Health AI Central
Peer-Reviewed Publication
Med Phys2025;52(5):3348-3359.May 1, 2025Journal Article

Radiotherapy dose prediction using off-the-shelf segmentation networks: A feasibility study with GammaPod planning.

Qingying Wang1, Mingli Chen1, Mahdieh Kazemimoghadam1, Zi Yang2, Kangning Zhang2, Xuejun Gu1,2, Weiguo Lu1
1Department of Radiation Oncology, The University of Texas Southwestern Medical Center, Dallas, Texas, USA.
2Department of Radiation Oncology, Stanford University, Stanford, California, USA.
PubMed PMC DOI PDF

Abstract

BACKGROUND: Radiotherapy requires precise, patient-specific treatment planning to achieve high-quality dose distributions that improve patient outcomes. Traditional manual planning is time-consuming and clinically impractical for performing necessary plan trade-off comparisons, including treatment modality selection, prescription dose settings, and organ at risk (OAR) constraints. A time-efficient…

Create a free account to keep reading

Free members get 10 full research views every month across publications, clinical trials, FDA clearances, adverse events, and NIH grants. No credit card required.

Create Free AccountSign In

Want unlimited research access? See Pro plans

Data Accuracy Notice: Research intelligence on Health AI Central is aggregated from public sources (PubMed, ClinicalTrials.gov, FDA, NIH, CMS, and others) and refreshed nightly. Classifications and derived metrics are produced by automated methods described in our Methodology. We recommend verifying critical data points against the primary sources before making decisions.