MR elastography to measure the effects of cancer and pathology fixation on prostate biomechanics, and comparison with<i>T</i><sub>1</sub>,<i>T</i><sub>2</sub>and ADC
Bibliographic record
Abstract
Abstract MRI is under evaluation for image-guided intervention for prostate cancer. The sensitivity and specificity of MRI parameters is determined via correlation with the gold-standard of histopathology. Whole-mount histopathology of prostatectomy specimens can be digitally registered to in vivo imaging for correlation. When biomechanical-based deformable registration is employed to account for deformation during histopathology processing, the ex vivo biomechanical properties are required. However, these properties are altered by pathology fixation, and vary with disease. Hence, this study employs magnetic resonance elastography (MRE) to measure ex vivo prostate biomechanical properties before and after fixation. A quasi-static MRE method was employed to measure high resolution maps of Young’s modulus ( E ) before and after fixation of canine prostate and prostatectomy specimens ( n = 4) from prostate cancer patients who had previously received radiation therapy. For comparison, T 1 , T 2 and apparent diffusion coefficient (ADC) were measured in parallel. E (kPa) varied across clinical anatomy and for histopathology-identified tumor: peripheral zone: 99(±22), central gland: 48(±37), tumor: 85(±53), and increased consistently with fixation (factor of 11 ± 5; p < 0.02). T 2 decreased consistently with fixation, while changes in T 1 and ADC were more complex and inconsistent. The biomechanics of the clinical prostate specimens varied greatly with fixation, and to a lesser extent with disease and anatomy. The data obtained will improve the precision of prostate pathology correlation, leading to more accurate disease detection and targeting.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".