The Fourth School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou 310053, China.
Hangzhou Cancer Hospital, Hangzhou 310005China.
J Clin Endocrinol Metab. 2024 Nov 18;109(12):3036-3045. doi: 10.1210/clinem/dgae364.
To develop and validate a radiomics-clinical combined model combining preoperative computed tomography (CT) and clinical data from patients with papillary thyroid carcinoma (PTC) to predict the efficacy of initial postoperative 131I treatment.
A total of 181 patients with PTC who received total thyroidectomy and initial 131I treatment were divided into training and testing sets (7:3 ratio). Univariate analysis and multivariate logistic regression were used to screen clinical factors affecting the therapeutic response to 131I treatment and construct a clinical model. Radiomics features extracted from preoperative CT images of PTCs were dimensionally reduced through recursive feature elimination and least absolute shrinkage and selection operator. Logistic regression was used to establish a radiomics model, and a radiomics-clinical combined model was developed by integrating the clinical model. The area under the curve (AUC), sensitivity, and specificity were used to evaluate the prediction performance of each model.
Multivariate analysis revealed that pre-131I treatment serum thyroglobulin was an independent clinical risk factor affecting the efficacy of initial 131I treatment (P = .002), and the AUC, sensitivity, and specificity for predicting the efficacy of initial 131I treatment were 0.895, 0.899, and 0.816, respectively. After dimensionality reduction, 14 key CT radiomics features of PTCs were included. The established radiomics model predicted the efficacy of 131I treatment in the training and testing sets with AUCs of 0.825 and 0.809, sensitivities of 0.828 and 0.636, and specificities of 0.745 and 0.944, respectively. The combined model improved the AUC, sensitivity, and specificity in both sets.
The preoperative CT-based radiomics model can effectively predict the efficacy of initial postoperative 131I treatment in patients with intermediate- or high-risk PTC, and the radiomics-clinical combined model exhibits better predictive performance.
建立并验证一种基于术前计算机断层扫描（CT）和临床数据的甲状腺乳头状癌（PTC）患者放射组学-临床联合模型，以预测初始术后 131I 治疗的疗效。
将 181 例接受甲状腺全切除术和初始 131I 治疗的 PTC 患者分为训练集和测试集（比例为 7:3）。采用单因素分析和多因素逻辑回归筛选影响 131I 治疗疗效的临床因素，并构建临床模型。从 PTC 患者术前 CT 图像中提取放射组学特征，通过递归特征消除和最小绝对收缩和选择算子进行降维。采用逻辑回归建立放射组学模型，并通过整合临床模型建立放射组学-临床联合模型。采用曲线下面积（AUC）、灵敏度和特异度评估各模型的预测性能。
多因素分析显示，治疗前血清甲状腺球蛋白是影响初始 131I 治疗疗效的独立临床危险因素（P =.002），预测初始 131I 治疗疗效的 AUC、灵敏度和特异度分别为 0.895、0.899 和 0.816。降维后，纳入 PTC 的 14 个关键 CT 放射组学特征。训练集和测试集中建立的放射组学模型预测 131I 治疗疗效的 AUC 分别为 0.825 和 0.809，灵敏度分别为 0.828 和 0.636，特异度分别为 0.745 和 0.944。联合模型提高了两组的 AUC、灵敏度和特异度。
基于术前 CT 的放射组学模型可有效预测中高危 PTC 患者初始术后 131I 治疗的疗效，放射组学-临床联合模型具有更好的预测性能。
