Chitalia, Rhea

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.

Background Immune checkpoint inhibitors (ICIs) improve survival in advanced non-small cell lung cancer (NSCLC), yet current biomarkers such as programmed death-ligand 1 (PD-L1) expression and response criteria (Response Evaluation Criteria in Solid Tumors, RECIST, V.1.1) align poorly with long-term survival. Radiomics has been proposed as a source of novel biomarkers, but standard radiomic approaches suffer from limited biological interpretability and poor generalizability across treatment settings. We address these gaps by developing the Quantitative Vessel Tortuosity (QVT) score, a biologically interpretable imaging biomarker that quantifies tumor vascular complexity—a known mediator of immune evasion—from routine imaging. We hypothesized that QVT score would improve prognostication and enable treatment response monitoring in ICI-treated NSCLC, independent of current biomarkers. Methods This retrospective, multicenter study analyzed 1,301 CT scans from 682 patients with ICI-treated NSCLC. An automated pipeline segmented lesions and tumor-associated vasculature within each scan, extracting 910 QVT features measuring vascular shape and complexity. Unsupervised clustering of these features in a discovery cohort (N=375) was performed to identify fundamental vascular phenotypes. A continuous QVT score was then derived using regularized logistic regression to map patients along this phenotypic spectrum. QVT score was externally validated in ICI monotherapy (N=172) and chemoimmunotherapy (N=135) cohorts. In a longitudinal cohort (n=143), early on-treatment QVT score changes were evaluated for overall survival (OS) association. Results Two robust vascular phenotypes emerged in the discovery cohort: a highly vascularized, chaotic “QVT High” phenotype with poor post-ICI OS and a “QVT Low” phenotype with normalized vasculature and improved ICI outcomes. The continuous QVT score was prognostic for ICI monotherapy (HR=1.17 per 0.1 increase, p=0.0028) and chemoimmunotherapy (HR = 1.23 per 0.1 increase, p = 4.9×10⁻⁵). High QVT status remained prognostic for both treatments after adjustment for PD-L1 and clinical variables (adjusted HR range: 2.13–2.38, p≤0.002). Early decreases in QVT score during therapy, indicating vascular normalization, were associated with improved OS (HR=1.93, p=0.0022) independent of RECIST best overall response and tumor volume change. Conclusions QVT score is a novel, biologically interpretable imaging biomarker that quantifies vascular complexity. It enables automated, non-invasive prediction and monitoring of ICI outcomes by capturing treatment-induced vascular remodeling. Integrating QVT score into clinical decision-making and drug development can address critical gaps in precision oncology.