Qian Tingting, Feng Xuhan, Zhou Yahan, Ling Shan, Yao Jincao, Lai Min, Chen Chen, Lin Jun, Xu Dong
Graduate School, The Second Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, 310014, China.
Department of Diagnostic Ultrasound Imaging & Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, 310022, China.
Endocrine. 2025 Mar 8. doi: 10.1007/s12020-025-04198-8.
Thyroid nodules classified within the Thyroid Imaging Reporting and Data Systems (TI-RADS) category 3-5 are typically regarded as having varying degrees of malignancy risk, with the risk increasing from TI-RADS 3 to TI-RADS 5. While some of these nodules may undergo fine-needle aspiration (FNA) biopsy to assess their nature, this procedure carries a risk of false negatives and inherent complications. To avoid the need for unnecessary biopsy examination, we explored a method for distinguishing the benign and malignant characteristics of thyroid TI-RADS 3-5 nodules based on deep-learning ultrasound images combined with computed tomography (CT).
Thyroid nodules, assessed as American College of Radiology (ACR) TI-RADS category 3-5 through conventional ultrasound, all of which had postoperative pathology results, were examined using both conventional ultrasound and CT before operation. We investigated the effectiveness of deep-learning models based on ultrasound alone, CT alone, and a combination of both imaging modalities using the following metrics: Area Under Curve (AUC), sensitivity, accuracy, and positive predictive value (PPV). Additionally, we compared the diagnostic efficacy of the combined methods with manual readings of ultrasound and CT.
A total of 768 thyroid nodules falling within TI-RADS categories 3-5 were identified across 768 patients. The dataset comprised 499 malignant and 269 benign cases. For the automatic identification of thyroid TI-RADS category 3-5 nodules, deep learning combined with ultrasound and CT demonstrated a significantly higher AUC (0.930; 95% CI: 0.892, 0.969) compared to the application of ultrasound alone AUC (0.901; 95% CI: 0.856, 0.947) or CT alone AUC (0.776; 95% CI: 0.713, 0.840). Additionally, the AUC of combined modalities surpassed that of radiologists'assessments using ultrasound alone AUCmean (0.725；95% CI:0.677, 0.773), CT alone AUCmean (0.617; 95% CI:0.564, 0.669). Deep learning method combined with ultrasound and CT imaging of thyroid can allow more accurate and precise classification of nodules within TI-RADS categories 3-5.
甲状腺影像报告和数据系统（TI-RADS）分类为3-5类的甲状腺结节通常被认为具有不同程度的恶性风险，风险从TI-RADS 3到TI-RADS 5逐渐增加。虽然其中一些结节可能会接受细针穿刺（FNA）活检以评估其性质，但该操作存在假阴性和固有并发症的风险。为了避免不必要的活检检查，我们探索了一种基于深度学习超声图像结合计算机断层扫描（CT）来区分甲状腺TI-RADS 3-5类结节的良性和恶性特征的方法。
通过传统超声评估为美国放射学会（ACR）TI-RADS 3-5类的甲状腺结节，所有这些结节均有术后病理结果，在手术前使用传统超声和CT进行检查。我们使用以下指标研究了基于单独超声、单独CT以及两种成像方式组合的深度学习模型的有效性：曲线下面积（AUC）、敏感性、准确性和阳性预测值（PPV）。此外，我们将组合方法的诊断效能与超声和CT的人工读数进行了比较。
共识别出768例患者的768个TI-RADS 3-5类甲状腺结节。数据集包括499例恶性和269例良性病例。对于甲状腺TI-RADS 3-5类结节的自动识别，与单独应用超声的AUC（0.901；95%CI：0.856，0.947）或单独CT的AUC（0.776；95%CI：0.713，0.840）相比，深度学习结合超声和CT显示出显著更高的AUC（0.930；95%CI：0.892，0.969）。此外，组合模式的AUC超过了放射科医生单独使用超声的评估AUC均值（0.725；95%CI：0.677，0.773）、单独CT的AUC均值（0.617；95%CI：0.564，0.669）。深度学习方法结合甲状腺超声和CT成像可以对TI-RADS 3-5类结节进行更准确和精确的分类。
