De Maria Marchiano Ruggero, Di Sante Gabriele, Piro Geny, Carbone Carmine, Tortora Giampaolo, Boldrini Luca, Pietragalla Antonella, Daniele Gennaro, Tredicine Maria, Cesario Alfredo, Valentini Vincenzo, Gallo Daniela, Babini Gabriele, D'Oria Marika, Scambia Giovanni
Department of Translational Medicine and Surgery, Section of General Pathology, Università Cattolica del Sacro Cuore, 00168 Rome, Italy.
Scientific Direction, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy.
J Pers Med. 2021 Mar 18;11(3):216. doi: 10.3390/jpm11030216.
The advent of Precision Medicine has globally revolutionized the approach of translational research suggesting a patient-centric vision with therapeutic choices driven by the identification of specific predictive biomarkers of response to avoid ineffective therapies and reduce adverse effects. The spread of "multi-omics" analysis and the use of sensors, together with the ability to acquire clinical, behavioral, and environmental information on a large scale, will allow the digitization of the state of health or disease of each person, and the creation of a global health management system capable of generating real-time knowledge and new opportunities for prevention and therapy in the individual person (high-definition medicine). Real world data-based translational applications represent a promising alternative to the traditional evidence-based medicine (EBM) approaches that are based on the use of randomized clinical trials to test the selected hypothesis. Multi-modality data integration is necessary for example in precision oncology where an Avatar interface allows several simulations in order to define the best therapeutic scheme for each cancer patient.
精准医学的出现已经在全球范围内彻底改变了转化研究的方法，它提出了一种以患者为中心的理念，其治疗选择由特定的反应预测生物标志物的识别驱动，以避免无效治疗并减少不良反应。“多组学”分析的普及、传感器的使用，以及大规模获取临床、行为和环境信息的能力，将使每个人的健康或疾病状态数字化，并创建一个全球健康管理系统，该系统能够为个人生成实时知识以及预防和治疗的新机会（高清医学）。基于真实世界数据的转化应用是传统循证医学（EBM）方法的一个有前景的替代方案，传统循证医学方法基于使用随机临床试验来检验所选假设。例如，在精准肿瘤学中，多模态数据整合是必要的，其中一个阿凡达界面允许进行多种模拟，以便为每个癌症患者确定最佳治疗方案。
