0000000001308349
AUTHOR
Jan Christian Kaiser
showing 5 related works from this author
Biologically based models of cancer risk in radiation research
2020
PURPOSE: In radiation risk analysis the state-of-the-art approach is based on descriptive models which link excess rates of cancer incidence and mortality to radiation exposure by statistical association. To estimate the number of sporadic and radiation-induced cases descriptive models apply parametric dose response function which directly determine the radiation risk. In biologically-based models of cancer risk (BBCR models) dose responses are implemented for key events on the biological level such as early mutations or clonal expansion of initiated cells. Influenced by radiation these events then shape the risk response on the epidemiological level. Although BBCR models facilitate a more …
Integration of a radiation biomarker into modeling of thyroid carcinogenesis and post-Chernobyl risk assessment
2016
Strong evidence for the statistical association between radiation exposure and disease has been produced for thyroid cancer by epidemiological studies after the Chernobyl accident. However, limitations of the epidemiological approach in order to explore health risks especially at low doses of radiation appear obvious. Statistical fluctuations due to small case numbers dominate the uncertainty of risk estimates. Molecular radiation markers have been searched extensively to separate radiation-induced cancer cases from sporadic cases. The overexpression of the CLIP2 gene is the most promising of these markers. It was found in the majority of papillary thyroid cancers (PTCs) from young patients…
Dose-dependent expression of CLIP2 in post-Chernobyl papillary thyroid carcinomas
2015
Summary This study showed a clear dose-response relationship for the CLIP2 radiation marker in post-Chernobyl papillary thyroid carcinoma cohorts for young patients and hints to different molecular mechanisms in tumors induced at low doses compared to moderate/high doses.
Biologically based models of cancer risk in radiation research
2020
Biologically based models of cancer risk in radiation research
Biologically-based models of cancer risk in radiation research
2020
Purpose: In radiation risk analysis the state-of-the-art approach is based on descriptive models which link excess rates of cancer incidence and mortality to radiation exposure by statistical association. To estimate the number of sporadic and radiation-induced cases descriptive models apply parametric dose response function which directly determine the radiation risk. In biologically-based models of cancer risk (BBCR models) dose responses are implemented for key events on the biological level such as early mutations or clonal expansion of initiated cells. Influenced by radiation these events then shape the risk response on the epidemiological level. Although BBCR models facilitate a more …