Comparison of Multiparametric MRI and the Combination of PSMA Plus MRI for the Intraprostatic Diagnosis of Prostate Cancer
Bibliographic record
Abstract
PURPOSE: The aim of this study was to perform a head-to-head comparison of multiparametric MRI (mpMRI) and the combination of prostate-specific membrane antigen (PSMA) PET plus MRI (PSMA + MRI) for detecting intraprostatic clinically significant prostate cancer (csPCa). PATIENTS AND METHODS: Relevant databases were searched through November 2023. Only studies directly comparing mpMRI and PSMA + MRI (PET/MRI or PET/CT + mpMRI) were included. A meta-analysis with a random-effects model was used to estimate pooled sensitivity, specificity, and area under the curve for each approach. RESULTS: A total of 19 studies were included. On a patient-level analysis, PSMA + MRI had higher sensitivity (9 studies) than mpMRI for csPCa detection (96% [95% confidence interval (CI): 92%, 98%] vs 89% [95% CI: 81%, 94%]; P = 0.04). The patient-level specificity (4 studies) of PSMA + MRI was 55% (95% CI: 31%-76%) compared with 50% (95% CI: 44%-57%) of mpMRI ( P = 0.67). Region-level sensitivity (10 studies) was 85% (95% CI: 74%-92%) for PSMA + MRI and 71% (95% CI: 58%-82%) for mpMRI ( P = 0.09), whereas specificity (4 studies) was 87% (95% CI: 76%-94%) and 90% (95% CI: 82%-95%), respectively ( P = 0.59). Lesion-level sensitivity and specificity were similar between modalities with pooled data from less than 4 studies. CONCLUSIONS: PSMA + MRI had superior pooled sensitivity and similar specificity for the detection of csPCa compared with mpMRI in this meta-analysis of head-to-head studies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".