Lesion volume on multiparametric magnetic resonance imaging as a non-invasive prognosticator for clinically significant prostate cancer
Bibliographic record
Abstract
Introduction: The association between prostate cancer (PCa) lesion volume on multiparametric magnetic resonance imaging (mpMRI) and clinically significant PCa (csPCa) remains a poorly studied aspect of diagnostic workup in patients with suspicion of PCa. The aim of this study was to assess the diagnostic value of mpMRI lesion volume in detecting csPCa. Material and methods: Patients with an elevated prostate-specific antigen (PSA) and suspicion of PCa underwent mpMRI as part of routine workup. Following this, patients underwent systematic and fusion targeted biopsy of the region of interest (ROI). All target lesions were sampled once in both axial and sagittal planes, with at least 2 cores per target. csPCa was defined as Gleason grade group ≥2, while highly suspicious lesions were considered as those with PI-RADS score ≥4. Multivariate logistic regression was performed for factors predicting csPCa. Results: Fifty men with a total of 108 mpMRI lesions were included, with a mean age of 71 ±6 years. 52% had prior negative biopsies. The mean lesion volume was 0.95 ±0.04 ml. Thirty-two patients (64%) had positive biopsies, among whom 20 had csPCa. Fifteen patients (30%) had highly suspicious PI-RADS lesions. Multivariate analysis demonstrated that capsular bulging, younger age, small prostate, highly suspicious lesions, high PSA density, and lesion volume >1mL were predictive of csPCa. Conclusions: Lesion volume on mpMRI may be used as a non-invasive indicator of csPCa. Future studies exploring the correlation between lesion volume and csPCa may enable patients to be monitored by non-invasive means, while ensuring early intervention when needed.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".