Atlas of Gynecologic Pathology: A Pattern Based Approach

Closely mirroring the daily sign-out process, Atlas of Gynecologic 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 1,000 high-quality photomicrographs capture the subtle morphologic spectrum and if applicable, pertinent immunohistochemical staining patterns encountered in gynecologic pathology, including variations of tumor types, non-tumor pathology, artifacts, and normal histology. Each image is captioned with key diagnostic considerations and includes call-outs showing subtle features and diagnostic clues. – Practical tools throughout the text include: – Tables that emphasize salient clinicopathologic features, management implications, and therapeutic options – Discussions of how and when to incorporate molecular tools – Checklists for key elements of the diagnostic approach and sample notes for inclusion in pathology reports – Relevant diagnostic algorithms – 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 – Comprehensive quiz provides 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. —- ISBN: 9781975124762 | ISBN10: 1975124766 | ISBN-13: 978-1975124762