Natural history of prostatic lesions on serial multiparametric magnetic resonance imaging
Bibliographic record
Abstract
INTRODUCTION: The natural history of prostatic lesions identified on multiparametric magnetic resonance imaging (mpMRI) is largely unknown. We aimed to describe changes observed over time on serial MRI. METHODS: All patients with ≥2 MRI studies between 2008 and 2015 at our institution were identified. MRI progression was defined as an increase in Prostate Imaging Reporting and Data System (PI-RADS; version 2) or size of existing lesions, or the appearance of a new lesion PIRADS ≥4. Patients on active surveillance (AS) were analyzed for correlation of MRI progression to biopsy reclassification. RESULTS: A total of 83 patients (54 on AS and 29 for diagnostic purposes) underwent serial MRI, with a mean interval of 1.9 years between scans. At baseline, 115 lesions (66 index, 49 non-index) were identified. Index lesions were more likely than non-index lesions to increase in size ≥2 mm (36.2 vs. 7.3 %; p=0.002). Overall progression was more likely to be seen among the index cohort (34.8 vs. 7.6%; p<0.001). New lesions with PIRADS ≥4 were seen on second imaging in 13 (16.5%) men, and became the index lesion in 29 cases (34.9%). Eighteen men on AS showed evidence of MRI progression (five with new lesions, 13 with progression of a previous lesion). Biopsy reclassification was present in three men (16.7%) with and seven men without MRI progression (19.4%). CONCLUSIONS: Overall changes in size and PIRADS scores of index lesions on MRI were small. New lesions were common, but usually did not alter management.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".