Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Clinical College of The Affiliated Central Hospital, School of Medcine, Lishui University, Lishui 323000, China; Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China.
Department of Head and Neck Surgery, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China.
Acad Radiol. 2024 Jan;31(1):142-156. doi: 10.1016/j.acra.2023.04.038. Epub 2023 Jun 4.
This study aimed to develop and validate a dual-energy CT (DECT)-based model for preoperative prediction of the number of central lymph node metastases (CLNMs) in clinically node-negative (cN0) papillary thyroid carcinoma (PTC) patients.
Between January 2016 and January 2021, 490 patients who underwent lobectomy or thyroidectomy, CLN dissection, and preoperative DECT examinations were enrolled and randomly allocated into the training (N = 345) and validation cohorts (N = 145). The patients' clinical characteristics and quantitative DECT parameters obtained on primary tumors were collected. Independent predictors of> 5 CLNMs were identified and integrated to construct a DECT-based prediction model, for which the area under the curve (AUC), calibration, and clinical usefulness were assessed. Risk group stratification was performed to distinguish patients with different recurrence risks.
More than 5 CLNMs were found in 75 (15.3%) cN0 PTC patients. Age, tumor size, normalized iodine concentration (NIC), normalized effective atomic number (nZ) and the slope of the spectral Hounsfield unit curve (λ) in the arterial phase were independently associated with> 5 CLNMs. The DECT-based nomogram that incorporated predictors demonstrated favorable performance in both cohorts (AUC: 0.842 and 0.848) and significantly outperformed the clinical model (AUC: 0.688 and 0.694). The nomogram showed good calibration and added clinical benefit for predicting> 5 CLNMs. The KaplanMeier curves for recurrence-free survival showed that the high- and low-risk groups stratified by the nomogram were significantly different.
The nomogram based on DECT parameters and clinical factors could facilitate preoperative prediction of the number of CLNMs in cN0 PTC patients.
本研究旨在建立并验证一种基于双能 CT（DECT）的模型，用于术前预测临床淋巴结阴性（cN0）甲状腺乳头状癌（PTC）患者中央淋巴结转移（CLNM）的数量。
2016 年 1 月至 2021 年 1 月，共纳入 490 例接受甲状腺叶切除术或甲状腺切除术、中央淋巴结清扫术及术前 DECT 检查的患者，并将其随机分配至训练队列（n=345）和验证队列（n=145）。收集患者的临床特征及原发肿瘤的定量 DECT 参数。确定并整合独立的>5 个 CLNM 预测因子以构建 DECT 预测模型，评估其曲线下面积（AUC）、校准度和临床实用性。进行风险分层以区分不同复发风险的患者。
在 490 例 cN0 PTC 患者中，有 75 例（15.3%）患者发现>5 个 CLNM。年龄、肿瘤大小、碘标准化浓度（NIC）、标准化有效原子序数（nZ）和动脉期光谱 CT 值斜率（λ）与>5 个 CLNM 独立相关。纳入预测因子的基于 DECT 的列线图在两个队列中均具有良好的表现（AUC：0.842 和 0.848），并显著优于临床模型（AUC：0.688 和 0.694）。该列线图具有良好的校准度，并为预测>5 个 CLNM 提供了附加的临床获益。基于列线图的无复发生存曲线显示，高低风险组之间差异显著。
基于 DECT 参数和临床因素的列线图有助于术前预测 cN0 PTC 患者的 CLNM 数量。
