Prognostic Value of Transfer Learning Based Features in Resectable Pancreatic Ductal Adenocarcinoma

Background: Pancreatic Ductal Adenocarcinoma (PDAC) is one of the most aggressive cancers with an extremely poor prognosis. Radiomics has shown prognostic ability in multiple types of cancer including PDAC. However, the prognostic value of traditional radiomics pipelines, which are based on hand-cra...
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Autor*in:	Yucheng Zhang [verfasserIn] Edrise M. Lobo-Mueller [verfasserIn] Paul Karanicolas [verfasserIn] Steven Gallinger [verfasserIn] Masoom A. Haider [verfasserIn] Farzad Khalvati [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	transfer learning radiomics prognosis pancreatic cancer survival analysis

Übergeordnetes Werk:	In: Frontiers in Artificial Intelligence - Frontiers Media S.A., 2019, 3(2020)
Übergeordnetes Werk:	volume:3 ; year:2020

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3389/frai.2020.550890

Katalog-ID:	DOAJ033197466

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520			\|a Background: Pancreatic Ductal Adenocarcinoma (PDAC) is one of the most aggressive cancers with an extremely poor prognosis. Radiomics has shown prognostic ability in multiple types of cancer including PDAC. However, the prognostic value of traditional radiomics pipelines, which are based on hand-crafted radiomic features alone is limited.Methods: Convolutional neural networks (CNNs) have been shown to outperform radiomics models in computer vision tasks. However, training a CNN from scratch requires a large sample size which is not feasible in most medical imaging studies. As an alternative solution, CNN-based transfer learning models have shown the potential for achieving reasonable performance using small datasets. In this work, we developed and validated a CNN-based transfer learning model for prognostication of overall survival in PDAC patients using two independent resectable PDAC cohorts.Results: The proposed transfer learning-based prognostication model for overall survival achieved the area under the receiver operating characteristic curve of 0.81 on the test cohort, which was significantly higher than that of the traditional radiomics model (0.54). To further assess the prognostic value of the models, the predicted probabilities of death generated from the two models were used as risk scores in a univariate Cox Proportional Hazard model and while the risk score from the traditional radiomics model was not associated with overall survival, the proposed transfer learning-based risk score had significant prognostic value with hazard ratio of 1.86 (95% Confidence Interval: 1.15–3.53, p-value: 0.04).Conclusions: This result suggests that transfer learning-based models may significantly improve prognostic performance in typical small sample size medical imaging studies.
650		4	\|a transfer learning
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650		4	\|a pancreatic cancer
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allfields	10.3389/frai.2020.550890 doi (DE-627)DOAJ033197466 (DE-599)DOAJ516f6b5d9a574f9998a87c77545a913e DE-627 ger DE-627 rakwb eng QA75.5-76.95 Yucheng Zhang verfasserin aut Prognostic Value of Transfer Learning Based Features in Resectable Pancreatic Ductal Adenocarcinoma 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background: Pancreatic Ductal Adenocarcinoma (PDAC) is one of the most aggressive cancers with an extremely poor prognosis. Radiomics has shown prognostic ability in multiple types of cancer including PDAC. However, the prognostic value of traditional radiomics pipelines, which are based on hand-crafted radiomic features alone is limited.Methods: Convolutional neural networks (CNNs) have been shown to outperform radiomics models in computer vision tasks. However, training a CNN from scratch requires a large sample size which is not feasible in most medical imaging studies. As an alternative solution, CNN-based transfer learning models have shown the potential for achieving reasonable performance using small datasets. In this work, we developed and validated a CNN-based transfer learning model for prognostication of overall survival in PDAC patients using two independent resectable PDAC cohorts.Results: The proposed transfer learning-based prognostication model for overall survival achieved the area under the receiver operating characteristic curve of 0.81 on the test cohort, which was significantly higher than that of the traditional radiomics model (0.54). To further assess the prognostic value of the models, the predicted probabilities of death generated from the two models were used as risk scores in a univariate Cox Proportional Hazard model and