Surgical Oncology - An Algorithmic Approach (Hardcover, 2003 ed.)

SURGICAL ONCOLOGY: AN ALGORITHMIC APPROACH FOR THE GENERAL SURGEON presents the full spectrum of current cancer treatment options available to the practicing general surgery or trainee. Through the pairing of 4-color algorithms and concise, clinically-oriented text, the reader can quickly visualize and understand the decision-making process. Separate algorithmic branches highlight surgical treatment, adjuvant therapies such as chemotherapy and radiation therapy, as well as combined modality treatment. Over 90 chapters address organ systems from head to toe and tackle special topics, including oncologic emergencies (compromised airway, mental status changes, spinal cord compression) diagnostic and therapeutic challenges (occult axillary metastases, the jaundiced cancer patient, ascites), and surgical adjuncts (management of pain in the cancer patient, nutritional support, genetic counseling, when to withdraw therapy). Two appendices will prove invaluable to the busy general surgeon--reviewing both the most commonly used chemotherapeutic agents and the principles of radiation oncology. More than 200 illustrations augment the textbook. SURGICAL ONCOLOGY: AN ALGORITHMIC APPROACH FOR THE GENERAL SURGEON is intended to be a useful, user-friendly reference for every general surgeon, surgical resident, surgical oncology fellow, and surgical oncologist. The book will also provide the medical oncologist with a up-to-date review of surgical procedures and the role of surgery in combined multimodality treatment.

General

Imprint:	Springer-Verlag New York
Country of origin:	United States
Release date:	2003
First published:	2003
Editors:	Theodore J. Saclarides • Keith W. Millikan • Constantine V Godellas
Dimensions:	279 x 216 x 42mm (L x W x T)
Format:	Hardcover
Pages:	778
Edition:	2003 ed.
ISBN-13:	978-0-387-95201-7
Categories:	Books > Medicine > Surgery > General surgery Books > Medicine > Clinical & internal medicine > Diseases & disorders > Oncology > General
LSN:	0-387-95201-2
Barcode:	9780387952017

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