Department of Oncology, Precision Oncology Center, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
Institute of Informatics, School of Management, University of Applied Sciences Western Switzerland (HES-SO), Sierre, Switzerland.
JCO Clin Cancer Inform. 2023 May;7:e2200126. doi: 10.1200/CCI.22.00126.
A semiautomated pipeline for the collection and curation of free-text and imaging real-world data (RWD) was developed to quantify cancer treatment outcomes in large-scale retrospective real-world studies. The objectives of this article are to illustrate the challenges of RWD extraction, to demonstrate approaches for quality assurance, and to showcase the potential of RWD for precision oncology.
We collected data from patients with advanced melanoma receiving immune checkpoint inhibitors at the Lausanne University Hospital. Cohort selection relied on semantically annotated electronic health records and was validated using process mining. The selected imaging examinations were segmented using an automatic commercial software prototype. A postprocessing algorithm enabled longitudinal lesion identification across imaging time points and consensus malignancy status prediction. Resulting data quality was evaluated against expert-annotated ground-truth and clinical outcomes obtained from radiology reports.
The cohort included 108 patients with melanoma and 465 imaging examinations (median, 3; range, 1-15 per patient). Process mining was used to assess clinical data quality and revealed the diversity of care pathways encountered in a real-world setting. Longitudinal postprocessing greatly improved the consistency of image-derived data compared with single time point segmentation results (classification precision increased from 53% to 86%). Image-derived progression-free survival resulting from postprocessing was comparable with the manually curated clinical reference (median survival of 286 336 days, = .89).
We presented a general pipeline for the collection and curation of text- and image-based RWD, together with specific strategies to improve reliability. We showed that the resulting disease progression measures match reference clinical assessments at the cohort level, indicating that this strategy has the potential to unlock large amounts of actionable retrospective real-world evidence from clinical records.
开发了一种用于收集和管理自由文本和成像真实世界数据（RWD）的半自动化管道，以在大规模回顾性真实世界研究中量化癌症治疗结果。本文的目的是说明 RWD 提取的挑战，展示质量保证方法，并展示 RWD 在精准肿瘤学中的潜力。
我们从洛桑大学医院接受免疫检查点抑制剂治疗的晚期黑色素瘤患者中收集数据。队列选择依赖于语义注释的电子健康记录，并使用流程挖掘进行验证。选择的成像检查使用自动商业软件原型进行分割。后处理算法能够在成像时间点之间进行纵向病变识别，并预测一致性恶性状态。根据专家注释的地面实况和从放射学报告中获得的临床结果评估所得数据质量。
该队列包括 108 例黑色素瘤患者和 465 次成像检查（中位数为 3 次；范围为每位患者 1-15 次）。流程挖掘用于评估临床数据质量，并揭示了真实环境中遇到的各种护理途径。与单次时间点分割结果相比，纵向后处理大大提高了图像衍生数据的一致性（分类精度从 53%提高到 86%）。后处理得出的图像衍生无进展生存期与手动策管的临床参考值相当（中位生存时间为 286 336 天， =.89）。
我们提出了一种用于收集和策管基于文本和图像的 RWD 的通用管道，以及提高可靠性的具体策略。我们表明，所得疾病进展测量值与队列水平的参考临床评估相匹配，这表明该策略有可能从临床记录中解锁大量可操作的回顾性真实世界证据。
