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ObjectiveIn this study, we aimed to investigate the classification of symptomatic plaques by evaluating the models generated via two different approaches, a radiomics-based machine learning (ML) approach, and an end-to-end learning approach which utilized deep learning (DL) techniques with several representative model frameworks.MethodsWe collected high-resolution magnetic resonance imaging (HRMRI) data from 104 patients with carotid artery stenosis, who were diagnosed with either symptomatic plaques (SPs) or asymptomatic plaques (ASPs), in two medical centers. 74 patients were diagnosed with SPs and 30 patients were ASPs. Sampling Perfection with Application-optimized Contrasts (SPACE) by using different flip angle Evolutions was used for MRI imaging. Repeated stratified five-fold cross-validation was used to evaluate the accuracy and receiver operating characteristic (ROC) of the trained classifier. The two proposed approaches were investigated to train the models separately. The difference in the model performance of the two proposed methods was quantitatively evaluated to find a better model to differentiate between SPs and ASPs.Results3D-SE-Densenet-121 model showed the best performance among all prediction models (AUC, accuracy, precision, sensitivity, and F1-score of 0.9300, 0.9308, 0.9008, 0.8588, and 0.8614, respectively), which were 0.0689, 0.1119, 0.1043, 0.0805, and 0.1089 higher than the best radiomics-based ML model (MLP). Decision curve analysis showed that the 3D-SE-Densenet-121 model delivered more net benefit than the best radiomics-based ML model (MLP) with a wider threshold probability.ConclusionThe DL models were able to accurately differentiate between symptomatic and asymptomatic carotid plaques with limited data, which outperformed radiomics-based ML models in identifying symptomatic plaques.

As a novel oncogene, the role of YEATS domain-containing protein 4 (YEATS4) in the occurrence, development, and treatment of tumors is now beginning to be appreciated. YEATS4 plays an important role in regulating DNA repair during replication. The upregulation of YEAST4 promotes DNA damage repair and prevents cell death, whereas its downregulation inhibits DNA replication and induces apoptosis. Additionally, accumulating evidence indicates that the aberrant activation of YEATS4 leads to changes in drug resistance, epithelial-mesenchymal transition and also in the migration and invasion capacity of tumor cells. Therefore, specific inhibition of the expression or activity of YEATS4 protein may be an effective strategy for inhibiting the proliferation, motility, differentiation, and/or survival of tumor cells. Taken together, YEATS4 has emerged as a potential target for multiple cancers and is an attractive protein for the development of small-molecule inhibitors. However, research on YEAST4 in tumor-related fields is limited and its biological functions, metabolism, and the regulatory mechanism of YEATS4 in numerous cancers remain undetermined. This review comprehensively and extensively summarizes the functions, structure and oncogenic roles of YEATS4 in cancer progression and aims to further contribute to the study of its underlying molecular mechanism and targeted drugs.