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

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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.

Methods

Two partitioned survival models were developed to assess the cost-effectiveness of Histotyping for two treatment cohorts: patients with CRC stage II and III. For each of the two cohorts, Histotyping was used for risk stratification to assign adjuvant chemotherapy and was compared with the standard of care (SOC) (adjuvant chemotherapy to all patients). Health outcomes measured in the model were quality-adjusted life years (QALYs) and life years (LYs) gained. Deterministic and probabilistic sensitivity analyses were performed to determine the impact of uncertainty. Scenario analyses were performed to assess the impact of the parameters with the greatest uncertainty.

Results

Risk-stratifying patients with CRC stage II and III using Histotyping was dominant (less costly and more effective) compared to SOC. In patients with CRC stage II, the net monetary benefit of Histotyping was 270,934 Norwegian kroners (NOK) (year of valuation is 2021), and the net health benefit of Histotyping was 0.99. In stage III, the net monetary benefit of Histotyping was 195,419 NOK, and the net health benefit of Histotyping was 0.71.

Conclusions

 Risk-stratifying patients with CRC using Histotyping prior to the administration of adjuvant chemotherapy is likely to be a cost-effective strategy in Norway.

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Acknowledgements

Thank you to Dr. Håvard E.G. Danielsen for facilitating and supporting the project and to Dr. Andreas Kleppe for thoughtful input and helpful discussions throughout the data structuring and analysis. Thank you to Dr. Arild Nesbakken and Dr. Ole Kristian Andersen for providing invaluable clinical expertise. We thank Mariann Mathisen of the academic library at Vestfold Hospital Trust for her assistance conducting a thorough and structured literature search. We would also like to thank the QUASAR 2 trial for access to the patient material and data as well as all participating patients.

Author information

Author notes

  1. Anna Kenseth and Dominika Kantorova have contributed equally to this work.

Authors and Affiliations

  1. Institute for Cancer Genetics and Informatics, Oslo University Hospital, Oslo, Norway

    Anna Kenseth, Dominika Kantorova, Hanne Askautrud & Jørn Evert Jacobsen

  2. Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK

    Mikyung Kelly Seo

  3. Department of Health Services Research and Policy, London School of Hygiene and Tropical Medicine, London, UK

    John Cairns

  4. Radcliffe Department of Medicine, Oxford University, Oxford, UK

    David Kerr

  5. Wolfson Institute of Population Health, Faculty of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom

    Mikyung Kelly Seo

  6. Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, United Kingdom

    Mikyung Kelly Seo

  7. Department of Health Management and Health Economics, Institute of Health and Society,, University of Oslo, Oslo, Norway

    Eline Aas

  8. Department of Research and Innovation, Vestfold Hospital Trust, Tønsberg, Norway

    Jørn Evert Jacobsen

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  1. Anna Kenseth
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Corresponding author

Correspondence to Mikyung Kelly Seo.

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Funding

This study was supported by the Norwegian Research Council through the DoMore Project (259204) and the Bench to Bedside Project (309610).

Conflict of interest

AK, DK, HA, and JEJ report having stocks in DoMore Diagnostics. All other authors declare no competing interests. The funder had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

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All necessary data and information are provided in this article.

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All authors consent to participate.

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Author contributions

Study concept and design: AK, DK, MKS, EA, JEJ. Data analysis: AK, DK, MKS. Model development: AK, DK, MKS. Manuscript writing, original draft: AK, DK. Manuscript review and editing: AK, DK, MKS, EA, JC, DK, HA, JEJ. Supervision: MKS

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Kenseth, A., Kantorova, D., Seo, M.K. et al. Is Risk-Stratifying Patients with Colorectal Cancer Using a Deep Learning-Based Prognostic Biomarker Cost-Effective?. PharmacoEconomics (2024). https://doi.org/10.1007/s40273-024-01371-1

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