Prediction of Significant Prostate Cancer at Prostate Biopsy and Per Core Detection Rate of Targeted and Systematic Biopsies Using Real-Time Shear Wave Elastography
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 it