Transurethral prostate magnetic resonance elastography: Prospective imaging requirements
Bibliographic record
Abstract
Tissue stiffness is known to undergo alterations when affected by prostate cancer and may serve as an indicator of the disease. Stiffness measurements can be made with magnetic resonance elastography performed using a transurethral actuator to generate shear waves in the prostate gland. The goal of this study was to help determine the imaging requirements of transurethral magnetic resonance elastography and to evaluate whether the spatial and stiffness resolution of this technique overlapped with the requirements for prostate cancer detection. Through the use of prostate-mimicking gelatin phantoms, frequencies of at least 400 Hz were necessary to obtain accurate stiffness measurements of 10 mm diameter inclusions, but the detection of inclusions with diameters as small as 4.75 mm was possible at 200 Hz. The shear wave attenuation coefficient was measured in vivo in the canine prostate gland, and was used to predict the detectable penetration depth of shear waves in prostate tissue. These results suggested that frequencies below 200 Hz could propagate to the prostate boundary with a signal to noise ratio (SNR) of 60 and an actuator capable of producing 60 μm displacements. These requirements are achievable with current imaging and actuator technologies, and motivate further investigation of magnetic resonance elastography for the targeting of prostate cancer.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".