Multiparametric MRI-based radiomics approach with deep transfer learning for preoperative prediction of Ki-67 status in sinonasal squamous cell carcinoma - Lin, Naier; Shi, Yiqian; Ye, Min; Wang, Luxi; Sha, Yan (Frontiers in Oncology, 2024)
Purpose: Based on comparison of different machine learning (ML) models, we developed the model that integrates traditional hand-crafted (HC) features and ResNet50 network-based deep transfer learning (DTL) features from multi-parametric MRI to predict Ki67 status in sinonasal squamous cell carcinoma (SNSCC). Methods: 231 SNSCC patients were retrospectively reviewed [training cohort (n = 185), test cohort (n = 46)]. Pathological grade, clinical and MRI characteristics were analyzed to choose the independent predictor. HC and DTL radiomics features were extracted from fat-saturated T2weighted imaging, contrast-enhanced T1-weighted imaging and apparent diffusion coefficient map. Then, HC and DTL features were fused to formulate the deep learning-based radiomics (DLR) features. After features selection and radiomics signature (RS) building, we compared the predictive ability of RS-HC, RS-DTL and RS-DLR. Results: No independent predictors were found based on pathological, clinical and MRI characteristics. After features selection, 42 HC and 10 DTL radiomics features were retained. The support vector machine (SVM), LightGBM and ExtraTrees (ET) were the best classier for RS-HC, RS-DTL and RS-DLR. In the training cohort, the predictive ability of RS-DLR was significantly better than those of RS-DTL and RS-HC (p < 0.050), in the test set, the area under curve (AUC) of RS-DLR (AUC = 0.817) was also the highest, but there was no significant difference of the performance between DLR-RS and HC-RS. Conclusions: Both the HC and DLR model showed favorable predictive efficacy for Ki-67 expression in patients with SNSCC. Especially, RS-DLR model represented an opportunity to advance the prediction ability.