Zhong Lichang, Shi Lin, Li Weimei, Zhou Liang, Wang Kui, Gu Liping
Department of Ultrasound in Medicine, Sixth People's Hospital Affiliated to Medical College of Shanghai Jiaotong University, Shanghai Institute of Ultrasound in Medicine, Shanghai, China.
Department of Information, Sixth People's Hospital Affiliated to Medical College of Shanghai Jiaotong University, Shanghai, China.
J Clin Ultrasound. 2025 May 21. doi: 10.1002/jcu.24058.
Our objective is to develop and validate a deep learning radiomics nomogram (DLRN) based on preoperative ultrasound images and clinical features, for predicting the malignancy of thyroid nodules with indeterminate cytology (Bethesda III).
Between June 2017 and June 2022, we conducted a retrospective study on 194 patients with surgically confirmed indeterminate cytology (Bethesda III) in our hospital. The training and internal validation cohorts were comprised of 155 and 39 patients, in a 7:3 ratio. To facilitate external validation, we selected an additional 80 patients from each of the remaining two medical centers. Utilizing preoperative ultrasound data, we obtained imaging markers that encompass both deep learning and manually radiomic features. After feature selection, we developed a comprehensive diagnostic model to evaluate the predictive value for Bethesda III benign and malignant cases. The model's diagnostic accuracy, calibration, and clinical applicability were systematically assessed.
The results showed that the prediction model, which integrated 512 DTL features extracted from the pre-trained Resnet34 network, ultrasound radiomics, and clinical features, exhibited superior stability in distinguishing between benign and malignant indeterminate thyroid nodules (Bethesda Class III). In the validation set, the AUC was 0.92 (95% CI: 0.831-1.000), and the accuracy, sensitivity, specificity, precision, and recall were 0.897, 0.882, 0.909, 0.882, and 0.882, respectively.
The comprehensive multidimensional data model based on deep transfer learning, ultrasound radiomics features, and clinical characteristics can effectively distinguish the benign and malignant indeterminate thyroid nodules (Bethesda Class III), providing valuable guidance for treatment selection in patients with indeterminate thyroid nodules (Bethesda Class III).
我们的目标是基于术前超声图像和临床特征，开发并验证一种深度学习放射组学列线图（DLRN），用于预测细针穿刺活检结果不确定（贝塞斯达III类）的甲状腺结节的恶性程度。
2017年6月至2022年6月期间，我们对我院194例手术确诊为细针穿刺活检结果不确定（贝塞斯达III类）的患者进行了回顾性研究。训练队列和内部验证队列分别由155例和39例患者组成，比例为7:3。为便于外部验证，我们从另外两个医疗中心各选取了80例患者。利用术前超声数据，我们获得了包括深度学习和手动放射组学特征在内的影像标志物。经过特征选择后，我们开发了一个综合诊断模型，以评估其对贝塞斯达III类良性和恶性病例的预测价值。系统评估了该模型的诊断准确性、校准度和临床适用性。
结果显示，整合了从预训练的Resnet34网络提取的512个DTL特征、超声放射组学特征和临床特征的预测模型，在区分良性和恶性甲状腺细针穿刺活检结果不确定结节（贝塞斯达III类）方面表现出卓越的稳定性。在验证集中，AUC为0.92（95%CI：0.831 - 1.000），准确性、敏感性特异性、阳性预测值和召回率分别为0.897、0.882、0.909、0.882和0.882。
基于深度迁移学习、超声放射组学特征和临床特征的综合多维数据模型能够有效区分甲状腺细针穿刺活检结果不确定的良性和恶性结节（贝塞斯达III类），为甲状腺细针穿刺活检结果不确定结节（贝塞斯达III类）患者的治疗选择提供有价值的指导。
