Luminal Water Imaging: A New MR Imaging T2 Mapping Technique for Prostate Cancer Diagnosis
Bibliographic record
Abstract
Purpose To assess the feasibility of luminal water imaging, a quantitative T2-based magnetic resonance (MR) imaging technique, for the detection and grading of prostate cancer (PCa). Materials and Methods Eighteen patients with biopsy-proven PCa provided informed consent to be included in this institutional human ethics board–approved prospective study between January 2015 and January 2016. Patients underwent 3.0-T MR imaging shortly before radical prostatectomy. T2 distributions were generated with a regularized non-negative least squares algorithm from multiecho spin-echo MR imaging data. From T2 distributions, maps of seven MR parameters, Ncomp, T2short, T2long, geometric mean T2 (gmT2), luminal water fraction (LWF), Ashort, and Along, were generated and compared with digitized images of hematoxylin-eosin–stained whole-mount histologic slices. A paired t test was used to determine significant differences between MR parameters in malignant and nonmalignant tissue. Correlation with Gleason score (GS) was evaluated with the Spearman rank correlation test. Diagnostic accuracy was evaluated by using logistic generalized linear mixed-effect models and receiver operating characteristic (ROC) analysis. Results The average values of four MR parameters (gmT2, Ashort, Along, and LWF) were significantly different between malignant and nonmalignant tissue. All MR parameters except for T2long showed significant correlation (P < .05) with GS in the peripheral zone. The highest correlation with GS was obtained for LWF (−0.78 ± 0.11, P < .001). ROC analysis demonstrated high accuracy for tumor detection, with the highest area under the ROC curve obtained for LWF (0.97 in the peripheral zone and 0.98 in the transition zone). Conclusion Results of this pilot study demonstrated the feasibility of luminal water imaging in the detection and grading of PCa. A study with a larger cohort of patients and a broader range of GS is required to further evaluate this new technique in clinical settings. © RSNA, 2017
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".