https://www.selleckchem.com/pr....oducts/verubecestat.
76-0.78 and 0.79-0.84. The nomogram had powerful diagnostic ability in all training, test I and II sets with AUCs of 0.90 (95 % CI, 0.86-0.94), 0.87 (95 % CI, 0.82-0.92) and 0.90 (95 % CI, 0.85-0.96) respectively. The net reclassification index revealed that the radiomics nomogram had significantly better performance than the clinical model (p-values 0.05). The deep learning radiomics model based on CT images is effective at discriminating serosa invasion in gastric cancer. The deep learning radiomics model based on CT images
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