Assessing the Role of Bi- and Multi-Parametric MRI in Prostate Cancer-A Regional Study
Bibliographic record
Abstract
Prostate MRI is a key diagnostic tool for prostate cancer (PCa), with current guidelines recommending multi-parametric MRI (mpMRI), which includes T2-weighted (T2W), diffusion-weighted (DWI), and dynamic contrast-enhanced (DCE) imaging. However, biparametric MRI (bpMRI), which omits DCE, is suggested to reduce scan time, cost, and potential contrast-related side effects. Limited research exists comparing bpMRI’s efficacy against mpMRI for detecting clinically significant prostate cancer (CsPCa) using the Prostate Imaging and Reporting Data System (PI-RADS v2.1).To compare the diagnostic performance of bpMRI and mpMRI for prostatic carcinoma and CsPCa detection. This study retrospectively evaluated 115 males over 40 years with elevated prostate-specific antigen (PSA) levels (≥15 ng/ml) who underwent mpMRI and had histopathological results. Two radiologists independently assessed suspected PCa lesions, assigning PI-RADS categories for bpMRI (report one) and mpMRI (report two). The reference standard was histopathological biopsy with Gleason scoring. Among 101 patients with suspected PCa, CsPCa was diagnosed in 45 cases using mpMRI, 39 with bpMRI, and 14 with DCE alone. The PI-RADS grading system showed strong agreement (kappa = 0.82) for bpMRI and near-perfect agreement (kappa = 0.912) for mpMRI. Sensitivity was slightly higher for mpMRI (98.4%) than bpMRI (96.7%) with (P < 0.001), while bpMRI demonstrated higher specificity (75.8% vs. 66.8%, P < 0.001) the detection rates of CsPCa for bpMRI and mpMRI were 51.50% and 53.40% respectively. The study concludes that bpMRI is non-inferior to mpMRI in CsPCa detection, making it a viable alternative while DCE remains valuable for PCa lesion detection.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".