Purpose To compare a deep learning model with a radiomics model in differentiating high-grade (LR-3, LR-4, LR-5) liver imaging reporting and data system (LI-RADS) liver tumors from low-grade (LR-1, LR-2) LI-RADS tumors based on the contrast-enhanced magnetic resonance images. Methods Magnetic resonance imaging scans of 361 suspected hepatocellular carcinoma patients were retrospectively reviewed. Lesion volume segmentation was manually performed by two radiologists, resulting in 426 lesions from the training set and 83 lesions from the test set. The radiomics model was constructed using a support vector machine (SVM) with pre-defined features, which was first selected using Chi-square test, followed by refining using binary least absolute shrinkage and selection operator (LASSO) regression. The deep learning model was established based on the DenseNet. Performance of the models was quantified by area under the receiver-operating characteristic curve (AUC), accuracy, sensitivity, specificity and F1-score. Results A set of 8 most informative features was selected from 1049 features to train the SVM classifier. The AUCs of the radiomics model were 0.857 (95% confidence interval [CI] 0.816–0.888) for the training set and 0.879 (95% CI 0.779–0.935) for the test set. The deep learning method achieved AUCs of 0.838 (95% CI 0.799–0.871) for the training set and 0.717 (95% CI 0.601–0.814) for the test set. The performance difference between these two models was assessed by t-test, which showed the results in both training and test sets were statistically significant. Conclusion The deep learning based model can be trained end-to-end with little extra domain knowledge, while the radiomics model requires complex feature selection. However, this process makes the radiomics model achieve better performance in this study with smaller computational cost and more potential on model interpretability.

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Relevant meta-analyses have confirmed the cardiovascular and renal benefits of sodium-glucose cotransporter 2 inhibitors (SGLT2i) and glucagon-like peptide-1 receptor agonists (GLP1RA) among patients with type 2 diabetes (T2D) and/or cardiorenal disease. However, it is not established whether the combination therapy of SGLT2i and GLP1RA will yield an additive benefit on cardiorenal endpoints. Lopez and colleagues recently did a cohort study (Lopez et al., Am. J. Cardiol., 2022, 181, 87–93) and aimed to address this issue. However, their findings are not consistent with those of previous studies. To confirm Lopez et al.’s findings (Lopez et al., Am. J. Cardiol., 2022, 181, 87–93) and address the aforementioned inconsistencies, we conducted a meta-analysis based on relevant studies. Our meta-analysis identified that SGLT2i + GLP1RA combination therapy was significantly associated with the reduced risks of cardiovascular/cerebrovascular atherosclerotic, heart failure-associated, and death outcomes compared with SGLT2i/GLP1RA monotherapy. These might support this combination therapy used for better reducing cardiovascular and death events in T2D patients, especially in those with high or very high cardiovascular risk. This is a commentary on a previous article (Lopez et al.’s study (Lopez et al., Am. J. Cardiol., 2022, 181, 87–93)) published outside of Frontiers. Therefore, we submitted this manuscript as an Opinion article, as suggested in the Author Guidelines.

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