Qian Tingting, Zhou Yahan, Yao Jincao, Ni Chen, Asif Sohaib, Chen Chen, Lv Lujiao, Ou Di, Xu Dong
Graduate School, The Second Clinical Medical College of Zhejiang Chinese Medical University, Hang Zhou, Zhejiang, 310014, China.
Department of Diagnostic Ultrasound Imaging &Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, 310022, China.
Endocrine. 2025 Mar;87(3):1060-1069. doi: 10.1007/s12020-024-04091-w. Epub 2024 Nov 18.
Cervical lymph node metastasis (CLNM) is the most common form of thyroid cancer metastasis. Accurate preoperative CLNM diagnosis is of more importance in patients with papillary thyroid cancer (PTC). However, there is currently no unified methods to objectively predict CLNM risk from ultrasonography in PTC patients.This study aimed to develop a deep learning (DL) model to help clinicians more accurately determine the existence of CLNM risk in patients with PTC and then assist them with treatment decisions.
Ultrasound dynamic videos of 388 patients with 717 thyroid nodules were retrospectively collected from Zhejiang Cancer Hospital between January 2020 and June 2022. Five deep learning (DL) models were investigated to examine its efficacy for predicting CLNM risks and their performances were also compared with those predicted using two-dimensional ultrasound static images.
In the testing dataset (n = 78), the DenseNet121 model trained on ultrasound dynamic videos outperformed the other four DL models as well as the DL model trained using the two-dimensional (2D) static images across all metrics. Specifically, using DenseNet121, the comparison between the 3D model and 2D model for all metrics are shown as below: AUROC: 0.903 versus 0.828, sensitivity: 0.877 versus 0.871, specificity: 0.865 versus 0.659.
This study demonstrated that the DenseNet121 model has the greatest potential in distinguishing CLNM from non-CLNM in patients with PTC. Dynamic videos also offered more information about the disease states which have proven to be more efficient and robust in identifying CLNM compared to statis images.
颈部淋巴结转移（CLNM）是甲状腺癌转移最常见的形式。准确的术前CLNM诊断对甲状腺乳头状癌（PTC）患者更为重要。然而，目前尚无统一的方法可从超声检查中客观预测PTC患者的CLNM风险。本研究旨在开发一种深度学习（DL）模型，以帮助临床医生更准确地判断PTC患者是否存在CLNM风险，进而辅助他们做出治疗决策。
回顾性收集了2020年1月至2022年6月期间在浙江省肿瘤医院就诊的388例患者的717个甲状腺结节的超声动态视频。研究了五种深度学习（DL）模型，以检验其预测CLNM风险的有效性，并将其性能与使用二维超声静态图像预测的性能进行比较。
在测试数据集（n = 78）中，基于超声动态视频训练的DenseNet121模型在所有指标上均优于其他四个DL模型以及使用二维（2D）静态图像训练的DL模型。具体而言，使用DenseNet121时，3D模型和2D模型在所有指标上的比较如下：曲线下面积（AUROC）：0.903对0.828，灵敏度：0.877对0.871，特异性：0.865对0.659。
本研究表明，DenseNet121模型在区分PTC患者的CLNM与非CLNM方面具有最大潜力。动态视频还提供了更多关于疾病状态的信息，与静态图像相比，已证明在识别CLNM方面更有效、更可靠。
