Lutz Sebastian, D'Angelo Alicia, Hammerl Sonja, Schmutz Maximilian, Claus Rainer, Fischer Nina M, Kramer Frank, Hammoud Zaynab
IT-Infrastructure for Translational Medical Research, University of Augsburg, Augsburg, Germany.
Institute of Digital Medicine (IDM), Medical Faculty, University of Augsburg, Augsburg, Germany.
Target Oncol. 2025 Jan;20(1):27-43. doi: 10.1007/s11523-024-01109-1. Epub 2024 Nov 28.
Molecular tumor boards (MTB) are interdisciplinary conferences involving various experts discussing patients with advanced tumors, to derive individualized treatment suggestions based on molecular variants. These discussions involve using heterogeneous internal data, such as patient clinical data, but also external resources such as knowledge databases for annotations and search for relevant clinical studies. This imposes a certain level of complexity that requires huge effort to homogenize the data and use it in a speedy manner to reach the needed treatment. For this purpose, most institutions involving an MTB are heading toward automation and digitalization of the process, hence reducing manual work requiring human intervention and subsequently time in deriving personalized treatment suggestions. The tools are also used to better visualize the patient's data, which allows a refined overview for the board members. In this paper, we present the results of our thorough literature research about MTBs, their process, the most common knowledge bases, and tools used to support this decision-making process.
分子肿瘤委员会(MTB)是跨学科会议,由各类专家共同讨论晚期肿瘤患者,以便根据分子变异得出个体化治疗建议。这些讨论不仅涉及使用患者临床数据等异质性内部数据,还涉及外部资源,如用于注释的知识数据库以及搜索相关临床研究。这带来了一定程度的复杂性,需要付出巨大努力来使数据同质化,并快速使用数据以获得所需的治疗方案。为此,大多数设有分子肿瘤委员会的机构都在朝着该流程的自动化和数字化方向发展,从而减少需要人工干预的手动工作,并进而减少得出个性化治疗建议所需的时间。这些工具还用于更好地可视化患者数据,使委员会成员能够有更清晰的总体了解。在本文中,我们展示了关于分子肿瘤委员会、其流程、最常见的知识库以及用于支持这一决策过程的工具的全面文献研究结果。