Abstract
Prediction of long-term survival in patients with invasive intraductal papillary mucinous neoplasm (IPMN) of the pancreas may aid in patient assessment, risk stratification and personalization of treatment. This study aimed to investigate the predictive ability of artificial neural networks (ANN) and LASSO regression in terms of 5-year disease-specific survival. ANN work in a non-linear fashion, having a potential advantage in analysis of variables with complex correlations compared to regression models. LASSO is a type of regression analysis facilitating variable selection and regularization. A total of 440 patients undergoing surgical treatment for invasive IPMN of the pancreas registered in the Surveillance, Epidemiology and End Results (SEER) database between 2004 and 2016 were analyzed. The dataset was prior to analysis randomly split into a modelling and test set (7:3). The accuracy, precision and F1 score for predicting mortality were 0.82, 0.83 and 0.89, respectively for ANN with variable selection compared to 0.79, 0.85 and 0.87, respectively for the LASSO-model. ANN using all variables showed similar accuracy, precision and F1 score of 0.81, 0.85 and 0.88, respectively compared to a logistic regression analysis. McNemar´s test showed no statistical difference between the models. The models showed high and similar performance with regard to accuracy and precision for predicting 5-year survival status.
Funder
Magnus Bergvall Foundation
Inga and John Hains Foundation for Medical Research
Clas Groschinsky Foundation
Gunnar Nilsson Foundation
Gyllenstiernska Krapperup Foundation
Erik and Angelica Sparre Research Foundation
Emil and Wera Cornell Foundation
Henning and Ida Persson Research Foundation
Anna Lisa and Sven-Eric Lundgren Foundation for Medical Research
Governmental Funding of Clinical Research within the National Health Service
Publisher
Public Library of Science (PLoS)
Cited by
5 articles.
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