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Abstract
BACKGROUND: Prostate changes classified as PI-RADS 3 are a clinical situation requiring diagnostic accuracy and minimization of invasive procedures. Exploring the potential value of texture analysis in magnetic resonance imaging for prostate cancer risk stratification is critical in modern medical diagnostics. AIM: To systematize and analyze current data on the application of texture analysis for prostate cancer risk stratification in patients with PI-RADS 3 and evaluate its diagnostic significance in differentiating clinically significant from clinically insignificant prostate cancer. MATERIALS AND METHODS: Articles published in the last 7 years were selected and analyzed from research reference and analytical databases (Medline and Scopus) using search engines (PubMed, Google Scholar, and eLibrary). Keywords related to texture analysis and radiomics regarding prostate cancer diagnosis and risk stratification were used. RESULTS: Analysis of the selected publications showed that machine learning and texture analysis significantly enhance the diagnostic accuracy of prostate cancer. These methods allow for more accurate risk stratification and determination of the actual need for biopsy, potentially leading to a reduction in unnecessary invasive procedures. CONCLUSION: Texture analysis potentially enhances diagnostic accuracy in cases of prostate gland changes classified as PI-RADS 3. However, further research focused on standardizing techniques and conducting multicenter clinical trials is required for its widespread clinical application.
Published Version
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