Deep transfer learning radiomics for distinguishing sinonasal malignancies: a preliminary MRI study

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. 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. 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. Combining MRI-based HC radiomics and DTL features with the SVM model enhanced differentiation between sinonasal SCC, NHL, and ACC.