AI-Guided Delineation of Gross Tumor Volume for Body Tumors: A Systematic Review
<b>Background</b>: 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 nu...
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MDPI AG
2025-03-01
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| Online Access: | https://www.mdpi.com/2075-4418/15/7/846 |
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| author | Lea Marie Pehrson Jens Petersen Nathalie Sarup Panduro Carsten Ammitzbøl Lauridsen Jonathan Frederik Carlsen Sune Darkner Michael Bachmann Nielsen Silvia Ingala |
| author_facet | Lea Marie Pehrson Jens Petersen Nathalie Sarup Panduro Carsten Ammitzbøl Lauridsen Jonathan Frederik Carlsen Sune Darkner Michael Bachmann Nielsen Silvia Ingala |
| author_sort | Lea Marie Pehrson |
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| description | <b>Background</b>: 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. <b>Methods</b>: 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. <b>Results</b>: After screening 2430 articles, 48 were included. The pooled diagnostic performance from the use of AI algorithms across different tumors and topological areas ranged 0.62–0.92 in dice similarity coefficient (DSC) and 1.33–47.10 mm in Hausdorff distance (HD). The algorithms with the highest DSC deployed an encoder–decoder architecture. <b>Conclusions</b>: 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. |
| format | Article |
| id | doaj-art-8830ede9fcd94b9ab0f7ad950f9da1e7 |
| institution | OA Journals |
| issn | 2075-4418 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj-art-8830ede9fcd94b9ab0f7ad950f9da1e72025-08-20T02:15:55ZengMDPI AGDiagnostics2075-44182025-03-0115784610.3390/diagnostics15070846AI-Guided Delineation of Gross Tumor Volume for Body Tumors: A Systematic ReviewLea Marie Pehrson0Jens Petersen1Nathalie Sarup Panduro2Carsten Ammitzbøl Lauridsen3Jonathan Frederik Carlsen4Sune Darkner5Michael Bachmann Nielsen6Silvia Ingala7Department of Diagnostic Radiology, Copenhagen University Hospital Rigshospitalet, 2100 Copenhagen, DenmarkDepartment of Computer Science, University of Copenhagen, 2100 Copenhagen, DenmarkDepartment of Diagnostic Radiology, Copenhagen University Hospital Rigshospitalet, 2100 Copenhagen, DenmarkDepartment of Diagnostic Radiology, Copenhagen University Hospital Rigshospitalet, 2100 Copenhagen, DenmarkDepartment of Diagnostic Radiology, Copenhagen University Hospital Rigshospitalet, 2100 Copenhagen, DenmarkDepartment of Computer Science, University of Copenhagen, 2100 Copenhagen, DenmarkDepartment of Diagnostic Radiology, Copenhagen University Hospital Rigshospitalet, 2100 Copenhagen, DenmarkDepartment of Diagnostic Radiology, Copenhagen University Hospital Rigshospitalet, 2100 Copenhagen, Denmark<b>Background</b>: 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. <b>Methods</b>: 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. <b>Results</b>: After screening 2430 articles, 48 were included. The pooled diagnostic performance from the use of AI algorithms across different tumors and topological areas ranged 0.62–0.92 in dice similarity coefficient (DSC) and 1.33–47.10 mm in Hausdorff distance (HD). The algorithms with the highest DSC deployed an encoder–decoder architecture. <b>Conclusions</b>: 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.https://www.mdpi.com/2075-4418/15/7/846gross tumor volumesegmentationartificial intelligence |
| spellingShingle | Lea Marie Pehrson Jens Petersen Nathalie Sarup Panduro Carsten Ammitzbøl Lauridsen Jonathan Frederik Carlsen Sune Darkner Michael Bachmann Nielsen Silvia Ingala AI-Guided Delineation of Gross Tumor Volume for Body Tumors: A Systematic Review Diagnostics gross tumor volume segmentation artificial intelligence |
| title | AI-Guided Delineation of Gross Tumor Volume for Body Tumors: A Systematic Review |
| title_full | AI-Guided Delineation of Gross Tumor Volume for Body Tumors: A Systematic Review |
| title_fullStr | AI-Guided Delineation of Gross Tumor Volume for Body Tumors: A Systematic Review |
| title_full_unstemmed | AI-Guided Delineation of Gross Tumor Volume for Body Tumors: A Systematic Review |
| title_short | AI-Guided Delineation of Gross Tumor Volume for Body Tumors: A Systematic Review |
| title_sort | ai guided delineation of gross tumor volume for body tumors a systematic review |
| topic | gross tumor volume segmentation artificial intelligence |
| url | https://www.mdpi.com/2075-4418/15/7/846 |
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