# Computerized histologic image predictor of cancer outcome

> **NIH NIH R01** · CASE WESTERN RESERVE UNIVERSITY · 2020 · $598,709

## Abstract

SUMMARY: There is an increased need for predictive and prognostic assays to distinguish more and less
aggressive phenotypes of cancer due to A) dramatic increase in cancer incidence and; B) improvements in
early diagnosis. Predictive assays in particular will allow for patients with less aggressive disease to be spared
more aggressive treatment. Most prognostic tests in the US and Europe are based on gene expression assays
(e.g. Oncotype DX (ODx)). Recent studies have shown extensive genetic heterogeneity among cancer cells
between tumors and even within the same tumor, suggesting that approaches for recommending therapy for a
patient based on the “average” molecular signal of many cells are overly simplistic.
 Interestingly, for a number of cancers, tumor grade (morphologic appearance on tissue as assessed
qualitatively or semi-quantitatively by a pathologist) has been found to be highly correlated with disease
outcome. However pathologic grade tends to suffer from significant inter-observer variability. Digitzation of
histological samples, or whole slide imaging, facilitates a quantitative approach towards evaluating disease
progression and predicting outcome, while also facilitating the adoption of telepathology. Recently, research
groups (including our own) have begun to show that computer extracted measurements of tumor morphology
(e.g. capturing nuclear orientation, texture, shape, architecture) from routine H&E stained cancer tissue images
can predict disease aggressiveness and treatment outcome. By computationally interrogating the entire tumor
landscape and its most invasive elements from a standard H&E slide, these approaches can allow for more
accurate capture of tumor heterogeneity, disease risk and hence the most appropriate treatment strategy.
 The goal of this academic-industrial partnership is to develop and validate a computerized histologic
image-based predictor (CHIP) to identify which early-stage, estrogen receptor positive (ER+) breast cancer
patients are candidates for hormonal therapy alone and which women are candidates for adjuvant
chemotherapy based off analysis of the pathology slides derived from biopsy and surgical specimens. Inspirata
Inc., a cancer diagnostics company which has recently licensed a number of histomorphometry based
technologies from the Madabhushi group, will bring quality management systems and production software
standards to help create a pre-commercial companion diagnostic test of the CHIP assay. Additionally Inspirata
Inc. will build a complete regulatory pathway for successful translation of the assay in the US and abroad.
Finally, the pre-commercial prototype of the CHIP assay will be independently validated using the same
strategy and data cohorts as ODx. Our approach has several advantages over molecular assays such as ODx
in that it (1) can interrogate the entire expanse of the pathology image enabling a more accurate capture of
tumor heterogeneity and hence disease risk, (2) is non-disrupti...

## Key facts

- **NIH application ID:** 9951006
- **Project number:** 5R01CA202752-05
- **Recipient organization:** CASE WESTERN RESERVE UNIVERSITY
- **Principal Investigator:** MICHAEL D FELDMAN
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $598,709
- **Award type:** 5
- **Project period:** 2016-07-01 → 2023-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9951006

## Citation

> US National Institutes of Health, RePORTER application 9951006, Computerized histologic image predictor of cancer outcome (5R01CA202752-05). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9951006. Licensed CC0.

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