{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Rojas-Pizarro R"],"funding":["Ministry of Education, Government of Chile | Comisión Nacional de Investigación Científica y Tecnológica","Ministry of Education, Government of Chile | Comisión Nacional de Investigación Científica y Tecnológica (National Commission for Scientific and Technological Research)","Ministry of Education, Government of Chile | National Commission for Scientific and Technological Research | Fondo de Fomento al Desarrollo Científico y Tecnológico (FONDEF)","Ministry of Education, Government of Chile | National Commission for Scientific and Technological Research | Fondo de Fomento al Desarrollo Científico y Tecnológico"],"pagination":["270"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12917031"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["13(1)"],"pubmed_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 meas"],"journal":["Scientific data"],"pubmed_title":["A CT Dataset with RECIST Measurements and Comprehensive Segmentation Masks for Tumors and Lymph Nodes."],"pmcid":["PMC12917031"],"funding_grant_id":["FONDEF ID23|10337","FONDECYT 1211988"],"pubmed_authors":["Pereira G","Perez N","Bravo-Bahamondez F","Rojas G","Gallardo-Badilla P","Caro-Flores F","Molina MM","Cabrera-Vives G","Ormeno-Candia C","Rojas-Pizarro R","Eyssautier MF","Vasquez-Venegas C","Santander F","Sanhueza N","Hartel S"],"additional_accession":[]},"is_claimable":false,"name":"A CT Dataset with RECIST Measurements and Comprehensive Segmentation Masks for Tumors and Lymph Nodes.","description":"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 meas","dates":{"release":"2026-01-01T00:00:00Z","publication":"2026 Jan","modification":"2026-07-16T10:07:41.503Z","creation":"2026-07-09T10:49:05.114Z"},"accession":"S-EPMC12917031","cross_references":{"pubmed":["41559108"],"doi":["10.1038/s41597-026-06597-6"]}}