Prediction of Significant Prostate Cancer at Prostate Biopsy and Per Core Detection Rate of Targeted and Systematic Biopsies Using Real-Time Shear Wave Elastography
Bibliographic record
Abstract
INTRODUCTION: Prostate cancer (PCa) detection is accompanied by overdiagnosis and mischaracterization of PCa. Therefore, new imaging modalities like shear wave elastography (SWE) are required. AIM: The aim of this study was to evaluate per-core detection rates (DRs) of targeted biopsies and systematic biopsies and to test if SWE findings can predict presence of clinically significant PCa (csPCa) at biopsy. PATIENTS AND METHODS: Overall, 95 patients scheduled for prostate biopsy in our center underwent SWE. SWE findings were classified into suspicious or normal. Targeted biopsies were taken in up to 3 SWE-suspicious areas. csPCa was defined as the presence of Gleason pattern ≥4, level of prostate-specific antigen ≥10 ng/ml or >2 positive cores. RESULTS: Overall DR for csPCa in our study cohort was 40%. Per-core DR for exclusively SWE-targeted cores versus systematic samples cores was 10.5 vs. 8.6% (p = 0.3). In the logistic regression models, individuals with suspicious SWE findings are at 6.4-fold higher risk of harboring csPCa (p = 0.03). Gain in predictive accuracy was 2.3% (0.82 vs. 0.84, p = 0.01). CONCLUSIONS: Presence of suspicious SWE findings is an independent predictor of csPCa. Therefore, SWE may be helpful in selecting patients for biopsy. Nonetheless, per-core DR for SWE-targeted cores was not statistically significant higher than DR of systematic sampled cores. Therefore, additional systematic biopsy is mandatory.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".