Abstract
PURPOSE: This study aimed to assess the diagnostic accuracy of combining MRI hand-crafted (HC) radiomics features with deep transfer learning (DTL) in identifying sinonasal squamous cell carcinoma (SCC), adenoid cystic carcinoma (ACC), and non-Hodgkin's lymphoma (NHL) using various machine learning (ML) models. METHODS: A retrospective analysis of 132 patients (50 with SCC, 42 with NHL, 40 with ACC) was conducted. The dataset was split 80/20 into training and testing cohorts. HC radiomics and DTL features were extracted from T2-weighted, ADC, and contrast-enhanced T1-weighted MRI images. ResNet50, a pre-trained convolutional neural network, was used for DTL feature extraction. LASSO regression was applied to select features and create radiomic signatures. Seven ML models were evaluated for classification performance. RESULTS: The radiomic signature included 24 hC and 8 DTL features. The support vector machine (SVM) model achieved the highest accuracy (92.6%) in the testing cohort. The SVM model's ROC analysis showed macro-average and micro-average AUC values of 0.98 and 0.99. AUCs for ACC, NHL, and SCC were 0.99, 0.97, and 1.00. K-nearest neighbors (KNN) and XGBoost also showed AUC values above 0.90. CONCLUSION: Combining MRI-based HC radiomics and DTL features with the SVM model enhanced differentiation between sinonasal SCC, NHL, and ACC.