Department of Medical Ultrasound, Jiangsu University Affiliated People's Hospital, Zhenjiang, Jiangsu Province, China.
School of Medicine, Jiangsu University, Zhenjiang, Jiangsu Province, China.
J Xray Sci Technol. 2023;31(6):1263-1280. doi: 10.3233/XST-230091.
Preoperative prediction of cervical lymph node metastasis (CLNM) in patients with papillary thyroid carcinoma (PTC) is significant for surgical decision-making.
This study aims to develop a dual-modal radiomics (DMR) model based on grayscale ultrasound (GSUS) and dual-energy computed tomography (DECT) for non-invasive CLNM in PTC.
In this study, 348 patients with pathologically confirmed PTC at Jiangsu University Affiliated People's Hospital who completed preoperative ultrasound (US) and DECT examinations were enrolled and randomly assigned to training (n = 261) and test (n = 87) cohorts. The enrolled patients were divided into two groups based on pathology findings namely, CLNM (n = 179) and CLNM-Free (n = 169). Radiomics features were extracted from GSUS images (464 features) and DECT images (960 features), respectively. Pearson correlation coefficient (PCC) and the least absolute shrinkage and selection operator (LASSO) regression with 10-fold cross-validation were then used to select CLNM-related features. Based on the selected features, GSUS, DECT, and GSUS combined DECT radiomics models were constructed by using a Support Vector Machine (SVM) classifier.
Three predictive models based on GSUS, DECT, and a combination of GSUS and DECT, yielded performance of areas under the curve (AUC) = 0.700 [95% confidence interval (CI), 0.662-0.706], 0.721 [95% CI, 0.683-0.727], and 0.760 [95% CI, 0.728-0.762] in the training dataset, and AUC = 0.643 [95% CI, 0.582-0.734], 0.680 [95% CI, 0.623-0.772], and 0.744 [95% CI, 0.686-0.784] in the test dataset, respectively. It shows that the predictive model combined GSUS and DECT outperforms both models using GSUS and DECT only.
The newly developed combined radiomics model could more accurately predict CLNM in PTC patients and aid in better surgical planning.
术前预测甲状腺乳头状癌（PTC）患者的颈部淋巴结转移（CLNM）对手术决策具有重要意义。
本研究旨在建立一种基于灰阶超声（GSUS）和双能计算机断层扫描（DECT）的双模态放射组学（DMR）模型，用于非侵入性预测 PTC 的 CLNM。
本研究纳入了在江苏大学附属医院经病理证实为 PTC 并完成术前超声（US）和 DECT 检查的 348 例患者，并将其随机分为训练（n=261）和测试（n=87）队列。根据病理结果将纳入的患者分为 CLNM 组（n=179）和 CLNM 阴性组（n=169）。分别从 GSUS 图像（464 个特征）和 DECT 图像（960 个特征）中提取放射组学特征。然后，使用 Pearson 相关系数（PCC）和 10 倍交叉验证的最小绝对收缩和选择算子（LASSO）回归选择与 CLNM 相关的特征。基于所选特征，使用支持向量机（SVM）分类器构建 GSUS、DECT 和 GSUS 联合 DECT 放射组学模型。
基于 GSUS、DECT 和 GSUS 联合 DECT 的三个预测模型，在训练数据集的 AUC 分别为 0.700[95%置信区间（CI），0.662-0.706]、0.721[95%CI，0.683-0.727]和 0.760[95%CI，0.728-0.762]，在测试数据集的 AUC 分别为 0.643[95%CI，0.582-0.734]、0.680[95%CI，0.623-0.772]和 0.744[95%CI，0.686-0.784]。这表明，与仅使用 GSUS 和 DECT 的两个模型相比，联合 GSUS 和 DECT 的预测模型表现更好。
新开发的联合放射组学模型可以更准确地预测 PTC 患者的 CLNM，有助于更好地进行手术规划。
