Huang Xingzhi, Wu Zhenghua, Zhou Aiyun, Min Xiang, Qi Qi, Zhang Cheng, Chen Songli, Xu Pan
Department of Ultrasonography, The First Affiliated Hospital of Nanchang University, Nanchang, China.
Department of Head and Neck Otolaryngology, The First Affiliated Hospital of Nanchang University, Nanchang, China.
Front Oncol. 2021 Oct 13;11:737847. doi: 10.3389/fonc.2021.737847. eCollection 2021.
To develop and validate a nomogram combining radiomics of B-mode ultrasound (BMUS) images and the American College of Radiology (ACR) Thyroid Imaging Reporting and Data System (TI-RADS) for predicting malignant thyroid nodules and improving the performance of the guideline.
A total of 451 thyroid nodules referred for surgery and proven pathologically at an academic referral center from January 2019 to September 2020 were retrospectively collected and randomly assigned to training and validation cohorts (7:3 ratio). A nomogram was developed through combining the BMUS radiomics score (Rad-Score) with ACR TI-RADS score (ACR-Score) in the training cohort; the performance of the nomogram was assessed with respect to discrimination, calibration, and clinical application in the validation and entire cohorts.
The ACR-Rad nomogram showed good calibration and yielded an AUC of 0.877 (95% CI 0.836-0.919) in the training cohort and 0.864 (95% CI 0.799-0.931) in the validation cohort, which were significantly better than the ACR-Score model ( < 0.001 and 0.031, respectively). The significantly improved AUC, net reclassification index (NRI), and integrated discriminatory improvement (IDI) of the nomogram were found for both senior and junior radiologists (all < 0.001). Decision curve analysis indicated that the nomogram was clinically useful. When cutoff values for 50% predicted malignancy risk (ACR-Rad_50%) were applied, the nomogram showed increased specificity, accuracy and positive predictive value (PPV), and decreased unnecessary fine-needle aspiration (FNA) rates in comparison to ACR TI-RADS.
The ACR-Rad nomogram has favorable value in predicting malignant thyroid nodules and improving performance of the ACR TI-RADS for senior and junior radiologists.
开发并验证一种列线图，该列线图结合了B超（BMUS）图像的影像组学和美国放射学会（ACR）甲状腺影像报告和数据系统（TI-RADS），用于预测甲状腺恶性结节并提高指南的效能。
回顾性收集2019年1月至2020年9月在一家学术转诊中心接受手术并经病理证实的451个甲状腺结节，并将其随机分配到训练组和验证组（比例为7:3）。通过将训练组中的BMUS影像组学评分（Rad-Score）与ACR TI-RADS评分（ACR-Score）相结合来开发列线图；在验证组和整个队列中，从区分度、校准度和临床应用方面评估列线图的性能。
ACR-Rad列线图显示出良好的校准度，在训练组中的曲线下面积（AUC）为0.877（95%CI 0.836-0.919），在验证组中的AUC为0.864（95%CI 0.799-0.931），显著优于ACR-Score模型（分别为<0.001和0.031）。对于高级和初级放射科医生而言，列线图的AUC、净重新分类指数（NRI）和综合鉴别改善（IDI）均有显著提高（均<0.001）。决策曲线分析表明该列线图具有临床实用性。当应用预测恶性风险为50%的截断值（ACR-Rad_50%）时，与ACR TI-RADS相比，列线图显示出更高的特异性、准确性和阳性预测值（PPV），以及更低的不必要细针穿刺（FNA）率。
ACR-Rad列线图在预测甲状腺恶性结节以及提高ACR TI-RADS对高级和初级放射科医生的效能方面具有良好价值。
