MRI suspicious lesions in patients under active surveillance
Bibliographic record
Abstract
INTRODUCTION: Active surveillance (AS) requires regular monitoring to detect disease progression. Multiparametric magnetic resonance imaging (mpMRI) and targeted biopsies are commonly used to identify clinically significant prostate cancer (csPC) in AS patients, yet their diagnostic value remains unclear among this population. METHODS: We conducted a retrospective study of patients who underwent mpMRI followed by combined prostate biopsies between 2017 and 2022. Patients were categorized into AS and non-AS groups. We compared the diagnostic yield of mpMRI suspicious Prostate Imaging-Reporting & Data System (PI-RADS) 3-5 lesions using comparisons of PI-RADS score distribution and detection rates of csPC from targeted biopsies between the groups. Logistic regression was used to assess associations between AS category and outcomes. csPC detection rates of targeted and combined biopsies were assessed as well. RESULTS: The study consisted of 600 patients, 158 in the AS group and 442 in the non-AS group. PI-RADS scores distribution and the number of suspicious lesions were similar between AS and non-AS groups. csPC detection rates from targeted biopsies were not different between AS and non-AS patients (32% vs. 30%, p=0.68), and AS was also not associated with the rates of csPC for each PI-RADS score. The addition of systematic biopsies did not increase csPC detection in AS patients (36% vs. 32%, p=0.47). CONCLUSIONS: Our findings suggest that mpMRI suspicious PI-RADS 3-5 lesions are reliable for csPC diagnosis during AS, and that targeted biopsies alone may be sufficient for its detection; however, further prospective research is needed to validate these results and optimize biopsy strategies within AS protocols.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".