Department of Endocrinology and Metabolism, Endocrine and Metabolic Disease Medical Center, Nanjing University Medical School Affiliated Drum Tower Hospital, Nanjing, China.
Department of Endocrinology and Metabolism, The Fifth People's Hospital of Suzhou Wujiang, Suzhou, China.
Front Endocrinol (Lausanne). 2022 Sep 5;13:938008. doi: 10.3389/fendo.2022.938008. eCollection 2022.
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（TNs）是一种常见的情况。更准确的术前恶性肿瘤诊断已成为首要关注的问题。本研究纳入了人口统计学、血清学、超声和活检数据，并旨在比较基于反向传播神经网络（BPNN）的新诊断预测模型与多变量逻辑回归模型，以指导手术决策。回顾性分析了 2090 例接受甲状腺手术的 TN 患者的记录。多变量逻辑回归分析表明，Bethesda 分类（OR=1.90，P<0.001）、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）和血清 Tg（OR=0.994，P=0.004）和 HDL-C（OR=0.23，P=0.001）水平是 PTC 和良性结节患者的统计学上显著的独立鉴别因素。BPNN 和回归模型在鉴别 PTC 和良性结节方面均表现出良好的准确性（曲线下面积 [AUC]，分别为 0.948 和 0.924）。值得注意的是，BPNN 模型的特异性（88.3% vs. 73.9%）和阴性预测值（83.7% vs. 45.8%）均高于回归模型，而敏感性（93.1% vs. 93.9%）则相似。基于 Bethesda 不确定细胞学分类的分层分析也得出了类似的结果。因此，基于人口统计学、血清学、超声和活检数据组合的 BPNN 和回归模型，这些数据在常规临床实践中均易于获得，可能有助于指导 TN 手术决策。
