NCI Office of Data Sharing (ODS) Data Jamboree (Abstract Submissions): Submission #9

Presenter Information
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First Name: Rakesh









Middle Initial: {Empty}









Last Name: Khanna









Degree(s): B.A.









Position/Title/Career Status: {Empty}









Organization: CBIIT Computational Genomics and Bioinformatics Branch









Organization Address:
Rockville, MD










Email: rakesh.khanna@nih.gov









Other (Please Specify): {Empty}













Abstract Information
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Abstract Category: Employment of statistical methods or existing computational, mathematical, or informatics tools









Abstract Keywords: Deep Learning, Foundation Model, Whole Slide Images, Brain, Feature Extraction









Abstract Title: Extracting Deep Learning Features from Childhood Brain Tumor Whole Slide Images for Multi-Modal Analysis









Abstract Summary:
### Background:
Whole slide images (WSIs) contain rich morphological information used to understand childhood brain tumors. However, accessing and processing this data requires significant computational overhead including specialized hardware and expertise.

### Methods
This project aims to faciliate a number of downstream analyses utilizing histopathological features by generating pre-computed embeddings from WSIs of 433 childhood brain tumor patients from the Childhood Cancer Data Initative-Molecular Characterization Initiative (CCDI-MCI), a heterogenous collection of CNS tumors including gliomas, astrocytomas, and medulloblastomas. 

Using **TRIDENT**, a toolkit for large-scale whole-slide image processing we will generate both patch-level (UNI2-h, CONCHv1.5, and Prov-Gigapath) and slide-level representations (Threads, Titan and CHEIF).

### Purpose
By providing pre-extracted features, we enable scientists to immediately incorporate histopathological information into their own analyses. We will demonstrate the potential utility of these feature sets through proof-of-concept downstream analyses according to available clinical and omics data received at the jamboree.

All extracted features will be publicly available enabling the broader pediatric cancer community to leverage imaging data in their investigations without the typical computational overhead.










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