Incorporating mpMRI biopsy data into established pre-RP nomograms: potential impact of an increasingly common clinical scenario
Bibliographic record
Abstract
BACKGROUND: We examine the practical application of multiparametric MRI (mpMRI) prostate biopsy data using established pre-RP nomograms and its potential implications on RP intraoperative decision-making. We hypothesize that current nomograms are suboptimal in predicting outcomes with mpMRI targeted biopsy (TBx) data. MATERIALS AND METHODS: Patients who underwent mpMRI-based TBx prior to RP were assessed using the MSKCC and Briganti nomograms with the following iterations: (1) Targeted (T) (targeted only), (2) Targeted and Systematic (TS) and (3) Targeted Augmented (TA) (targeted core data; assumed negative systematic cores for 12 total cores). Nomogram outcomes, lymph node involvement (LNI), extracapsular extension (ECE), organ-confined disease (OCD), seminal vesicle invasion (SVI), were compared across iterations. Clinically significant impact on management was defined as a change in LNI risk above or below 2% (Δ2) or 5% (Δ5). RESULTS: A total of 217 men met inclusion criteria. Overall, the TA iteration had more conservative nomogram outcomes than the T. Moreover, TA better predicted RP pathology for all four outcomes when compared with the T. In the entire cohort, Δ2 and Δ5 were 16.6-25.8% and 20.3-39.2%, respectively. In the subset of 190 patients with targeted and systematic cores, TA was a better approximation of TS outcomes than T in 71% (MSKCC) and 82% (Briganti) of patients. CONCLUSION: In established pre-RP nomograms, mpMRI-based TBx often yield variable and discordant results when compared with systematic biopsies. Future nomograms must better incorporate mpMRI TBx core data. In the interim, augmenting TBx data may serve to bridge the gap.
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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 | 0.037 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".