Integrating Machine Learning for Early Mortality Prediction in Lung Adenosquamous Carcinoma: A Web-Based Prognostic Model.

Abstract

IntroductionCombined with the characteristics of adenocarcinoma and squamous cell carcinoma, lung adenosquamous carcinoma (ASC) is an uncommon histological subtype of lung cancer with more aggressive biological behavior. This study aimed to quantify the 90-day mortality rate in patients with ASC, identify associated features, and develop a predictive machine learning model.MethodsThis retrospective study obtained data from the Surveillance, Epidemiology, and End Results (SEER) program database, covering the period from 2000 to 2018. Through univariate logistic regression and Lasso analyses, significant prognostic features were determined. We developed predictive models using XGBoost, logistic regression, and AJCC staging algorithms, assessing their performance via metrics such as the Area Under the Receiver Operating Characteristic Curve (AUC), Decision Curve Analysis (DCA), Kolmogorov-Smirnov (KS) statistic, and calibration plots. Restricted Cubic Splines (RCS) were employed to assess potential non-linear relationships between continuous features and survival outcomes.ResultsOur analysis of 2820 eligible patients identified 6 clinical features significantly affecting outcomes. The XGBoost model exhibited exceptional discriminatory power, with AUC scores of 0.97 in the training set and 0.84 in the validation set, surpassing other models in all datasets according to AUC, KS score, DCA, and calibration analyses. RCS analysis showed a non-linear association between tumor size and prognosis, with a cutoff size of 44 mm. Moreover, we integrated the model into a web-based platform to enhance its accessibility.ConclusionsWe present a novel machine learning model, supported by an easily accessible web-based platform, to guide personalized clinical decision-making and optimize treatment strategies for patients with ASC.