AI-Guided Delineation of Gross Tumor Volume for Body Tumors: A Systematic Review.

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 0.62-0.92 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.

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.