Comparison of micro-ultrasound and multiparametric MRI imaging for prostate cancer: A multicenter prospective analysis.
Bibliographic record
Abstract
296 Background: This study aims to compare the sensitivity, specificity, NPV and PPV of mpMRI with the novel high-resolution micro-ultrasound imaging modality. This approach offers the benefits of simplicity, a single intervention for imaging and biopsy, leveraging the low cost of ultrasound. Micro-ultrasound may be used to image suspicious lesions and target biopsies in real-time with or without additional MRI-based targets. Methods: Data from 9 sites was aggregated, totaling 866 subjects presenting for ExactVu micro-ultrasound guided biopsy with available mpMRI studies. Samples in all subjects were taken from mpMRI targets and micro-ultrasound targets, with up to 12 systematic samples filled in. Various strategies were used for mpMRI target sampling including cognitive fusion with micro-ultrasound, separate software-fusion systems, and software-fusion using the micro-ultrasound FusionVu system. Clinically significant cancer was considered any Gleason Sum > 6 and targeted samples were taken for PI-RADS > 2 or PRI-MUS1 > 2 lesions. Results: Overall, 39% of all biopsy cases were positive for clinically significant PCa. mpMRI demonstrated 89% sensitivity and NPV of 77%. Compared to mpMRI, micro-ultrasound sensitivity (95%) and NPV (87%) were higher. Micro-ultrasound was less specific (21% vs 23% for mpMRI) with similar PPV (44% vs 43%). The aggregate effect demonstrates higher sensitivity for csPCa with micro-ultrasound compared to mpMRI (p<0.01). Conclusions: Micro-ultrasound is an attractive option for screening and targeted biopsy. Sensitivity and NPV appear superior to MRI, but specificity is mildly reduced. Further larger-scale studies are required for validation of these findings. References: Ghai, S. et al., “Assessing Cancer Risk in Novel 29 MHz Micro-Ultrasound Images of the Prostate”, Journal of Urology, 2016 Aug;196(2):562-9. Clinical trial information: NCT03938376.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".