MRI assessment of pathological stage and surgical margins in anterior prostate cancer (APC) using subjective and quantitative analysis
Bibliographic record
Abstract
PURPOSE: To evaluate magnetic resonance imaging (MRI) for assessment of extraprostatic extension (EPE) and positive surgical margins (PSM) in anterior prostate cancer (APC). MATERIALS AND METHODS: -weighted-MRI, invasion of AFMS and EPE. Comparisons were performed between APCs by EPE/PSM using chi-square, multivariable analysis, and receiver operator characteristic (ROC) analysis. RESULTS: The prevalence of EPE and PSM were 52% (13/25) and 36% (9/25). Tumor sizes were larger with EPE (22.5 ± 8.4 vs. 14.7 ± 6.3, P = 0.02) and PSM (23.0 ± 9.3 vs. 16.4 ± 7.0, P = 0.06). Area under ROC curve (AUC-ROC) for the diagnosis of EPE by tumor size was 0.77 (95% confidence interval [CI] 0.58-0.95); ≥16 mm size = sensitivity/specificity 69.2/66.7%. Maximal leading edge of tumor was greater with EPE (2.4 ± 2.2 vs. -0.2 ± 3.0) and PSM (2.8 ± 2.3 vs. -0.3 ± 2.5), (P = 0.023, 0.031). AUC-ROC for diagnosis of EPE/PSM by leading edge was 0.78 (CI 0.57-0.97) and 0.75 (CI 0.56-0.94). A ≥1 mm leading edge yielded sensitivity/specificity of 76.9/75.0% and 77.8/62.5% for diagnosis of EPE/PSM. 60-72% (15-18/25) tumors invaded AFMS (k = 0.74), which was not associated with EPE/PSM (P = 0.12-0.14). Radiologists' assessment of EPE had sensitivity/specificity of 61.5-69.2/50.0-75.0% (k = 0.53). CONCLUSION: Tumor size and leading edge of tumor relative to AFMS may enable diagnosis of EPE and positive surgical margins in APC. LEVEL OF EVIDENCE: 2 J. MAGN. RESON. IMAGING 2017;45:1296-1303.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 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".