Background Higher enrolment rates of cancer patients into clinical trials are necessary to increase cancer survival. As a prerequisite, an improved semiautomated matching of patient characteristics with clinical trial eligibility criteria is needed. This is based on the computer interpretability, i.e., structurability of eligibility criteria texts. To increase structurability, the common content, phrasing, and structuring problems of oncological eligibility criteria need to be better understood.

Objectives We aimed to identify oncological eligibility criteria that were not possible to be structured by our manual approach and categorize them by the underlying structuring problem. Our results shall contribute to improved criteria phrasing in the future as a prerequisite for increased structurability.

Methods The inclusion and exclusion criteria of 159 oncological studies from the Clinical Trial Information System of the National Center for Tumor Diseases Heidelberg were manually structured and grouped into content-related subcategories. Criteria identified as not structurable were analyzed further and manually categorized by the underlying structuring problem.

Results The structuring of criteria resulted in 4,742 smallest meaningful components (SMCs) distributed across seven main categories (Diagnosis, Therapy, Laboratory, Study, Findings, Demographics, and Lifestyle, Others). A proportion of 645 SMCs (13.60%) was not possible to be structured due to content- and structure-related issues. Of these, a subset of 415 SMCs (64.34%) was considered not remediable, as supplementary medical knowledge would have been needed or the linkage among the sentence components was too complex. The main category “Diagnosis and Study” contained these two subcategories to the largest parts and thus were the least structurable. In the inclusion criteria, reasons for lacking structurability varied, while missing supplementary medical knowledge was the largest factor within the exclusion criteria.

Conclusion Our results suggest that further improvement of eligibility criterion phrasing only marginally contributes to increased structurability. Instead, physician-based confirmation of the matching results and the exclusion of factors harming the patient or biasing the study is needed.

背景 提高癌症患者临床试验的入组率是提高癌症存活率的必要条件。作为先决条件，需要改进患者特征与临床试验资格标准的半自动匹配。这是基于计算机可解释性，即资格标准文本的结构性。为了提高结构性，需要更好地理解肿瘤学资格标准的常见内容、措辞和结构问题。

目标 我们旨在确定无法通过我们的手动方法构建的肿瘤学资格标准，并按潜在的结构问题对其进行分类。我们的结果将有助于在未来改进标准措辞，作为提高可结构化性的先决条件。

方法 对来自海德堡国家肿瘤疾病中心临床试验信息系统的 159 项肿瘤学研究的纳入和排除标准进行人工构建，并按照内容相关的子类别进行分组。被确定为不可结构化的标准被进一步分析，并根据潜在的结构化问题进行手动分类。

结果 标准的结构产生了 4,742 个最小有意义成分 (SMC)，分布在七个主要类别（诊断、治疗、实验室、研究、发现、人口统计和生活方式、其他）中。由于内容和结构相关的问题，645 个 SMC（13.60%）的一部分无法结构化。其中，415 个 SMC 的子集（64.34%）被认为是不可补救的，因为需要补充医学知识或句子成分之间的联系过于复杂。主要类别“诊断和研究”包含这两个子类别的最大部分，因此是最不可结构化的。在纳入标准中，缺乏结构化的原因各不相同，而缺乏补充医学知识是排除标准中的最大因素。

结论 我们的结果表明，资格标准措辞的进一步改进仅略微有助于提高可结构化性。相反，需要对匹配结果进行基于医生的确认，并排除伤害患者或使研究产生偏见的因素。