Establishment Of A Machine Learning Model For Early And Differential Diagnosis Of Pancreatic Ductal Adenocarcinoma Using Laboratory Routine Data

Early diagnosis and clear differentiation of pancreatic ductal adenocarcinoma (PDAC) from chronic pancreatitis (CP) is clinically challenging. A machine learning model is developed for the diagnosis of PDAC. The model is induced using a dataset of 13 987 participants, of which 12 402 are used for training the model and the remaining 1585 for testing purposes. One thousand sixty-six laboratory variables are reduced to 18 measures using standard filtering and feature importance methods. Then, five machine learning classifiers are evaluated for the study. Hyperparameter optimization for each classifier is carried out, and the optimal algorithm is established using a tenfold cross validation on the training data. Finally, gradient boosting decision tree-based ternary classifier composed of 18 routine laboratory variables (GBDT-TC18) is established. In the test cohort, GBDT-TC18 differentiates PDAC from CP and healthy control (HC) with an accuracy better than carbohydrate antigen 19-9 (CA19-9)-based diagnosis. It also maintains a high diagnostic accuracy for stages I, IIA, and IIB PDAC, small-sized PDAC, body and tail adenocarcinoma, CA19-9-negative PDAC, and nonjaundice PDAC. What's more, GBDT-TC18 shows a higher accuracy than CA19-9 in distinguishing PDAC from CP. GBDT-TC18 can be used to augment the capability of doctors for early and differential diagnosis of PDAC.