This enduring webcast provides practical guidance for laboratories preparing to implement digital pathology for cancer biomarker testing. Drawing on real-world experience, faculty discuss digital pathology infrastructure, scanner and image analysis validation, QA/QC processes, workflow design, and reporting considerations. The webcast also highlights how image analysis and emerging AI applications can support biomarker quantification, strengthen laboratory workflows, and advance clinical implementation.
Course topics include:
- Digital pathology readiness for cancer biomarker testing
- Infrastructure, validation, and workflow integration
- QA/QC for scanner and image analysis workflows
- Image analysis for biomarker quantification
- Emerging applications of AI in pathology
- Practical lessons for clinical implementation
Target Audience
This activity has been designed to meet the educational needs of pathologists and laboratory
professionals.
Learning Objectives
Upon completion of this activity, you will be able to:
- Apply CAP- and FDA-aligned validation practices for whole-slide imaging and image analysis assays, including case set selection, acceptance criteria, documentation, reproducibility testing, and change control procedures prior to clinical deployment.
- Implement governance and quality control frameworks for digital pathology and AI-enabled biomarker testing, including scanner calibration monitoring, color consistency checks, algorithm performance verification, storage and retention policies, access control, audit trails, and documentation of AI-assisted results in clinical reports.
- Evaluate and select appropriate image analysis solutions for tumor biomarker testing by comparing custom-developed algorithms with commercially available assays based on clinical use case, validation requirements, regulatory considerations, and institutional resources.
- Design a laboratory workflow for digital biomarker testing by outlining the required infrastructure for digital pathology, including whole-slide imaging, image analysis platforms, data storage, and integration with laboratory information systems.
Faculty
Ibrahim Abukhiran, M.B.B.S.
Assistant Professor, Pathology
Director, Leidos Computational Pathology Fellowship
Director, Pathology Image Analysis Laboratory
Associate Director, AI Operations, Computational Pathology and Artificial Intelligence Center of Excellence (CPACE)
University of Pittsburgh Medical Center (UPMC) / University of Pittsburgh School of Medicine
Lindsey Seigh, BS, MEd
Expert Medical Lab Scientist, Pathology Image Analysis Laboratory
Faculty Disclosures
Ibrahim Abukhiran and Lindsey Seigh have no financial relationships to disclose.
Accreditation Statement
The American Society for Clinical Pathology (ASCP) is accredited by the Accreditation Council for Continuing Medical Education (ACCME) to provide continuing medical education (CME) for physicians.
Credit Designation Statement
The ASCP designates this activity for a maximum of 1.0 AMA PRA Category 1 Credit(s)â„¢. Physicians should claim only credit commensurate with the extent of their participation in the activity.
ASCP designates this activity for a maximum of 1.0 CMLE credit. This activity meets CMP and state re-licensure requirements for laboratory personnel.
For questions regarding CME credit, please contact ASCP Customer Service at 1-800-267-2727, option 2, in the US & Canada or internationally at access code + 3-1-312-541-4890. Monday-Friday, 8am-5pm CT.
Method of Participation
To complete the activity and receive credit, the participant must participate in the course. CME certificates will be provided online.
Commercial Support
This activity is supported by an independent educational grant from AstraZeneca Pharmaceuticals.