Combining Prostate-Specific Antigen Parameters With Prostate Imaging Reporting and Data System Score Version 2.0 to Improve Its Diagnostic Accuracy
Bibliographic record
Abstract
Background: Any non-invasive test that can predict the absence of prostate cancer (PCa) or absence of clinically significant PCa (CSPCa) is necessary, as it can reduce the number of unnecessary biopsies in patients with gray zone prostate-specific antigen (PSA, 4 - 10 ng/mL). This study evaluated the diagnostic performance of free PSA% and PSA density (PSAD), and Prostate Imaging Reporting and Data System (PIRADS) score (version 2.0) alone and combined in predicting CSPCa in patients with PSA between 4 and 10 ng/mL. Methods: This prospective study included a total of 104 consecutive patients with lower urinary tract symptoms (LUTS) and serum PSA between 4 and 10 ng/mL, with or without abnormal digital rectal examination (DRE) findings or any hypoechoic lesion on ultrasound sonography of prostate and without prior transrectal ultrasound (TRUS) biopsy of prostate. PIRADS score was calculated using multi-parametric magnetic resonance imaging (mp-MRI) before TRUS biopsy of prostate. Relationships among PIRADS score, PSAD, free PSA% and presence of CSPCa in TRUS biopsy were statistically analyzed. Results: In patients with CSPCa, significantly higher median age (P = 0.001), PSA level (P < 0.001), PSAD (P < 0.001) and significantly lower prostate volume (P < 0.001) and free PSA% were observed as compared to patients with non-CSPCa. Significantly higher proportion of patients with CSPCa showed PIRADS positive test compared to those with non-CSPCa (86.4% vs. 53.3%, P < 0.001). Cut-off values for PSAD and free PSA% were 0.12 ng/mL 2 and 25%, respectively. Age, PSAD and free PSA% were significant predictors of PCa, while age and PSAD were significant predictors of CSPCa. Criteria 2, 3 and 4 demonstrated higher specificity and positive predictive value (PPV) in predicting CSPCa as compared to criterion 1. The overall accuracies of criterion 1, 2, 3 and 4 were 64.42%, 85.58%, 80.77% and 79.81%, respectively. The area under the curve (AUC) values of criterion 2, 3 and 4 were higher (0.827, 0.732 and 0.792) than criterion 1 (0.665). Conclusion: Using PIRADS score for predicting CSPCa as a screening test, criteria 2, 3 and 4 have much higher diagnostic performance and present accuracy of mp-MRI to predict CSPCa can be increased with addition of PSAD and free PSA%. World J Oncol. 2019;10(6):218-225 doi: https://doi.org/10.14740/wjon1230
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".