ArticleClinical Medicine Insights. Oncology2023
Evaluating the Role of Morphological Parameters in the Prostate Transition Zone in PHI-Based Predictive Models for Detecting Gray Zone Prostate Cancer.
Article in Clinical Medicine Insights. Oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed, 2 citations in OpenAlex.
- Volume-adjusted p2PSA-derived indices for predicting prostate cancer: the PROPHET study.International journal of clinical oncology · 2026Observational
- Deep learning based on multiparametric MRI for differentiating benign from malignant PI-RADS 3 prostate lesions: a dual-center study.Scientific reports · 2026Article
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: The early detection of clinically significant prostate cancer (csPCa) through the integration of multidimensional parameters presents a promising avenue for improving survival outcomes for this fatal disease. This study aimed to assess the contribution of prostate transition zone (TZ) to predictive models based on the prostate health index (PHI), with the goal of enhancing early detection of csPCa in the prostate-specific antigen (PSA) gray zone. Methods: In this observational cross-sectional study, a total of 177 PSA gray zone patients (total prostate-specific antigen [tPSA] level ranging from 4.0 to 10.0 ng/mL) were recruited and received PHI detections from August 2020 to March 2022. Prostatic morphologies especially the TZ morphological parameters were measured by transrectal ultrasound (TRUS). Results: Univariable logistic regression indicated prostatic morphological parameters including total prostate volume (PV) indexes and transitional zone volume indexes were all associated with csPCa ( Conclusion: Our data confirmed the value of prostate TZ morphological parameters and suggested a significant advantage for the TZ-adjusted PHI predictive model (PHI-TZD) compared with PHI and PHID in the early detection of gray zone csPCa under specific conditions.
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