Abstract 3751: QVT Subscore: An interpretable radiomic panel of tumor vascularity reveals treatment-specific mechanisms-of-action (MoA) in NSCLC
Abstract Background: Tumor angiogenesis drives treatment resistance by fostering an immunosuppressive microenvironment. VEGF inhibitors (VEGFi) target the vasculature, while immune checkpoint inhibitors (ICI) do not address this mechanism directly. Quantitative Vessel Tortuosity (QVT) Score is a radiomic biomarker of vascularity previously shown to be associated with ICI response. Here, we introduce QVT Subscore, an interpretable panel that isolates distinct vascular attributes (e.g., branching, curvature, radius), enabling treatment-specific insight into vascularity MoA and vascular normalization. Methods: QVT Subscores are derived by grouping the 910 QVT features comprising QVT Score into six biological categories, summarized by principal component analysis. The panel was evaluated in 557 patients from the phase 3 SWOG S0819 trial and a real-world ICI monotherapy cohort (n=147). QVT score/subscore changes from baseline to first on-treatment CTs were assessed by paired t-test and substratified by objective response (OR). Results: While the mono-chemotherapy showed no vascularity changes, VEGFi regimens reduced QVT Score, with Branch and Curvature Subscores decreasing significantly but Vessel Volume unchanged. In contrast, ICI recipients exhibited significant increases in QVT Score and 5/6 Subscores, showing opposite changes to VEGFi in four subscores and differing effects on vessel Radius and Volume. VEGFi produced the greatest vascularity decrease in patients achieving OR, with milder decreases in non-OR. For ICI, OR showed negligible QVT change, while vascularity significantly increased in non-OR across all subscores except Radius. Conclusions: The QVT Subscore panel captures granular tumor vascular remodeling from different treatments, providing mechanistically grounded insights into clinical trial arm performance. Future work will assess the panel’s potential for identifying regimens that benefit from adding VEGFi. Citation Format: Haojia Li, Pushkar Mutha, Young Kwang Chae, Kai Zhang, Liam Il-Young Chung, Amogh Hiremath, Rhea Chitalia, Justin Yau, Omid Haji-Maghsoudi, Trishan Arul, Vamsidhar Velcheti, Anant Madabhushi, Nathaniel Braman. QVT Subscore: An interpretable radiomic panel of tumor vascularity reveals treatment-specific mechanisms-of-action (MoA) in NSCLC [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 3751.
Authors
- Liam Il‐Young Chung (ORCID: https://orcid.org/0000-0001-6541-793X)
- Rhea Chitalia (ORCID: https://orcid.org/0000-0001-7880-930X)
- Amogh Hiremath (ORCID: https://orcid.org/0000-0001-6937-9141)
- Young Kwang Chae (ORCID: https://orcid.org/0000-0003-1557-7235)
- Nathaniel Braman (ORCID: https://orcid.org/0000-0001-5603-0821)
- P. Mutha
- Omid Haji-Maghsoudi
- Kai Zhang
- Justin Yau
- Vamsidhar Velcheti
- Haojia Li
- Trishan Arul
- Anant Madabhushi
Institutions
- Northwestern University (US)
- The Wallace H. Coulter Department of Biomedical Engineering (US)
- Mayo Clinic in Florida (US)
Publication Details
- Journal
- Cancer Research
- Published
- 2026-04-03
- DOI
- https://doi.org/10.1158/1538-7445.am2026-3751
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00