Combined Modality Therapy Versus Chemotherapy Alone as An Induction Regimen for Primary CNS Lymphoma: a Decision Analysis.
Bibliographic record
Abstract
Abstract Abstract 1504 Background: Induction therapy for non-HIV, primary CNS lymphoma is centered on high-dose methotrexate (MTX) as the most effective chemotherapeutic agent. Combined modality therapy (CMT) using high-dose MTX and whole brain radiotherapy (WBRT) has improved response rates compared to chemotherapy alone. The trade-off is a significant risk of delayed, treatment-related neurotoxicity (NT), leading to significant morbidity and mortality. This risk of NT is greater in patients over the age of 60. As data from randomized controlled trials is not available and it is unlikely to be generated in the future, we performed a decision analysis to explore the implications of the trade-off between improved tumor control with CMT, and decreased NT with chemotherapy. Our analyses compared life expectancy and quality-adjusted life expectancy with these two strategies. Methods: We developed a Markov decision-analytic model to compare CMT versus chemotherapy alone for a hypothetical cohort of 60 year old patients newly-diagnosed with primary CNS lymphoma. The model simulates the clinical course of patients over a 5 year time horizon, with the end-points of life expectancy and quality-adjusted life expectancy. The baseline probabilities used in the model were derived from a systematic review of published studies. Key variables included response, relapse and survival rates with CMT versus chemotherapy alone, risk of developing NT with each strategy, including severe NT, and the estimated survival once NT develops. The model incorporated data on health state utilities, which were derived from a survey of expert physicians who treat patients with primary CNS lymphoma. All patients who received chemotherapy alone as induction therapy were assumed to have received salvage radiotherapy upon relapse. The utility of having active disease was assumed to overcome any ill effects from neurotoxicity. Sensitivity analyses were performed for key variables. Results: The life expectancy was 2.96 years for the CMT strategy and 2.82 years for the chemotherapy alone strategy (with deferred salvage radiotherapy). This yielded a net benefit of 0.14 years for the CMT strategy. The quality-adjusted life expectancies for the two strategies were 2.01 and 1.73 quality-adjusted life years (QALYs), with an expected benefit from CMT as induction therapy of 0.28 QALYs. Sensitivity analysis demonstrated that the model was robust to key variables. The model favoured treating patients with CMT unless the hazard ratio (HR) of time to first relapse with chemotherapy alone versus CMT was <1.02, a lower HR than reported in the literature (base case assumption HR: 2.12). The model was robust to the plausible ranges in probability of developing NT (base probability: 0.18 at 1yr). It favoured treating patients with CMT unless the rate of neurotoxicity was more than 73% at 1yr, a rate not encountered in the published literature. On microsimulation (100 000 trials), 85% of the simulations showed that CMT was the preferred strategy compared to chemotherapy alone. Conclusion: The preferred induction strategy for patients with primary CNS lymphoma appears to be CMT. This strategy maximizes life expectancy, and quality adjusted life years. The model was robust to sensitivity analyses of key variables tested through the plausible ranges obtained from the published literature. Disclosures: No relevant conflicts of interest to declare.
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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 | 0.024 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.010 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".