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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Autor*in:	Zarrukh Baig MD [verfasserIn] Nawaf Abu-Omar MD [verfasserIn] Rayyan Khan MSc [verfasserIn] Carlos Verdiales BSc [verfasserIn] Ryan Frehlick BSc [verfasserIn] John Shaw MD [verfasserIn] Fang-Xiang Wu PhD, Eng SMIEE [verfasserIn] Yigang Luo MD [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Übergeordnetes Werk:	In: Technology in Cancer Research & Treatment - SAGE Publishing, 2020, 20(2021)
Übergeordnetes Werk:	volume:20 ; year:2021

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1177/15330338211050767

Katalog-ID:	DOAJ051431041

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spelling	10.1177/15330338211050767 doi (DE-627)DOAJ051431041 (DE-599)DOAJa01b67a0c3df42a5a008833818b16f4d DE-627 ger DE-627 rakwb eng RC254-282 Zarrukh Baig MD verfasserin aut Prognosticating Outcome in Pancreatic Head Cancer With the use of a Machine Learning Algorithm 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. Neoplasms. Tumors. Oncology. Including cancer and carcinogens Nawaf Abu-Omar MD verfasserin aut Rayyan Khan MSc verfasserin aut Carlos Verdiales BSc verfasserin aut Ryan Frehlick BSc verfasserin aut John Shaw MD verfasserin aut Fang-Xiang Wu PhD, Eng SMIEE verfasserin aut Yigang Luo MD verfasserin aut In Technology in Cancer Research & Treatment SAGE Publishing, 2020 20(2021) (DE-627)507184734 (DE-600)2220436-2 15330338 nnns volume:20 year:2021 https://doi.org/10.1177/15330338211050767 kostenfrei https://doaj.org/article/a01b67a0c3df42a5a008833818b16f4d kostenfrei https://doi.org/10.1177/15330338211050767 kostenfrei https://doaj.org/toc/1533-0338 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 20 2021
allfields_unstemmed	10.1177/15330338211050767 doi (DE-627)DOAJ051431041 (DE-599)DOAJa01b67a0c3df42a5a008833818b16f4d DE-627 ger DE-627 rakwb eng RC254-282 Zarrukh Baig MD verfasserin aut Prognosticating Outcome in Pancreatic Head Cancer With the use of a Machine Learning Algorithm 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. Neoplasms. Tumors. Oncology. Including cancer and carcinogens Nawaf Abu-Omar MD verfasserin aut Rayyan Khan MSc verfasserin aut Carlos Verdiales BSc verfasserin aut Ryan Frehlick BSc verfasserin aut John Shaw MD verfasserin aut Fang-Xiang Wu PhD, Eng SMIEE verfasserin aut Yigang Luo MD verfasserin aut In Technology in Cancer Research & Treatment SAGE Publishing, 2020 20(2021) (DE-627)507184734 (DE-600)2220436-2 15330338 nnns volume:20 year:2021 https://doi.org/10.1177/15330338211050767 kostenfrei https://doaj.org/article/a01b67a0c3df42a5a008833818b16f4d kostenfrei https://doi.org/10.1177/15330338211050767 kostenfrei https://doaj.org/toc/1533-0338 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 20 2021
allfieldsGer	10.1177/15330338211050767 doi (DE-627)DOAJ051431041 (DE-599)DOAJa01b67a0c3df42a5a008833818b16f4d DE-627 ger DE-627 rakwb eng RC254-282 Zarrukh Baig MD verfasserin aut Prognosticating Outcome in Pancreatic Head Cancer With the use of a Machine Learning Algorithm 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. Neoplasms. Tumors. Oncology. Including cancer and carcinogens Nawaf Abu-Omar MD verfasserin aut Rayyan Khan MSc verfasserin aut Carlos Verdiales BSc verfasserin aut Ryan Frehlick BSc verfasserin aut John Shaw MD verfasserin aut Fang-Xiang Wu PhD, Eng SMIEE verfasserin aut Yigang Luo MD verfasserin aut In Technology in Cancer Research & Treatment SAGE Publishing, 2020 20(2021) (DE-627)507184734 (DE-600)2220436-2 15330338 nnns volume:20 year:2021 https://doi.org/10.1177/15330338211050767 kostenfrei https://doaj.org/article/a01b67a0c3df42a5a008833818b16f4d kostenfrei https://doi.org/10.1177/15330338211050767 kostenfrei https://doaj.org/toc/1533-0338 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 20 2021
allfieldsSound	10.1177/15330338211050767 doi (DE-627)DOAJ051431041 (DE-599)DOAJa01b67a0c3df42a5a008833818b16f4d DE-627 ger DE-627 rakwb eng RC254-282 Zarrukh Baig MD verfasserin aut Prognosticating Outcome in Pancreatic Head Cancer With the use of a Machine Learning Algorithm 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. Neoplasms. Tumors. Oncology. Including cancer and carcinogens Nawaf Abu-Omar MD verfasserin aut Rayyan Khan MSc verfasserin aut Carlos Verdiales BSc verfasserin aut Ryan Frehlick BSc verfasserin aut John Shaw MD verfasserin aut Fang-Xiang Wu PhD, Eng SMIEE verfasserin aut Yigang Luo MD verfasserin aut In Technology in Cancer Research & Treatment SAGE Publishing, 2020 20(2021) (DE-627)507184734 (DE-600)2220436-2 15330338 nnns volume:20 year:2021 https://doi.org/10.1177/15330338211050767 kostenfrei https://doaj.org/article/a01b67a0c3df42a5a008833818b16f4d kostenfrei https://doi.org/10.1177/15330338211050767 kostenfrei https://doaj.org/toc/1533-0338 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 20 2021
language	English
source	In Technology in Cancer Research & Treatment 20(2021) volume:20 year:2021
sourceStr	In Technology in Cancer Research & Treatment 20(2021) volume:20 year:2021
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institution	findex.gbv.de
topic_facet	Neoplasms. Tumors. Oncology. Including cancer and carcinogens
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container_title	Technology in Cancer Research & Treatment
authorswithroles_txt_mv	Zarrukh Baig MD @@aut@@ Nawaf Abu-Omar MD @@aut@@ Rayyan Khan MSc @@aut@@ Carlos Verdiales BSc @@aut@@ Ryan Frehlick BSc @@aut@@ John Shaw MD @@aut@@ Fang-Xiang Wu PhD, Eng SMIEE @@aut@@ Yigang Luo MD @@aut@@
publishDateDaySort_date	2021-01-01T00:00:00Z
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callnumber-first	R - Medicine
author	Zarrukh Baig MD
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topic_title	RC254-282 Prognosticating Outcome in Pancreatic Head Cancer With the use of a Machine Learning Algorithm
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title	Prognosticating Outcome in Pancreatic Head Cancer With the use of a Machine Learning Algorithm
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title_full	Prognosticating Outcome in Pancreatic Head Cancer With the use of a Machine Learning Algorithm
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title_auth	Prognosticating Outcome in Pancreatic Head Cancer With the use of a Machine Learning Algorithm
abstract	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.
abstractGer	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.
abstract_unstemmed	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.
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title_short	Prognosticating Outcome in Pancreatic Head Cancer With the use of a Machine Learning Algorithm
url	https://doi.org/10.1177/15330338211050767 https://doaj.org/article/a01b67a0c3df42a5a008833818b16f4d https://doaj.org/toc/1533-0338
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author2	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
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