Chen Zihan, Huang Zongwei, Pan Yuhui, Weng Youliang, Wu Zijie, Wang Jing, Wu Wenxi, Hong Xinyi, Chen Xin, Qiu Sufang
Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital (Fujian Branch of Fudan University Shanghai Cancer Center), Fuzhou, Fujian, China.
Department Radiation oncology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital (Fujian Branch of Fudan University Shanghai Cancer Center), Fuzhou, 350014, Fujian, China.
Eur J Med Res. 2025 Apr 1;30(1):218. doi: 10.1186/s40001-025-02425-6.
Studies on the epidemiological characteristics, treatment strategies and prognosis of nasal and paranasal sinus cancer are still relatively limited.
This study analyzed the age-adjusted incidence rates of nasal and paranasal sinus cancer from 1975 to 2020 using SEER database data. We conducted an in-depth examination of patients diagnosed between 2004 and 2015 with SEER*Stat software. A retrospective study from Fujian Provincial Cancer Hospital (2013-2020) provided an external validation set. Multiple imputation methods in R were used to address missing data. Survival analyses were performed using Kaplan-Meier and Cox proportional hazards models. Additionally, ten advanced machine learning models were utilized and evaluated in Python to predict patient survival outcomes.
This study analyzed data from 3,190 patients. The annual percent change (APC) in incidence rates per 100 000 person-years was 0.36 until 2012, subsequently decreasing to - 1.79. Among various predictive models, the gradient boosting classifier demonstrated superior performance with an area under the curve (AUC) of 0.699 and an accuracy rate of 0.708. Chemotherapy did not significantly influence overall mortality risk (HR = 0.93, 95% CI 0.82-1.05, P = 0.27). Chemotherapy showed potential benefits in specific patient subgroups.
This study revealed a declining trend in incidence rates beginning in 2012. The gradient boosting model demonstrated robust performance, playing a crucial role in predicting patient prognosis and the significance of chemotherapy.
关于鼻及鼻窦癌的流行病学特征、治疗策略和预后的研究仍然相对有限。
本研究使用监测、流行病学和最终结果（SEER）数据库数据，分析了1975年至2020年鼻及鼻窦癌的年龄调整发病率。我们使用SEER*Stat软件对2004年至2015年期间诊断的患者进行了深入检查。福建省肿瘤医院（2013 - 2020年）的一项回顾性研究提供了外部验证集。使用R语言中的多重插补方法处理缺失数据。使用Kaplan - Meier法和Cox比例风险模型进行生存分析。此外，在Python中使用并评估了十种先进的机器学习模型来预测患者的生存结果。
本研究分析了3190例患者的数据。每10万人年发病率的年度百分比变化（APC）在2012年之前为0.36，随后降至 - 1.79。在各种预测模型中，梯度提升分类器表现出卓越性能，曲线下面积（AUC）为0.699，准确率为0.708。化疗对总体死亡风险没有显著影响（风险比[HR] = 0.93，95%置信区间[CI] 0.82 - 1.05，P = 0.27）。化疗在特定患者亚组中显示出潜在益处。
本研究揭示了2012年开始的发病率下降趋势。梯度提升模型表现出强大性能，在预测患者预后和化疗的意义方面发挥了关键作用。
