目的:现有的嗜铬细胞瘤/副神经节瘤(PPGL)转移预测模型在不同中心表现出高度异质性。因此,本研究旨在建立基于不同算法的多变量综合预测模型。
方法:回顾性收集2007-2022年北京协和医院接受手术切除的PPGL患者资料。以7:3的比例将患者随机分为训练集和测试集。随后,决策树,随机森林,和逻辑模型构建用于转移预测的训练集和Cox模型用于无转移生存(MFS)预测的总人群。此外,将Ki-67指数和肿瘤大小转化为分类变量以调整模型。测试集用于评估模型的辨别和校准,最佳模型可视化为列线图。比较有和没有危险因素的患者的临床特征和MFS。
结果:共纳入198例转移患者59例,并分为训练集(n=138)和测试集(n=60)。在所有型号中,logistic回归模型显示转移预测的最佳区分,AUC为0.891(95%CI,0.793-0.990),整合SDHB种系突变[OR:96.72(95%CI,16.61-940.79)],S-100(-)[OR:11.22(95%CI,3.04-58.51)],通过免疫组织化学(IHC)评估ATRX(-)[OR:8.42(95%CI,2.73-29.24)]和Ki-67≥3%[OR:7.98(95%CI,2.27-32.24)],肿瘤大小≥5cm[OR:4.59(95%CI,1.34-19.13)]。包括上述危险因素的多变量Cox模型在预测术后MFS方面也显示出高C指数0.860(95%CI,0.810-0.911)。此外,有上述危险因素的患者MFS明显较差(P≤0.001).
结论:本研究中建立的模型为临床医生预测PPGL患者的转移和MFS提供了替代和可靠的工具。更重要的是,这项研究首次揭示ATRX的IHC可以作为PPGL转移的独立预测因子。
OBJECTIVE: The existing prediction models for metastasis in pheochromocytomas/paragangliomas (PPGLs) showed high heterogeneity in different centers. Therefore, this study aimed to establish new prediction models integrating multiple variables based on different algorithms.
METHODS: Data of patients with PPGLs undergoing surgical resection at the Peking Union Medical College Hospital from 2007 to 2022 were collected retrospectively. Patients were randomly divided into the training and testing sets in a ratio of 7:3. Subsequently, decision trees, random forest, and logistic models were constructed for metastasis prediction with the training set and Cox models for metastasis-free survival (MFS) prediction with the total population. Additionally, Ki-67 index and tumor size were transformed into categorical variables for adjusting models. The testing set was used to assess the discrimination and calibration of models and the optimal models were visualized as nomograms. Clinical characteristics and MFS were compared between patients with and without risk factors.
RESULTS: A total of 198 patients with 59 cases of metastasis were included and classified into the training set (n = 138) and testing set (n = 60). Among all models, the logistic regression model showed the best discrimination for metastasis prediction with an AUC of 0.891 (95% CI, 0.793-0.990), integrating SDHB germline mutations [OR: 96.72 (95% CI, 16.61-940.79)], S-100 (-) [OR: 11.22 (95% CI, 3.04-58.51)], ATRX (-) [OR: 8.42 (95% CI, 2.73-29.24)] and Ki-67 ≥ 3% [OR: 7.98 (95% CI, 2.27-32.24)] evaluated through immunohistochemistry (IHC), and tumor size ≥ 5 cm [OR: 4.59 (95% CI, 1.34-19.13)]. The multivariate Cox model including the above risk factors also showed a high C-index of 0.860 (95% CI, 0.810-0.911) in predicting MFS after surgery. Furthermore, patients with the above risk factors showed a significantly poorer MFS (P ≤ 0.001).
CONCLUSIONS: Models established in this study provided alternative and reliable tools for clinicians to predict PPGLs patients\' metastasis and MFS. More importantly, this study revealed for the first time that IHC of ATRX could act as an independent predictor of metastasis in PPGLs.