"In estimating the stock market," she says, "people try to predict how stock will behave based on historical data and the company's portfolio." The mathematical model uses data on how tumor motion has changed during a course of radiation treatment in addition to real-time images of a tumor to calculate how much confidence the physcists can have about an instantaneous tumor position estimate. The goal of the work is to reduce the number of times intrafraction X-ray needs to be triggered as tumor localization measurement, thereby reducing the total amount of radiation a patient receives.
With a typical image-guided radiotherapy (IGRT) protocol, X-rays are used at a fixed frequency to validate the location of the tumor target. This rate may be increased to improve the localization accuracy. Ruan's model, however, which she calls adaptive, aims to accurately localize tumors in real-time by imaging smarter, rather than more frequently. It makes online decisions as to whether or not it is necessary to take a new X-ray image during treatment. In test cases that she will present, imaging frequency was reduced by 40 to 50 per cent without sacrificing tumor localization accuracy, meaning that the X-ray dosage to the patient was essentially halved or close to halved.
Reducing imaging radiation is an important goal for oncologists because radiation is associated with secondary malignancies, especially in pediatric patients who typically live for a long time after surviving their cancer. The model should be helpful in these cases, and particularly for tumors that because of their location lung, thorax, and abdomen are difficult to locate because of the body movement that occurs as patients breathe.
The Presentation " Reducing Imaging Dose Without Sacrificing Target Localization Accuracy: A Feasibility Study Byli
|Contact: Jason Socrates Bardi|
American Institute of Physics