Application of contrast-enhanced CT-driven multimodal machine learning models for pulmonary metastasis prediction in head and neck adenoid cystic carcinoma.

Objective: This study explores radiomics and deep learning for predicting pulmonary metastasis in head and neck Adenoid Cystic Carcinoma (ACC), assessing machine learning(ML) algorithms' model performance.

Methods: The study retrospectively analyzed contrast-enhanced CT imaging data and clinical records from 130 patients with pathologically confirmed ACC in the head and neck region. The dataset was randomly split into training and test sets at a 7:3 ratio. Radiomic features and deep learning-derived features were extracted and subsequently integrated through multi-feature fusion. Z-score normalization was applied to training and test sets. Hypothesis testing selected significant features, followed by LASSO regression (5-fold CV) identifying 7 predictive features. Nine machine learning algorithms were employed to build predictive models for ACC pulmonary metastasis: ada, KNN, rf, NB, GLM, LDA, rpart, SVM-RBF, and GBM. Models were trained using the training set and tested on the test set. Model performance was evaluated using metrics such as recall, sensitivity, PPV, F1-score, precision, prevalence, NPV, specificity, accuracy, detection rate, detection prevalence, and balanced accuracy.

Results: Machine learning models based on multi-feature fusion of enhanced CT, utilizing KNN, SVM, rpart, GBM, NB, GLM, and LDA, demonstrated AUC values in the test set of 0.687, 0.863, 0.737, 0.793, 0.763, 0.867, and 0.844, respectively. Rf and ada showed significant overfitting. Among these, GBM and GLM showed higher stability in predicting pulmonary metastasis of head and neck ACC.

Conclusion: Radiomics and deep learning methods based on enhanced CT imaging can provide effective auxiliary tools for predicting pulmonary metastasis in head and neck ACC patients, showing promising potential for clinical application.