Fusion of Magnetic Resonance Imaging and Real-Time Elastography to Visualize Prostate Cancer: A Prospective Analysis using Whole Mount Sections after Radical Prostatectomy
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Bibliographic record
Abstract
PURPOSE: To determine whether the fusion of multiparametric magnetic resonance imaging (MRI) with transrectal real-time elastography (RTE) improves the visualization of PCa lesions compared to MRI alone. MATERIALS AND METHODS: In a prospective setting, 45 patients with biopsy-proven PCa received prostate MRI prior to radical prostatectomy (RP). T2 and diffusion-weighted imaging (T2WI/DW-MRI) and, if applicable, dynamic contrast-enhanced sequences (T2WI/DW/DCE-MRI) were used to perform MRI/RTE fusion. The probability of PCa on MRI was graded according to the PI-RADS score for 12 different prostate sectors per patient. MRI images were fused with RTE to stratify suspicious from non-suspicious sectors. Imaging results were compared to whole mount sections using nonparametrical receiver operating characteristic curves and the area under these curves (AUC). RESULTS: 41 of 45 patients were eligible for final analyses. Histopathology confirmed PCa in 261 (53%) of 492 prostate sectors. MRI alone provided an AUC of 0.62 (T2WI/DW-MRI) and 0.65 (T2WI/DW/DCE-MRI) to predict PCa and was meaningfully enhanced to 0.75 (T2WI/DW-MRI) and 0.74 (T2WI/DW/DCE-MRI) using MRI/RTE fusion. Sole MRI showed a sensitivity and specificity of 57.9% and 61% with the best results for ventral prostate sectors whereas RTE was superior in dorsal and apical sectors. MRI/RTE fusion improved sensitivity and specificity to 65.9% and 75.3%, respectively. Additional use of DCE sequences showed a sensitivity and specificity of 65% and 55.7% for MRI and 72.1% and 66% for MRI/RTE fusion. CONCLUSION: MRI/RTE fusion provides improved PCa visualization by combining the strength of both imaging techniques in regard to prostate zonal anatomy and thereby might improve future biopsy-guided PCa detection.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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