Machine learning model based on clinical and imaging features for predicting fungal infections in children with leukemia

BACKGROUND: Due to immunosuppression, mucosal barrier injury, and prolonged neutropenia resulting from both the disease and chemotherapy, along with the frequent use of broad-spectrum antibiotics and glucocorticoids, children with leukemia are at a high risk of invasive fungal disease (IFD). The present study aimed to develop an effective machine learning model to predict fungal infections in children with leukemia. METHODS: A total of 247 pediatric patients diagnosed with leukemia and concurrent infections were evaluated. Five distinct ML classifiers-Random Forest, Logistic Regression, Support Vector Machine (SVM), Naïve Bayes, and K-Nearest Neighbors-were employed to construct predictive classification models. These models were trained using three distinct feature sets: (1) clinical features exclusively, (2) imaging features exclusively, and (3) an integrated feature set comprising both clinical and imaging data. The predictive model was validated prospectively in an independent cohort of 61 patients. Model performance was evaluated through cross-validation techniques to ensure robustness and generalizability. To validate the clinical applicability of the ML models, their diagnostic performance was systematically compared against that of three radiologists with varying experience levels: Reader A (3 years), Reader B (6 years), and Reader C (11 years). RESULTS: Among the five classifiers evaluated, models using both clinical and imaging features consistently outperformed those relying solely on either clinical or imaging features. Notably, the SVM algorithm exhibited the highest overall predictive performance. Within the SVM algorithm, the validation set achieved the mean area under the curve (AUC) values of 0.825 with clinical features alone, 0.852 with imaging features alone, and 0.947 when both clinical and imaging features were combined. The corresponding mean AUC values for the test set were 0.777, 0.797, and 0.879. Furthermore, a comparative analysis between the classification results of the SVM model and the diagnostic assessments provided by three radiologists demonstrated that the SVM consistently outperformed the radiologists across key performance metrics. CONCLUSIONS: The SVM algorithm demonstrates robust efficacy in predicting fungal infections among pediatric patients diagnosed with leukemia. Within the predictive model, the variables that exhibited the greatest influence included pleural thickening, neutropenia, hormone therapy, CRP level, mediastinal lymphadenopathy, and the presence of pleural effusion.