Atlas of Genitourinary Pathology A Pattern Based Approach, First edition

Closely mirroring the daily sign-out process, Atlas of Genitourinary 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 presentation 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 1,200 high-quality photomicrographs capture the subtle morphologic spectrum of both biopsy and resection specimens of the prostate, bladder, kidney, testis, and the male genital tract. 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 immunohistochemical, and if necessary, molecular tools
Checklists for key elements of the diagnostic approach and sample notes for inclusion in pathology reports.
Photographs of select gross specimens, and numerous histologic correlates
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.