Fully automated volumetric measurement of malignant pleural mesothelioma by deep learning AI: validation and comparison with modified RECIST response criteria.

Journal: Thorax
PMID: 35110367

Abstract

BACKGROUND: In malignant pleural mesothelioma (MPM), complex tumour morphology results in inconsistent radiological response assessment. Promising volumetric methods require automation to be practical. We developed a fully automated Convolutional Neural Network (CNN) for this purpose, performed blinded validation and compared CNN and human response classification and survival prediction in patients treated with chemotherapy.

Authors

  • Andrew C Kidd
    Glasgow Pleural Disease Unit, Queen Elizabeth University Hospital, Glasgow, UK.
  • Owen Anderson
    School of Computing Science, University of Glasgow, Glasgow, UK.
  • Gordon W Cowell
    Department of Imaging, Queen Elizabeth University Hospital, Glasgow, UK.
  • Alexander J Weir
    Canon Medical Research Europe Ltd, Edinburgh, UK.
  • Jeremy P Voisey
    Canon Medical Research Europe Ltd, Edinburgh, UK.
  • Matthew Evison
    Department of Respiratory Medicine, University Hospital of South Manchester, Manchester, UK.
  • Selina Tsim
    Glasgow Pleural Disease Unit, Queen Elizabeth University Hospital, Glasgow, UK.
  • Keith A Goatman
    Canon Medical Research Europe Ltd, Edinburgh, UK.
  • Kevin G Blyth
    Glasgow Pleural Disease Unit, Queen Elizabeth University Hospital, Glasgow, UK Kevin.Blyth@glasgow.ac.uk.

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