SHORT COURSE

Wednesday Morning - March 21, 8:00 AM - 12:00 Noon




41. Pattern-based Algorithms in Diagnostic Liver Pathology

Romil Saxena, M.D., Indiana University School of Medicine, Indianapolis, IN and Neil D. Theise, M.D., Beth Israel Medical Center, New York, NY

This course adopts a pattern-based approach to the diagnosis of liver biopsies and provides algorithms which will aid the practicing pathologist in arriving at a diagnosis, ruling out other closely associated diseases and avoiding common pitfalls. We will provide guidelines for the most clinically relevant information that should be included in the final pathologic diagnosis. This course is intended for general pathologists of all levels of expertise including pathologists starting out in practice or pathologists who desire specialization in liver pathology along with trainees of all levels.

Participants will be introduced to an algorithm to identify the predominant pattern of injury in a liver biopsy followed by a description of the salient features of the seven common histological patterns of liver injury viz. portal cellular infiltrates, ductular reaction, lobular injury, steatosis, fibrosis, near-normal appearance and solid and cystic mass lesions. This will be followed by a discussion of the clinical and histologic features that help to distinguish the various diseases that fall under each pattern. We will focus, though not exclusively, on the most common of these diseases and end up with the information that is most clinically relevant. Upon completion of this course, participants should be able to: 1) Clarify algorithms that will aid the practicing pathologist in arriving at a diagnosis, ruling out other closely associated diseases and avoiding common pitfalls; 2) Describe guidelines for the most clinically relevant information that should be included in the final pathologic diagnosis. Registrants will receive a syllabus at the meeting, and materials will be made available after the meeting on the USCAP website.

(NEW COURSE) This course may be used for CME credits or SAM credits.