Department of Radiology, Hangzhou Ninth People's Hospital, No. 98, Yilong Road, Qiantang District, Hangzhou, 310012, China.
Department of Radiology, Affiliated Hangzhou First People's Hospital, Westlake University School of Medicine, No. 261, Huansha Road, Shangcheng District, Hangzhou, 310006, China.
Sci Rep. 2024 Jul 9;14(1):15828. doi: 10.1038/s41598-024-66304-6.
The central lymph node metastasis (CLNM) status in the cervical region serves as a pivotal determinant for the extent of surgical intervention and prognosis in papillary thyroid carcinoma (PTC). This paper seeks to devise and validate a predictive model based on clinical parameters for the early anticipation of high-volume CLNM (hv-CLNM, > 5 nodes) in high-risk patients. A retrospective analysis of the pathological and clinical data of patients with PTC who underwent surgical treatment at Medical Centers A and B was conducted. The data from Center A was randomly divided into training and validation sets in an 8:2 ratio, with those from Center B serving as the test set. Multifactor logistic regression was harnessed in the training set to select variables and construct a predictive model. The generalization ability of the model was assessed in the validation and test sets. The model was evaluated through the receiver operating characteristic area under the curve (AUC) to predict the efficiency of hv-CLNM. The goodness of fit of the model was examined via the Brier verification technique. The incidence of hv-CLNM in 5897 PTC patients attained 4.8%. The occurrence rates in males and females were 9.4% (128/1365) and 3.4% (156/4532), respectively. Multifactor logistic regression unraveled male gender (OR = 2.17, p < .001), multifocality (OR = 4.06, p < .001), and lesion size (OR = 1.08 per increase of 1 mm, p < .001) as risk factors, while age emerged as a protective factor (OR = 0.95 per an increase of 1 year, p < .001). The model constructed with four predictive variables within the training set exhibited an AUC of 0.847 ([95%CI] 0.815-0.878). In the validation and test sets, the AUCs were 0.831 (0.783-0.879) and 0.845 (0.789-0.901), respectively, with Brier scores of 0.037, 0.041, and 0.056. Subgroup analysis unveiled AUCs for the prediction model in PTC lesion size groups (≤ 10 mm and > 10 mm) as 0.803 (0.757-0.85) and 0.747 (0.709-0.785), age groups (≤ 31 years and > 31 years) as 0.778 (0.720-0.881) and 0.837 (0.806-0.867), multifocal and solitary cases as 0.803 (0.767-0.838) and 0.809 (0.769-0.849), and Hashimoto's thyroiditis (HT) and non-HT cases as 0.845 (0.793-0.897) and 0.845 (0.819-0.871). Male gender, multifocality, and larger lesion size are risk factors for hv-CLNM in PTC patients, whereas age serves as a protective factor. The clinical predictive model developed in this research facilitates the early identification of high-risk patients for hv-CLNM, thereby assisting physicians in more efficacious risk stratification management for PTC patients.
中央区淋巴结转移(CLNM)状态是影响甲状腺乳头状癌(PTC)手术范围和预后的关键因素。本研究旨在建立并验证一种基于临床参数的预测模型,用于早期预测高危患者的高容量中央区淋巴结转移(hv-CLNM,>5 个淋巴结)。
对在医疗中心 A 和 B 接受手术治疗的 PTC 患者的病理和临床数据进行回顾性分析。中心 A 的数据随机分为 8:2 的训练集和验证集,中心 B 的数据为测试集。利用多因素逻辑回归在训练集中选择变量并构建预测模型。在验证集和测试集中评估模型的泛化能力。通过受试者工作特征曲线下面积(AUC)评估模型对预测 hv-CLNM 的效率。通过 Brier 验证技术评估模型的拟合优度。
在 5897 例 PTC 患者中,hv-CLNM 的发生率为 4.8%。男性和女性的发生率分别为 9.4%(128/1365)和 3.4%(156/4532)。多因素逻辑回归揭示了男性(OR=2.17,p<.001)、多灶性(OR=4.06,p<.001)和病变大小(OR=1.08,每增加 1mm,p<.001)是危险因素,而年龄是保护因素(OR=0.95,每增加 1 岁,p<.001)。在训练集中构建的包含四个预测变量的模型的 AUC 为 0.847([95%CI]0.815-0.878)。在验证集和测试集中,AUC 分别为 0.831(0.783-0.879)和 0.845(0.789-0.901),Brier 评分分别为 0.037、0.041 和 0.056。亚组分析显示,在 PTC 病变大小组(≤10mm 和>10mm)中,预测模型的 AUC 分别为 0.803(0.757-0.85)和 0.747(0.709-0.785),年龄组(≤31 岁和>31 岁)中为 0.778(0.720-0.881)和 0.837(0.806-0.867),多灶性和单灶性病例中为 0.803(0.767-0.838)和 0.809(0.769-0.849),桥本甲状腺炎(HT)和非 HT 病例中为 0.845(0.793-0.897)和 0.845(0.819-0.871)。
男性、多灶性和较大的病变大小是 PTC 患者发生 hv-CLNM 的危险因素,而年龄是保护因素。本研究建立的临床预测模型有助于早期识别高危患者的 hv-CLNM,从而帮助医生对 PTC 患者进行更有效的风险分层管理。