Cost-effectiveness analysis of a circulating tumor DNA-based molecular residual disease assay to guide adjuvant chemotherapy decisions in patients with resectable early stage colorectal cancer.
Bibliographic record
Abstract
e18929 Background: An ongoing prospective study in patients with stage II or III colorectal cancer (CRC) has demonstrated ctDNA testing to be prognostic of recurrence risk and predictive of adjuvant chemotherapy (ACT) benefit. From the perspective of a US payer, we aimed to investigate the cost effectiveness of incorporating ctDNA analysis using the commercially available molecular residual disease (MRD) assay (Signatera™) into standard practice to aid in adjuvant chemotherapy decision-making, where traditional high-risk clinicopathological (CP) features have not shown a significant benefit of ACT in Stage II CRC. Methods: We developed a decision model to project lifetime clinical and economic consequences of different adjuvant treatment-guiding strategies. The model was parameterized using a follow-up of up to 2-years from a recently published data from GALAXY study and the cost data from the US literature. GALAXY is an observational arm of the ongoing CIRCULATE-Japan study (UMIN000039205) that analysed pre-surgical and post-surgical ctDNA in patients with stage II-IV resectable CRC. Costs are presented in 2023 US dollars. Future costs and benefits were discounted at 3%. Results: Compared to the CP predictors alone-based strategy, ctDNA analysis using the MRD assay in combination with CP predictors-based strategy led to an increase of 0.8 life years and 0.7 quality adjusted-Life Years (QALY) and cost savings of $9,771 per patient. Conclusions: The addition of MRD detection by ctDNA to traditional CP predictors aids in adjuvant chemotherapy decision-making in patients with stage II or III resectable CRC and is likely to be cost effective in the US healthcare system. ctDNA testing should therefore be considered for adoption in this disease setting.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".