Clinical prostate T<sub>2</sub> quantification using magnetization‐prepared spiral imaging
Bibliographic record
Abstract
T(2) quantification may augment diagnostic T(2)-weighted imaging; to improve cancer detection via auto-segmentation of multi-parametric acquisitions and to potentiate longitudinal studies of prostate cancer. However, robust quantitative techniques are not generally accessible or refined for clinical translation. This research describes the adaptation of a magnetization-prepared spiral imaging technique, termed T2prep for prostate application, providing whole gland coverage within a 5-min interval with considerable insensitivity to radio-frequency (RF) inhomogeneities. Clinical piloting in two cohorts with distinct clinical histories demonstrated the anticipated differences in zonal and tumor T(2), including tumor T(2) shortening compared to peripheral zone, and post-radiotherapy shortening of peripheral zone T(2). SNR calculations were performed for data acquired with or without an endo-rectal coil in tandem with a torso phased array, to judge the potential for voxel-based T(2) mapping and thereby support focal biological characterization of cancer, hypoxia, and response to therapy within regions of dense cancer burden.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".