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临床医生在个性化治疗规划中面临的挑战:文献综述及俄罗斯国家癌症计划的初步结果

Challenges Faced by Clinicians in the Personalized Treatment Planning: A Literature Review and the First Results of the Russian National Cancer Program.

作者信息

Shegai P V, Shatalov P A, Zabolotneva A A, Falaleeva N A, Ivanov S A, Kaprin A D

机构信息

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.

出版信息

Crit Care Res Pract. 2021 Sep 23;2021:6649771. doi: 10.1155/2021/6649771. eCollection 2021.

Abstract

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.

摘要

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。

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本文引用的文献

1
Estimated Cost of Anticancer Therapy Directed by Comprehensive Genomic Profiling in a Single-Center Study.
JCO Precis Oncol. 2018 Nov 2;2. doi: 10.1200/PO.18.00074. eCollection 2018.
2
Cancer statistics, 2020.
CA Cancer J Clin. 2020 Jan;70(1):7-30. doi: 10.3322/caac.21590. Epub 2020 Jan 8.
3
A view on drug resistance in cancer.
Nature. 2019 Nov;575(7782):299-309. doi: 10.1038/s41586-019-1730-1. Epub 2019 Nov 13.
4
Cost-comparison analysis of a multiplatform tumour profiling service to guide advanced cancer treatment.
Cost Eff Resour Alloc. 2019 Oct 21;17:23. doi: 10.1186/s12962-019-0191-6. eCollection 2019.
5
Targeted drug combination therapy design based on driver genes.
Oncotarget. 2019 Sep 3;10(51):5255-5266. doi: 10.18632/oncotarget.26985.
6
The potential for artificial intelligence in healthcare.
Future Healthc J. 2019 Jun;6(2):94-98. doi: 10.7861/futurehosp.6-2-94.
7
Artificial Intelligence in Drug Treatment.
Annu Rev Pharmacol Toxicol. 2020 Jan 6;60:353-369. doi: 10.1146/annurev-pharmtox-010919-023746. Epub 2019 Jul 26.
8
In-silico Prediction of Synergistic Anti-Cancer Drug Combinations Using Multi-omics Data.
Sci Rep. 2019 Jun 20;9(1):8949. doi: 10.1038/s41598-019-45236-6.
10
The current state of molecular testing in the treatment of patients with solid tumors, 2019.
CA Cancer J Clin. 2019 Jul;69(4):305-343. doi: 10.3322/caac.21560. Epub 2019 May 22.

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