Comparison of conventional transrectal ultrasound, magnetic resonance imaging, and micro-ultrasound for visualizing prostate cancer in an active surveillance population: A feasibility study
Bibliographic record
Abstract
INTRODUCTION: Active surveillance monitoring of prostate cancer is unique in that most patients have low-grade disease that is not well-visualized by any common imaging technique. High-resolution (29 MHz) micro-ultrasound is a new, real-time modality that has been demonstrated to be sensitive to significant prostate cancer and effective for biopsy targeting. This study compares micro-ultrasound imaging with magnetic resonance imaging (MRI) and conventional ultrasound for visualizing prostate cancer in active surveillance. METHODS: Nine patients on active surveillance were imaged with multiparametric (mp) MRI prior to biopsy. During the biopsy procedure, imaging and target identification was first performed using conventional ultrasound, then using micro-ultrasound. The mpMRI report was then unblinded and used to determine cognitive fusion targets. Using micro-ultrasound, biopsy samples were taken from targets in each modality, plus 12 systematic samples. RESULTS: mpMRI and micro-ultrasound both demonstrated superior sensitivity to Gleason sum 7 or higher cancer compared to conventional ultrasound (p=0.02 McNemar's test). Micro-ultrasound detected 89% of clinically significant cancer, compared to 56% for mpMRI. CONCLUSIONS: Micro-ultrasound may provide similar sensitivity to clinically significant prostate cancer as mpMRI and visualize all significant mpMRI targets. Unlike mpMRI, micro-ultrasound is performed in the office, in real-time during the biopsy procedure, and so is expected to maintain the cost-effectiveness of conventional ultrasound. Larger studies are needed before these results may be applied in a clinical setting.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".