https://www.selleckchem.com/pr....oducts/rottlerin.htm
8% at the optimal decision point, which outperformed all observer readers' performance (AUC 0.846±0.031). For pre-invasive and invasive classification of malignant SSNs, the 3D CNN also achieved satisfactory AUC of 0.908 (95% CI 0.877-0.939), sensitivity of 87.4%, and specificity of 80.8%. The deep-learning model showed its potential to accurately identify the malignancy and invasiveness of SSNs and thus can help surgeons make treatment decisions. The deep-learning model showed its potential to accurately identify the malignancy and i
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