# RadxTools for assessing tumor treatment response on imaging

> **NIH NIH U01** · CASE WESTERN RESERVE UNIVERSITY · 2022 · $365,668

## Abstract

ABSTRACT: Over 1.6 million patients in the U.S. annually undergo chemo- or radiation- as first-line cancer
therapy. After therapy, the most significant challenge for oncologists is identifying non-responders (those with
residual or progressive disease), which could allow them to be switched to alternative therapies. Similarly, if
those with stable or regressing disease were identified early and reliably, patients could avoid unnecessary and
highly morbid surgeries or biopsies for disease confirmation. Unfortunately, expert assessment of post-treatment
imaging is challenging, as residual disease is visually confounded with benign treatment-induced changes on
imaging. There is hence a critical need for dedicated radiomic (computerized feature extraction from imaging)
and informatics approaches to enable reliable post-treatment tumor assessment. Such tools will need to account
for: (1) Limited well-curated data resources with deeply annotated pathology-validated radiographic datasets, for
discovery and validation of new imaging and radiomic markers for post-treatment characterization in vivo; (2)
Need for specialized radiomics tools that specifically quantify morphological perturbations in response to
shrinkage/growth of the lesion for identifying progressive disease (versus benign confounders), despite presence
of treatment-induced artifacts (exacerbated noise, reduced contrast, poor resolution); and (3) Lack of
comprehensive quality control (QC) tools to identify which of a plethora of radiomic features are both
discriminable as well as generalizable to variations between sites and scanners. To address these challenges,
we propose RadxTools, a new image informatics toolkit comprising three modules: (a) RadQC to enable quality
control of radiomics features across multi-site imaging cohorts, (b) RadTx comprising new radiomics tools which
capture local surface morphometric changes and subtle structural deformations unique to tumor response on
post-treatment imaging, and (c) RadPathFuse for creating deeply annotated learning sets by spatially mapping
post-treatment changes from ex vivo surgically excised histopathology specimens onto pre-operative in vivo
imaging. RadxTools will be evaluated in the context of post-treatment characterization for use cases in
distinguishing (a) radiation effects from cancer recurrence for brain tumors; and (b) complete/partial vs
incomplete chemoradiation response for rectal cancers. Deliverables and Dissemination: Our team has had a
successful history of disseminating informatics tools (>1000 downloads), including our most recent release of
RadTx which has been integrated into 3 informatics platforms. By organizing community resources and targeted
workshops, as well as releasing highly curated data cohorts, our team is uniquely positioned to disseminate
RadxTools to the radiomics/imaging community, professional societies, and oncology working groups. Our
deliverables will include tool prototypes as modules within 5 QIN...

## Key facts

- **NIH application ID:** 10477947
- **Project number:** 5U01CA248226-03
- **Recipient organization:** CASE WESTERN RESERVE UNIVERSITY
- **Principal Investigator:** Pallavi Tiwari
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $365,668
- **Award type:** 5
- **Project period:** 2020-07-01 → 2024-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10477947

## Citation

> US National Institutes of Health, RePORTER application 10477947, RadxTools for assessing tumor treatment response on imaging (5U01CA248226-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10477947. Licensed CC0.

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