4.7 Article

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

Journal

SCIENTIFIC REPORTS
Volume 11, Issue 1, Pages -

Publisher

NATURE PORTFOLIO
DOI: 10.1038/s41598-021-80998-y

Keywords

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Funding

  1. Ontario Institute for Cancer Research (PanCuRx Translational Research Initiative) through the Government of Ontario
  2. Wallace McCain Centre for Pancreatic Cancer - Princess Margaret Cancer Foundation
  3. Terry Fox Research Institute
  4. Canadian Cancer Society Research Institute
  5. Pancreatic Cancer Canada Foundation

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The study found weak linear relationships between deep transfer learning features and radiomics features, indicating a potential complementary relationship. By combining radiomics and transfer learning features, the prognosis performance for predicting overall survival in PDAC patients can be significantly improved.
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).

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