# Development and Validation of Prognostic Radiomic Markers of Response and Recurrence for Patients with Colorectal Liver Metastases

> **NIH NIH R01** · SLOAN-KETTERING INST CAN RESEARCH · 2022 · $251,390

## Abstract

PROJECT SUMMARY
Colorectal cancer is the second deadliest cancer in the United States. Black men and women are 20% more
likely to get colorectal cancer and 40% more likely to die from it than most other groups. They are more likely to
develop colorectal cancer at a younger age, be diagnosed at a later stage, and are more likely to die of their
disease. Patients do not die of primary colon cancer; they die of liver metastases with a dismal <10% of patients
surviving past three years. The goal of our parent R01 award is to develop robust machine-learning based
imaging biomarkers (radiomics) for personalized treatment of colorectal liver metastases (CRLM). While the
development of these markers is extremely promising, machine-learning models are increasingly shown to be
biased so understanding the impact of our biomarkers on Black patients that are more likely to be harmed by
artificial intelligence (AI) is imperative. This proposal will not solve the totality of disparities that Black patients
are experiencing in colorectal cancer but rather, the objectives of the supplemental application are to highlight
areas of concern in tandem, as AI-based biomarkers are developed and broadcast this information to AI
stakeholders. There are currently no validated imaging biomarkers for CRLM patients, a gap that our parent R01
addresses. Our currently funded activities are focused on computational and statistical biases: with large
datasets from multiple institutions (Memorial Sloan Kettering Cancer Center in New York and MD Anderson
Cancer Center in Houston), and state-of-the-art AI techniques with carefully controlled data acquisition and
annotation, which are vital to the development of robust biomarkers. None of these standard practices, while
important, are a panacea against bias: our proposed supplemental activities will begin to address human and
systemic biases that are currently overlooked. The proposed studies are innovative because they will examine,
for the first time, bias in AI colorectal cancer algorithms in tandem with a socio-technical perspective provided
by our new team members. Our work will systematically dispel the myth that there is perfect data by
comprehensively evaluating sources of bias and offer a path for AI stakeholders (patients, developers, and
users). Given the breadth and complexity of the proposed work, we will not only disseminate our work through
traditional methods (publications and talks), but we will also create a podcast. This multi-channel approach will
ensure the timely dissemination of important aspects of the work that could inform policy to diverse audiences
in an accessible format. Importantly, our work challenges the fundamental assumption in AI development that
enough data will overcome any biases, which is simply untrue. The data are already biased.

## Key facts

- **NIH application ID:** 10599583
- **Project number:** 3R01CA233888-04S1
- **Recipient organization:** SLOAN-KETTERING INST CAN RESEARCH
- **Principal Investigator:** Richard Kinh Gian Do
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $251,390
- **Award type:** 3
- **Project period:** 2019-03-01 → 2024-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10599583

## Citation

> US National Institutes of Health, RePORTER application 10599583, Development and Validation of Prognostic Radiomic Markers of Response and Recurrence for Patients with Colorectal Liver Metastases (3R01CA233888-04S1). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10599583. Licensed CC0.

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