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

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.