Xu Xiongye, Liu Baomo, Su Yan, Dong Peixin, Wang Shuaishuai, Deng Jiating, Lin Ziying, Huang Lixia, Li Shaoli, Gu Jincui, Zhou Yanbin
Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
J Thorac Dis. 2024 Aug 31;16(8):5152-5166. doi: 10.21037/jtd-23-1927. Epub 2024 Aug 15.
Pulmonary large-cell neuroendocrine carcinoma (PLCNEC) is a rare and highly malignant lung cancer. Due to the paucity of data from clinical studies, its clinical characteristics and treatment remain controversial. The present study explored factors influencing the prognosis and survival outcomes of patients with PLCNEC and developed a dependable prognostic model using machine learning.
The clinical data of PLCNEC patients were extracted from the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2020. A total of 2,897 PLCNEC patients were enrolled and univariate and multivariate Cox regression analyses were performed to explore independent prognostic factors for disease-specific survival (DSS). Ten machine learning algorithms were utilized to predict the 2-year survival. The clinicopathological data collected from The First Affiliated Hospital of Sun Yat-sen University between 2010 and 2022 were used to test the trained machine.
Sex [hazard ratio (HR) 1.168, 95% confidence interval (CI): 1.063-1.284], age (HR 1.262, 95% CI: 1.144-1.391), surgery (HR 0.481, 95% CI: 0.413-0.559), chemotherapy (HR 0.450, 95% CI: 0.404-0.501), bone metastasis (HR 1.284, 95% CI: 1.124-1.466), brain metastasis (HR 1.167, 95% CI: 1.023-1.331), liver metastasis (HR 1.223, 95% CI: 1.069-1.399), American Joint Committee on Cancer-Node (AJCC-N), and tumor stage were independent prognostic factors. The gradient boosting decision tree (GBDT) performed better than other models, with an F1-score of 0.791 and an area under the curve of 0.831.
Male, age ≥65 years, distant metastasis to the bone, liver, and brain are associated with a worse prognosis in PLCNEC patients, while surgery and chemotherapy are associated with improved prognosis. GBDT showed promising performance in predicting 2-year survival, which can serve as a valuable reference for clinical diagnosis and treatment of PLCNEC.
肺大细胞神经内分泌癌（PLCNEC）是一种罕见且高度恶性的肺癌。由于临床研究数据有限，其临床特征和治疗方法仍存在争议。本研究探讨了影响PLCNEC患者预后和生存结局的因素，并使用机器学习建立了一个可靠的预后模型。
从监测、流行病学和最终结果（SEER）数据库中提取2010年至2020年间PLCNEC患者的临床数据。共纳入2897例PLCNEC患者，进行单因素和多因素Cox回归分析，以探讨疾病特异性生存（DSS）的独立预后因素。使用十种机器学习算法预测2年生存率。收集2010年至2022年间中山大学附属第一医院的临床病理数据，用于测试训练好的模型。
性别[风险比（HR）1.168，95%置信区间（CI）：1.063 - 1.284]、年龄（HR 1.262，95% CI：1.144 - 1.391）、手术（HR 0.481，95% CI：0.413 - 0.559）、化疗（HR 0.450，95% CI：0.404 - 0.501）、骨转移（HR 1.284，95% CI：1.124 - 1.466）、脑转移（HR 1.167，95% CI：1.023 - 1.331）、肝转移（HR 1.223，95% CI：1.069 - 1.399）、美国癌症联合委员会淋巴结（AJCC - N）和肿瘤分期是独立的预后因素。梯度提升决策树（GBDT）的表现优于其他模型，F1分数为0.791，曲线下面积为0.831。
男性、年龄≥65岁、骨、肝和脑远处转移与PLCNEC患者预后较差相关，而手术和化疗与预后改善相关。GBDT在预测2年生存率方面表现出良好的性能，可为PLCNEC的临床诊断和治疗提供有价值的参考。
