Department of Urology, IEO, European Institute of Oncology IRCCS, 20141 Milan, Italy.
Department of Oncology and Hemato-Oncology, University of Milan, 20122 Milan, Italy.
Int J Mol Sci. 2021 Sep 15;22(18):9971. doi: 10.3390/ijms22189971.
Radiomics and genomics represent two of the most promising fields of cancer research, designed to improve the risk stratification and disease management of patients with prostate cancer (PCa). Radiomics involves a conversion of imaging derivate quantitative features using manual or automated algorithms, enhancing existing data through mathematical analysis. This could increase the clinical value in PCa management. To extract features from imaging methods such as magnetic resonance imaging (MRI), the empiric nature of the analysis using machine learning and artificial intelligence could help make the best clinical decisions. Genomics information can be explained or decoded by radiomics. The development of methodologies can create more-efficient predictive models and can better characterize the molecular features of PCa. Additionally, the identification of new imaging biomarkers can overcome the known heterogeneity of PCa, by non-invasive radiological assessment of the whole specific organ. In the future, the validation of recent findings, in large, randomized cohorts of PCa patients, can establish the role of radiogenomics. Briefly, we aimed to review the current literature of highly quantitative and qualitative results from well-designed studies for the diagnoses, treatment, and follow-up of prostate cancer, based on radiomics, genomics and radiogenomics research.
放射组学和基因组学代表了癌症研究中最有前途的两个领域,旨在改善前列腺癌 (PCa) 患者的风险分层和疾病管理。放射组学涉及使用手动或自动算法将成像衍生的定量特征转换,通过数学分析增强现有数据。这可以增加 PCa 管理中的临床价值。为了从磁共振成像 (MRI) 等成像方法中提取特征,使用机器学习和人工智能的分析经验性质可以帮助做出最佳的临床决策。基因组学信息可以通过放射组学来解释或解码。方法学的发展可以创建更有效的预测模型,并更好地描述 PCa 的分子特征。此外,通过对整个特定器官进行非侵入性的放射学评估,可以识别新的成像生物标志物,从而克服 PCa 的已知异质性。将来,可以在大量随机分组的 PCa 患者中验证最近的研究结果,以确定放射基因组学的作用。总之,我们旨在基于放射组学、基因组学和放射基因组学研究,回顾基于高度定量和定性结果的最新文献,以诊断、治疗和随访前列腺癌。