NCI Office of Data Sharing (ODS) Data Jamboree (Abstract Submissions): Submission #9
Submission information
Submission Number: 9
Submission ID: 144817
Submission UUID: 779c98c9-5f63-4322-84a3-f9a52cbd4f31
Submission URI: /nci/ods-data-jamboree/abstractsubmissions
Submission Update: /nci/ods-data-jamboree/abstractsubmissions?token=FMkJ3p6ucr0GVuR_8ronebgeKH4oEm4qwCzHqXbjhTo
Created: Mon, 06/16/2025 - 14:58
Completed: Mon, 06/16/2025 - 14:58
Changed: Mon, 06/16/2025 - 14:58
Remote IP address: 10.208.28.250
Submitted by: Anonymous
Language: English
Is draft: No
Presenter Information
Rakesh
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Khanna
B.A.
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CBIIT Computational Genomics and Bioinformatics Branch
Rockville, MD
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Abstract Information
Employment of statistical methods or existing computational, mathematical, or informatics tools
Deep Learning, Foundation Model, Whole Slide Images, Brain, Feature Extraction
Extracting Deep Learning Features from Childhood Brain Tumor Whole Slide Images for Multi-Modal Analysis
### 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.
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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