Junior Research Group (Bio-) Medical Data Science, Faculty of Medicine, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany.
Data Integration Centre, University Hospital Halle (Saale), Halle (Saale), Germany.
JMIR Res Protoc. 2024 Sep 4;13:e58705. doi: 10.2196/58705.
Understanding the similarities of patients with cancer is essential to advancing personalized medicine, improving patient outcomes, and developing more effective and individualized treatments. It enables researchers to discover important patterns, biomarkers, and treatment strategies that can have a significant impact on cancer research and oncology. In addition, the identification of previously successfully treated patients supports oncologists in making treatment decisions for a new patient who is clinically or molecularly similar to the previous patient.
The planned review aims to systematically summarize, map, and describe existing evidence to understand how patient similarity is defined and used in cancer research and clinical care.
To systematically identify relevant studies and to ensure reproducibility and transparency of the review process, a comprehensive literature search will be conducted in several bibliographic databases, including Web of Science, PubMed, LIVIVIVO, and MEDLINE, covering the period from 1998 to February 2024. After the initial duplicate deletion phase, a study selection phase will be applied using Rayyan, which consists of 3 distinct steps: title and abstract screening, disagreement resolution, and full-text screening. To ensure the integrity and quality of the selection process, each of these steps is preceded by a pilot testing phase. This methodological process will culminate in the presentation of the final research results in a structured form according to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) flowchart. The protocol has been registered in the Journal of Medical Internet Research.
This protocol outlines the methodologies used in conducting the scoping review. A search of the specified electronic databases and after removing duplicates resulted in 1183 unique records. As of March 2024, the review process has moved to the full-text evaluation phase. At this stage, data extraction will be conducted using a pretested chart template.
The scoping review protocol, centered on these main concepts, aims to systematically map the available evidence on patient similarity among patients with cancer. By defining the types of data sources, approaches, and methods used in the field, and aligning these with the research questions, the review will provide a foundation for future research and clinical application in personalized cancer care. This protocol will guide the literature search, data extraction, and synthesis of findings to achieve the review's objectives.
INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/58705.
了解癌症患者之间的相似之处对于推进个性化医学、改善患者预后以及开发更有效和个体化的治疗方法至关重要。它使研究人员能够发现重要的模式、生物标志物和治疗策略，这些策略对癌症研究和肿瘤学有重大影响。此外，鉴定以前成功治疗过的患者有助于肿瘤学家为与以前患者在临床或分子上相似的新患者做出治疗决策。
计划的综述旨在系统地总结、映射和描述现有证据，以了解患者相似性在癌症研究和临床护理中的定义和使用方式。
为了系统地识别相关研究，并确保综述过程的可重复性和透明度，将在几个书目数据库中进行全面的文献检索，包括 Web of Science、PubMed、LIVIVO 和 MEDLINE，涵盖时间范围为 1998 年至 2024 年 2 月。在初始重复删除阶段之后，将使用 Rayyan 进行研究选择阶段，该阶段由 3 个不同步骤组成：标题和摘要筛选、分歧解决和全文筛选。为了确保选择过程的完整性和质量，每个步骤都在前一个阶段进行试点测试。这一方法过程将以根据 PRISMA-ScR（系统评价和荟萃分析扩展的首选报告项目用于范围综述）流程图呈现最终研究结果的形式结束。该方案已在《医学互联网研究杂志》上注册。
本方案概述了进行范围综述所使用的方法。对指定的电子数据库进行搜索并去除重复项后，得到了 1183 条独特的记录。截至 2024 年 3 月，审查过程已进入全文评估阶段。在这个阶段，将使用预测试图表模板进行数据提取。
该方案以这些主要概念为中心，旨在系统地绘制关于癌症患者之间患者相似性的现有证据图谱。通过定义该领域中使用的数据来源、方法和方法的类型，并将其与研究问题对齐，该综述将为个性化癌症护理的未来研究和临床应用提供基础。本方案将指导文献检索、数据提取和结果综合，以实现综述目标。
国际注册报告标识符（IRRID）：DERR1-10.2196/58705.
