Evaluation of MRI for diagnosis of extraprostatic extension in prostate cancer
Bibliographic record
Abstract
Purpose To assess the ability of magnetic resonance imaging (MRI) to diagnose extraprostatic extension (EPE) in prostate cancer. Materials and Methods With Institutional Review Board (IRB) approval, 149 men with 170 ≥0.5 mL tumors underwent preoperative 3T MRI followed by radical prostatectomy (RP) between 2012–2015. Two blinded radiologists (R1/R2) assessed tumors using Prostate Imaging Reporting and Data System (PI‐RADS) v2, subjectively evaluated for the presence of EPE, measured tumor size, and length of capsular contact (LCC). A third blinded radiologist, using MRI‐RP‐maps, measured whole‐lesion: apparent diffusion coefficient (ADC) mean/centile and histogram features. Comparisons were performed using chi‐square, logistic regression, and receiver operator characteristic (ROC) analysis. Results The subjective EPE assessment showed high specificity (SPEC = 75.4/91.3% [R1/R2]), low sensitivity (SENS = 43.3/43.6% [R1/R2]), and area‐under (AU) ROC curve = 0.67 (confidence interval [CI] 0.61–0.73) R1 and 0.61 (CI 0.53–0.70) R2; (k = 0.33). PI‐RADS v2 scores were strongly associated with EPE ( P < 0.001 / P = 0.008; R1/R2) with AU‐ROC curve = 0.72 (0.64–0.79) R1 and 0.61 (0.53–0.70) R2; (k = 0.44). Tumors with EPE were larger (18.8 ± 7.8 [median 17, range 6–51] vs. 18.8 ± 4.9 [12, 6–28] mm) and had greater LCC (21.1 ± 14.9 [16, 1–85] vs. 13.6 ± 6.1 [11.5, 4–30] mm); P < 0.001 and 0.002, respectively. AU‐ROC for size was 0.73 (0.64–0.80) and LCC was 0.69 (0.60–0.76), respectively. Optimal SENS/SPEC for diagnosis of EPE were: size ≥15 mm = 67.7/66.7% and LCC ≥11 mm = 84.9/44.8%. 10 th ‐centile ADC and ADC entropy were both associated with EPE ( P = 0.02 and < 0.001), with AU‐ROC = 0.56 (0.47–0.65) and 0.76 (0.69–0.83), respectively. Optimal SENS/SPEC for diagnosis of EPE with entropy ≥6.99 was 63.3/75.0%. 25 th ‐centile ADC trended towards being significantly lower with EPE ( P = 0.06) with no difference in other ADC metrics ( P = 0.25–0.88). Size, LCC, and ADC entropy improved sensitivity but reduced specificity compared with subjective analysis with no difference in overall accuracy ( P = 0.38). Conclusion Measurements of tumor size, capsular contact, and ADC entropy improve sensitivity but reduce specificity for diagnosis of EPE compared to subjective assessment. Level of Evidence: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:176–185.
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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 | 0.003 | 0.009 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".