Decision Tools for Radiation Oncology: Prognosis, Treatment Response and Toxicity (Medical Radiology) PDF ebook Free
Decision Tools for Radiation Oncology: Prognosis, Treatment Response and Toxicity (Medical Radiology) PDF Download
by Carsten Nieder (Editor), Laurie E. Gaspar (Editor)
A look at the recent oncology literature or a search of the common databases reveals a steadily increasing number of nomograms and other prognostic models. These models may predict the risk of relapse, lymphatic spread of a given malignancy, toxicity, survival, etc. Pathology information, gene signatures, and clinical data may all be used to compute the models. This trend reflects increasingly individualized treatment concepts, the need for approaches that achieve a favorable balance between effectiveness and side-effects, and the goal of optimal resource utilization reflecting prognostic knowledge. In order to avoid misuse, it is important to understand the limits and caveats of prognostic and predictive models. This book provides a comprehensive overview of such decision tools for radiation oncology, stratified by disease site, which will enable readers to make informed choices in daily clinical practice and to critically follow the future development of new tools in the field.
- Series:Medical Radiology
- Hardcover:305 pages
- Publisher:Springer; 2014 edition (March 13, 2014)
Practicing radiation oncologists have to make several important decisions during treatment planning and realization, one patient at a time. Questions such as “is radiotherapy indicated, what is the optimal dose/fractionation regimen, what is the optimal technique and dose distribution, what are the risks and side effects” have to be addressed. This is often done in larger multidisciplinary teams, and ideally based on solid scientific evidence. Compared to earlier decades, we have now an incredibly large tool box, allowing for assessment of tumor biology and its surrogates, imaging biomarkers, host genetics, and dynamic tumor changes during treatment, to name a few. New research adding to these fields is being presented at each of the major international oncology meetings, including but not limited to prognostic scores and nomograms. It is critical to appraise the methodological strengths and weaknesses of such research and to put into context established decision tools. The purpose of this book is to provide practicing radiation oncologists, as well as those in training, with a concise overview of the most important and up-to-date information pertaining to general and diagnosis-specific decision tools including staging systems. We strongly recommend starting with the introductory chapters, which provide necessary background information on statistical methods, principles of biomarker development, gene expression analyses, and other topics that are crucial for those who want to fully understand the applicability and limitations of prediction tools. Going towards increasingly individualized cancer therapy, we still need to rely on systematic evidence and sound treatment algorithms. We are most grateful for the enthusiasm and courtesy all chapter authors showed during preparation of this truly international volume and for the fruitful discussion with many colleagues. We also appreciate the excellent support from the publisher. We hope that the reader will find this book to be a useful summary of new or refined decision tools and how they contribute to state-of-the-art radiation therapy. Only continued basic and clinical research will provide a better basis for tolerable and efficacious treatment regimens, exploiting the promises put forward by the emerging concepts of personalized medicine and adaptive radiation therapy.