Prognostic Value of Response Evaluation Using PSMA PET/CT in Patients with Metastatic Prostate Cancer (RECIP 1.0): A Systematic Review and Meta-analysis
Bibliographic record
Abstract
RATIONALE AND OBJECTIVES: Recently, the Response Evaluation Using PSMA PET/CT in Patients with Metastatic Castration-Resistant Prostate Cancer (RECIP 1.0) was proposed to better evaluate treatment response in prostate cancer patients using PET/CT with prostate-specific membrane antigen (PSMA) than more traditional approaches like metabolic PET evaluation response criteria in solid tumor (PERCIST 1.0). This system showed promising results in single-center studies. We aim to evaluate the prognostic performance of RECIP 1.0 in assessing treatment outcomes in metastatic prostate cancer patients with a systematic review and meta-analysis. MATERIALS AND METHODS: Searches were conducted across PubMed/MEDLINE, EMBASE, and Web of Science databases through March 2024. Only studies involving patients with metastatic prostate cancer who underwent PSMA PET/CT to assess therapeutic response and who were evaluated using the RECIP 1.0 criteria were included. Pooled hazard ratios for mortality and concordance indices (c-index) of RECIP were assessed. A secondary analysis compared RECIP 1.0 to PSMA PET Progression Criteria (PPP) in head-to-head studies. RESULTS: From an initial 553 reports, eight met the eligibility criteria after full-text review (n=516 patients) and six underwent quantitative analysis. RECIP 1.0 significantly differentiated between disease progression and non-progression in terms of mortality risk (HR: 3.48; 95% CI: 2.64-4.59). A sub-analysis of three studies with 174 patients demonstrated a pooled c-index of 0.68 (95% CI: 0.65-0.71). Comparison involving 224 patients from three studies indicated a non-significant trend favoring RECIP 1.0 over PPP. CONCLUSION: RECIP 1.0 offers robust prognostic value for assessing metastatic prostate cancer treatment outcomes.
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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 | 0.016 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.028 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".