Table_1_Risk factors and diagnostic prediction models for papillary thyroid carcinoma.docx

Thyroid nodules (TNs) represent a common scenario. More accurate pre-operative diagnosis of malignancy has become an overriding concern. This study incorporated demographic, serological, ultrasound, and biopsy data and aimed to compare a new diagnostic prediction model based on Back Propagation Neural Network (BPNN) with multivariate logistic regression model, to guide the decision of surgery. Records of 2,090 patients with TNs who underwent thyroid surgery were retrospectively reviewed. Multivariate logistic regression analysis indicated that Bethesda category (OR=1.90, P<0.001), TIRADS (OR=2.55, P<0.001), age (OR=0.97, P=0.002), nodule size (OR=0.53, P<0.001), and serum levels of Tg (OR=0.994, P=0.004) and HDL-C (OR=0.23, P=0.001) were statistically significant independent differentiators for patients with PTC and benign nodules. Both BPNN and regression models showed good accuracy in differentiating PTC from benign nodules (area under the curve [AUC], 0.948 and 0.924, respectively). Notably, the BPNN model showed a higher specificity (88.3% vs. 73.9%) and negative predictive value (83.7% vs. 45.8%) than the regression model, while the sensitivity (93.1% vs. 93.9%) was similar between two models. Stratified analysis based on Bethesda indeterminate cytology categories showed similar findings. Therefore, BPNN and regression models based on a combination of demographic, serological, ultrasound, and biopsy data, all of which were readily available in routine clinical practice, might help guide the decision of surgery for TNs.

甲状腺结节（Thyroid Nodules, TNs）为临床常见病症，实现更为精准的术前恶性病变诊断已成为当前的核心关切。本研究纳入人口学、血清学、超声及活检相关数据，旨在对比一种基于反向传播神经网络（Back Propagation Neural Network, BPNN）的新型诊断预测模型与多因素logistic回归模型，以辅助甲状腺结节手术决策的制定。本研究回顾性分析了2090例接受甲状腺手术的甲状腺结节患者的临床资料。多因素logistic回归分析结果显示，贝塞斯达分类（Bethesda Category，OR=1.90，P<0.001）、甲状腺影像报告和数据系统（Thyroid Imaging Reporting and Data System, TIRADS，OR=2.55，P<0.001）、年龄（OR=0.97，P=0.002）、结节大小（OR=0.53，P<0.001）以及血清Tg（OR=0.994，P=0.004）与高密度脂蛋白胆固醇（High-Density Lipoprotein Cholesterol, HDL-C，OR=0.23，P=0.001）水平，均为区分乳头状甲状腺癌（Papillary Thyroid Carcinoma, PTC）患者与良性结节患者的独立统计学显著影响因素。反向传播神经网络模型与回归模型在区分乳头状甲状腺癌与良性结节时均表现出良好的诊断效能，受试者工作特征曲线下面积（Area Under the Curve, AUC）分别为0.948与0.924。值得注意的是，相较于回归模型，反向传播神经网络模型的特异性（88.3% vs. 73.9%）与阴性预测值（83.7% vs. 45.8%）更高，而二者的敏感性（93.1% vs. 93.9%）无显著差异。基于贝塞斯达不确定细胞学分类的分层分析也得到了相似的结果。综上，基于临床常规即可获取的人口学、血清学、超声及活检数据构建的反向传播神经网络模型与回归模型，可辅助甲状腺结节患者的手术决策制定。