Using multiparametric Magnetic Resonance Imaging and Prostate Specific Membrane Antigen Positron Emission Tomography to detect and delineate the gross tumour volume of intraprostatic lesions – A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Radiation therapy is used frequently for patients with prostate cancer. Dose escalation to intraprostatic lesions (IPLs) has been shown to improve oncologic outcomes, without increasing toxicity. Both multiparametric MRI (mpMRI) and PSMA PET can be used to identify IPLs. MATERIALS AND METHODS: A systematic review was conducted to determine the ability of mpMRI, PSMA PET and their combination to detect IPLs prior to radical prostatectomy (RP) as correlated with the histology. Trials included patients that had mpMRI, PSMA PET, or both, prior to RP. The quality of the histopathological-radiological co-registration was assessed as high or low for each study. Recorded outcomes include sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC). A meta-analysis was conducted using a bivariate model to determine the pooled sensitivity and specificity for each imaging modality. This systematic review was registered through PROSPERO (CRD42023389092). RESULTS: Altogether, 42 studies were included in the systematic review. Of these, 20 could be included in the meta-analysis. The pooled sensitivity (95 % CI), specificity (95 % CI) and AUROC for mpMRI (n = 13 studies) were 64.7 % (50.2 % - 76.9 %), 86.4 % (79.7 % - 91.1 %), and 0.852; the pooled outcomes for PSMA PET (n = 12) were 75.7 % (64.0 % - 84.5 %), 87.1 % (80.2 % - 91.9 %), and 0.889; for their combination (n = 5), 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 histopathological-radiological co-registration, IPL delineation recommendations varied by study and the imaging modality used. CONCLUSION: All of mpMRI, PSMA PET or their combination were found to have very good diagnostic outcomes for detecting IPLs. Recommendations for delineating IPLs varied based on the imaging modalities used and between research groups. Consensus guidelines for IPL delineation would help with creating consistency for focal boost radiation treatments in future studies.
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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 | 0.015 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.037 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".