Role of serial multiparametric magnetic resonance imaging in prostate cancer active surveillance
Bibliographic record
Abstract
AIM: To examine whether addition of 3T multiparametric magnetic resonance imaging (mpMRI) to an active surveillance protocol could detect aggressive or progressive prostate cancer. METHODS: Twenty-three patients with low risk disease were enrolled on this active surveillance study, all of which had Gleason score 6 or less disease. All patients had clinical assessments, including digital rectal examination and prostate specific antigen (PSA) testing, every 6 mo with annual 3T mpMRI scans with gadolinium contrast and minimum sextant prostate biopsies. The MRI images were anonymized of patient identifiers and clinical information and each scan underwent radiological review without the other results known. Descriptive statistics for demographics and follow-up as well as the sensitivity and specificity of mpMRI to identify prostate cancer and progressive disease were calculated. RESULTS: During follow-up (median 24.8 mo) 11 of 23 patients with low-risk prostate cancer had disease progression and were taken off study to receive definitive treatment. Disease progression was identified through upstaging of Gleason score on subsequent biopsies for all 11 patients with only 2 patients also having a PSA doubling time of less than 2 years. All 23 patients had biopsy confirmed prostate cancer but only 10 had a positive index of suspicion on mpMRI scans at baseline (43.5% sensitivity). Aggressive disease prediction from baseline mpMRI scans had satisfactory specificity (81.8%) but low sensitivity (58.3%). Twenty-two patients had serial mpMRI scans and evidence of disease progression was seen for 3 patients all of whom had upstaging of Gleason score on biopsy (30% specificity and 100% sensitivity). CONCLUSION: Addition of mpMRI imaging in active surveillance decision making may help in identifying aggressive disease amongst men with indolent prostate cancer earlier than traditional methods.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.011 |
| 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 | 0.001 | 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 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".