Diagnostic accuracy of surface coil magnetic resonance imaging at 1.5 T for local staging of elevated risk prostate cancer
Bibliographic record
Abstract
INTRODUCTION: Preoperative prostate cancer stage predicts prognosis and affects treatment decisions. The purpose of this study was to estimate the sensitivity and specificity of surface coil magnetic resonance imaging (MRI) for prostate cancer stage using surgical pathologic data as the reference standard. METHODS: High-risk patients (>/=cT3 or PSA >/=20 ng/mL or Gleason >/=8) and selected intermediate-risk patients (clinically bulky disease on exam or biopsy, cT2b/c, or Gleason 7 with >/=3 of 5 biopsy cores positive in a lobe) routinely received a pelvic MRI at our institution. The images of identified patients were reviewed by one radiologist who was blinded to clinical information. The radiologist reported presence or absence of tumour within each lobe of the prostate. Extraprostatic extension (EPE), seminal vesicle (SV) invasion and pelvic lymph node (PLN) metastasis were also reported. Radiological findings were compared with prostatectomy pathology reports. RESULTS: During the study period, about 320 radical prostatectomies were performed. Of these, 32 had a preoperative surface coil pelvic MRI adequate for analysis. Pathologically, 53 of 64 (82.8%) prostate lobes contained tumour, 17 (26.6%) lobes had associated EPE, 12 (18.8%) had SV involvement and 7 (10.9%) sets of PLNs contained cancer. Magnetic resonance imaging sensitivity and specificity were, respectively, 94.3% and 81.8% for tumour location, 82.4% and 87.2% for EPE, 83.3% and 92.3% for SV invasion and 71.4% and 94.7% for PLN involvement. INTERPRETATION: Surface coil MRI accurately stages many prostate cancer patients with elevated risk of extraprostatic disease. This mode of imaging may be reasonable at centres that do not have endorectal coil MRI.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.001 | 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".