Lesion volume on multiparametric magnetic resonance imaging as a non-invasive prognosticator for clinically significant prostate cancer
Why this work is in the frame
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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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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it