Department of thyroid surgery, The Third Affiliated Hospital of Soochow University, Changzhou First People's Hospital, Changzhou, Jiangsu, China (J.W.F., G.F.Q., J.Y., L.Z.H., W.X.W., Y.J.).
Department of Ultrasound, The Third Affiliated Hospital of Soochow University, Changzhou First People's Hospital, Changzhou, Jiangsu, China (S.Q.L.).
Acad Radiol. 2024 Jun;31(6):2292-2305. doi: 10.1016/j.acra.2023.12.008. Epub 2024 Jan 16.
This investigation sought to create and verify a nomogram utilizing ultrasound radiomics and crucial clinical features to preoperatively identify central lymph node metastasis (CLNM) in patients diagnosed with papillary thyroid carcinoma (PTC).
We enrolled 1069 patients with PTC between January 2022 and January 2023. All patients were randomly divided into a training cohort (n = 748) and a validation cohort (n = 321). We extracted 129 radiomics features from the original gray-scale ultrasound image. Then minimum Redundancy-Maximum Relevance and Least Absolute Shrinkage and Selection Operator regression were used to select the CLNM-related features and calculate the radiomic signature. Incorporating the radiomic signature and clinical risk factors, a clinical-radiomics nomogram was constructed using multivariable logistic regression. The predictive performance of clinical-radiomics nomogram was evaluated by calibration, discrimination, and clinical utility in the training and validation cohorts.
The clinical-radiomics nomogram which consisted of five predictors (age, tumor size, margin, lateral lymph node metastasis, and radiomics signature), showed good calibration and discrimination in both the training (AUC 0.960; 95% CI, 0.947-0.972) and the validation (AUC 0.925; 95% CI, 0.895-0.955) cohorts. Discrimination of the clinical-radiomics nomogram showed better discriminative ability than the clinical signature, radiomics signature, and conventional ultrasound model in both the training and validation cohorts. Decision curve analysis showed satisfactory clinical utility of the nomogram.
The clinical-radiomics nomogram incorporating radiomic signature and key clinical features was efficacious in predicting CLNM in PTC patients.
本研究旨在创建并验证一个列线图，利用超声放射组学和关键临床特征，术前识别诊断为甲状腺乳头状癌（PTC）患者的中央淋巴结转移（CLNM）。
我们纳入了 2022 年 1 月至 2023 年 1 月期间的 1069 例 PTC 患者。所有患者被随机分为训练队列（n=748）和验证队列（n=321）。我们从原始灰度超声图像中提取了 129 个放射组学特征。然后采用最小冗余最大相关性和最小绝对收缩和选择算子回归选择与 CLNM 相关的特征并计算放射组学特征。通过多变量逻辑回归将放射组学特征和临床危险因素纳入，构建临床放射组学列线图。在训练和验证队列中，通过校准、区分度和临床实用性评估临床放射组学列线图的预测性能。
由五个预测因素（年龄、肿瘤大小、边界、侧方淋巴结转移和放射组学特征）组成的临床放射组学列线图在训练（AUC 0.960；95%CI，0.947-0.972）和验证（AUC 0.925；95%CI，0.895-0.955）队列中均具有良好的校准和区分度。在训练和验证队列中，临床放射组学列线图的区分度均显示出比临床特征、放射组学特征和常规超声模型更好的区分能力。决策曲线分析显示列线图具有良好的临床实用性。
该列线图整合了放射组学特征和关键临床特征，可有效预测 PTC 患者的 CLNM。
