Shegai P V, Shatalov P A, Zabolotneva A A, Falaleeva N A, Ivanov S A, Kaprin A D
Federal State Budgetary Institution National Medical Research Radiological Center of the Ministry of Health of the Russian Federation, Kaluga Region, Koroleva Str. 4., Obninsk 249036, Russia.
Pirogov Russian National Research Medical University, Ostrovitianov Str. 1, Moscow 117997, Russia.
Crit Care Res Pract. 2021 Sep 23;2021:6649771. doi: 10.1155/2021/6649771. eCollection 2021.
Advances in cancer molecular profiling have enabled the development of more effective approaches to the diagnosis and personalized treatment of tumors. However, treatment planning has become more labor intensive, requiring hours or even days of clinician effort to optimize an individual patient case in a trial-and-error manner. Lessons learned from the world cancer programs provide insights into ways to develop approaches for the treatment strategy definition which can be introduced into clinical practice. This article highlights the variety of breakthroughs in patients' cancer treatment and some challenges that this field faces now in Russia. In this report, we consider the key characteristics for planning an optimal clinical treatment regimen and which should be included in the algorithm of clinical decision support systems. We discuss the perspectives of implementing artificial intelligence-based systems in cancer treatment planning in Russia.
癌症分子图谱分析的进展推动了更有效的肿瘤诊断和个性化治疗方法的发展。然而,治疗方案规划变得更加耗费人力,需要临床医生花费数小时甚至数天,通过反复试验的方式来优化单个患者的病例。从全球癌症项目中吸取的经验教训为制定可引入临床实践的治疗策略定义方法提供了思路。本文重点介绍了患者癌症治疗方面的各种突破以及俄罗斯该领域目前面临的一些挑战。在本报告中,我们考虑了规划最佳临床治疗方案的关键特征,这些特征应纳入临床决策支持系统的算法中。我们还讨论了在俄罗斯癌症治疗规划中实施基于人工智能的系统的前景。