Fusion of Magnetic Resonance Imaging and Real-Time Elastography to Visualize Prostate Cancer: A Prospective Analysis using Whole Mount Sections after Radical Prostatectomy
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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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| 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".