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Use of deep learning for detection, characterisation and prediction of metastatic disease from computerised tomography: a systematic review
  1. Natesh Shivakumar,
  2. Anirudh Chandrashekar,
  3. Ashok Inderraj Handa,
  4. Regent Lee
  1. Nuffield Department of Surgical Sciences, University of Oxford, Oxford, Oxfordshire, UK
  1. Correspondence to Regent Lee, Nuffield Department of Surgical Sciences, University of Oxford, Oxford, Oxfordshire, UK; regent.lee{at}nds.ox.ac.uk

Abstract

CT is widely used for diagnosis, staging and management of cancer. The presence of metastasis has significant implications on treatment and prognosis. Deep learning (DL), a form of machine learning, where layers of programmed algorithms interpret and recognise patterns, may have a potential role in CT image analysis. This review aims to provide an overview on the use of DL in CT image analysis in the diagnostic evaluation of metastatic disease. A total of 29 studies were included which could be grouped together into three areas of research: the use of deep learning on the detection of metastatic disease from CT imaging, characterisation of lesions on CT into metastasis and prediction of the presence or development of metastasis based on the primary tumour. In conclusion, DL in CT image analysis could have a potential role in evaluating metastatic disease; however, prospective clinical trials investigating its clinical value are required.

  • surgery
  • oncology
  • radiology & imaging

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Footnotes

  • Twitter @regentlee

  • Contributors NS: Concept, data collection, first reviewer, initial draft of the manuscript and revisions. AC: Data collection, second reviewer and revisions of the manuscript. AH: Concept and revisions of the manuscript. RL: Concept, design, third reviewer for discrepancies and revisions of the manuscript.

  • Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

  • Competing interests None declared.

  • Patient consent for publication Not required.

  • Provenance and peer review Not commissioned; externally peer reviewed.

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