# Implementation of Machine Learning Workflows in Primary Brain Tumor Diagnostics

> **NIH NIH R03** · OHIO STATE UNIVERSITY · 2020 · $156,000

## Abstract

Abstract
The diagnosis of primary brain tumors requires a layered approach of histologic, anatomic and molecular
features to generate an integrated diagnosis with clinical and prognostic significance. The diagnostic workup of
diffuse gliomas in particular requires a panel of immunohistochemical stains with a subset of tumors requiring
additional molecular testing to reach a diagnostic category recognized by the World Health Organization. In
the United States and worldwide, scarce resources are available to perform these tests, so methods that
improve pre-test probabilities and decrease false positive results have significant clinical and financial impact.
Our long-term goal is to improve and standardize testing and diagnoses for brain tumor patients worldwide by
validating new diagnostic workflows using digital imaging, immunohistochemical tests, open source computing
platforms and machine learning algorithms to improve diagnostic capabilities. We will achieve these goals by
completing three specific aims in this R03 Pilot/Feasibility project. First, we will determine the extent to which
predictive diagnostic models developed from public domain data show generalizability to cases evaluated at a
tertiary brain cancer care center. We have already generated a prototype statistical predictive model, which we
will expand to all CNS tumor types and test with data from patients at James Cancer Hospital. Second, we will
generate and validate models that predict the probability of false-positive 1p/19q FISH testing using
histological features from OLIG2-immunostained brain tumor slides obtained from whole slide imaging.
Lastly, we will consolidate data containing whole slide digital images, immunohistochemical features, clinical
data and molecular features of diffuse gliomas. Consolidating these data will allow us to begin data analysis
correlating histological images to immunohistochemical and molecular features. This dataset will represent
the core dataset that upon which we will base our next R01-level proposal. To achieve these goals, we have
assembled a multidisciplinary team composed of an image analysis expert and neuropathologist (JO), a
molecular neuropathologist (DT), and a high dimensionality bioinformaticist (JZ). The Ohio State University
is the first US cancer center to transition to complete whole slide imaging, and therefore we are in a unique
position to generate a significant, vertical advance in improving diagnostic accuracy in neuropathology with
modern Pathology Informatics approaches.

## Key facts

- **NIH application ID:** 9954242
- **Project number:** 1R03NS116334-01
- **Recipient organization:** OHIO STATE UNIVERSITY
- **Principal Investigator:** Jose Javier Otero
- **Activity code:** R03 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $156,000
- **Award type:** 1
- **Project period:** 2020-06-01 → 2021-11-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9954242

## Citation

> US National Institutes of Health, RePORTER application 9954242, Implementation of Machine Learning Workflows in Primary Brain Tumor Diagnostics (1R03NS116334-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/9954242. Licensed CC0.

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