while the risk score from the traditional radiomics model was not associated with overall survival, the proposed transfer learning-based risk score had significant prognostic value with hazard ratio of 1.86 (95% Confidence Interval: 1.15–3.53, p-value: 0.04).Conclusions: This result suggests that transfer learning-based models may significantly improve prognostic performance in typical small sample size medical imaging studies. transfer learning radiomics prognosis pancreatic cancer survival analysis Electronic computers. Computer science Edrise M. Lobo-Mueller verfasserin aut Paul Karanicolas verfasserin aut Steven Gallinger verfasserin aut Masoom A. Haider verfasserin aut Masoom A. Haider verfasserin aut Farzad Khalvati verfasserin aut Farzad Khalvati verfasserin aut Farzad Khalvati verfasserin aut In Frontiers in Artificial Intelligence Frontiers Media S.A., 2019 3(2020) (DE-627)1047358654 (DE-600)2957496-1 26248212 nnns volume:3 year:2020 https://doi.org/10.3389/frai.2020.550890 kostenfrei https://doaj.org/article/516f6b5d9a574f9998a87c77545a913e kostenfrei https://www.frontiersin.org/article/10.3389/frai.2020.550890/full kostenfrei https://doaj.org/toc/2624-8212 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2507 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 3 2020
spelling	10.3389/frai.2020.550890 doi (DE-627)DOAJ033197466 (DE-599)DOAJ516f6b5d9a574f9998a87c77545a913e DE-627 ger DE-627 rakwb eng QA75.5-76.95 Yucheng Zhang verfasserin aut Prognostic Value of Transfer Learning Based Features in Resectable Pancreatic Ductal Adenocarcinoma 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background: Pancreatic Ductal Adenocarcinoma (PDAC) is one of the most aggressive cancers with an extremely poor prognosis. Radiomics has shown prognostic ability in multiple types of cancer including PDAC. However, the prognostic value of traditional radiomics pipelines, which are based on hand-crafted radiomic features alone is limited.Methods: Convolutional neural networks (CNNs) have been shown to outperform radiomics models in computer vision tasks. However, training a CNN from scratch requires a large sample size which is not feasible in most medical imaging studies. As an alternative solution, CNN-based transfer learning models have shown the potential for achieving reasonable performance using small datasets. In this work, we developed and validated a CNN-based transfer learning model for prognostication of overall survival in PDAC patients using two independent resectable PDAC cohorts.Results: The proposed transfer learning-based prognostication model for overall survival achieved the area under the receiver operating characteristic curve of 0.81 on the test cohort, which was significantly higher than that of the traditional radiomics model (0.54). To further assess the prognostic value of the models, the predicted probabilities of death generated from the two models were used as risk scores in a univariate Cox Proportional Hazard model and while the risk score from the traditional radiomics model was not associated with overall survival, the proposed transfer learning-based risk score had significant prognostic value with hazard ratio of 1.86 (95% Confidence Interval: 1.15–3.53, p-value: 0.04).Conclusions: This result suggests that transfer learning-based models may significantly improve prognostic performance in typical small sample size medical imaging studies. transfer learning radiomics prognosis pancreatic cancer survival analysis Electronic computers. Computer science Edrise M. Lobo-Mueller verfasserin aut Paul Karanicolas verfasserin aut Steven Gallinger verfasserin aut Masoom A. Haider verfasserin aut Masoom A. Haider verfasserin aut Farzad Khalvati verfasserin aut Farzad Khalvati verfasserin aut Farzad Khalvati verfasserin aut In Frontiers in Artificial Intelligence Frontiers Media S.A., 2019 3(2020) (DE-627)1047358654 (DE-600)2957496-1 26248212 nnns volume:3 year:2020 https://doi.org/10.3389/frai.2020.550890 kostenfrei https://doaj.org/article/516f6b5d9a574f9998a87c77545a913e kostenfrei https://www.frontiersin.org/article/10.3389/frai.2020.550890/full kostenfrei https://doaj.org/toc/2624-8212 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2507 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 3 2020
