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Updated: Jun 10, 2025
A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound
Published on: March 21, 2025
Molecular Correlates of Prostate Cancer Visibility on Multiparametric Magnetic Resonance Imaging: A Systematic Review
Tamás Fazekas1, Maximilian Pallauf2, Jakub Kufel3
1Department of Urology, Comprehensive Cancer Center, Medical University of Vienna, Vienna, Austria; Department of Urology, Semmelweis University, Budapest, Hungary; Centre for Translational Medicine, Semmelweis University, Budapest, Hungary.
Background And Objective:
Although prostate magnetic resonance imaging (MRI) is increasingly used to diagnose and stage prostate cancer (PCa), the biologic and clinical significance of MRI visibility of the disease is unclear. Our aim was to examine the existing knowledge regarding the molecular correlates of MRI visibility of PCa.
Methods:
The PubMed, Scopus, and Web of Science databases were queried through November 2023. We defined MRI-visible and MRI-invisible lesions based on the Prostate Imaging Reporting and Data System (PI-RADS) score, and compared these based on the genomic, transcriptomic, and proteomic characteristics.
Key Findings And Limitations:
From 2015 individual records, 25 were selected for qualitative data synthesis. Current evidence supports the polygenic nature of MRI visibility, primarily influenced by genes related to stroma, adhesion, and cellular organization. Several gene signatures related to MRI visibility were associated with oncologic outcomes, which support that tumors appearing as PI-RADS 4-5 lesions harbor lethal disease. Accordingly, MRI-invisible tumors detected by systematic biopsies were, generally, less aggressive and had a more favorable prognosis; however, some MRI-invisible tumors harbored molecular features of biologically aggressive PCa. Among the commercially available prognostic gene panels, only Decipher was strongly associated with MRI visibility.
Conclusions And Clinical Implications:
High PI-RADS score is associated with biologically and clinically aggressive PCa molecular phenotypes, and could potentially be used as a biomarker. However, MRI-invisible lesions can harbor adverse features, advocating the continued use of systemic biopsies. Further research to refine the integration of imaging data to prognostic assessment is warranted.
Patient Summary:
Magnetic resonance imaging visibility of prostate cancer is a polygenic trait. Higher Prostate Imaging Reporting and Data System scores are associated with features of biologically and clinically aggressive cancer.
Insights
Prostate cancer (PCa) visibility on MRI is polygenic. High PI-RADS scores indicate aggressive PCa, but some invisible tumors also show aggressive features, necessitating biopsies.
Area of Science:
- Oncology
- Radiology
- Genomics
Background:
- Prostate magnetic resonance imaging (MRI) is increasingly used for prostate cancer (PCa) diagnosis and staging.
- The molecular basis and clinical significance of MRI visibility in PCa remain unclear.
Purpose of the Study:
- To investigate the molecular correlates of MRI visibility in prostate cancer.
- To understand the relationship between MRI appearance and PCa aggressiveness.
Main Methods:
- Systematic review of PubMed, Scopus, and Web of Science databases up to November 2023.
- Defined MRI-visible (PI-RADS 4-5) and MRI-invisible lesions.
- Compared genomic, transcriptomic, and proteomic characteristics of lesions.
Main Results:
- MRI visibility of PCa is a polygenic trait influenced by genes in stroma, adhesion, and cellular organization.
- High PI-RADS scores (MRI-visible) correlate with aggressive molecular features and oncologic outcomes.
- Some MRI-invisible tumors possess aggressive molecular traits, and the Decipher gene panel is associated with MRI visibility.
Conclusions:
- High PI-RADS scores indicate biologically and clinically aggressive PCa and may serve as a biomarker.
- MRI-invisible lesions can harbor adverse features, supporting the continued use of systematic biopsies.
- Further research is needed to integrate imaging data into prognostic assessments.
