# Rad-path-omic tools for rectal cancer treatment evaluation

> **NIH NIH F31** · CASE WESTERN RESERVE UNIVERSITY · 2020 · $26,372

## Abstract

PROJECT SUMMARY: Of the estimated 43,030 patients who will be newly diagnosed with rectal cancer in 2018,
a majority will receive neoadjuvant chemoradiation (NAC) to reduce tumor burden. All patients ultimately undergo
an aggressive excision of the rectum, of which 25% exhibit complete pathologic response (pCR, i.e. disease-
free after NAC) on the post-surgical specimen. These patients have therefore been subjected to an unnecessary,
morbid procedure resulting in quality of life issues, in the absence of any definitive, non-invasive biomarkers for
NAC response in vivo. While multi-parametric MRI is utilized to pre-operatively assess tumor response and
regression to NAC, expert interpretation is confounded and variable due to overlapping appearance of benign
treatment effects (e.g. fibrosis, ulceration) and residual tumor.
 Recently, more quantitative characterization of lesions has been enabled via radiomics, involving high-
throughput, computerized extraction of textural or kinetic attributes from imaging. Radiomic maps of the tumor
environment can depict presence of different tissue types based on their structural and functional characteristics,
visualized as regions of “low” and ‘high” feature expression. In fact, the post-NAC tumor environment on the
excised rectal tissue specimen has been shown to reflect a variety and organization in different pathologic tissue
types, also linked to patient prognosis and outcome. However, existing radiomic approaches only attempt to
characterize the overall heterogeneity in a tissue region, as they lack “ground truth” to quantify tissue types and
their organization on post-NAC MRI. A more comprehensive and accurate predictor for pCR based off multi-
parametric MRI could thus be constructed by (a) quantifying the density and arrangement of structural and
functional attributes on post-NAC rectal MRIs, and (b) optimizing radiomic descriptors against pathologically
validated information of post-NAC tissue types on MRI, via spatial correlation with pathology.
 In this proposal, I will develop novel radiomic tools in conjunction with spatially co-localized “ground truth”
pathology to build a predictor for identifying rectal cancer patients exhibiting pCR via post-NAC MRI. Aim 1 will
involve developing and evaluating a novel radiomic descriptor to quantify spatial organization of morphologic (via
structural MRI) and physiologic (via contrast enhancement functional MRI) heterogeneity of the post-NAC lesion
environment. Aim 2 will focus on optimizing this radiomic organization descriptor to capture distinctive tissue
organization associated with pCR, via spatial mapping of post-surgical pathology information onto pre-operative
MRI. My novel descriptor will be evaluated and validated via a machine learning predictor to identify patients
exhibiting pCR using a discovery and a hold-out validation cohort, acquired from 2 different institutions; and
compared with clinical markers of response. My project will build on pro...

## Key facts

- **NIH application ID:** 9916627
- **Project number:** 5F31CA216935-02
- **Recipient organization:** CASE WESTERN RESERVE UNIVERSITY
- **Principal Investigator:** Jacob Antunes
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $26,372
- **Award type:** 5
- **Project period:** 2019-06-01 → 2020-10-01

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9916627

## Citation

> US National Institutes of Health, RePORTER application 9916627, Rad-path-omic tools for rectal cancer treatment evaluation (5F31CA216935-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9916627. Licensed CC0.

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