■根据指南,主动监测(AS)已被推荐为低风险(LR)甲状腺乳头状微癌(PTMC)的一线治疗策略。然而,术前影像学和细针穿刺不能排除一小群侵袭性PTMC伴大量淋巴结微转移的患者,向周围软组织外侵入,或来自AS候选者的高度恶性肿瘤。
■在2,809名PTMC患者中,根据纳入标准,本研究纳入2,473例患者。采用后向逐步多因素logistic回归分析筛选临床特征和超声特征,以确定高危(HR)患者的独立预测因素。根据选定的危险因素制定并验证列线图,以在手术前识别“LR”PTMC患者中的HR亚组。
■为了识别独立的风险因素,采用反向逐步法进行多变量logistic回归分析,发现男性[3.91(2.58-5.92)],年龄较大[0.94(0.92-0.96)],最大肿瘤直径[26.7(10.57-69.22)],双边性[1.44(1.01-2.3)],和多灶性[1.14(1.01-2.26)]是HR组的独立预测因子。基于这些独立的风险因素,建立了预测HR概率的列线图模型.C指数为0.806(95%CI,0.765-0.847),这表明列线图在预测HR概率方面具有令人满意的准确性。
■放在一起,我们开发并验证了一个列线图模型来预测PTMC的HR,这可能有助于患者咨询和促进治疗相关的决策。
Active surveillance (AS) has been recommended as the first-line treatment strategy for low-risk (LR) papillary thyroid microcarcinoma (PTMC) according to the guidelines. However, preoperative imaging and fine-needle aspiration could not rule out a small group of patients with aggressive PTMC with large-volume lymph node micro-metastasis, extrathryoidal invasion to surrounding soft tissue, or high-grade malignancy from the AS candidates.
Among 2,809 PTMC patients, 2,473 patients were enrolled in this study according to the inclusion criteria. Backward stepwise multivariate logistic regression analysis was used to filter clinical characteristics and ultrasound features to identify independent predictors of high-risk (HR) patients. A nomogram was developed and validated according to selected risk factors for the identification of an HR subgroup among \"LR\" PTMC patients before operation.
For identifying independent risk factors, multivariable logistic regression analysis was performed using the backward stepwise method and revealed that male sex [3.91 (2.58-5.92)], older age [0.94 (0.92-0.96)], largest tumor diameter [26.7 (10.57-69.22)], bilaterality [1.44 (1.01-2.3)], and multifocality [1.14 (1.01-2.26)] were independent predictors of the HR group. Based on these independent risk factors, a nomogram model was developed for predicting the probability of HR. The C index was 0.806 (95% CI, 0.765-0.847), which indicated satisfactory accuracy of the nomogram in predicting the probability of HR.
Taken together, we developed and validated a nomogram model to predict HR of PTMC, which could be useful for patient counseling and facilitating treatment-related decision-making.