Prospective evaluation of the value of dynamic contrast enhanced (DCE) imaging for prostate cancer detection, with pathology correlation.
Bibliographic record
Abstract
INTRODUCTION: The aim of this study was to evaluate the value of dynamic contrast enhanced (DCE) imaging in multi-parametric prostate MRI (mpMRI) for the detection and staging of prostate cancer in comparison with T2W and DWI images alone in biparametric MRI (bpMRI) in treatment naïve patients. MATERIALS AND METHODS: One hundred consecutive patients who underwent a prostate MRI at our institution from June-August 2017, as well as a systematic ultrasound-guided prostate biopsy or prostatectomy, were included. Strictly following PIRADSv2, the MRI studies were independently interpreted by a body radiologist and a body-imaging fellow on two different occasions 8-10 weeks apart. Initially, with all mpMRI sequences and then without the DCE sequence (bpMRI). The readers were blinded to the clinical information. Ethics approval was obtained. RESULTS: One hundred treatment-naïve patients were included (median age 64, age range 48-81, mean PSA 10.3). There was almost perfect intra-observer agreement for mpMRI versus bpMRI for both readers [Cohen's Kappa (k) 0.88-0.86] and substantial inter-observer agreement (k = 0.74 for mpMRI and 0.76 for bpMRI). The sensitivity and specificity did not significantly change between multi-parametric and bi-parametric MRI (Sensitivity 91.7% and 90%, Specificity of 85.5% and 85% for mpMRI and bpMRI, respectively). CONCLUSION: Based on our findings, prostate MRI without DCE (bpMRI) is of comparable diagnostic accuracy to mpMRI in treatment-naïve patients. Performing prostate MRI without DCE (bpMRI) will reduce acquisition time, decrease cost and potentially improve patient safety.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".