Identifying chemotherapy beneficiaries in nasal and paranasal sinus cancers: epidemiological trends and machine learning insights.

Abstract

Background: Studies on the epidemiological characteristics, treatment strategies and prognosis of nasal and paranasal sinus cancer are still relatively limited.

Methods: 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.

Results: 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.

Conclusions: 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.