Prostate cancer topography and tumour conspicuity on multiparametric magnetic resonance imaging: A systematic review and meta-analysis
Bibliographic record
Abstract
Introduction The implications of tumour location on mpMRI conspicuity are not fully understood. Identifying topographical correlates that influence conspicuity may improve outcomes. Here, we present the first systematic review and meta-analysis describing the effect of tumour location on prostate cancer conspicuity on mpMRI. Methods Medline, PubMed, EMBASE and Cochrane databases were systematically searched and results were assessed as per the PRISMA statement. Differential tumour conspicuity on mpMRI was compared between cancers in the peripheral zone (PZ), transitional zone (TZ), base, apex, anterior and posterior. Meta-analysis was conducted to compare diagnostic odds ratios (DOR) of mpMRI detection for tumours in the PZ and TZ. PROSPERO registration: CRD42021228087. Results Thematic synthesis showed apical and basal tumours had reduced conspicuity compared to mid-gland tumours. Cancer in the TZ demonstrated increased conspicuity on T2-weighted imaging, whilst PZ cancers had higher conspicuity on diffusion-weighted and dynamic contrast enhancement imaging. mpMRI had better diagnostic accuracy for PZ lesions, albeit higher specificity for TZ lesions. Meta-analysis showed an increased DOR for PZ tumours (OR: 7.206 [95% CI: 4.991;10.403], compared to TZ (OR: 5.310 [95% CI: 3.082; 9.151]). However, the test for subgroup differences was not significant (p = 0.2743). Conclusions Cancer in the apex or base of the prostate may be less conspicuous than mid-gland tumours. Similarly, TZ cancer appears to have reduced conspicuity compared to PZ cancer, however, meta-analysis did not show a significant difference between DOR. Future larger studies with prospective datasets are required to clarify the relationship between tumour position and conspicuity.
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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 | 0.013 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.022 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".