Development and validation of a nomogram for predicting lung cancer based on acoustic-clinical features.

Objective: Lung cancer-with its global prevalence and critical need for early diagnosis and treatment-is the focus of our study. This study aimed to develop a nomogram based on acoustic-clinical features-a tool that could significantly enhance the clinical prediction of lung cancer. Methods: We reviewed the voice data and clinical information of 350 individuals: 189 pathologically confirmed lung cancer patients and 161 non-lung cancer patients, which included 77 patients with benign pulmonary lesions and 84 healthy volunteers. First, acoustic features were extracted from all participants, and optimal features were selected by least absolute shrinkage and selection operator (LASSO) regression. Subsequently, by integrating acoustic features and clinical features, a nomogram for predicting lung cancer was developed using a multivariate logistic regression model. The performance of the nomogram was evaluated by the area under the receiver operating characteristic curve (AUC) and the calibration curve. The clinical utility was estimated by decision curve analysis (DCA) to confirm the predictive value of the nomogram. Furthermore, the nomogram model was compared with predictive models that were developed using six additional machine-learning (ML) methods. Results: Our acoustic-clinical nomogram model demonstrated a strong discriminative ability, with AUCs of 0.774 (95% confidence interval [CI], 0.716-0.832) and 0.714 (95% CI: 0.616-0.811) in the training and test sets, respectively. The nomogram achieved an accuracy of 0.642, a sensitivity of 0.673, and a specificity of 0.611 in the test set. The calibration curve showed excellent agreement between the predicted and actual values, and the DCA curve underscored the clinical usefulness of our nomogram. Notably, our nomogram model outperformed other models in terms of AUC, accuracy, and specificity. Conclusion: The acoustic-clinical nomogram developed in this study demonstrates robust discrimination, calibration, and clinical application value. This nomogram, a unique contribution to the field, provides a reliable tool for predicting lung cancer.