allfields_unstemmed	10.3389/frai.2020.550890 doi (DE-627)DOAJ033197466 (DE-599)DOAJ516f6b5d9a574f9998a87c77545a913e DE-627 ger DE-627 rakwb eng QA75.5-76.95 Yucheng Zhang verfasserin aut Prognostic Value of Transfer Learning Based Features in Resectable Pancreatic Ductal Adenocarcinoma 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background: Pancreatic Ductal Adenocarcinoma (PDAC) is one of the most aggressive cancers with an extremely poor prognosis. Radiomics has shown prognostic ability in multiple types of cancer including PDAC. However, the prognostic value of traditional radiomics pipelines, which are based on hand-crafted radiomic features alone is limited.Methods: Convolutional neural networks (CNNs) have been shown to outperform radiomics models in computer vision tasks. However, training a CNN from scratch requires a large sample size which is not feasible in most medical imaging studies. As an alternative solution, CNN-based transfer learning models have shown the potential for achieving reasonable performance using small datasets. In this work, we developed and validated a CNN-based transfer learning model for prognostication of overall survival in PDAC patients using two independent resectable PDAC cohorts.Results: The proposed transfer learning-based prognostication model for overall survival achieved the area under the receiver operating characteristic curve of 0.81 on the test cohort, which was significantly higher than that of the traditional radiomics model (0.54). To further assess the prognostic value of the models, the predicted probabilities of death generated from the two models were used as risk scores in a univariate Cox Proportional Hazard model and while the risk score from the traditional radiomics model was not associated with overall survival, the proposed transfer learning-based risk score had significant prognostic value with hazard ratio of 1.86 (95% Confidence Interval: 1.15–3.53, p-value: 0.04).Conclusions: This result suggests that transfer learning-based models may significantly improve prognostic performance in typical small sample size medical imaging studies. transfer learning radiomics prognosis pancreatic cancer survival analysis Electronic computers. Computer science Edrise M. Lobo-Mueller verfasserin aut Paul Karanicolas verfasserin aut Steven Gallinger verfasserin aut Masoom A. Haider verfasserin aut Masoom A. Haider verfasserin aut Farzad Khalvati verfasserin aut Farzad Khalvati verfasserin aut Farzad Khalvati verfasserin aut In Frontiers in Artificial Intelligence Frontiers Media S.A., 2019 3(2020) (DE-627)1047358654 (DE-600)2957496-1 26248212 nnns volume:3 year:2020 https://doi.org/10.3389/frai.2020.550890 kostenfrei https://doaj.org/article/516f6b5d9a574f9998a87c77545a913e kostenfrei https://www.frontiersin.org/article/10.3389/frai.2020.550890/full kostenfrei https://doaj.org/toc/2624-8212 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2507 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 3 2020
allfieldsGer	10.3389/frai.2020.550890 doi (DE-627)DOAJ033197466 (DE-599)DOAJ516f6b5d9a574f9998a87c77545a913e DE-627 ger DE-627 rakwb eng QA75.5-76.95 Yucheng Zhang verfasserin aut Prognostic Value of Transfer Learning Based Features in Resectable Pancreatic Ductal Adenocarcinoma 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background: Pancreatic Ductal Adenocarcinoma (PDAC) is one of the most aggressive cancers with an extremely poor prognosis. Radiomics has shown prognostic ability in multiple types of cancer including PDAC. However, the prognostic value of traditional radiomics pipelines, which are based on hand-crafted radiomic features alone is limited.Methods: Convolutional neural networks (CNNs) have been shown to outperform radiomics models in computer vision tasks. However, training a CNN from scratch requires a large sample size which is not feasible in most medical imaging studies. As an alternative solution, CNN-based transfer learning models have shown the potential for achieving reasonable performance using small datasets. In this work, we developed and validated a CNN-based transfer learning model for prognostication of overall survival in PDAC patients using two independent resectable PDAC cohorts.Results: The proposed transfer learning-based prognostication model for overall survival achieved the area under the receiver operating characteristic curve of 0.81 on the test cohort, which was significantly higher than that of the traditional radiomics model (0.54). To further assess the prognostic value of the models, the predicted probabilities of death generated from the two models were used as risk scores in a univariate Cox Proportional Hazard model and while the risk score from the traditional radiomics model was not associated with overall survival, the proposed transfer learning-based risk score had significant prognostic value with hazard ratio of 1.86 (95% Confidence Interval: 1.15–3.53, p-value: 0.04).Conclusions: This result suggests that transfer learning-based models may significantly improve prognostic performance in typical small sample size medical imaging studies. transfer learning radiomics prognosis