Detection and delineation of intraprostatic lesions (IPLs) using multiparametric magnetic resonance imaging (mpMRI) and prostate specific membrane antigen positron emission tomography (PSMA PET) for patients with prostate cancer: A systematic review and meta-analysis.
Bibliographic record
Abstract
287 Background: External beam radiation therapy (EBRT) is a common treatment used for patients with prostate cancer, however, isolated local failures (ILFs) can occur in up to 13% of patients receiving EBRT. Most ILFs occur at the location of the previously known intraprostatic lesions (IPLs). The FLAME trial has shown improved cancer outcomes for patients treated with EBRT who receive a focal radiation boost to their IPL. Both mpMRI and PSMA PET can be used to localize IPL for these patients. Methods: A systematic review and meta-analysis was conducted for the use of mpMRI and PSMA PET for the detection and delineation of IPLs for patients with localized prostate cancer. Trials were included if patients received either mpMRI, PSMA PET, or both, prior to radical prostatectomy (RP). IPLs on RP specimens were used as the reference standard. The quality of the co-registration between imaging and histopathology was assessed as high or low for each study included in the systematic review. Outcomes that were recorded include sensitivity and specificity, among others. A meta-analysis was conducted using a bivariate model to determine the sensitivity, specificity, and area under the receiver operating curve (AUROC) of mpMRI, PSMA PET, and their combination. This systematic review was registered through PROSPERO (CRD42023389092). Results: After exclusions, 20 studies were incorporated into the bivariate meta-analysis. In total, there were 13 studies using mpMRI (n = 541 patients), 12 studies that used PSMA PET (n = 327), and 5 studies that used a combination (n = 180). The pooled sensitivity (95% CI), specificity (95% CI) and AUROC for mpMRI were 64.7% (50.2% – 76.9%), 86.4% (79.7% - 91.1%), and 0.852; the pooled outcomes for PSMA PET were 75.7% (64.0% - 84.5%), 87.1% (80.2% - 91.9%), and 0.889; for their combination, the pooled outcomes were 70.3% (64.1% - 75.9%), 81.9% (71.9% - 88.8%), and 0.796. When reviewing studies with a high-quality co-registration between imaging and histopathology, IPL delineation recommendations included using a 1-2 mm margin when delineating on mpMRI, using a SUV max threshold of 20-30% for PSMA PET, and using the union of mpMRI and PSMA PET when using both modalities. Conclusions: In this study, we conducted a meta-analysis of studies that used mpMRI, PSMA PET or their combination prior to RP for patients with localized prostate cancer. The pooled sensitivity and specificity for each modality had overlapping 95% confidence intervals, suggesting the modalities have similar accuracy in detecting IPLs on RP specimens. There were different delineation recommendations based on the imaging modalities used in studies with a high-quality co-registration of imaging and histopathology.
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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 | 0.011 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.028 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".