Prognosticating Outcome in Pancreatic Head Cancer With the use of a Machine Learning Algorithm

Background: The purpose of this project is to identify prognostic features in resectable pancreatic head adenocarcinoma and use these features to develop a machine learning algorithm that prognosticates survival for patients pursuing pancreaticoduodenectomy. Methods: A retrospective cohort study of...

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Autores principales:	Zarrukh Baig MD, Nawaf Abu-Omar MD, Rayyan Khan MSc, Carlos Verdiales BSc, Ryan Frehlick BSc, John Shaw MD, Fang-Xiang Wu PhD, Eng SMIEE, Yigang Luo MD
Formato:	article
Lenguaje:	EN
Publicado:	SAGE Publishing 2021
Materias:	Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282
Acceso en línea:	https://doaj.org/article/a01b67a0c3df42a5a008833818b16f4d
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spelling	oai:doaj.org-article:a01b67a0c3df42a5a008833818b16f4d2021-11-05T22:33:55ZPrognosticating Outcome in Pancreatic Head Cancer With the use of a Machine Learning Algorithm1533-033810.1177/15330338211050767https://doaj.org/article/a01b67a0c3df42a5a008833818b16f4d2021-11-01T00:00:00Zhttps://doi.org/10.1177/15330338211050767https://doaj.org/toc/1533-0338Background: The purpose of this project is to identify prognostic features in resectable pancreatic head adenocarcinoma and use these features to develop a machine learning algorithm that prognosticates survival for patients pursuing pancreaticoduodenectomy. Methods: A retrospective cohort study of 93 patients who underwent a pancreaticoduodenectomy was performed. The patients were analyzed in 2 groups: Group 1 (n = 38) comprised of patients who survived < 2 years, and Group 2 (n = 55) comprised of patients who survived > 2 years. After comparing the two groups, 9 categorical features and 2 continuous features (11 total) were selected to be statistically significant (p < .05) in predicting outcome after surgery. These 11 features were used to train a machine learning algorithm that prognosticates survival. Results: The algorithm obtained 75% accuracy, 41.9% sensitivity, and 97.5% specificity in predicting whether survival is less than 2 years after surgery. Conclusion: A supervised machine learning algorithm that prognosticates survival can be a useful tool to personalize treatment plans for patients with pancreatic cancer.Zarrukh Baig MDNawaf Abu-Omar MDRayyan Khan MScCarlos Verdiales BScRyan Frehlick BScJohn Shaw MDFang-Xiang Wu PhD, Eng SMIEEYigang Luo MDSAGE PublishingarticleNeoplasms. Tumors. Oncology. Including cancer and carcinogensRC254-282ENTechnology in Cancer Research & Treatment, Vol 20 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282
spellingShingle	Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282 Zarrukh Baig MD Nawaf Abu-Omar MD Rayyan Khan MSc Carlos Verdiales BSc Ryan Frehlick BSc John Shaw MD Fang-Xiang Wu PhD, Eng SMIEE Yigang Luo MD Prognosticating Outcome in Pancreatic Head Cancer With the use of a Machine Learning Algorithm
description	Background: The purpose of this project is to identify prognostic features in resectable pancreatic head adenocarcinoma and use these features to develop a machine learning algorithm that prognosticates survival for patients pursuing pancreaticoduodenectomy. Methods: A retrospective cohort study of 93 patients who underwent a pancreaticoduodenectomy was performed. The patients were analyzed in 2 groups: Group 1 (n = 38) comprised of patients who survived < 2 years, and Group 2 (n = 55) comprised of patients who survived > 2 years. After comparing the two groups, 9 categorical features and 2 continuous features (11 total) were selected to be statistically significant (p < .05) in predicting outcome after surgery. These 11 features were used to train a machine learning algorithm that prognosticates survival. Results: The algorithm obtained 75% accuracy, 41.9% sensitivity, and 97.5% specificity in predicting whether survival is less than 2 years after surgery. Conclusion: A supervised machine learning algorithm that prognosticates survival can be a useful tool to personalize treatment plans for patients with pancreatic cancer.
format	article
author	Zarrukh Baig MD Nawaf Abu-Omar MD Rayyan Khan MSc Carlos Verdiales BSc Ryan Frehlick BSc John Shaw MD Fang-Xiang Wu PhD, Eng SMIEE Yigang Luo MD
author_facet	Zarrukh Baig MD Nawaf Abu-Omar MD Rayyan Khan MSc Carlos Verdiales BSc Ryan Frehlick BSc John Shaw MD Fang-Xiang Wu PhD, Eng SMIEE Yigang Luo MD
author_sort	Zarrukh Baig MD
title	Prognosticating Outcome in Pancreatic Head Cancer With the use of a Machine Learning Algorithm
title_short	Prognosticating Outcome in Pancreatic Head Cancer With the use of a Machine Learning Algorithm
title_full	Prognosticating Outcome in Pancreatic Head Cancer With the use of a Machine Learning Algorithm
title_fullStr	Prognosticating Outcome in Pancreatic Head Cancer With the use of a Machine Learning Algorithm
title_full_unstemmed	Prognosticating Outcome in Pancreatic Head Cancer With the use of a Machine Learning Algorithm
title_sort	prognosticating outcome in pancreatic head cancer with the use of a machine learning algorithm
publisher	SAGE Publishing
publishDate	2021
url	https://doaj.org/article/a01b67a0c3df42a5a008833818b16f4d
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Prognosticating Outcome in Pancreatic Head Cancer With the use of a Machine Learning Algorithm

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