Childhood Cancer Data Initiative Annual Symposium (Abstract Registration)

Abstract Submission Form

Abstract Submission for Poster Presentation

Attendees interested in presenting a poster should complete the form below to submit their work. A confirmation email will be sent upon successful submission. Poster presentations will take place on Monday, October 6, from 3:00–5:00 p.m. E.T.

Best Abstract Award

Abstracts will be evaluated based on the following criteria:

Significance and Impact:
- Relevance of the topic and its potential contribution
- Evidence of innovative data use and/or potential to increase relevant data for sharing in the CCDI Ecosystem
Scientific Rigor:
- Clearly stated research question
- Well-suited methodologies to address the problem Detailed experimental methods
- Clarity: Content is well-organized, easy to follow, and effectively communicated

The deadline for abstracts for poster presentations closed on September 1, 2025. If you have questions about submitting abstracts for poster presentations, contact Subhashini Jagu at subhashini.jagu@nih.gov.

Submission information

Submission Number: 54

Submission ID: 150456

Submission UUID: e937c80f-e97b-46cf-a8ab-e1de3537311d

Submission URI: /nci/ccdisymposium/abstract

Submission Update: /nci/ccdisymposium/abstract?token=Ta5EC_uLmT5pqXyI6EZulaFmLGSfKSQB-YfIZvOlMhQ

Created: Sun, 08/31/2025 - 03:56

Completed: Sun, 08/31/2025 - 04:00

Changed: Sun, 08/31/2025 - 04:00

Remote IP address: 10.208.24.230

Submitted by: Anonymous

Language: English

Is draft: No

Webform: Childhood Cancer Data Initiative Annual Symposium (Abstracts Submission)

Submitted to: Childhood Cancer Data Initiative Annual Symposium (Abstract Registration)

Abstract Submission for Poster Presentation

Poster size should not exceed 4 ft. (height) by 6 ft. (width)

Abstract Title:

Abstract:

The Childhood Cancer Data Initiative (CCDI) strives to accelerate pediatric cancer research by uniting clinical records with emerging spatial omics data but lacks a unified framework for joint analysis. Although CCDI and public repositories provide rich patient records and high-resolution spatial profiles, no unified data standards and framework exist to extract, harmonize, and process these multimodal datasets for interpretable discovery. 
We developed the GAIPO (Graph Artificial Intelligence for Pediatric Oncology) clinicospatial data standard and platform, enabling graph artificial intelligence (AI) on the CCDI ecosystem. The GAIPO clinicospatial standard unifies spatial assay formats, spatial resolutions, and registration of spatial data across modalities. To promote interoperability and reproducibility, we define a spatial data model that encompasses data standards, formats, and interfaces to integrate imaging and omics data through spatial graph representation, thereby enabling the implementation of explainable graph AI models. The GAIPO platform provides: flexible interfaces to CCDI APIs and public portals for retrieval of clinical metadata, spatial omics data, and associated histopathology images; a preprocessing pipeline for omics and imaging data; a graph AI module that integrates multiple modalities for downstream analysis; and an interpretation module to identify important features and cells and reveal critical enrichment pathways and cell-cell interactions. We demonstrated GAIPO’s functionalities by using xSiGra as the explainable graph AI model, graph Grad-CAM as the interpretability module, ScPCA as the CCDI-participating resource, and Wilms tumor as the pediatric cancer. 
The GAIPO standards and platform facilitate graph AI on clinicospatial data in precision oncology for pediatric cancers.

Authors:

Please use the following formatting for these degrees: M.D. Ph.D. MPH

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