Agyekum Enock Adjei, Yuzhi Zhang, Fang Yu, Agyekum Doris Nti, Wang Xian, Issaka Eliasu, Li CuiRong, Shen Xiangjun, Qian Xiaoqin, Wu Xinping
Department of Ultrasound, Affiliated People's Hospital of Jiangsu University, Zhenjiang, 212002, China.
School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang, Jiangsu Province, China.
Sci Rep. 2025 Jul 1;15(1):21708. doi: 10.1038/s41598-025-05551-7.
This study aimed to develop and validate convolutional neural network (CNN) models for distinguishing follicular thyroid carcinoma (FTC) from follicular thyroid adenoma (FTA). Additionally, this current study compared the performance of CNN models with the American College of Radiology Thyroid Imaging Reporting and Data System (ACR-TIRADS) and Chinese Thyroid Imaging Reporting and Data System (C-TIRADS) ultrasound-based malignancy risk stratification systems. A total of 327 eligible patients with FTC and FTA who underwent preoperative thyroid ultrasound examination were retrospectively enrolled between August 2017, and August 2024. Patients were randomly assigned to a training cohort (n = 263) and a test cohort (n = 64) in an 8:2 ratio using stratified sampling. Five CNN models, including VGG16, ResNet101, MobileNetV2, ResNet152, and ResNet50, pre-trained with ImageNet, were developed and tested to distinguish FTC from FTA. The CNN models exhibited good performance, yielding areas under the receiver operating characteristic curve (AUC) ranging from 0.64 to 0.77. The ResNet152 model demonstrated the highest AUC (0.77; 95% CI, 0.67-0.87) for distinguishing between FTC and FTA. Decision curve and calibration curve analyses demonstrated the models' favorable clinical value and calibration. Furthermore, when comparing the performance of the developed models with that of the C-TIRADS and ACR-TIRADS systems, the models developed in this study demonstrated superior performance. This can potentially guide appropriate management of FTC in patients with follicular neoplasms.
本研究旨在开发并验证用于区分甲状腺滤泡癌（FTC）和甲状腺滤泡性腺瘤（FTA）的卷积神经网络（CNN）模型。此外，本研究还将CNN模型的性能与美国放射学会甲状腺影像报告和数据系统（ACR-TIRADS）以及中国甲状腺影像报告和数据系统（C-TIRADS）基于超声的恶性风险分层系统进行了比较。2017年8月至2024年8月期间，共回顾性纳入了327例接受术前甲状腺超声检查的符合条件的FTC和FTA患者。采用分层抽样的方法，将患者以8:2的比例随机分配到训练队列（n = 263）和测试队列（n = 64）。开发并测试了五个使用ImageNet预训练的CNN模型，包括VGG16、ResNet101、MobileNetV2、ResNet152和ResNet50，以区分FTC和FTA。这些CNN模型表现出良好的性能，受试者操作特征曲线（AUC）下面积范围为0.64至0.77。ResNet152模型在区分FTC和FTA方面表现出最高的AUC（0.77；95%CI，0.67 - 0.87）。决策曲线和校准曲线分析证明了这些模型具有良好的临床价值和校准效果。此外，在将开发的模型与C-TIRADS和ACR-TIRADS系统的性能进行比较时，本研究开发的模型表现更优。这可能有助于指导对滤泡性肿瘤患者的FTC进行适当管理。
