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

serial: '9'
sid: '144817'
uuid: 779c98c9-5f63-4322-84a3-f9a52cbd4f31
uri: /nci/ods-data-jamboree/abstractsubmissions
created: '1750100282'
completed: '1750100282'
changed: '1750100282'
in_draft: '0'
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remote_addr: 10.208.28.250
uid: '0'
langcode: en
webform_id: nci_office_of_data_sharing_abstr
entity_type: node
entity_id: '2107'
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sticky: '0'
notes: ''
metatag: meta
data:
  category: 'Employment of statistical methods or existing computational, mathematical, or informatics tools'
  degree_s_: B.A.
  email: rakesh.khanna@nih.gov
  first_name: Rakesh
  keywords_abstracts: 'Deep Learning, Foundation Model, Whole Slide Images, Brain, Feature Extraction'
  last_name: Khanna
  middle_initial: ''
  organization: 'CBIIT Computational Genomics and Bioinformatics Branch'
  organization_address:
    address: ''
    address_2: ''
    city: 'Rockville, MD'
    country: ''
    postal_code: ''
    state_province: ''
  other_please_specify_: ''
  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.
  title: ''
  ttile: 'Extracting Deep Learning Features from Childhood Brain Tumor Whole Slide Images for Multi-Modal Analysis'
  upload_abstract: ''