Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2026 Jan-Dec:25:15330338261428377.
doi: 10.1177/15330338261428377. Epub 2026 Feb 27.

Predicting Stereotactic Body Radiation Therapy Response Using an AI-Based Tumor Vessel Biomarker

Affiliations

Predicting Stereotactic Body Radiation Therapy Response Using an AI-Based Tumor Vessel Biomarker

Jun Hyeong Park et al. Technol Cancer Res Treat. 2026 Jan-Dec.

Abstract

IntroductionAbnormal tumor vasculature impairs oxygen delivery and induces hypoxia, contributing to treatment resistance and poor prognosis in non-small cell lung cancer (NSCLC). Although radiation therapy can modulate tumor vessels, its effects vary widely due to vascular heterogeneity. Therefore, a reliable and noninvasive method to quantify vascular abnormality is needed to better predict treatment outcomes.MethodsWe developed a deep learning-based imaging biomarker, the Vessel Risk Score (VRS), to quantify tumor vascular abnormality from contrast-enhanced CT scans. Trained on multi-institutional data from 126 NSCLC patients treated with hypofractionated radiotherapy, the model learned vascular morphology patterns from tumor-vessel images. Using these learned patterns, vascular heterogeneity was quantified as the distributional difference from normal vessel morphology. The generalizability of VRS was then evaluated in an external cohort of 128 early-stage NSCLC patients who underwent stereotactic body radiotherapy (SBRT).ResultsVRS showed significantly better prediction of SBRT radiation therapy response compared to vessel density. The VRS of the responder group was 0.494 (95% CI: 0.47-0.52), significantly lower than the non-responder group's 0.578 (95% CI: 0.54-0.62). Additionally, patients with high VRS showed significantly shorter PFS compared to those with low VRS (p < 0.05). In Cox multivariate analysis, VRS emerged as the only significant predictor among vessel density and other clinical variables (p < 0.05).ConclusionThe proposed AI-derived VRS provides a noninvasive and reproducible measure of tumor vascular abnormality, offering improved prediction of radiation therapy response and prognosis compared with vessel density. This approach may extend to prognostic assessment in other cancer types where vascular morphology plays a critical role.

Keywords: SBRT; artificial intelligence; biomarker; non-small cell lung cancer (NSCLC); tumor vessel.

PubMed Disclaimer

Conflict of interest statement

Declaration of Conflicting InterestsThe authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.