Sci‐Fri AM: MRI and Diagnostic Imaging ‐ 04: How does prostate biopsy guidance error impact pathologic cancer risk assessment?
Bibliographic record
Abstract
Purpose: MRI‐targeted, 3D transrectal ultrasound (TRUS)‐guided prostate biopsy aims to reduce the 21–47% false negative rate 1 of clinical 2D TRUS‐guided sextant biopsy, but still has a substantial false negative rate. This could be improved via biopsy needle target optimization, accounting for uncertainties due to guidance system errors and image registration errors. As an initial step toward this broader goal, we elucidated the impact of biopsy needle delivery error on the probability of obtaining tumour samples and on core involvement. These are both important parameters to patient risk stratification and treatment decision. Methods: We investigated this for cancer of all grades, and separately for intermediate/high grade (≥Gleason 4+3) cancer. We used expert‐contoured gold‐standard prostatectomy histology to simulate targeted biopsies using an isotropic Gaussian needle delivery error from 1 to 6 mm, and investigated the amount of cancer obtained in each biopsy core as determined by histology. Results: Needle delivery error resulted in core involvement variability that could influence treatment decisions; the presence or absence of cancer in 1/3 or more of each needle core can be attributed to needle delivery error of 4 mm (as observed in practice 2 ). Conclusions: Repeated biopsies of the same tumour target can yield percent core involvement measures with sufficient variability to influence the decision between active surveillance and treatment. However, this may be mitigated by making more than one biopsy attempt at selected tumour targets.
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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 | 0.017 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".