Decision Tools for Radiation Oncology

Prognosis, Treatment Response and Toxicity

  • Carsten Nieder
  • Laurie E. Gaspar

Part of the Medical Radiology book series (MEDRAD)

Also part of the Radiation Oncology book sub series (Med Radiol Radiat Oncol)

Table of contents

  1. Front Matter
    Pages i-xiii
  2. Meredith Elana Giuliani, Andrew J. Hope, Anthony Fyles
    Pages 1-6
  3. Vincenzo Valentini, Andrea Damiani, Andre Dekker, Nicola Dinapoli
    Pages 7-28
  4. Maria A. Thomas, Ramachandran Rashmi, Jacqueline Payton, Imran Zoberi, Julie K. Schwarz
    Pages 29-46
  5. Stephanie E. Weiss, Lynn Chang
    Pages 47-59
  6. Carsten Nieder
    Pages 61-75
  7. Christine M. Fisher, Rachel A. Rabinovitch
    Pages 77-89
  8. Hale Basak Caglar, Francesc Casas, Luhua Wang, Nenad Filipovic, Branislav Jeremic
    Pages 91-106
  9. Robert L Eil, F. E. M. Voncken, J. Torres-Roca, Charles R Thomas Jr.
    Pages 107-125
  10. Trevor Leong
    Pages 127-140
  11. Carsten Nieder, Thomas B. Brunner
    Pages 141-150
  12. Christine F. Lauro, Tracey E. Schefter
    Pages 151-166
  13. Joanna Y. Chin, Nataliya Kovalchuk, Lisa A. Kachnic
    Pages 167-184
  14. Melissa R. Young, Susan A. Higgins, William Yuh, Nina A. Mayr
    Pages 185-219
  15. Ping Jiang, Juergen Dunst
    Pages 221-229
  16. Cordula Petersen, Rudolf Schwarz
    Pages 231-240
  17. William P. Levin, Thomas F. DeLaney
    Pages 241-255
  18. Chris R. Kelsey, Lynn D. Wilson
    Pages 257-278
  19. Paul W. Sperduto, Laurie E. Gaspar
    Pages 279-287
  20. Marko Popovic, Michael Poon, Erin Wong, Danielle Rodin, Kenneth Li, Florence Mok et al.
    Pages 289-301

About this book

Introduction

A look at the recent oncology literature or a search of one of the common databases reveals a steadily increasing number of nomograms and other prognostic models, some of which are also available in the form of web-based tools. 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 and also the need for approaches that achieve a favorable balance between effectiveness and side-effects. Moreover, optimal resource utilization requires prognostic knowledge, for example to avoid lengthy and aggressive treatment courses in patients with a short survival expectation. 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.

Keywords

Nomogram Predictive Factors Prognostic Factors Prognostic Scores Radiation Oncology Radiotherapy Toxicity

Editors and affiliations

  • Carsten Nieder
    • 1
  • Laurie E. Gaspar
    • 2
  1. 1.Department of OncologyNordland Hospital Trust Bodø University of TromsøBodøNorway
  2. 2.Department of Radiation OncologyUniversity of Colorado School of MedicineAuroraUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-642-37102-8
  • Copyright Information Springer-Verlag Berlin Heidelberg 2014
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Medicine
  • Print ISBN 978-3-642-37101-1
  • Online ISBN 978-3-642-37102-8
  • Series Print ISSN 0942-5373
  • Series Online ISSN 2197-4187
  • About this book
Industry Sectors
Pharma