Prostate cancer detection with magnetic resonance imaging (MRI)/cognitive fusion biopsy: Comparing standard and targeted prostate biopsy with final prostatectomy histology
Bibliographic record
Abstract
INTRODUCTION: The use of multiparametric magnetic resonance imaging (MRI) with targeted biopsies of the prostate improves the diagnosis of clinically significant prostate cancer. Recent studies have shown that targeted prostate biopsies also more accurately predict final histopathology after radical prostatectomy (RP). There are three broad techniques for performing MRI-targeted prostate biopsy: cognitive MRI/ultrasound (US) fusion, software MRI/US fusion, and in-bore MRI-guided. Current practices recommend that a standard systematic 12-core prostate biopsy be performed, as well as targeted biopsies in patients with positive MRI findings. This study aimed to evaluate the accuracy of histological grading of cognitive MRI/US fusion prostate biopsy by comparing the histology from the targeted biopsy specimens (TB), standard systematic specimens (SB), and the combination of both (CB) specimens with the final histological grade from subsequent prostatectomy. METHODS: A retrospective, single-center review of 115 patients who underwent standard systematic and cognitive MRI/US-targeted biopsy of the prostate before undergoing a RP between 2016 and 2019 was performed. MRI findings, biopsy, final histology International Society of Urological Pathology (ISUP) grades, and patient demographics were collected. Cochran's Q test and McNemar test were used to compare the differences in upgrading, downgrading, and concordance between each biopsy group. RESULTS: The concordance between SB, TB, and CB biopsy were 28.7%, 49.6%, and 50.4%, respectively. There was no significant difference in concordance between TB and CB. Patients were more likely to be downgraded on the final histology when comparing CB with TB alone (26.1% vs. 16.5%, p<0.05). In cases where an ISUP grade 1 cancer was diagnosed on TB (n=24), there was a 62.5% chance that the final histology would be upgraded. In the same sample, when combined with a SB, the risk of upgrading on final histology was reduced to 37.5%. CONCLUSIONS: Although grading concordance between TB and CB were similar, the concomitant use of a SB significantly reduced the rate of upgrading in the final RP histopathology. CB may result in better decision-making regarding treatment options and also have implications for intraoperative planning.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".