Executive Summary
Accelerate tumor detection in histopathology workflows to support research and clinical trials with AI-driven insights.
The AI system, built on GenAI foundations and fine-tuned on expert-annotated slides, delivered a 95%+ accuracy in identifying tumor regions, reduced manual review burden by 70%, and enabled biomarker discovery and trial cohort enrichment across oncology programs.
Organizational Profile
The client, a multinational pharma company with a global network of clinical labs and research partners, conducts large-scale translational oncology research. As digital pathology adoption increased, identifying tumor regions manually across thousands of gigapixel WSIs became a bottleneck—slowing biomarker research, patient stratification, and trial recruitment.
They partnered with GenPhase.ai to build a robust, AI-powered tumor detection pipeline that could generalize across cancer types and lab settings while remaining explainable and compliant.
Business Challenge
Manual annotation and tumor detection in WSIs were plagued by:
- High inter-observer variability across pathologists and labs
- Long turnaround times due to manual review and pathologists’ burnouts
- Limited scalability for use in trials, where thousands of WSIs are reviewed
- Inability to rapidly iterate or stratify based on tumor microenvironment (TME) features
This not only slowed research workflows but also impacted biomarker validation timelines and delayed trial readiness.
Technical Solution
The team implemented a custom AI pipeline powered by foundation models for histopathology image understanding. Key components included:
This architecture supported both batch inference for retrospective analysis and real-time inference in ongoing clinical workflows.
Implementation Approach
Phase 1: Data Aggregation & Annotation
Phase 2: Model Development
Phase 3: Infrastructure & Inference Pipeline
Phase 4: Lab Rollout & Feedback Loop
Business Impact
The solution delivered transformative outcomes:
- >95% Accuracy in tumor detection across cancer types
- 70% Reduction in manual annotation time per slide
- 3x Faster biomarker cohort identification for Phase 2 trials
- Deployed as a scalable, auditable module for research and trial settings
Strategic Significance
This initiative went beyond automation—it enabled strategic transformation in oncology R&D:
- Accelerated time-to-insight for biomarker and drug-response analysis
- Enabled data harmonization across global labs with varying stain conditions
- Built a reusable foundation for AI-first pathology workflows in clinical development
- Created IP and proprietary datasets from AI-assisted tumor classification
The project set the stage for broader applications in target discovery, companion diagnostics, and pathology report automation.
Looking Ahead
Post-successful tumor detection deployment, the organization is now exploring:
- TME Profiling Models for stromal, immune, and necrotic subregions
- WSI-level Prognostic & Diagnostic Models using AI-derived spatial features
- Active Learning Loops with expert feedback to continuously improve accuracy
- Integration with LIMS/EMR systems for seamless clinical trial reporting
The modular pipeline is also being adapted for non-oncology use cases
Key Takeaways
This use case illustrates how pharma and lab ecosystems can harness GenAI to:
- Accelerate manual tumor detection by pathologists with explainable AI without any compromise in quality and also increase efficiency
- Standardize digital pathology pipelines across trials and sites
- Build future-ready infrastructure for precision medicine and trial acceleration
Let’s Transform Your Pathology Workflows
Discover how GenPhase.ai can help your organization apply foundation models to pathology images and build scalable, validated AI tools for oncology and beyond. Contact us to explore your use case.