwent treatment at our hospital between March 2016 and December 2021 were included in this study. The patients were grouped into a training cohort (n=115) and a validation cohort (n=49). Radiomic features were extracted from the conventional ultrasound (US), contrast-enhanced ultrasound (CEUS) and strain elastography-ultrasound (SE-US) images of patients with PTC. Multivariate logistic regression analysis was used to identify the independent risk factors. FAE software was used for radiomic feature extraction and the construction of different prediction models. The diagnostic performance of each model was evaluated and compared in terms of the area under the curve (AUC), sensitivity, specificity, accuracy, negative predictive value (NPV) and positive predictive value (PPV). RStudio software was used to develop the decision curve and assess the clinical value of the prediction model.
The clinical-multimodal ultrasound radiomics model developed in this study can successfully detect CLNM in PTC patients. A total of 3720 radiomic features (930 features per modality) were extracted from the ROIs of the multimodal images, and 15 representative features were ultimately screened. The combined model showed the best prediction performance in both the training and validation cohorts, with AUCs of 0.957 (95% CI: 0.918-0.987) and 0.932 (95% CI: 0.822-0.984), respectively. Decision curve analysis revealed that the combined model was superior to the other models.
The clinical-multimodal ultrasound radiomics model constructed with multimodal ultrasound radiomic features and clinical risk factors has favorable potential and high diagnostic value for predicting CLNM in PTC patients.
甲状腺乳头状癌（PTC）是一种具有亲淋巴性的恶性肿瘤，与颈部淋巴结转移（CLNM，包括中央区和侧方淋巴结转移）相关，这会影响患者的治疗效果和预后。准确的术前识别将为诊断和治疗策略的制定提供有价值的参考信息。本研究的目的是建立并验证一种用于预测PTC患者CLNM的临床-多模态超声影像组学模型。
本研究纳入了2016年3月至2021年12月在我院接受治疗的164例PTC患者。将患者分为训练队列（n = 115）和验证队列（n = 49）。从PTC患者的常规超声（US）、超声造影（CEUS）和应变弹性成像超声（SE-US）图像中提取影像组学特征。采用多因素逻辑回归分析确定独立危险因素。使用FAE软件进行影像组学特征提取和不同预测模型的构建。根据曲线下面积（AUC）、敏感性、特异性、准确性、阴性预测值（NPV）和阳性预测值（PPV）评估并比较各模型的诊断性能。使用RStudio软件绘制决策曲线并评估预测模型的临床价值。
本研究建立的临床-多模态超声影像组学模型能够成功检测PTC患者的CLNM。从多模态图像的感兴趣区共提取了3720个影像组学特征（每个模态930个特征），最终筛选出15个代表性特征。联合模型在训练队列和验证队列中均表现出最佳的预测性能，AUC分别为0.957（95%CI：0.918 - 0.987）和0.932（95%CI：0.822 - 0.984）。决策曲线分析显示联合模型优于其他模型。
基于多模态超声影像组学特征和临床危险因素构建的临床-多模态超声影像组学模型在预测PTC患者CLNM方面具有良好的潜力和较高的诊断价值。
