MR measurement of luminal water in prostate gland: Quantitative correlation between MRI and histology
Bibliographic record
Abstract
Purpose To determine the relationship between parameters measured from luminal water imaging (LWI), a new magnetic resonance imaging (MRI) T 2 mapping technique, and the corresponding tissue composition in prostate. Materials and Methods In all, 17 patients with prostate cancer were examined with a 3D multiecho spin echo sequence at 3T prior to undergoing radical prostatectomy. Maps of seven MR parameters, called N, T 2‐short , T 2‐long , A short , A long , geometric mean T 2 time (gm T 2 ), and luminal water fraction (LWF), were generated using nonnegative least squares (NNLS) analysis of the T 2 decay curves. MR parametric maps were correlated to digitized whole‐mount histology sections. Percentage area of tissue components, including luminal space, nuclei, and cytoplasm plus stroma, was measured on the histology sections by using color‐based image segmentation. Spearman's rank correlation test was used to evaluate the correlation between MR parameters and the corresponding tissue components, with particular attention paid to the correlation between LWF and percentage area of luminal space. Results N, T 2‐short , A long , gm T 2 , and LWF showed significant correlation ( P < 0.05) with percentage area of luminal space and stroma plus cytoplasm. T 2‐short and gm T 2 also showed significant correlation ( P < 0.05) with percentage area of nuclei. Overall, the strongest correlation was observed between LWF and luminal space (Spearman's coefficient of rank correlation = 0.75, P < 0.001). Conclusion Results of this study show that LWF measured with MRI is strongly correlated with the fractional amount of luminal space in prostatic tissue. This result suggests that LWI can potentially be applied for evaluation of prostatic diseases in which the extent of luminal space differs between normal and abnormal tissues. Level of Evidence: 1 Technical Efficacy: Stage 1 J. MAGN. RESON. IMAGING 2017;46:861–869
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".