An explainable radiomics model based on multiparametric magnetic resonance for differentiating benign and malignant orbital tumors.

OBJECTIVE

To develop and internally test a multiparametric radiomics combined model for differentiating benign and malignant orbital tumors.

PATIENTS AND

METHODS

This retrospective study analyzed 147 patients from two centers (December 2014 to March 2024) with pathologically confirmed orbital tumors and preoperative contrast-enhanced magnetic resonance imaging(MRI). After image preprocessing, 3668 radiomics features were extracted from T2-weighted imaging (T2WI) and contrast-enhanced T1-weighted imaging (CE T1WI) sequences. Feature reduction and selection were performed using the t-test/U-test, Pearson correlation coefficient, minimum redundancy maximum relevance (mRMR), and least absolute shrinkage and selection operator (LASSO) regression. Three machine learning algorithms, logistic regression (LR), naive Bayes classifier (NaiveBayes), and Multilayer perceptron (MLP) were used to construct radiomics models. A combined radiomics model (CRM), defined as an MLP-based model incorporating selected features from both T2WI and CE T1WI sequences, was subsequently built and integrated with clinical factors to create a radiomics nomogram. Model performance was evaluated using the area under the curve (AUC), accuracy, sensitivity, and specificity. Decision curve analysis (DCA) assessed clinical utility, and SHapley Additive exPlanations (SHAP) provided model interpretability.

RESULTS

Six key radiomics features were selected to establish the CRM. The MLP-based model achieved the highest AUC among the individual machine learning models in both training and test cohorts. The CRM demonstrated superior performance compared to models based solely on T2WI or CE T1WI, with AUCs of 0.877 (training cohort) and 0.860 (test cohort). The final nomogram, integrating the CRM and clinical factors, showed favorable discriminatory performance, achieving AUCs of 0.890 and 0.846 in the training and test cohorts, respectively. SHAP analysis identified 'squareroot_firstorder_Skewness_CE T1WI' and 'wavelet_LLH_glcm_Correlation_CE T1WI' as important predictors for malignant orbital tumors.

CONCLUSION

This study presents an effective and explainable multiparametric MRI radiomics model that accurately differentiates benign from malignant orbital tumors. The developed nomogram demonstrates promising performance within the internal validation framework and may provide supportive information for clinical decision-making pending further external validation.