Prolaris Cell Cycle Progression Test for Localized Prostate Cancer: A Health Technology Assessment.
Bibliographic record
Abstract
BACKGROUND: Prostate cancer is very common and many localized tumours are non-aggressive. Determining which cancers are aggressive is important for choosing the most appropriate treatment (e.g., surgery, radiation, active surveillance). Current clinical risk stratification is reliable in forecasting the prognosis of groups of men with similar clinical and pathologic characteristics, but there is residual uncertainty at the individual level. The Prolaris cell cycle progression (CCP) test, a genomic test that estimates how fast tumour cells are proliferating, could potentially be used to improve the accuracy of individual risk assessment. This health technology assessment sought to determine the clinical utility, economic impact, and patients' perceptions of the value of the CCP test in low- and intermediate-risk localized prostate cancer. METHODS: We conducted a systematic review of the clinical and economic evidence of the CCP test in low-and intermediate-risk, localized prostate cancer. Medical and health economic databases were searched from 2010 to June or July 2016. The critical appraisal of the clinical evidence included risk of bias and the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) Working Group criteria. We also analyzed the potential budget impact of adding the CCP test into current practice, from the perspective the Ontario Ministry of Health and Long-Term Care. Finally, we conducted qualitative interviews with men with prostate cancer, on the factors that influenced their treatment decision-making. RESULTS: For the review of clinical effectiveness, we screened 3,021 citations, and two before-after studies met our inclusion criteria. In one study, the results of the CCP test appeared to change the treatment plan (from initial to final plan) in 64.9% of cases overall (GRADE rating of the quality of evidence: Very low). In the other study, the CCP test changed the treatment received in nearly half of cases overall, compared with the initial plan (GRADE: Very low). No evidence was available on clinical outcomes of patients whose treatment was informed by CCP results. For the review of cost-effectiveness, 100 citations were identified and screened. No studies met the inclusion criteria. In our economic evaluation, we estimated that publicly funding the CCP test would result in a total net budget impact of $41.3 million in the first 5 years, mostly due to the cost of the CCP test. In our model, the relatively small cost savings ($7.3 million) due to treatment change (increased use of active surveillance and decreased use of interventional treatment) was not large enough to offset the high cost of the test. Patients viewed the test as potentially helpful but, due to the complexity of treatment decision-making, were unsure the test would ultimately change their treatment choices. CONCLUSIONS: We found no evidence to demonstrate the impact of the Prolaris CCP test on patient-important clinical outcomes. The limited evidence available shows that the test appears to provide information that, when considered in addition to clinical risk stratification, may change the treatment plan or actual treatment for some low- and intermediate-risk prostate cancer patients. As a result, there is insufficient data to inform the cost-effectiveness of the CCP test. Publicly funding the CCP test would result in a large incremental cost to the provincial budget.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.034 | 0.156 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.014 |
| Bibliometrics | 0.025 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".