Department of Pathology, SUNY Downstate Medical Center, Brooklyn, NY, USA.
Department of Pathology, LSU Health Sciences Center, New Orleans, LA, USA.
Cancer Med. 2021 Apr;10(8):2723-2731. doi: 10.1002/cam4.3866. Epub 2021 Mar 24.
Thyroid nodules have a low prevalence of malignancy and most proven cancers do not behave aggressively. Thus, risk-stratification of nodules is a critical step to avoid surgical overtreatment. We hypothesized that a risk management system superior to those currently in use could be created to reduce the number of clinically indeterminate nodules (i.e., the "gray zone") by concurrently considering the malignancy risks conferred by clinical, ultrasonographic, and cytologic variables.
Thyroidectomy cases were reviewed from three institutions. Their benign versus malignant outcome was used to evaluate the variables for correlation. A binary logistic regression model was trained and, using indeterminate nodules with Bethesda III and IV results, validated. A scoring nomogram was designed to demonstrate the application of the model in clinical practice.
One hundred thirty thyroidectomies (28% malignant) met inclusion criteria. The final logistic regression model included difficulty in swallowing, hypothyroidism, echogenicity, hypervascularity, margins, calcification, and cytology diagnosis as input parameters. The model was highly successful in determining the outcome (p value: 0.001) with a R (Nagelkerke) score of 0.93. The area under the curve as determined by receiver operating characteristics was 0.91. The accuracy of the model on the training dataset was 93% (sensitivity and specificity 92% and 96%, respectively) and, on the validation dataset, 80% (sensitivity and specificity 91% and 67%, respectively).
We report a model for risk assessment of thyroid nodules that has the potential to significantly reduce indeterminates and surgical overtreatment. We illustrate its application via a straightforward nomogram, which integrates clinical, ultrasonographic, and cytologic data, and can be used to create clear, evidence-based management plans for patients.
甲状腺结节的恶性率较低，大多数已证实的癌症也不会表现出侵袭性。因此，对结节进行风险分层是避免过度手术治疗的关键步骤。我们假设可以创建一种优于目前使用的风险管理系统，通过同时考虑临床、超声和细胞学变量所带来的恶性风险，来减少临床上不确定的结节数量（即“灰色区域”）。
回顾了来自三个机构的甲状腺切除术病例。使用其良性与恶性结果来评估变量之间的相关性。训练了一个二项逻辑回归模型，并使用 Bethesda III 和 IV 结果的不确定结节进行了验证。设计了一个评分诺模图来展示该模型在临床实践中的应用。
符合纳入标准的 130 例甲状腺切除术（28%为恶性）。最终的逻辑回归模型包括吞咽困难、甲状腺功能减退、回声、血流丰富、边界、钙化和细胞学诊断作为输入参数。该模型在确定结果方面非常成功（p 值：0.001），R（Nagelkerke）得分为 0.93。通过接收者操作特征确定的曲线下面积为 0.91。该模型在训练数据集上的准确率为 93%（敏感性和特异性分别为 92%和 96%），在验证数据集上的准确率为 80%（敏感性和特异性分别为 91%和 67%）。
我们报告了一种用于评估甲状腺结节风险的模型，该模型有可能显著减少不确定病例和过度手术治疗。我们通过一个简单的诺模图说明了其应用，该诺模图整合了临床、超声和细胞学数据，并可用于为患者制定明确的、基于证据的管理计划。
