Related Experiment Video
Updated: Apr 14, 2026
Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence
Published on: June 10, 2025
From prostate-specific antigen to precision: The future of prostate cancer diagnosis with artificial intelligence,
Helena Margot Flôres Soares da Silva1,2, Juan Gómez Rivas3,4, Paula Mata Déniz3
1Faculdade de Medicina da, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil.
Background:
Prostate cancer (PCa) diagnosis has historically relied on prostate-specific antigen (PSA) testing. Although PSA screening significantly reduces mortality rates, it is limited by its low specificity and the risk of overdiagnosis and overtreatment. These limitations highlight the need for more accurate diagnostic approaches that can be combined with PSA testing. Emerging technologies, such as artificial intelligence (AI), novel biomarkers, and advanced imaging techniques, offer promising avenues to enhance the accuracy and efficiency of PCa diagnosis and risk stratification.
Materials And Methods:
This review comprehensively analyzes the current literature on the use of AI, machine learning, novel biomarkers, and imaging tools, particularly multiparametric magnetic resonance imaging and digital pathology, for the diagnosis of PCa. Studies on AI-driven image interpretation, lesion segmentation, radiomics, genomic classifiers, and multimodal data integration were evaluated. This study also considers the technical, regulatory, and ethical challenges related to the clinical implementation of AI technologies.
Results:
Artificial intelligence demonstrated significant utility in multiparametric magnetic resonance imaging interpretation, enhancing lesion detection, segmentation, and Gleason grading with high accuracy and reproducibility. In pathology, AI algorithms improve the diagnostic consistency of digital slides and assist with automated Gleason scoring. Genomic tools, such as Oncotype DX, when combined with AI, allow for individualized risk prediction. Multimodal models that integrate imaging, clinical, and molecular data outperform traditional PSA-based strategies and reduce unnecessary biopsies.
Conclusions:
The transition from PSA-centered to AI-driven, biomarker-supported, image-enhanced diagnosis marks a critical evolution in PCa care. While these technologies promise improved diagnostic accuracy compared with that with PSA alone, PSA will remain a foundation for model construction and risk stratification. Personalized treatment strategies and the successful clinical integration of AI depend on harmonized regulations, large-scale validation, equitable access, and transparent algorithm design. Future screening and treatment pathways for PCa are likely to be shaped by these multimodal precision diagnostic frameworks.
