Chapter title |
Optimizing Radiotherapy with Immunotherapeutic Approaches
|
---|---|
Chapter number | 3 |
Book title |
Immunotherapy
|
Published in |
Advances in experimental medicine and biology, January 2017
|
DOI | 10.1007/978-3-319-53156-4_3 |
Pubmed ID | |
Book ISBNs |
978-3-31-953155-7, 978-3-31-953156-4, 978-3-31-953155-7, 978-3-31-953156-4
|
Authors |
Jonathan E. Schoenhals, Tijana Skrepnik, Ugur Selek, Maria A. Cortez, Ailin Li, James W. Welsh |
Abstract |
Several factors must be considered to successfully integrate immunotherapy with radiation into clinical practice. One such factor is that concepts arising from preclinical work must be tested in combination with radiation in preclinical models to better understand how combination therapy will work in patients; examples include checkpoint inhibitors, tumor growth factor-beta (TGF-β) inhibitors, and natural killer (NK) cell therapy. Also, many radiation fields and fractionation schedules typically used in radiation therapy had been standardized before the introduction of advanced techniques for radiation planning and delivery that account for changes in tumor size, location, and motion during treatment, as well as uncertainties introduced by variations in patient setup between treatment fractions. As a result, radiation therapy may involve the use of large treatment volumes, often encompassing nodal regions that may not be irradiated with more conformal techniques. Traditional forms of radiation in particular pose challenges for combination trials with immunotherapy. This chapter explores these issues in more detail and provides insights as to how radiation therapy can be optimized to combine with immunotherapy. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 35 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Bachelor | 4 | 11% |
Student > Master | 4 | 11% |
Researcher | 2 | 6% |
Student > Ph. D. Student | 2 | 6% |
Other | 1 | 3% |
Other | 3 | 9% |
Unknown | 19 | 54% |
Readers by discipline | Count | As % |
---|---|---|
Medicine and Dentistry | 10 | 29% |
Biochemistry, Genetics and Molecular Biology | 3 | 9% |
Agricultural and Biological Sciences | 1 | 3% |
Unknown | 21 | 60% |