预测美国放射学会甲状腺影像报告和数据系统（ACR TI-RADS）3类结节中的甲状腺微小乳头状癌：基于肿瘤内和肿瘤周围超声图像的放射组学分析
Predicting papillary thyroid microcarcinoma in American College of Radiology Thyroid Imaging Reporting and Data System (ACR TI-RADS) 3 nodules: radiomics analysis based on intratumoral and peritumoral ultrasound images.
作者信息
Chen Zhang, Zhan Wenting, He Huiliao, Yu Haolong, Huang Xiaoyan, Wu Zhijing, Yang Yan
机构信息
Department of Ultrasound Imaging, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China.
Wenzhou Key Laboratory of Structural and Functional Imaging, Wenzhou, China.
出版信息
Gland Surg. 2024 Jun 30;13(6):897-909. doi: 10.21037/gs-24-30. Epub 2024 Jun 20.
BACKGROUND
A subset of patients undergoing thyroid surgery for presumed benign thyroid disease presented with papillary thyroid microcarcinoma (PTMC). A non-invasive and precise method for early recognition of PTMC are urgently needed. The aim of this study was to construct and validate a nomogram that combines intratumoral and peritumoral radiomics features as well as clinical features for predicting PTMC in the American College of Radiology Thyroid Imaging Reporting and Data System (ACR TI-RADS) 3 nodules using ultrasonography.
METHODS
A retrospective review was conducted on a cohort of 221 patients who presented with ACR TI-RADS 3 nodules. These patients were subsequently pathologically diagnosed with either PTMC or benign thyroid nodules. These patients were randomly divided into a training and test cohort with an 8:2 ratio for developing the clinical model, intratumor-region model, peritumor-region model and the combined-region model respectively. The radiomics features were extracted from ultrasound (US) images of each patient. We employed K-nearest neighbor (KNN) model as the base model for building the radiomics signature and clinical signature. Finally, a radiomics-clinical nomogram that combined intratumoral and peritumoral radiomics features as well as clinical features was developed. The prediction performance of each model was assessed by the area under the curve (AUC), sensitivity, specificity and calibration curve.
RESULTS
A total of 23 radiomics features were selected to develop radiomics models. The combined-region radiomics model showed favorable prediction efficiency in both the training dataset (AUC: 0.955) and the test dataset (AUC: 0.923). A radiomics-clinical nomogram was constructed and achieved excellent calibration and discrimination, which yielded an AUC value of 0.950, a sensitivity of 0.950 and a specificity of 0.920.
CONCLUSIONS
This study proposed the nomogram that contributes to the accurate and intuitive identification of PTMC in ACR TI-RADS 3 nodules.
背景
一部分因疑似良性甲状腺疾病接受甲状腺手术的患者被诊断为甲状腺微小乳头状癌（PTMC）。迫切需要一种非侵入性且精确的方法来早期识别PTMC。本研究的目的是构建并验证一种列线图，该列线图结合肿瘤内和肿瘤周围的影像组学特征以及临床特征，用于使用超声预测美国放射学会甲状腺影像报告和数据系统（ACR TI-RADS）3类结节中的PTMC。
方法
对221例ACR TI-RADS 3类结节患者进行回顾性研究。这些患者随后经病理诊断为PTMC或良性甲状腺结节。将这些患者以8:2的比例随机分为训练组和测试组，分别用于建立临床模型、肿瘤内区域模型、肿瘤周围区域模型和联合区域模型。从每位患者的超声（US）图像中提取影像组学特征。我们采用K近邻（KNN）模型作为构建影像组学特征和临床特征的基础模型。最后，开发了一种结合肿瘤内和肿瘤周围影像组学特征以及临床特征的影像组学-临床列线图。通过曲线下面积（AUC）、敏感性、特异性和校准曲线评估每个模型的预测性能。
结果
共选择23个影像组学特征来建立影像组学模型。联合区域影像组学模型在训练数据集（AUC：0.955）和测试数据集（AUC：0.923）中均显示出良好的预测效率。构建了一种影像组学-临床列线图，其校准和区分度良好，AUC值为0.950，敏感性为0.950，特异性为0.920。
结论
本研究提出的列线图有助于准确直观地识别ACR TI-RADS 3类结节中的PTMC。
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