Improving prognostic performance in resectable pancreatic ductal adenocarcinoma using radiomics and deep learning features fusion in CT images

Abstract As an analytic pipeline for quantitative imaging feature extraction and analysis, radiomics has grown rapidly in the past decade. On the other hand, recent advances in deep learning and transfer learning have shown significant potential in the quantitative medical imaging field, raising the...

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Autores principales:	Yucheng Zhang, Edrise M. Lobo-Mueller, Paul Karanicolas, Steven Gallinger, Masoom A. Haider, Farzad Khalvati
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/3d233bf30f804aacb0fa45f8d87db39d
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spelling	oai:doaj.org-article:3d233bf30f804aacb0fa45f8d87db39d2021-12-02T14:01:24ZImproving prognostic performance in resectable pancreatic ductal adenocarcinoma using radiomics and deep learning features fusion in CT images10.1038/s41598-021-80998-y2045-2322https://doaj.org/article/3d233bf30f804aacb0fa45f8d87db39d2021-01-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-80998-yhttps://doaj.org/toc/2045-2322Abstract As an analytic pipeline for quantitative imaging feature extraction and analysis, radiomics has grown rapidly in the past decade. On the other hand, recent advances in deep learning and transfer learning have shown significant potential in the quantitative medical imaging field, raising the research question of whether deep transfer learning features have predictive information in addition to radiomics features. In this study, using CT images from Pancreatic Ductal Adenocarcinoma (PDAC) patients recruited in two independent hospitals, we discovered most transfer learning features have weak linear relationships with radiomics features, suggesting a potential complementary relationship between these two feature sets. We also tested the prognostic performance for overall survival using four feature fusion and reduction methods for combining radiomics and transfer learning features and compared the results with our proposed risk score-based feature fusion method. It was shown that the risk score-based feature fusion method significantly improves the prognosis performance for predicting overall survival in PDAC patients compared to other traditional feature reduction methods used in previous radiomics studies (40% increase in area under ROC curve (AUC) yielding AUC of 0.84).Yucheng ZhangEdrise M. Lobo-MuellerPaul KaranicolasSteven GallingerMasoom A. HaiderFarzad KhalvatiNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Yucheng Zhang Edrise M. Lobo-Mueller Paul Karanicolas Steven Gallinger Masoom A. Haider Farzad Khalvati Improving prognostic performance in resectable pancreatic ductal adenocarcinoma using radiomics and deep learning features fusion in CT images
description	Abstract As an analytic pipeline for quantitative imaging feature extraction and analysis, radiomics has grown rapidly in the past decade. On the other hand, recent advances in deep learning and transfer learning have shown significant potential in the quantitative medical imaging field, raising the research question of whether deep transfer learning features have predictive information in addition to radiomics features. In this study, using CT images from Pancreatic Ductal Adenocarcinoma (PDAC) patients recruited in two independent hospitals, we discovered most transfer learning features have weak linear relationships with radiomics features, suggesting a potential complementary relationship between these two feature sets. We also tested the prognostic performance for overall survival using four feature fusion and reduction methods for combining radiomics and transfer learning features and compared the results with our proposed risk score-based feature fusion method. It was shown that the risk score-based feature fusion method significantly improves the prognosis performance for predicting overall survival in PDAC patients compared to other traditional feature reduction methods used in previous radiomics studies (40% increase in area under ROC curve (AUC) yielding AUC of 0.84).
format	article
author	Yucheng Zhang Edrise M. Lobo-Mueller Paul Karanicolas Steven Gallinger Masoom A. Haider Farzad Khalvati
author_facet	Yucheng Zhang Edrise M. Lobo-Mueller Paul Karanicolas Steven Gallinger Masoom A. Haider Farzad Khalvati
author_sort	Yucheng Zhang
title	Improving prognostic performance in resectable pancreatic ductal adenocarcinoma using radiomics and deep learning features fusion in CT images
title_short	Improving prognostic performance in resectable pancreatic ductal adenocarcinoma using radiomics and deep learning features fusion in CT images
title_full	Improving prognostic performance in resectable pancreatic ductal adenocarcinoma using radiomics and deep learning features fusion in CT images
title_fullStr	Improving prognostic performance in resectable pancreatic ductal adenocarcinoma using radiomics and deep learning features fusion in CT images
title_full_unstemmed	Improving prognostic performance in resectable pancreatic ductal adenocarcinoma using radiomics and deep learning features fusion in CT images
title_sort	improving prognostic performance in resectable pancreatic ductal adenocarcinoma using radiomics and deep learning features fusion in ct images
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/3d233bf30f804aacb0fa45f8d87db39d
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Improving prognostic performance in resectable pancreatic ductal adenocarcinoma using radiomics and deep learning features fusion in CT images

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