Closely mirroring the daily sign-out process, Atlas of Lymph Node Pathology: A Pattern Based Approach 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. More than 1,500 high-quality photomicrographs depict reactive and neoplastic processes involving lymph nodes, capturing the full spectrum of morphologic changes associated with common abnormalities, including relatively rare conditions. Captions include a morphologic description, highlighting subtle features and key diagnostic considerations. Practical tools throughout the text include: Tables that emphasize salient clinicopathologic features, management implications, and therapeutic options Discussions of how and when to incorporate immunohistochemical and special stains, as well as the utilization of flow cytometry and molecular tools Checklists for key elements of the diagnostic approach and sample notes for inclusion in pathology reports Relevant endoscopic images, photographs of select gross specimens, and medical figures 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 synthesize complicated or especially challenging topics Quizzes in every chapter 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.