Department of Ultrasound, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, 270 Dong'an Road, Shanghai, 200032, China.
Department of Ultrasound, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200025, China.
BMC Med Imaging. 2022 May 2;22(1):82. doi: 10.1186/s12880-022-00809-2.
An accurate preoperative assessment of cervical lymph node metastasis (LNM) is important for choosing an optimal therapeutic strategy for papillary thyroid carcinoma (PTC) patients. This study aimed to develop and validate two ultrasound (US) nomograms for the individual prediction of central and lateral compartment LNM in patients with PTC.
A total of 720 PTC patients from 3 institutions were enrolled in this study. They were categorized into a primary cohort, an internal validation, and two external validation cohorts. Radiomics features were extracted from conventional US images. LASSO regression was used to select optimized features to construct the radiomics signature. Two nomograms integrating independent clinical variables and radiomics signature were established with multivariate logistic regression. The performance of the nomograms was assessed with regard to discrimination, calibration, and clinical usefulness.
The radiomics scores were significantly higher in patients with central/lateral LNM. A radiomics nomogram indicated good discrimination for central compartment LNM, with an area under the curve (AUC) of 0.875 in the training set, the corresponding value in the validation sets were 0.856, 0.870 and 0.870, respectively. Another nomogram for predicting lateral LNM also demonstrated good performance with an AUC of 0.938 and 0.905 in the training and internal validation cohorts, respectively. The AUC for the two external validation cohorts were 0.881 and 0.903, respectively. The clinical utility of the nomograms was confirmed by the decision curve analysis.
The nomograms proposed here have favorable performance for preoperatively predicting cervical LNM, hold promise for optimizing the personalized treatment, and might greatly facilitate the decision-making in clinical practice.
准确评估甲状腺乳头状癌（PTC）患者颈部淋巴结转移（LNM）对于选择最佳治疗策略至关重要。本研究旨在建立并验证两种超声（US）列线图，以分别预测 PTC 患者中央区和侧区 LNM。
本研究共纳入 3 家中心的 720 例 PTC 患者，分为原始队列、内部验证队列和 2 个外部验证队列。从常规 US 图像中提取放射组学特征。采用 LASSO 回归选择优化特征以构建放射组学特征。利用多元逻辑回归建立纳入独立临床变量和放射组学特征的 2 个列线图。采用鉴别度、校准度和临床实用性评估列线图的性能。
有中央/侧区 LNM 的患者放射组学评分显著更高。放射组学列线图对中央区 LNM 具有良好的鉴别能力，在训练集、验证集 1、验证集 2 的曲线下面积（AUC）分别为 0.875、0.856、0.870 和 0.870。另一个预测侧区 LNM 的列线图也表现出良好的性能，在训练集和内部验证集的 AUC 分别为 0.938 和 0.905。外部验证集 1 和验证集 2 的 AUC 分别为 0.881 和 0.903。决策曲线分析证实了列线图的临床实用性。
本研究提出的列线图在预测颈部 LNM 方面具有良好的性能，有望优化个体化治疗，并可能极大地促进临床实践中的决策。
