Selected as a Doody's Core Title for 2025! Closely mirroring the daily sign-out process, Atlas of Gastrointestinal Pathology: A Pattern-Based Approach to Non-Neoplastic Biopsies, 2nd Edition , by Drs. Maryam Pezhouh and Meredith Pittman, is a highly illustrated, efficient guide to accurate diagnosis. This practical reference uses a proven, pattern-based approach to clearly explain how to interpret challenging cases by highlighting red flags in the clinical chart and locating hidden clues in the slides. Useful as a daily “scope-side guide, it features numerous clinical and educational features that help you find pertinent information, reach a correct diagnosis, and assemble a thorough and streamlined pathology report. A companion volume for Atlas of Gastrointestinal Pathology is also available and focuses on neoplastic conditions. More than 1,000 high-quality, full-color images illustrate the full morphologic spectrum of the major patterns of non-neoplastic GI tract pathology, providing abundant and varied examples of each diagnosis. Each image is captioned with key diagnostic considerations and includes callouts showing subtle features and diagnostic clues. Practical tools throughout the text include: Tables that emphasize salient clinicopathologic features, management implications, and therapeutic options Checklists for key elements of the diagnostic approach and sample notes for inclusion in pathology reports Brief reviews of normal histology that provide contrast to succeeding patterns “Pearls and Pitfalls and “Near Misses sections with lessons from real life sign-out experience “Frequently Asked Questions sections that discuss common diagnostic dilemmas “Sample Note sections that offer a template of how to sign out cases from the simple to the complex Comprehensive quizzes that provide experience with high-yield, board-style teaching topics Enrich your eBook reading experience Read directly on your preferred device(s), such as computer, tablet, or smartphone. Easily convert to audiobook, powering your content with natural language text-to-speech.