Investigating the ability of multiparametric MRI to exclude significant prostate cancer prior to transperineal biopsy
Bibliographic record
Abstract
INTRODUCTION: We characterized false negative prostate magnetic resonance imaging (MRI) reporting by using histology derived from MRI-transrectal ultrasound (TRUS)-guided transperineal (MTTP) fusion biopsies. METHODS: In total, 148 consecutive patients were retrospectively reviewed. Men underwent multiparametric MRI (mpMRI), reported by a consultant/attending radiologist in line with European Society of Urogenital Radiology (ESUR) standards. MTTP biopsy of the lesions was performed according to the Ginsburg recommendations. Cases with an MRI-histology mismatch were identified and underwent a second read by an experienced radiologist. A third review was performed with direct histology comparison to determine a true miss from an MRI-occult cancer. Statistical analysis was performed with McNemar's test. RESULTS: False negative lesions were identified in 29 MRI examinations (19.6%), with a total of 46 lesions. Most false negative lesions (21/46) were located in the anterior sectors of the prostate. The second read led to a significant decrease of false-negative lesions with 7/29 further studies identified as positive on a patient-by-patient basis (24.1% of studies, p = 0.016) and 11/46 lesions (23.9%; p = 0.001). Of these, 30 lesions following the first read and 23 lesions after the second read were considered significant cancer according to the University College London criteria. However, on direct comparison with histology, most lesions were MRI occult. CONCLUSION: We demonstrate that MRI can fail to detect clinically relevant lesions. Improved results were achieved with a second read but despite this, a number of lesions remain MRI-occult. Further advances in imaging are required to reduce false negative results.
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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 | 0.003 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| 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".