The feasibility of endorectal MR elastography for prostate cancer localization
Bibliographic record
Abstract
The objectives of this study were to evaluate the feasibility of using a rigid radio-frequency receiver endorectal coil for intracavitary prostate magnetic resonance elastography (MRE) and to demonstrate the capability of this technique for generating stiffness maps over a typical prostate volume. An endorectal coil is currently used to help improve the signal-to-noise ratio of images acquired with multiparametric magnetic resonance imaging. We propose that this same coil could also serve to generate shear waves in the prostate gland during imaging, opening up the possibility of incorporating prostate stiffness characterization into multiparametric magnetic resonance imaging. Prostate cancer has been shown to change the elasticity of tissue, suggesting that stiffness imaging (elastography) may provide supplementary diagnostic information. A rigid endorectal coil was mechanically coupled to a piezoceramic actuator and used to investigate full volume (27 slices, 2-mm thick) endorectal MRE in a prostate mimicking phantom. The low-amplitude vibrations (± 8-38 μm displacements) necessary to perform endorectal MRE did not affect the signal-to noise ratio of the coil and endorectal MRE was capable of resolving 0.1 cc (0.6 cm diameter) spherical inclusion volumes. Therefore, the results of this study, in combination with current clinical practice, motivate clinical evaluation of endorectal MRE in patients.
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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 | 0.002 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".