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https://www.selleckchem.com/products/GSK1059615.html This study was conducted to establish and validate a non-contrast T1 map-based radiomic nomogram for predicting major adverse cardiac events (MACEs) in patients with acute ST-segment elevation myocardial infarction (STEMI) undergoing percutaneous coronary intervention (PCI). This retrospective study included 157 consecutive patients (training sets, 109 patients; test sets, 48 patients) with acute STEMI undergoing PCI. An open-source radiomics software was used to segment the myocardium on the non-contrast T1 mapping and extract features. A radiomic signature was constructed to predict MACEs using the least absolute shrinkage and selection operator method. The performance of the radiomic nomogram for predicting MACEs in both the training and test sets was evaluated by its discrimination, calibration, and clinical usefulness. The radiomic signature showed a good prognostic ability in the training sets with an AUC of 0.94 (95% CI, 0.86 to 1.00) and F1 score of 0.71, which was confirmed in the test sets witional cardiac MRI parameters in predicting MACEs in acute STEMI patients. • The non-contrast T1 mapping-based radiomic nomogram can be used for prediction of MACEs and improvement of risk stratification in acute STEMI. To evaluate the performance of a multiparametric MRI radiomics-based nomogram for the individualised prediction of synchronous distant metastasis (SDM) in patients with clear cell renal cell carcinoma (ccRCC). Two-hundred and one patients (training cohort n = 126; internal validation cohort n = 39; external validation cohort n = 36) with ccRCC were retrospectively enrolled between January 2013 and June 2019. In the training cohort, the optimal MRI radiomics features were selected and combined to calculate the radiomics score (Rad-score). Incorporating Rad-score and SDM-related clinicoradiologic characteristics, the radiomics-based nomogram was established by multivariable logistic regression analysis,

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