典型的肺类癌(TPC)的定义是缓慢的生长,经常需要手术干预。尽管如此,肿瘤切除后的长期结局尚不清楚.这项研究检查了影响TPC患者总生存率(OS)的因素,利用来自监控的数据,流行病学,和2000年至2018年的最终结果数据库。我们采用Lasso-Cox分析来识别预后特征,并使用随机森林开发了各种模型,XGBoost,和Cox回归算法。随后,我们使用诸如曲线下面积(AUC)、校准图,Brier得分,和决策曲线分析(DCA)。在2687名患者中,我们确定了5个显著影响OS的临床特征.值得注意的是,随机森林模型表现出很强的性能,在训练集中实现0.744/0.757的5年和7年AUC值,在验证集中实现0.715/0.740,分别,表现优于其他型号。此外,我们开发了一个基于网络的平台,旨在方便访问该模型。本研究为医疗保健专业人员提供了机器学习模型和基于Web的支持系统,协助肿瘤切除术后TPC患者的个性化治疗决策。
Typical Pulmonary Carcinoid (TPC) is defined by its slow growth, frequently necessitating surgical intervention. Despite this, the long-term outcomes following tumor resection are not well understood. This study examined the factors impacting Overall Survival (OS) in patients with TPC, leveraging data from the Surveillance, Epidemiology, and End Results database spanning from 2000 to 2018. We employed Lasso-Cox analysis to identify prognostic features and developed various models using Random Forest, XGBoost, and Cox regression algorithms. Subsequently, we assessed model performance using metrics such as Area Under the Curve (AUC), calibration plot, Brier score, and Decision Curve Analysis (DCA). Among the 2687 patients, we identified five clinical features significantly affecting OS. Notably, the Random Forest model exhibited strong performance, achieving 5- and 7-year AUC values of 0.744/0.757 in the training set and 0.715/0.740 in the validation set, respectively, outperforming other models. Additionally, we developed a web-based platform aimed at facilitating easy access to the model. This study presents a machine learning model and a web-based support system for healthcare professionals, assisting in personalized treatment decisions for patients with TPC post-tumor resection.