Independent Risk Factors and Nomogram-Based Prediction of Pulmonary Fungal Infection in Lung Cancer Inpatients: A Single-Center Retrospective Study

Purpose: To investigate independent risk factors and construct an internally validated risk prediction model for invasive pulmonary fungal infection (IPFI) in patients with lung cancer. Patients and Methods: Clinical data from 250 consecutive lung cancer inpatients admitted to Nanchong Central Hospital between February 2022 and March 2025 were retrospectively analyzed; 41 patients developed IPFI and 209 did not. Patients were randomly assigned to a training set (n=175) and a validation set (n=75) at a 7:3 ratio. Candidate predictors were screened by univariate logistic regression, reduced using least absolute shrinkage and selection operator (LASSO) regression with 10-fold cross-validation, and entered into multivariable logistic regression to construct a nomogram. Model performance was evaluated using bootstrap internal validation (1000 resamples), calibration curves and goodness-of-fit testing, receiver operating characteristic analysis, and decision curve analysis. Results: Diabetes mellitus, invasive procedures, systemic glucocorticoid use, lower CD4+ T-cell count, and length of hospital stay >14 days were associated with IPFI and were retained as independent predictors in the final model. The model showed good discrimination, with an area under the curve of 0.876 (95% CI: 0.809-0.944) in the training set and 0.861 (95% CI: 0.750-0.973) in the validation set, and demonstrated clinical net benefit across threshold probability ranges of 0.03-0.90 (training) and 0.04-0.78 (validation). Conclusion: This nomogram may support early risk stratification for IPFI among lung cancer inpatients, while confirmation in external, multi-center cohorts is needed before broader clinical application.