A CT Dataset with RECIST Measurements and Comprehensive Segmentation Masks for Tumors and Lymph Nodes.
Authors
Affiliations (12)
Affiliations (12)
- Laboratory for Scientific Image Analysis SCIAN-Lab, Interdisciplinary Nucleus for Biology and Genetics, Institute of Biomedical Sciences ICBM, Faculty of Medicine, University of Chile, Av. Independencia 1027, Santiago, 8380453, Chile.
- Department of Medical Technology, Faculty of Medicine, University of Chile, Av. Independencia 1027, Santiago, 8380453, Chile.
- Department of Computer Science, Faculty of Engineering, Universidad de Concepción, Edmundo Larenas 219, Concepción, 4030000, Chile.
- Radiology Department, Clinical Hospital University of Chile, University of Chile, Dr. Carlos Lorca Tobar 999, Santiago, 8380420, Chile.
- Centro de Informática Médica y Telemedicina CIMT, Institute of Biomedical Sciences ICBM, Faculty of Medicine, University of Chile, Av. Independencia 1027, Santiago, 8380453, Chile.
- Laboratory for Scientific Image Analysis SCIAN-Lab, Interdisciplinary Nucleus for Biology and Genetics, Institute of Biomedical Sciences ICBM, Faculty of Medicine, University of Chile, Av. Independencia 1027, Santiago, 8380453, Chile. [email protected].
- Centro de Informática Médica y Telemedicina CIMT, Institute of Biomedical Sciences ICBM, Faculty of Medicine, University of Chile, Av. Independencia 1027, Santiago, 8380453, Chile. [email protected].
- Biomedical Neuroscience Institute BNI, Faculty of Medicine, University of Chile, Av. Independencia 1027, Santiago, 8380453, Chile. [email protected].
- National Center for Health Information Systems CENS, Av. Independencia 1027, Santiago, 8380453, Chile. [email protected].
- Centro de Modelamiento Matemático, Universidad de Chile, Santiago, Beauchef 851, Casilla 170-3, Santiago, Chile. [email protected].
- Department of Computer Science, Faculty of Engineering, Universidad de Concepción, Edmundo Larenas 219, Concepción, 4030000, Chile. [email protected].
- Center for Data and Artificial Intelligence, Universidad de Concepción, Concepción, Chile. [email protected].
Abstract
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.