pancreatic cancer survival analysis Electronic computers. Computer science Edrise M. Lobo-Mueller verfasserin aut Paul Karanicolas verfasserin aut Steven Gallinger verfasserin aut Masoom A. Haider verfasserin aut Masoom A. Haider verfasserin aut Farzad Khalvati verfasserin aut Farzad Khalvati verfasserin aut Farzad Khalvati verfasserin aut In Frontiers in Artificial Intelligence Frontiers Media S.A., 2019 3(2020) (DE-627)1047358654 (DE-600)2957496-1 26248212 nnns volume:3 year:2020 https://doi.org/10.3389/frai.2020.550890 kostenfrei https://doaj.org/article/516f6b5d9a574f9998a87c77545a913e kostenfrei https://www.frontiersin.org/article/10.3389/frai.2020.550890/full kostenfrei https://doaj.org/toc/2624-8212 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2507 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 3 2020
allfieldsSound	10.3389/frai.2020.550890 doi (DE-627)DOAJ033197466 (DE-599)DOAJ516f6b5d9a574f9998a87c77545a913e DE-627 ger DE-627 rakwb eng QA75.5-76.95 Yucheng Zhang verfasserin aut Prognostic Value of Transfer Learning Based Features in Resectable Pancreatic Ductal Adenocarcinoma 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background: Pancreatic Ductal Adenocarcinoma (PDAC) is one of the most aggressive cancers with an extremely poor prognosis. Radiomics has shown prognostic ability in multiple types of cancer including PDAC. However, the prognostic value of traditional radiomics pipelines, which are based on hand-crafted radiomic features alone is limited.Methods: Convolutional neural networks (CNNs) have been shown to outperform radiomics models in computer vision tasks. However, training a CNN from scratch requires a large sample size which is not feasible in most medical imaging studies. As an alternative solution, CNN-based transfer learning models have shown the potential for achieving reasonable performance using small datasets. In this work, we developed and validated a CNN-based transfer learning model for prognostication of overall survival in PDAC patients using two independent resectable PDAC cohorts.Results: The proposed transfer learning-based prognostication model for overall survival achieved the area under the receiver operating characteristic curve of 0.81 on the test cohort, which was significantly higher than that of the traditional radiomics model (0.54). To further assess the prognostic value of the models, the predicted probabilities of death generated from the two models were used as risk scores in a univariate Cox Proportional Hazard model and while the risk score from the traditional radiomics model was not associated with overall survival, the proposed transfer learning-based risk score had significant prognostic value with hazard ratio of 1.86 (95% Confidence Interval: 1.15–3.53, p-value: 0.04).Conclusions: This result suggests that transfer learning-based models may significantly improve prognostic performance in typical small sample size medical imaging studies. transfer learning radiomics prognosis pancreatic cancer survival analysis Electronic computers. Computer science Edrise M. Lobo-Mueller verfasserin aut Paul Karanicolas verfasserin aut Steven Gallinger verfasserin aut Masoom A. Haider verfasserin aut Masoom A. Haider verfasserin aut Farzad Khalvati verfasserin aut Farzad Khalvati verfasserin aut Farzad Khalvati verfasserin aut In Frontiers in Artificial Intelligence Frontiers Media S.A., 2019 3(2020) (DE-627)1047358654 (DE-600)2957496-1 26248212 nnns volume:3 year:2020 https://doi.org/10.3389/frai.2020.550890 kostenfrei https://doaj.org/article/516f6b5d9a574f9998a87c77545a913e kostenfrei https://www.frontiersin.org/article/10.3389/frai.2020.550890/full kostenfrei https://doaj.org/toc/2624-8212 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2507 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 3 2020
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callnumber-first	Q - Science
author	Yucheng Zhang
spellingShingle	Yucheng Zhang misc QA75.5-76.95 misc transfer learning misc radiomics misc prognosis misc pancreatic cancer misc survival analysis misc Electronic computers. Computer science Prognostic Value of Transfer Learning Based Features in Resectable Pancreatic Ductal Adenocarcinoma
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topic_title	QA75.5-76.95 Prognostic Value of Transfer Learning Based Features in Resectable Pancreatic Ductal Adenocarcinoma transfer learning radiomics prognosis pancreatic cancer survival analysis
topic	misc QA75.5-76.95 misc transfer learning misc radiomics misc prognosis misc pancreatic cancer misc survival analysis misc Electronic computers. Computer science
topic_unstemmed	misc QA75.5-76.95 misc transfer learning misc radiomics misc prognosis misc pancreatic cancer misc survival analysis misc Electronic computers. Computer science
