Overview
The International Association for the Study of Lung Cancer (IASLC) needed a platform to coordinate global research validation studies — enabling medical institutions worldwide to run standardized algorithms on lung imaging data while maintaining data privacy and computational consistency.
Intellitech Solutions engineered IASLC-ELIC (Early Lung Imaging Confederation), a distributed hub-and-spoke platform that orchestrates algorithm execution across globally-distributed research sites, automatically provisions cloud compute resources, and coordinates results through secure messaging infrastructure.
Challenge
Medical imaging research requires consistent algorithm execution across multiple international sites, but traditional approaches faced critical limitations:
- Data privacy regulations prevent centralized storage of patient imaging data
- Algorithm validation requires identical execution environments across diverse IT infrastructures
- Computational requirements vary dramatically based on imaging data volume and algorithm complexity
- Research sites lack uniform technical capabilities and resources
- Coordinating multi-site studies requires real-time visibility into processing status and results
The platform needed to bring algorithms to the data rather than data to algorithms, while maintaining scientific rigor and operational efficiency.
Solution
Intellitech Solutions designed IASLC-ELIC as a hub-and-spoke architecture where a central hub coordinates multiple autonomous spoke nodes deployed at research sites worldwide.
Core Architecture
- Central Hub – Grails-based web application manages experiments, algorithms, participants, and global orchestration through an intuitive researcher interface.
- Distributed Spokes – Lightweight Java agents deployed at research sites receive algorithm execution requests, provision AWS EC2 compute resources on-demand, and return results securely.
- Message Coordination – ActiveMQ messaging infrastructure ensures reliable communication between hub and spokes, handling network variability and geographic distribution.
- Containerized Algorithms – All algorithms run in Docker containers, guaranteeing consistent execution environments regardless of underlying infrastructure.
- Auto-Scaling Compute – Spokes dynamically provision AWS EC2 instances based on workload, spinning up resources when experiments start and terminating them when complete.
- Secure Data Handling – Imaging data never leaves the local spoke site; only algorithm results are transmitted back to the hub.
Deployment Model
- Hub deployed on AWS with MySQL database for experiment metadata
- Spokes deployed globally on AWS EC2 instances at participating research institutions
- SFTP endpoints at each spoke for secure data upload by clinical sites
- Automated spoke provisioning with configuration templates
Global Research Network
IASLC-ELIC Global Network – Distributed spoke nodes deployed across multiple continents coordinate algorithm execution while maintaining local data privacy
Results
Global Scale
Deployed spokes across 10+ international research sites spanning North America, Europe, Asia, and Africa
Data Privacy Compliance
Zero patient data transfer — all imaging analysis occurs locally at spoke sites
Cost Efficiency
On-demand AWS EC2 provisioning reduced idle compute costs by 80% compared to dedicated infrastructure
Scientific Validation
Enabled multi-site algorithm validation studies with guaranteed environmental consistency
Operational Automation
Reduced manual coordination overhead from weeks to hours per multi-site study
Containerized Reproducibility
Docker-based algorithm packaging ensured identical execution across all sites
Impact
IASLC-ELIC transformed global lung cancer research collaboration by solving the fundamental tension between data privacy requirements and algorithm validation needs — enabling institutions to participate in multi-site studies without compromising patient confidentiality or scientific rigor.
The hub-and-spoke architecture proved that distributed computing can maintain centralized control while respecting geographic, regulatory, and infrastructure constraints. By bringing algorithms to data rather than data to algorithms, IASLC-ELIC established a replicable model for privacy-preserving medical research infrastructure.
This platform demonstrates Intellitech Solutions' expertise in designing resilient distributed systems that solve real-world constraints through thoughtful architecture — combining messaging infrastructure, cloud auto-scaling, and containerization to create a globally-coordinated research network.
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