Ben Jiangyuan, Yv Qiying, Zhu Pengfei, Ren Junhao, Zhou Pu, Chen Guifang, He Ying
Cancer Research Center Nantong, Affiliated Tumor Hospital of Nantong University, and Medical School of Nantong University, Nantong, China.
Department of Ultrasound, Affiliated Tumor Hospital of Nantong University, Nantong, Jiangsu, China.
Front Oncol. 2025 Jul 16;15:1604951. doi: 10.3389/fonc.2025.1604951. eCollection 2025.
This study aimed to construct a model by applying radiomics and machine learning (ML) to multimodal ultrasound images (including grayscale, elastography and microflow images) along with clinical data to predict central lymph node metastasis (CLNM) in patients with papillary thyroid cancer (PTC).
A cohort of 213 patients who underwent thyroidectomy accompanied by lymph node dissection (LND) and were pathologically diagnosed with PTC postoperatively was enrolled and randomized to the training cohort (n = 170) or testing cohort (n = 43). Radiomics features were extracted from multimodal images and subsequently screened via the least absolute shrinkage and selection operator (LASSO). The same methods were applied to screen clinical features. Nine ML algorithms were used to construct clinical models, radiomics models and fusion models. Model performance was assessed via receiver operating characteristic curves (ROC), decision curve analysis (DCA), and Delong test. Finally, the optimal model was interpreted and visualized via Shapley additive explanation (SHAP).
In each modality, 1561 features were extracted from the ultrasound images. Sixteen features were ultimately retained, including 6 grayscale features, 6 elastography features, and 4 microflow features. From the clinical features, including gender, age, traditional ultrasound signs and serological indicators, 2 relevant features were selected. Among the prediction models, the fusion model constructed by Multilayer Perceptron (MLP) algorithm showed the best diagnostic performance, outperforming the other models in both the training cohort (AUC = 0.886) and the testing cohort (AUC = 0.873).
The fusion model based on clinical data and multimodal ultrasound radiomics has better predictive ability and net clinical benefit for CLNM in patients with PTC, confirms the diagnostic value of microflow images for CLNM, and can help to evaluate patients' preoperative lymph node status and make the correct decision on the surgical procedure.
本研究旨在通过将放射组学和机器学习（ML）应用于多模态超声图像（包括灰度、弹性成像和微流图像）以及临床数据，构建一个模型，以预测甲状腺乳头状癌（PTC）患者的中央淋巴结转移（CLNM）。
纳入213例接受甲状腺切除术并伴有淋巴结清扫（LND）且术后经病理诊断为PTC的患者，随机分为训练队列（n = 170）或测试队列（n = 43）。从多模态图像中提取放射组学特征，随后通过最小绝对收缩和选择算子（LASSO）进行筛选。采用相同方法筛选临床特征。使用九种ML算法构建临床模型、放射组学模型和融合模型。通过受试者操作特征曲线（ROC）、决策曲线分析（DCA）和德龙检验评估模型性能。最后，通过夏普利值加法解释（SHAP）对最佳模型进行解释和可视化。
在每种模态下，从超声图像中提取了1561个特征。最终保留了16个特征，包括6个灰度特征、6个弹性成像特征和4个微流特征。从包括性别、年龄、传统超声征象和血清学指标在内的临床特征中，选择了2个相关特征。在预测模型中，由多层感知器（MLP）算法构建的融合模型显示出最佳诊断性能，在训练队列（AUC = 0.886）和测试队列（AUC = 0.873）中均优于其他模型。
基于临床数据和多模态超声放射组学的融合模型对PTC患者的CLNM具有更好的预测能力和净临床效益，证实了微流图像对CLNM的诊断价值，并有助于评估患者术前淋巴结状态，对手术方案做出正确决策。
