Grant: Collaborative Health Research Projects

It is estimated that 3.5% of the population are cancer survivors, a prevalence that has tripled in North America since 1971. For many patients cured of their initial cancer, their treatment can give rise to significant delayed morbidity, and those originally diagnosed at young ages have a particularly high burden of late effects including second cancers (SC), and cardiovascular disease (CVD). For example, recent research suggests that childhood cancer survivors in particular face significant late morbidity or mortality during adulthood, with excess risks reducing life expectancy by as much as 28%. Much of the late toxicity is related to exposure to radiation therapy as part of initial treatment. The development of new therapeutic strategies that reduce long-term treatment related toxicity is a major theme in pediatric oncology. A major obstacle to the rational development of less toxic but effective new treatment strategies is that most published estimates of the risks late toxicity are based on outdated therapy, and conceal large variation in risks among individual patients. These limitations make it difficult to use these risk estimates to develop new treatments intended to reduce the occurrence of late effects. Simply eliminating radiation therapy without an understanding of the magnitude of the potential benefit has resulted in poor outcomes and in some clinical trials led to unacceptably high relapse rates. The overall aim of this study is to utilize recent advances in image processing, radiation dosimetry and biomathematical modeling to develop robust and flexible models of the treatment-related risks of SC and CVD for children with Hodgkin Lymphoma. With collaborators in the Children's Oncology Group (COG), we will work towards developing tools that will allow clinicians and clinical trialists to prospectively estimate the reduction in the risks of SC and CVD that would be expected with new changes in HL treatment. These models could be applied to individuals, to optimize the treatment for specific patients, or to groups, to aid the design of clinical trials.