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Updated: Jun 13, 2026
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Microarray-based Identification of Individual HERV Loci Expression: Application to Biomarker Discovery in Prostate Cancer
Published on: November 2, 2013
Biomarkers for Precision Prognosis in Prostate Cancer: Imaging, Molecular, and Integrated Approaches
Zahra Khazaei1,2, Frédéric Pouliot2, Louis Archambault1,2
1Département de Physique, de Génie Physique et D'optique, Faculté des Sciences et de Génie, Université Laval, Québec, QC G1V 0A6, Canada.
Cancers
|June 12, 2026
Summary
Accurate prostate cancer (PCa) prognosis relies on advanced imaging and non-imaging biomarkers. Combining these tools refines risk stratification, guiding personalized treatment and monitoring for better patient outcomes.
Area of Science:
- Urology
- Oncology
- Medical Imaging
Background:
- Prostate cancer (PCa) presents diverse clinical behaviors, necessitating precise prognostic assessment for effective treatment and monitoring.
- The International Society of Urological Pathology (ISUP) Grade Group classification aids in assessing tumor grade and molecular heterogeneity.
- Current challenges include differentiating indolent from aggressive PCa to optimize patient management.
Purpose of the Study:
- To review recent advancements in imaging and non-imaging biomarkers for prostate cancer risk stratification, treatment planning, and disease monitoring.
- To evaluate the prognostic capabilities of various imaging modalities in predicting recurrence, progression, and survival.
- To discuss the role of non-imaging biomarkers and emerging technologies in enhancing personalized medicine for PCa.
Main Methods:
- Comprehensive review of current literature on imaging biomarkers including multiparametric MRI (mpMRI), WB-MRI, PET/CT, PET/MRI, CT, and TRUS.
- Analysis of non-imaging biomarkers such as PSA, histopathology, biochemical markers, genomic classifiers, and ctDNA.
- Exploration of emerging techniques like radiomics, liquid biopsies, and AI-driven multimodal integration.
Main Results:
- Imaging biomarkers demonstrate significant potential in detecting PCa and predicting clinical outcomes.
- Non-imaging biomarkers, including PSA and genomic classifiers, provide crucial prognostic information.
- Emerging approaches like radiomics and AI integration show promise for multimodal data fusion and enhanced decision-making.
Conclusions:
- Combining imaging and molecular data is crucial for refining prognostic models in prostate cancer.
- Precision medicine in PCa can be accelerated through the integration of diverse biomarkers.
- Future research should focus on validating and translating these multimodal approaches into clinical practice.