topic_browse	misc QA75.5-76.95 misc transfer learning misc radiomics misc prognosis misc pancreatic cancer misc survival analysis misc Electronic computers. Computer science
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title	Prognostic Value of Transfer Learning Based Features in Resectable Pancreatic Ductal Adenocarcinoma
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title_full	Prognostic Value of Transfer Learning Based Features in Resectable Pancreatic Ductal Adenocarcinoma
author_sort	Yucheng Zhang
journal	Frontiers in Artificial Intelligence
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author_browse	Yucheng Zhang Edrise M. Lobo-Mueller Paul Karanicolas Steven Gallinger Masoom A. Haider Farzad Khalvati
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title_sort	prognostic value of transfer learning based features in resectable pancreatic ductal adenocarcinoma
callnumber	QA75.5-76.95
title_auth	Prognostic Value of Transfer Learning Based Features in Resectable Pancreatic Ductal Adenocarcinoma
abstract	Background: Pancreatic Ductal Adenocarcinoma (PDAC) is one of the most aggressive cancers with an extremely poor prognosis. Radiomics has shown prognostic ability in multiple types of cancer including PDAC. However, the prognostic value of traditional radiomics pipelines, which are based on hand-crafted radiomic features alone is limited.Methods: Convolutional neural networks (CNNs) have been shown to outperform radiomics models in computer vision tasks. However, training a CNN from scratch requires a large sample size which is not feasible in most medical imaging studies. As an alternative solution, CNN-based transfer learning models have shown the potential for achieving reasonable performance using small datasets. In this work, we developed and validated a CNN-based transfer learning model for prognostication of overall survival in PDAC patients using two independent resectable PDAC cohorts.Results: The proposed transfer learning-based prognostication model for overall survival achieved the area under the receiver operating characteristic curve of 0.81 on the test cohort, which was significantly higher than that of the traditional radiomics model (0.54). To further assess the prognostic value of the models, the predicted probabilities of death generated from the two models were used as risk scores in a univariate Cox Proportional Hazard model and while the risk score from the traditional radiomics model was not associated with overall survival, the proposed transfer learning-based risk score had significant prognostic value with hazard ratio of 1.86 (95% Confidence Interval: 1.15–3.53, p-value: 0.04).Conclusions: This result suggests that transfer learning-based models may significantly improve prognostic performance in typical small sample size medical imaging studies.
abstractGer	Background: Pancreatic Ductal Adenocarcinoma (PDAC) is one of the most aggressive cancers with an extremely poor prognosis. Radiomics has shown prognostic ability in multiple types of cancer including PDAC. However, the prognostic value of traditional radiomics pipelines, which are based on hand-crafted radiomic features alone is limited.Methods: Convolutional neural networks (CNNs) have been shown to outperform radiomics models in computer vision tasks. However, training a CNN from scratch requires a large sample size which is not feasible in most medical imaging studies. As an alternative solution, CNN-based transfer learning models have shown the potential for achieving reasonable performance using small datasets. In this work, we developed and validated a CNN-based transfer learning model for prognostication of overall survival in PDAC patients using two independent resectable PDAC cohorts.Results: The proposed transfer learning-based prognostication model for overall survival achieved the area under the receiver operating characteristic curve of 0.81 on the test cohort, which was significantly higher than that of the traditional radiomics model (0.54). To further assess the prognostic value of the models, the predicted probabilities of death generated from the two models were used as risk scores in a univariate Cox Proportional Hazard model and while the risk score from the traditional radiomics model was not associated with overall survival, the proposed transfer learning-based risk score had significant prognostic value with hazard ratio of 1.86 (95% Confidence Interval: 1.15–3.53, p-value: 0.04).Conclusions: This result suggests that transfer learning-based models may significantly improve prognostic performance in typical small sample size medical imaging studies.
