Prediction Factors for Detecting Clinically Significant Prostate Cancer in a PSA Gray Zone (4–10 ng/mL): A Systematic Review
Abstract
Prostate cancer (PCa) is one of the most commonly diagnosed malignancies among men worldwide. Prostate-specific antigen (PSA) testing has improved early detection; however, PSA levels within the so-called “gray zone” (4–10 ng/mL) remain a diagnostic challenge because of their limited specificity and the associated risk of unnecessary biopsies. In this clinical context, an important objective is the accurate identification of clinically significant prostate cancer (csPCa), defined as disease with a higher likelihood of progression or clinical impact. In recent years, several diagnostic approaches and risk prediction models have been proposed to improve csPCa detection in patients within the PSA gray zone. These models combine clinical parameters, PSA-derived indices, and imaging findings—particularly magnetic resonance imaging (MRI)—and, in some cases, incorporate advanced biomarkers or radiomic features. Nevertheless, considerable heterogeneity exists across studies with respect to predictor selection, model construction, and reported diagnostic performance. This systematic review aims to synthesize current evidence on the diagnostic characteristics and predictive models used to detect clinically significant prostate cancer in men with PSA levels between 4 and 10 ng/mL. For consistency, heterogeneous outcome terms used in the included studies (e.g., “probable csPCa”, “significant cancer”, “clinically important PCa”) were harmonized and analyzed under the unified term csPCa. By identifying the most consistently reported predictors and comparing univariate with multivariate approaches, this review seeks to support clinical decision-making and to highlight areas for future research in prostate cancer diagnosis within the PSA gray zone.
Fields of Study & Subjects
Prostate Cancer Diagnosis and Treatment
Radiomics and Machine Learning in Medical Imaging
Prostate Cancer Treatment and Research
Publication details
- Journal / source
- Applied Sciences
- Publisher
- MDPI AG
- Volume
- 16
- Issue
- 6
- Pages
- 2975–2975
- Publication date
- 19 March 2026
- Language
- EN