Published July 2021 | Version v1
Journal article Metadata-only

Can magnetic resonance imaging radiomics of the pancreas predict postoperative pancreatic fistula?

Creators

  • 1. University of Zurich, Zurich (Switzerland)
  • 2. Department of Nuclear Medicine, University Hospital Zurich, Zurich (Switzerland)
  • 3. Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Zurich (Switzerland)
  • 4. Department of Hepatobiliary Surgery and Liver Transplantation, St. Vincent's University Hospital, Dublin (Ireland)
  • 5. Department of Surgery and Transplantation, University Hospital Zurich, Zurich (Switzerland)
  • 6. Department of Surgery and Transplantation, University Medical Center, Schleswig-Holstein, Campus Kiel (Germany)

Description

Highlights: • MRI-radiomics can help identifying patients at risk for postoperative pancreatic fistula after pancreaticoduodenectomy. • The diagnostic performance of an MRI-radiomics approach exceeds that of established clinical risk scores. • Radiomics reveal additional information from readily available pancreatic imaging datasets invisible to the bare eye. To evaluate whether a magnetic resonance imaging (MRI) radiomics-based machine learning classifier can predict postoperative pancreatic fistula (POPF) after pancreaticoduodenectomy (PD) and to compare its performance to T1 signal intensity ratio (T1 SIratio).

Availability note (English)

Available from http://dx.doi.org/10.1016/j.ejrad.2021.109733

Additional details

Identifiers

DOI
10.1016/j.ejrad.2021.109733;
PII
S0720048X2100214X;

Publishing Information

Journal Title
European Journal of Radiology
Journal Volume
140
Journal Page Range
vp.
ISSN
0720-048X
CODEN
EJRADR

INIS

Country of Publication
Netherlands
Country of Input or Organization
International Atomic Energy Agency (IAEA)
INIS RN
53110460
Subject category
S62: RADIOLOGY AND NUCLEAR MEDICINE;
Descriptors DEI
COMPARATIVE EVALUATIONS; DIAGNOSIS; EYES; HAZARDS; MACHINE LEARNING; MAGNETIC RESONANCE; NMR IMAGING; PANCREAS; PATIENTS; RADIOMICS; SIGNALS
Descriptors DEC
ALGORITHMS; ARTIFICIAL INTELLIGENCE; BODY; DIAGNOSTIC TECHNIQUES; DIGESTIVE SYSTEM; ENDOCRINE GLANDS; EVALUATION; FACE; GLANDS; HEAD; LEARNING; MATHEMATICAL LOGIC; MEDICINE; NUCLEAR MEDICINE; ORGANS; RADIOLOGY; RESONANCE; SENSE ORGANS

Optional Information

Copyright
Copyright (c) 2021 Elsevier B.V. All rights reserved.