Artificial neural networks versus LASSO regression for the prediction of long-term survival after surgery for invasive IPMN of the pancreas.

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) an...

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Autores principales:	Linus Aronsson, Roland Andersson, Daniel Ansari
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/e9b0b7f5c7264aa09fc5a07cbf9edc8e
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spelling	oai:doaj.org-article:e9b0b7f5c7264aa09fc5a07cbf9edc8e2021-11-25T06:19:24ZArtificial neural networks versus LASSO regression for the prediction of long-term survival after surgery for invasive IPMN of the pancreas.1932-620310.1371/journal.pone.0249206https://doaj.org/article/e9b0b7f5c7264aa09fc5a07cbf9edc8e2021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0249206https://doaj.org/toc/1932-6203Prediction 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.Linus AronssonRoland AnderssonDaniel AnsariPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 3, p e0249206 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Linus Aronsson Roland Andersson Daniel Ansari Artificial neural networks versus LASSO regression for the prediction of long-term survival after surgery for invasive IPMN of the pancreas.
description	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.
format	article
author	Linus Aronsson Roland Andersson Daniel Ansari
author_facet	Linus Aronsson Roland Andersson Daniel Ansari
author_sort	Linus Aronsson
title	Artificial neural networks versus LASSO regression for the prediction of long-term survival after surgery for invasive IPMN of the pancreas.
title_short	Artificial neural networks versus LASSO regression for the prediction of long-term survival after surgery for invasive IPMN of the pancreas.
title_full	Artificial neural networks versus LASSO regression for the prediction of long-term survival after surgery for invasive IPMN of the pancreas.
title_fullStr	Artificial neural networks versus LASSO regression for the prediction of long-term survival after surgery for invasive IPMN of the pancreas.
title_full_unstemmed	Artificial neural networks versus LASSO regression for the prediction of long-term survival after surgery for invasive IPMN of the pancreas.
title_sort	artificial neural networks versus lasso regression for the prediction of long-term survival after surgery for invasive ipmn of the pancreas.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/e9b0b7f5c7264aa09fc5a07cbf9edc8e
work_keys_str_mv	AT linusaronsson artificialneuralnetworksversuslassoregressionforthepredictionoflongtermsurvivalaftersurgeryforinvasiveipmnofthepancreas AT rolandandersson artificialneuralnetworksversuslassoregressionforthepredictionoflongtermsurvivalaftersurgeryforinvasiveipmnofthepancreas AT danielansari artificialneuralnetworksversuslassoregressionforthepredictionoflongtermsurvivalaftersurgeryforinvasiveipmnofthepancreas
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Artificial neural networks versus LASSO regression for the prediction of long-term survival after surgery for invasive IPMN of the pancreas.

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