Fisher’s exact test, 95% confidence intervals (CI), and receiver operating characteristic area under the curve (ROC-AUC) were used. Individual radiomic feature importance was evaluated by assessment of tree node Gini impurity decrease and recursive feature elimination. Histopathological subtype of tumor samples was assessed by immunohistochemistry in 21 IVC patients. A random forest ML algorithm was fit to the training cohort and tested in the IVC. Tumors were segmented on preoperative apparent diffusion coefficient (ADC) maps, and radiomic features were extracted. One hundred two patients with histopathologically proven PDAC were retrospectively assessed as training cohort, and 30 prospectively accrued and retrospectively enrolled patients served as independent validation cohort (IVC). A very friendly user interface and easy to use controls.To develop a supervised machine learning (ML) algorithm predicting above- versus below-median overall survival (OS) from diffusion-weighted imaging-derived radiomic features in patients with pancreatic ductal adenocarcinoma (PDAC). Take the last chance to save the humans from extinction! Eradicating zombies has never been more satisfying! Get behind the wheel and play one of the best survival racing games! Keep upgrading your car to become better and better!Īmazing looking graphics! Well-crafted sound design and realistic vehicle physics.

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