Optimising the Diagnosis of Prostate Cancer in the Era of Multiparametric Magnetic Resonance Imaging: A Cost-effectiveness Analysis Based on the Prostate MR Imaging Study (PROMIS)
Bibliographic record
Abstract
BACKGROUND: The current recommendation of using transrectal ultrasound-guided biopsy (TRUSB) to diagnose prostate cancer misses clinically significant (CS) cancers. More sensitive biopsies (eg, template prostate mapping biopsy [TPMB]) are too resource intensive for routine use, and there is little evidence on multiparametric magnetic resonance imaging (MPMRI). OBJECTIVE: To identify the most effective and cost-effective way of using these tests to detect CS prostate cancer. DESIGN, SETTING, AND PARTICIPANTS: Cost-effectiveness modelling of health outcomes and costs of men referred to secondary care with a suspicion of prostate cancer prior to any biopsy in the UK National Health Service using information from the diagnostic Prostate MR Imaging Study (PROMIS). INTERVENTION: Combinations of MPMRI, TRUSB, and TPMB, using different definitions and diagnostic cut-offs for CS cancer. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Strategies that detect the most CS cancers given testing costs, and incremental cost-effectiveness ratios (ICERs) in quality-adjusted life years (QALYs) given long-term costs. RESULTS AND LIMITATIONS: The use of MPMRI first and then up to two MRI-targeted TRUSBs detects more CS cancers per pound spent than a strategy using TRUSB first (sensitivity = 0.95 [95% confidence interval {CI} 0.92-0.98] vs 0.91 [95% CI 0.86-0.94]) and is cost effective (ICER = £7,076 [€8350/QALY gained]). The limitations stem from the evidence base in the accuracy of MRI-targeted biopsy and the long-term outcomes of men with CS prostate cancer. CONCLUSIONS: An MPMRI-first strategy is effective and cost effective for the diagnosis of CS prostate cancer. These findings are sensitive to the test costs, sensitivity of MRI-targeted TRUSB, and long-term outcomes of men with cancer, which warrant more empirical research. This analysis can inform the development of clinical guidelines. PATIENT SUMMARY: We found that, under certain assumptions, the use of multiparametric magnetic resonance imaging first and then up to two transrectal ultrasound-guided biopsy is better than the current clinical standard and is good value for money.
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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 | 0.014 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".