Childhood Cancer Data Initiative Annual Symposium (Abstract Registration): Submission #62
Submission information
              Submission Number: 62
  Submission ID: 150498
  Submission UUID: 299b6cda-baca-4882-b02f-3d87ed4f96cd
      Submission URI: /nci/ccdisymposium/abstract
          
      Created: Mon, 09/01/2025 - 22:56
  Completed: Mon, 09/01/2025 - 23:52
  Changed: Mon, 09/01/2025 - 23:52
  Remote IP address: 10.208.24.230
  Submitted by: Anonymous
  Language: English
  Is draft: No
    
      
    
          Abstract Submission for Poster Presentation
              
      
  
  
  Digital Early Diagosis for Pediatric Brain Tumors: Sedation-Minimizing, Data-Driven Pathway
  
  
  
  
      
  
  
  More than the tumor itself, the delayed diagnosis of pediatric brain tumor remains a global challenge. Through professional and public advice, the UK's HeadSmart awareness program could successfully reduce the median of total diagnostic interval (TDI) from 14.4 weeks in 2006 to 6.7 weeks in 2013. There are few low- and middle-income areas that still use similar structured techniques. But existing delays of low-grade tumors of 6-7 weeks highlights clinical and system-level issues. To improve the early-diagnosis route for pediatric brain tumors, we aim to design a system that combines age-stratified triage, sedation-minimizing imaging, mobile decision assistance, and real-world data collection. The system first defines red flag symptom threshold and imaging flow. This is further validated by the Parent Companion app for symptom tracking, and triage guidance. This significantly reduces TDI through better patient-system tracking, and promotes widespread adoption of digital tools to reduce manual burden of clinician’s automating tasks. The accuracy will be verified via vignette-based assessments and real-world discrepancy testing against standard head circumference measurements. This effort uses big data integration and digital health assessment to regulate and transform pediatric neuro-oncology diagnoses. This system aims to achieve data-driven early detection and scalable global childhood cancer therapy.
  
  
  
  
      
  
  
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  Open Health Systems Laboratory (OHSL)