Saturday, September 7, 2024
9/7/2024
Our current clinical approach to re-irradiation is not based on data. Guidelines vary widely and are not rooted in observed toxicities. A frequently cited reason for that is both the scarcity of existing outcome data, as well as its heterogeneity: there are multiple radiation courses per patient with variable time between treatments. It is correct that the lack of data is a severe problem, though one that will be solved given the steep increase in the number of re-irradiation cases and published data. What the current lack of data obscures is a much more fundamental problem: that even if we had enough outcome data, there is no conceptual framework or outcome model to fit that data to. The need for such an approach to guide clinical treatment of re-irradiation is urgent. While re- irradiation used to be rare, patient volumes are growing rapidly: studies report that already 1 in 6 patients that come for radiotherapy have received prior radiation to the involved or a nearby site. To overcome the challenges described above, we propose a novel approach to guide re-irradiation based on imaging the evolution of normal tissue damage and recovery. In this space, data is more plentiful, with imaging at defined, clinically relevant follow-up timepoints. Furthermore we can build on our success in describing and analyzing normal tissue response to radiation. To overcome the challenges described above, we propose a novel framework to guide re-irradiation based on two innovative concepts: 1) Quantifying the evolution of normal tissue damage and recovery after RT. The time factor that is so important in re-irradiation can be studied using the evolution of radiation-induced normal tissue damage on imaging. 2) Normal tissue radiosensitivity in an individual patient is conserved between RT courses. While this seems intuitive, it is not known if normal tissue response in patients correlates when re-irradiating them. We have unique preliminary indicating that there is a strong relationship, and proving this hypothesis would enable us to integrate the response to the first treatment into the planning of the second. Based on these two concepts we formulated our two independent aims. In SA1 we will describe normal tissue recovery based on follow-up imaging using imaging biomarkers of liver and lung injury after radiotherpay. We hypothesize that we can parameterize the dose-dependent recovery from radiation-induced injury using a single, patient-specific recovery parameter in normal liver (SA1a) and lung (SA1b) after radiotherapy. In SA2 we will explore feasibility of personalization of re-RT based on the observed normal tissue response at first RT. Our working hypothesis is that the observed radiation-induced image changes after the first RT predicts normal tissue response at second RT, when corrected for the initially given dose distribution. Upon completion of these aims we will possess a generalizable framework for re-irradiation that allows us to incorporate the patient’s individual response to the first radiation treatment, and the time that has passed since, into the planning of re-irradiation.
HHS’ Tracking Accountability in Government Grants System (TAGGS) website provides access to detailed descriptions of grants, loans, aggregated direct payments and other types of financial assistance awarded by the HHS Operating Divisions (OPDIVs) and the Office of the Secretary Staff Divisions (STAFFDIVs).
