Predicting Cancer in Pancreatic Cystic Lesions Through Artificial Intelligence

Predict the presence of malignant degeneration (defined as: high grade dysplasia, in situ PADC, or T1 PADC) in pancreatic cystic lesion(s) using artificial intelligence model based on clinical, biochemical, and radiological features. This will be measured through Area Under the Receiver Operator Characteristic curve (AUROC) assesment. AUROC varies between 0.5 and 1, corresponding to no class separation capacity and full class separation capacity, respectively.

the number of true positives and true negatives among all predictions. It varies between 0 (no correct prediction) to 1 (full correct predictions).

The number of true positives divided by all the positive predictions (true positives and false positives). It varies between 0 (no correct prediction) to 1 (full correct predictions).

The number of true positives divided by the actual positive instances in the dataset (true positives and false negatives). It varies between 0 (no correct prediction) to 1 (full correct predictions).

the aritmethic mean of sensitivity and specificity. It varies between 0 (no correct prediction) to 1 (full correct predictions).

It combines precision and recall. It ranges from 0-100%, and a higher F1 score denotes a better quality classifier.

A visual representation of true positives, false positives, true negatives, and false negatives. It is depicted through a table.

It indicates how close the prediction probability is to the corresponding actual/true value (0 or 1 in case of binary classification). The more the predicted probability diverges from the actual value, the higher is the log-loss value.

A metric used to measure the level of agreement between two raters which can be a useful tool to gauge the performance of a classification model. It accounts for the fact that the raters may happen to agree on some items purely by chance. It varies between 0 (no correct prediction) to 1 (full correct predictions).