Berek and Hacker’s Gynecologic Oncology Seventh Edition

Selected as a Doody's Core Title for 2022 and 2023! Evidence-based, superbly illustrated, and easy to read, Berek & Hacker’s Gynecologic Oncology, Seventh Edition, remains your reference of choice for authoritative information on every aspect of gynecologic malignancies. Templated chapters provide quick access to guidance on everything from general principles through diagnosis and medical and surgical management. This fully revised edition offers the practical, state-of-the-art coverage you need when caring for women with preinvasive disease; ovarian, breast, uterine, cervical, vulvar, and vaginal cancers; and gestational trophoblastic disease. All chapters have been carefully updated to reflect the most current literature and practice recommendations. Presents summaries on signs and symptoms, staging, treatment, relevant basic science, genetics, and molecular issues. Key topics include preoperative care, laparoscopy, robotics, nutritional therapy, cancer in pregnancy, surgical techniques, symptom relief, and palliative care. Includes new chapters on genetics; cancer cell biology; and biologic, targeted, and immune therapies, as well as completely rewritten chapters on chemotherapy and communication skills. Features a new, visually appealing two-column design similar to Berek & Novak’s Gynecology, 16th Edition. Helps you visualize key concepts with full-color illustrations and drawings, pathology slides, and clinically relevant diagrams. Presents the knowledge and expertise of global leaders in gynecologic oncology, keeping you at the forefront of your field and preparing you for board exams. Clearly translates basic science to clinical practice, making it an excellent everyday resource for gynecologic oncologists and fellows, general gynecologists, and medical and radiation oncologists. Enrich Your eBook Reading ExperienceRead 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. Read more