{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T01:09:37Z","timestamp":1785460177252,"version":"3.56.0"},"reference-count":55,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T00:00:00Z","timestamp":1742947200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Innovation Fund Denmark (IFD)","award":["0176-00013B"],"award-info":[{"award-number":["0176-00013B"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background: Approximately 50% of all oncological patients undergo radiation therapy, where personalized planning of treatment relies on gross tumor volume (GTV) delineation. Manual delineation of GTV is time-consuming, operator-dependent, and prone to variability. An increasing number of studies apply artificial intelligence (AI) techniques to automate such delineation processes. Methods: To perform a systematic review comparing the performance of AI models in tumor delineations within the body (thoracic cavity, esophagus, abdomen, and pelvis, or soft tissue and bone). A retrospective search of five electronic databases was performed between January 2017 and February 2025. Original research studies developing and\/or validating algorithms delineating GTV in CT, MRI, and\/or PET were included. The Checklist for Artificial Intelligence in Medical Imaging (CLAIM) and Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis statement and checklist (TRIPOD) were used to assess the risk, bias, and reporting adherence. Results: After screening 2430 articles, 48 were included. The pooled diagnostic performance from the use of AI algorithms across different tumors and topological areas ranged 0.62\u20130.92 in dice similarity coefficient (DSC) and 1.33\u201347.10 mm in Hausdorff distance (HD). The algorithms with the highest DSC deployed an encoder\u2013decoder architecture. Conclusions: AI algorithms demonstrate a high level of concordance with clinicians in GTV delineation. Translation to clinical settings requires the building of trust, improvement in performance and robustness of results, and testing in prospective studies and randomized controlled trials.<\/jats:p>","DOI":"10.3390\/diagnostics15070846","type":"journal-article","created":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T11:35:47Z","timestamp":1742988947000},"page":"846","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["AI-Guided Delineation of Gross Tumor Volume for Body Tumors: A Systematic Review"],"prefix":"10.3390","volume":"15","author":[{"given":"Lea Marie","family":"Pehrson","sequence":"first","affiliation":[{"name":"Department of Diagnostic Radiology, Copenhagen University Hospital Rigshospitalet, 2100 Copenhagen, Denmark"},{"name":"Department of Clinical Medicine, University of Copenhagen, 2100 Copenhagen, Denmark"},{"name":"Department of Computer Science, University of Copenhagen, 2100 Copenhagen, Denmark"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jens","family":"Petersen","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Copenhagen, 2100 Copenhagen, Denmark"},{"name":"Department of Oncology, Rigshospitalet, 2100 Copenhagen, Denmark"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6987-1125","authenticated-orcid":false,"given":"Nathalie Sarup","family":"Panduro","sequence":"additional","affiliation":[{"name":"Department of Diagnostic Radiology, Copenhagen University Hospital Rigshospitalet, 2100 Copenhagen, Denmark"},{"name":"Department of Clinical Medicine, University of Copenhagen, 2100 Copenhagen, Denmark"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Carsten Ammitzb\u00f8l","family":"Lauridsen","sequence":"additional","affiliation":[{"name":"Department of Diagnostic Radiology, Copenhagen University Hospital Rigshospitalet, 2100 Copenhagen, Denmark"},{"name":"Radiography Education, University College Copenhagen, 2200 Copenhagen, Denmark"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6724-7281","authenticated-orcid":false,"given":"Jonathan Frederik","family":"Carlsen","sequence":"additional","affiliation":[{"name":"Department of Diagnostic Radiology, Copenhagen University Hospital Rigshospitalet, 2100 Copenhagen, Denmark"},{"name":"Department of Clinical Medicine, University of Copenhagen, 2100 Copenhagen, Denmark"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sune","family":"Darkner","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Copenhagen, 2100 Copenhagen, Denmark"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9380-1688","authenticated-orcid":false,"given":"Michael Bachmann","family":"Nielsen","sequence":"additional","affiliation":[{"name":"Department of Diagnostic Radiology, Copenhagen University Hospital Rigshospitalet, 2100 Copenhagen, Denmark"},{"name":"Department of Clinical Medicine, University of Copenhagen, 2100 Copenhagen, Denmark"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Silvia","family":"Ingala","sequence":"additional","affiliation":[{"name":"Department of Diagnostic Radiology, Copenhagen University Hospital Rigshospitalet, 2100 Copenhagen, Denmark"},{"name":"Cerebriu A\/S, 1434 Copenhagen, Denmark"},{"name":"Department of Diagnostic Radiology, Copenhagen University Hospital Herlev and Gentofte, 2730 Herlev, Denmark"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,3,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1102\/1470-7330.2004.0054","article-title":"Defining the tumour and target volumes for radiotherapy","volume":"4","author":"Burnet","year":"2004","journal-title":"Cancer Imaging"},{"key":"ref_2","unstructured":"Jaffray, D., and Gospodarowicz, M. 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