ArticleBMC urology2026
Additional predictive value of systemic inflammation indices beyond prostate specific antigen density for clinically significant prostate cancer.
Article in BMC 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
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThis study aimed to investigate whether systemic inflammation indices provide additional predictive value beyond conventional clinical parameters, particularly prostate-specific antigen (PSA) and prostate-specific antigen density (PSAD), for the detection of clinically significant prostate cancer (csPCa) prior to transrectal ultrasound (TRUS)-guided prostate biopsy.
methodsThis retrospective, single-center study included patients who underwent TRUS-guided prostate biopsy due to elevated PSA levels and/or suspicious digital rectal examination findings. Data from 527 patients who underwent prostate biopsy were analyzed. Demographic data, serum PSA levels, prostate volume, PSAD, and prebiopsy complete blood count parameters were recorded. Systemic inflammatory indices including neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), monocyte-to-lymphocyte ratio (MLR), systemic immune-inflammation index (SII), and pan-immune inflammation value (PIV) were calculated. Patients were classified into three groups: benign prostate pathology, clinically insignificant prostate cancer (ciPCa), and csPCa, based on histopathological findings. Receiver operating characteristic (ROC) curve analyses and multinomial logistic regression models adjusted for age and PSAD were performed to assess predictive performance.
resultsSignificant differences were observed among the benign, ciPCa and csPCa groups in terms of age, total PSA, prostate volume, and PSAD (all p < 0.001), with csPCa patients being older and exhibiting higher PSA and PSAD values. Among the inflammatory indices, only MLR differed significantly, being higher in the csPCa group compared to the ciPCa group (p = 0.028). ROC analysis identified MLR as a weak but statistically significant predictor of csPCa (AUC = 0.566, p = 0.011). In age- and PSAD adjusted regression analyses, PLR and SII were inversely associated with ciPCa, while age and PSAD ≥ 0.15 remained independent predictors of csPCa. Additionally, each 100-unit increase in PIV significantly reduced the likelihood of ciPCa (OR = 0.886, p = 0.026).
conclusionSystemic inflammatory indices do not provide sufficient predictive power to replace established clinical parameters in pre-biopsy decision-making. PSA, PSAD and age remain the most reliable predictors of csPCa, while selected inflammatory markers may offer limited complementary information for risk stratification.
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.