abstract_unstemmed	Background: Pancreatic Ductal Adenocarcinoma (PDAC) is one of the most aggressive cancers with an extremely poor prognosis. Radiomics has shown prognostic ability in multiple types of cancer including PDAC. However, the prognostic value of traditional radiomics pipelines, which are based on hand-crafted radiomic features alone is limited.Methods: Convolutional neural networks (CNNs) have been shown to outperform radiomics models in computer vision tasks. However, training a CNN from scratch requires a large sample size which is not feasible in most medical imaging studies. As an alternative solution, CNN-based transfer learning models have shown the potential for achieving reasonable performance using small datasets. In this work, we developed and validated a CNN-based transfer learning model for prognostication of overall survival in PDAC patients using two independent resectable PDAC cohorts.Results: The proposed transfer learning-based prognostication model for overall survival achieved the area under the receiver operating characteristic curve of 0.81 on the test cohort, which was significantly higher than that of the traditional radiomics model (0.54). To further assess the prognostic value of the models, the predicted probabilities of death generated from the two models were used as risk scores in a univariate Cox Proportional Hazard model and while the risk score from the traditional radiomics model was not associated with overall survival, the proposed transfer learning-based risk score had significant prognostic value with hazard ratio of 1.86 (95% Confidence Interval: 1.15–3.53, p-value: 0.04).Conclusions: This result suggests that transfer learning-based models may significantly improve prognostic performance in typical small sample size medical imaging studies.
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title_short	Prognostic Value of Transfer Learning Based Features in Resectable Pancreatic Ductal Adenocarcinoma
url	https://doi.org/10.3389/frai.2020.550890 https://doaj.org/article/516f6b5d9a574f9998a87c77545a913e https://www.frontiersin.org/article/10.3389/frai.2020.550890/full https://doaj.org/toc/2624-8212
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author2	Edrise M. Lobo-Mueller Paul Karanicolas Steven Gallinger Masoom A. Haider Farzad Khalvati
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up_date	2024-07-03T16:21:14.795Z
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In this work, we developed and validated a CNN-based transfer learning model for prognostication of overall survival in PDAC patients using two independent resectable PDAC cohorts.Results: The proposed transfer learning-based prognostication model for overall survival achieved the area under the receiver operating characteristic curve of 0.81 on the test cohort, which was significantly higher than that of the traditional radiomics model (0.54). To further assess the prognostic value of the models, the predicted probabilities of death generated from the two models were used as risk scores in a univariate Cox Proportional Hazard model and while the risk score from the traditional radiomics model was not associated with overall survival, the proposed transfer learning-based risk score had significant prognostic value with hazard ratio of 1.86 (95% Confidence Interval: 1.15–3.53, p-value: 0.04).Conclusions: This result suggests that transfer learning-based models may significantly improve prognostic performance in typical small sample size medical imaging studies.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">transfer learning</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">radiomics</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">prognosis</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">pancreatic cancer</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">survival analysis</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Electronic computers. Computer science</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Edrise M. Lobo-Mueller</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Paul Karanicolas</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Steven Gallinger</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Masoom A. Haider</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Masoom A. Haider</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Farzad Khalvati</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Farzad Khalvati</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Farzad Khalvati</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Frontiers in Artificial Intelligence</subfield><subfield code="d">Frontiers Media S.A., 2019</subfield><subfield code="g">3(2020)</subfield><subfield code="w">(DE-627)1047358654</subfield><subfield code="w">(DE-600)2957496-1</subfield><subfield code="x">26248212</subfield><subfield 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