# Artificial Intelligence Imaging Predictors for Rectal Cancer Management

> **NIH VA I01** · LOUIS STOKES CLEVELAND VA MEDICAL CENTER · 2024 · —

## Abstract

ABSTRACT: Colorectal cancers are the third most frequently occurring cancer in the military, occurring in up to
8% of Veterans. Veteran rectal cancer patients are often diagnosed earlier relative to the general population, but
do not have improved survival rates; indicating limitations of the existing “one-size-fits-all” therapeutic protocol.
The lack of personalized therapy in rectal cancers disproportionately impacts Veteran colorectal cancer patients
who tend to be older (median age 67.5 years) and often have a higher comorbidity burden than the general U.S.
population. A major clinical question thus remains selection of an appropriate combination of neoadjuvant
therapy, chemotherapy, and surgery to ensure optimal patient outcomes for Veteran rectal cancer patients.
For instance, total neoadjuvant therapy (neoadjuvant chemotherapy and chemoradiation) can enable ~30% of
rectal cancer patients to achieve a pathologic complete response (no residual tumor). These patients are ideal
candidates for organ-preserving strategies and non-operative management, such as a “watch-and-wait” (W&W)
protocol. Conversely, up to 20% of patients may not exhibit any pathologic regression after neoadjuvant therapy,
but instead suffer tumor invasion to surrounding structures (lymphatic, vascular, or perineural) and thus a higher
chance of life-threatening distant metastasis. Both of these issues particularly impact Veteran rectal cancer
patients due their increased age, additional financial and psychological stresses, and survival benefits when
administered targeted adjuvant therapy to combat metastasis. Unfortunately, there is a paucity of clinically
validated markers predictive or diagnostic of response to chemoradiation in rectal cancers and MR imaging has
limited sensitivity/specificity for detecting treatment response or mutational/invasion status. This suggests a
critical need for accurate and objective non-invasive markers to (a) identify Veteran rectal cancer patients who
have achieved complete response after neoadjuvant therapy so they can be safely recommended W&W, as well
as (b) predict which Veteran patients will see added benefit from chemotherapy beyond chemoradiation alone.
Our initial set of novel artificial intelligence-based descriptors (known as radiomics) that capture heterogeneity,
morphometry, as well as specialized measurements of lesion complexity on MRI have shown significant success
for (a) distinguishing pathologic responders to chemoradiation with up to AUC=0.86, (b) predicting high-risk
metastatic rectal tumors with up to AUC=0.81, as well as (c) capturing heterogeneity in pathologic tissue
organization associated with response and metastasis well as mutation status (N>200 patients, 3 institutions
including the Northeast Ohio VA). In this VA Merit Award, we propose to develop and optimize our AI radiomic
descriptors to build clinically actionable computational image Rectal Response Classifier (ciRRC) tools to
help (a) proactively select h...

## Key facts

- **NIH application ID:** 10807273
- **Project number:** 1I01BX006439-01
- **Recipient organization:** LOUIS STOKES CLEVELAND VA MEDICAL CENTER
- **Principal Investigator:** Satish Easwar Viswanath
- **Activity code:** I01 (R01, R21, SBIR, etc.)
- **Funding institute:** VA
- **Fiscal year:** 2024
- **Award amount:** —
- **Award type:** 1
- **Project period:** 2024-07-01 → 2028-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10807273

## Citation

> US National Institutes of Health, RePORTER application 10807273, Artificial Intelligence Imaging Predictors for Rectal Cancer Management (1I01BX006439-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10807273. Licensed CC0.

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