ArticleTranslational andrology and urology2026
Development and internal validation of a clinical model to increase the diagnostic probability of prostate cancer in patients with prostate-specific antigen levels of 4.0-10.0 ng/mL.
Article in Translational andrology and urology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Distinguishing prostate cancer (PCa) from benign prostatic hyperplasia (BPH) in the prostate-specific antigen (PSA) gray zone (4.0-10.0 ng/mL) remains a clinical challenge due to the limited specificity of PSA. While complex radiomics and AI models exist, their high resource requirements limit accessibility. We constructed a diagnostic model to distinguish PCa from BPH in patients with PSA ranging 4.0 to 10.0 ng/mL. Methods: This retrospective study recruited consecutive458 participants from January 2017 to September 2019. Eligible patients had PSA levels between 4.0-10.0 ng/mL and underwent prostate biopsy or surgery with histopathological confirmation of PCa or BPH by two independent pathologists. Candidate predictors included age, PSA, free PSA (FPSA), free/total PSA ratio (F/T ratio), prostate volume (PV), PSA density (PSAD), white blood cell count (WBC), neutrophil (NE), lymphocyte (LY), hemoglobin (HB), platelet (PLT), alanine aminotransferase (ALT), aspartate transaminase (AST), total protein (TP), albumin (ALB), globulin (GLB), creatinine (CREA), urea nitrogen (UREA), and phosphorus (PHOS). Multivariable logistic regression was applied to screen independent risk factors, with bootstrap resampling for internal validation. Model discrimination, calibration and clinical net benefit were assessed via receiver operating characteristic (ROC) curves, calibration plots and decision curve analysis (DCA). Results: Among 458 patients (192 with PCa, 266 with BPH), the PCa group had significantly higher PSA, PSAD, AST, TP, ALB, GLB and PHOS, but lower FPSA, F/T ratio and PV (all P<0.05). Age showed marginal significance (P=0.07) and was entered into the regression according to the P<0.1 screening standard. Age, PSA, FPSA, PV, AST, GLB and PHOS were independent PCa predictors. The model's area under the curve (AUC) was 0.834 [95% confidence interval (CI): 0.799-0.867], with optimism-corrected C-index 0.832 (95% CI: 0.796-0.869). Calibration (calibration: 0.92; Hosmer-Lemeshow test P=0.49) and DCA confirmed stable performance and clinical utility. At the Youden-index cutoff of 0.503, sensitivity reached 69.79% (95% CI: 62.7-76.2%), specificity 80.83% (95% CI: 75.5-85.4%). Conclusions: This clinical model shows promise for identifying PCa risk in patients with PSA 4.0-10.0 ng/mL; however, external validation in independent cohorts is needed before clinical implementation.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.