前列腺癌(prostate cancer,PCa)是男性常见的恶性肿瘤之一,其筛查和临床诊断主要依赖血清前列腺特异性抗原(prostate-specific antigen,PSA)、多参数磁共振成像(multiparametric magnetic resonance imaging,mpMRI)和经直肠超声检查(transrectal ultrasonography,TRUS)等.前列腺根据其组织学特征被划分为3个腺性区域,即外周带、移行带和中央带,每个区域PCa的发生率和预后存在差异.分子生物学可以帮助人们更好地理解PCa发生发展的分子特征,特别是各个区域独特的基因组学和蛋白组学特征.基于PCa的空间分布的研究,开展病灶特异性的局灶治疗或局部治疗,有效控制肿瘤进展,有助于保障患者的生活质量.近年来,人工智能技术在PCa的诊断与治疗方面展现出潜在的应用价值,有望提高空间分布诊断的准确率,辅助治疗决策,但需要在实践中验证其可靠性.
Advances in clinical research on the spatial distribution of prostate cancer
Prostate cancer(PCa)is one of the most common malignant tumors in men,and the clinical diag-nosis and screening of PCa mainly rely on serum prostate-specific antigen(PSA),multiparametric magnetic reso-nance imaging(mpMRI),and transrectal ultrasonography(TRUS)for prostate examination.The human prostate is divided into three zones based on its histological characteristics:peripheral zone,transition zone,and central zone.The incidence,prognosis,and clinical outcomes of PCa vary in each zone.Molecular biology research can help better understand the unique molecular,genomic,and zonal cellular characteristics of tumor progression and invasive differences.Local treatments targeting the spatial distribution of specific PCa can effectively control tumor progression,ensuring patients'quality of life.In recent years,artificial intelligence technology has shown potential application value in the diagnosis and treatment of PCa,aiming to improve diagnostic accuracy and preci-sion,but further research and validation are needed to determine its effectiveness and reliability.
prostate cancerspatial distributionperipheral zonetransitional zonecentral zone
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