A CT Dataset with RECIST Measurements and Comprehensive Segmentation Masks for Tumors and Lymph Nodes

The Response Evaluation Criteria in Solid Tumors (RECIST 1.1) protocol is the gold standard for assessing treatment response in oncological clinical trials and routine practice. It requires radiologists to review and select appropriate target lesions, and perform precise diameter measurements, making the process labor-intensive and variable. Artificial Intelligence (AI) holds great promise for automating this workflow, but progress is hindered by the lack of public datasets with comprehensive lesion annotations and RECIST-compliant measurements. We address this gap by presenting a dataset of 1,246 manually segmented lesions from 58 CT scans of 22 cancer patients treated at the Clinical Hospital of the University of Chile (HCUCH). All cases were evaluated under RECIST 1.1, with diameter measurements reported for 82 target lesions. This resource supports diverse applications, including validating automated RECIST tools, applying radiomics to study metastatic heterogeneity, benchmarking segmentation algorithms, and advancing foundation models in medical imaging. By including data from a Latin American institution, this dataset also promotes global representation in the development of generalizable medical AI tools.实体瘤疗效评价标准1.1版（Response Evaluation Criteria in Solid Tumors 1.1, RECIST 1.1）方案是肿瘤学临床试验与临床常规实践中评估治疗响应的金标准。该方案要求放射科医师审阅并遴选合适的靶病灶，开展精准的直径测量，这使得该流程既耗时费力，又存在人为变异。人工智能（Artificial Intelligence, AI）有望实现该流程的自动化，但当前因缺乏包含全面病灶标注与符合RECIST标准测量值的公开数据集，其研发进展受到了阻碍。为此，我们构建了一款数据集资源，其包含来自智利大学临床医院（Clinical Hospital of the University of Chile, HCUCH）收治的22名癌症患者的58次CT扫描中的1246个手动分割病灶。所有病例均按照RECIST 1.1标准完成评估，其中82个靶病灶记录了直径测量值。该数据集可支撑多种应用场景，包括验证自动化RECIST评估工具、运用放射组学研究肿瘤转移异质性、对分割算法开展性能基准测试，以及推动医学影像领域基础模型的发展。由于本数据集纳入了来自拉美地区医疗机构的数据，其还有助于提升可推广型医疗AI工具开发过程中的全球代表性。