Is Risk-Stratifying Patients with Colorectal Cancer Using a Deep Learning-Based Prognostic Biomarker Cost-Effective?

Journal: PharmacoEconomics
PMID: 38584239

Abstract

OBJECTIVES: Accurate risk stratification of patients with stage II and III colorectal cancer (CRC) prior to treatment selection enables limited health resources to be efficiently allocated to patients who are likely to benefit from adjuvant chemotherapy. We aimed to investigate the cost-effectiveness of a recently developed deep learning-based prognostic method, Histotyping, from the perspective of the Norwegian healthcare system.

Authors

  • Anna Kenseth
    Institute for Cancer Genetics and Informatics, Oslo University Hospital, Oslo, Norway.
  • Dominika Kantorova
    Institute for Cancer Genetics and Informatics, Oslo University Hospital, Oslo, Norway.
  • Mikyung Kelly Seo
    Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK. ks2024@cam.ac.uk.
  • Eline Aas
    Department of Health Management and Health Economics, Institute of Health and Society,, University of Oslo, Oslo, Norway.
  • John Cairns
    Department of Health Services Research and Policy, London School of Hygiene and Tropical Medicine, London, UK.
  • David Kerr
    1 Sansum Diabetes Research Institute, Santa Barbara, CA, USA.
  • Hanne Askautrud
    Institute for Cancer Genetics and Informatics, Oslo University Hospital, Oslo, Norway.
  • Jørn Evert Jacobsen
    Institute for Cancer Genetics and Informatics, Oslo University Hospital, Oslo, Norway.

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