The dose-response relationship for cardiovascular disease is not necessarily linear.
Uwe SchneiderMarina ErnstMatthias HartmannPublished in: Radiation oncology (London, England) (2017)
The probability for a complication after radiotherapy is usually a function of dose and volume in the organ or tissue of interest. In most epidemiological studies the risk for a complication is stratified in terms of dose, but not irradiated volume. We show that the obtained risk cannot generally be applied to radiotherapy patients.The epidemiological data of Darby et al. (N Engl J Med 368:2527, 2013) who found a linear relationship between the excess relative risk of major coronary events as function of mean heart dose in patients treated with tangential breast irradiation are analyzed. We have used the relative seriality model for a partly irradiated heart ("a lot to a little") which models radiation therapy using two tangential fields. The relative seriality model was then used to predict NTCP of cardiovascular disease for a homogenously irradiated heart ("a little to a lot"). The relative seriality model was fitted to the data of Darby et al. (N Engl J Med 368:2527, 2013) for tangential breast irradiation. For the situation "a little to a lot" it was found that the dose-response relationship is sigmoidal and contradicts the findings of Darby et al. (N Engl J Med 368:2527, 2013). It was shown in this work that epidemiological studies which predict a linear dose-response relationship for cardiovascular disease can be reproduced by bio-physical models for normal tissue complication. For irradiation situations which were not included in the epidemiological studies, e.g. a homogenous irradiation of the heart ("a little to a lot") the dose-response curve can be different. This could have consequences whether or not IMRT should be used for treating breast cancer. We believe that the results of epidemiological studies should not be generally used to predict normal tissue complications. It is better to use such data to optimize bio-physical models which can then be applied (with caution) to general treatment situations.
Keyphrases
- cardiovascular disease
- radiation therapy
- radiation induced
- heart failure
- case control
- electronic health record
- early stage
- mental health
- atrial fibrillation
- big data
- type diabetes
- end stage renal disease
- ejection fraction
- coronary artery disease
- coronary artery
- cardiovascular events
- machine learning
- squamous cell carcinoma
- peritoneal dialysis
- artificial intelligence
- metabolic syndrome
- patient reported outcomes
- cardiovascular risk factors
- aortic stenosis
- young adults
- deep learning
- patient reported
- transcatheter aortic valve replacement
- breast cancer risk
- aortic valve