# Computerized platform for interactive annotation and topological characterization of tumor associated vasculature for predicting response to immunotherapy in lung cancer

> **NIH NIH R21** · STATE UNIVERSITY NEW YORK STONY BROOK · 2022 · $217,410

## Abstract

SUMMARY: The tumor microenvironment (TME) vascular network harbors a compelling amount of anatomical
and physiological information embedded on the imaging scale. Although techniques like Radiomics have shown
significant promise in several medical imaging applications, such approaches are limited to capturing properties
such as lesion morphology and texture, and cannot comprehensively characterize or visualize the properties of
the aberrant TME vasculature. We hypothesize that angiogenesis manifests as characteristic topological and
geometrical patterns of vasculature in the nodule periphery, and is associated with disease progression and
outcome. In this project, we propose to leverage these topological and geometrical constructs in building
adaptive segmentation, quantification, and visualization tools for tumor associated vasculature. To demonstrate
the clinical efficacy of these new tools in therapy response assessment, we propose to target unmet clinical
needs in response prediction of lung immunotherapy. Fewer than 20% non-small cell lung cancer (NSCLC)
patients treated with immune checkpoint inhibitors (ICIs) respond favorably. Additionally, the associated costs
are extremely high. Molecular markers and metrics evaluating changes in tumor size have not been very effective
in predicting and monitoring response to ICIs. Intra- and peritumoral radiomic features have been recently shown
to outperform traditional biomarkers in outcome prediction. None of the existing markers, however, consider the
tumor associated vasculature in the clinical assessment of TME despite strong evidence of its role in determining
disease progression and response to therapy. One critical obstacle is the lack of an efficient and easy-to-use 3-
dimensional (D) vasculature annotation tool for clinicians. Despite rich literature, it is difficult to train an automatic
segmentation model due of the highly heterogeneous and complex 3D morphology of vasculature. This is
especially challenging near nodule periphery, where the pathological vasculature exhibits abnormal yet clinically
relevant geometry and topology. We aim to 1) build a human-in-the-loop vasculature visualization and
segmentation framework based on topological active learning, 2) characterize the topology and geometry of the
extracted vessels to obtain a set of novel vascular radiomic markers, and 3) use the developed suite of
quantitative vascular biomarkers to establish a risk scoring system for predicting clinical benefit for NSCLC
patients undergoing ICI therapy. Specifically, these tools will be optimized to identify patients who will benefit
from ICIs on pre-treatment CT. A major strength of our work is to provide clinicians an intuitive informatics
platform to visualize topological and geometrical attributes of aberrant vasculature, thereby enabling them to
better understand the role of vessel architecture in disease progression from a phenotypic perspective. The team
will train these biologically interp...

## Key facts

- **NIH application ID:** 10424637
- **Project number:** 1R21CA258493-01A1
- **Recipient organization:** STATE UNIVERSITY NEW YORK STONY BROOK
- **Principal Investigator:** Chao Chen
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $217,410
- **Award type:** 1
- **Project period:** 2022-05-01 → 2024-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10424637

## Citation

> US National Institutes of Health, RePORTER application 10424637, Computerized platform for interactive annotation and topological characterization of tumor associated vasculature for predicting response to immunotherapy in lung cancer (1R21CA258493-01A1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10424637. Licensed CC0.

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