{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,15]],"date-time":"2025-08-15T01:04:51Z","timestamp":1755219891161,"version":"3.43.0"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"type":"electronic","value":"9781643686080"}],"license":[{"start":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T00:00:00Z","timestamp":1754524800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,8,7]]},"abstract":"<jats:p>Despite the existence of ICD-O for tumor classification, the broader ICD-10 system is often used in practice. While OncoTree is significant in research and molecular tumor boards, it provides a more detailed classification based on molecular and histological characteristics, crucial for clinical trial enrollment and data comparison. Therefore, a mapping between ICD-10 and OncoTree was developed. The mapping uses SNOMED CT as an intermediary step because both ICD-10 and OncoTree are structured differently. During the mapping process, some challenges arose, such as differences in the structure of the coding systems and inaccurate mappings. Despite this, the approach achieved an accuracy rate of 86.18%, which is considered satisfactory. Future efforts will focus on refining the mapping process to enhance its integration into production systems.<\/jats:p>","DOI":"10.3233\/shti250796","type":"book-chapter","created":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T11:31:23Z","timestamp":1754566283000},"source":"Crossref","is-referenced-by-count":0,"title":["Mapping ICD-10 Codes for Oncology Diseases to OncoTree: Lessons Learned"],"prefix":"10.3233","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9775-0657","authenticated-orcid":false,"given":"Tessa","family":"Ohlsen","sequence":"first","affiliation":[{"name":"Institute of Medical Biometric and Statistics, Section for Clinical Research IT, University of Luebeck and University Hospital Schleswig-Holstein, Luebeck, Germany"},{"name":"Institute for Medical Informatics, University of Luebeck, Luebeck, Germany"}]},{"given":"Anke","family":"Neumann","sequence":"additional","affiliation":[{"name":"Institute of Medical Biometric and Statistics, Section for Clinical Research IT, University of Luebeck and University Hospital Schleswig-Holstein, Luebeck, Germany"},{"name":"Institute for Medical Informatics, University of Luebeck, Luebeck, Germany"}]},{"given":"Josef","family":"Ingenerf","sequence":"additional","affiliation":[{"name":"Institute for Medical Informatics, University of Luebeck, Luebeck, Germany"}]},{"given":"Niklas","family":"Reimer","sequence":"additional","affiliation":[{"name":"Medical Data Integration Center, University Hospital Schleswig-Holstein, Germany"},{"name":"University Cancer Center Schleswig-Holstein, University Hospital Schleswig-Holstein, Luebeck, Germany"},{"name":"Medical Systems Biology Group, L\u00fcbeck Institute of Experimental Dermatology, University of Luebeck, Luebeck, Germany"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","MEDINFO 2025 \u2014 Healthcare Smart \u00d7 Medicine Deep"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI250796","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T11:31:23Z","timestamp":1754566283000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI250796"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,7]]},"ISBN":["9781643686080"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti250796","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"type":"print","value":"0926-9630"},{"type":"electronic","value":"1879-8365"}],"subject":[],"published":{"date-parts":[[2025,8,7]]}}}