Bardoscia Lilia, Sardaro Angela, Quattrocchi Mariagrazia, Cocuzza Paola, Ciurlia Elisa, Furfaro Ilaria, Gilio Maria Antonietta, Mignogna Marcello, Detti Beatrice, Ingrosso Gianluca
Radiation Oncology Unit, Oncology Department, S. Luca Hospital, Azienda USL Toscana Nord Ovest, 55100 Lucca, Italy.
Radiation Oncology Unit, Vito Fazzi Hospital, 73100 Lecce, Italy.
J Pers Med. 2025 Aug 11;15(8):367. doi: 10.3390/jpm15080367.
High-risk prostate cancer (PC) accounts for 50-75% of 10-year relapse after primary treatment. Routine clinicopathological parameters for PC patient stratification have proven insufficient to inform clinical decisions in this setting. Tumor genomic profiling allowed overcoming the limits of diagnostic accuracy in the field of PC, integrated with radiomic features, automated platforms, evaluation of patient-related factors (age, performance status, comorbidity) and tumor-related factors (risk class, volume, T stage). In this scenario, the use of biomarkers to guide decision-making in localized, high-risk PC is evolving actively and rapidly. Additional tests for prostate-specific antigen have demonstrated superior sensitivity and specificity for detecting clinically significant PC, as well as commercially available genomic classifiers improving the risk prediction of disease recurrence/progression/metastasis, in combination with common clinical variables. This narrative review aimed to summarize the state of the art on the utility and evolution of old and emerging biomarkers in the diagnosis and prognosis of localized, high-risk PC, and the potential for their application in clinical practice. We focused on the theoretical molecular foundation of prostate carcinogenesis and explored the impact of genomic profiling, next-generation sequencing, and artificial intelligence in the extrapolation of customized features able to predict disease aggressiveness and possibly drive personalized therapeutic decisions.
高危前列腺癌（PC）占初始治疗后10年复发病例的50 - 75%。事实证明，用于PC患者分层的常规临床病理参数不足以在此情况下指导临床决策。肿瘤基因组分析能够克服PC领域诊断准确性的局限，它与放射组学特征、自动化平台、患者相关因素（年龄、体能状态、合并症）及肿瘤相关因素（风险类别、体积、T分期）相结合。在这种情况下，利用生物标志物指导局限性高危PC的决策制定正在积极且迅速地发展。前列腺特异性抗原的其他检测方法已显示出在检测具有临床意义的PC方面具有更高的敏感性和特异性，以及与常见临床变量相结合的、可提高疾病复发/进展/转移风险预测能力的市售基因组分类器。本叙述性综述旨在总结在局限性高危PC的诊断和预后中，新旧生物标志物的效用及演变情况，以及它们在临床实践中的应用潜力。我们聚焦于前列腺癌发生的理论分子基础，并探讨了基因组分析、下一代测序和人工智能在推断能够预测疾病侵袭性并可能推动个性化治疗决策的定制特征方面的影响。
