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Change in Mean CT Intensity of Lung Tumors During Radiation Treatment

R Mahon

R Mahon*, N Tennyson , E Weiss , G Hugo , Virginia Commonwealth University, Richmond, VA


SU-E-J-267 (Sunday, July 12, 2015) 3:00 PM - 6:00 PM Room: Exhibit Hall

Purpose: To evaluate CT intensity change of lung tumors during radiation therapy.

Methods: Repeated 4D CT images were acquired on a CT simulator during the course of therapy for 27 lung cancer patients on IRB approved protocols. All subjects received definitive radiation treatment ± chemotherapy. CT scans were completed prior to treatment, and 2-7 times during the treatment course. Primary tumor was delineated by an experienced Radiation Oncologist. Contours were thresholded between -100 HU and 200 HU to remove airways and bone. Correlations between the change in the mean tumor intensity and initial tumor intensity, SUVmax, and tumor volume change rate were investigated. Reproducibility was assessed by evaluating the variation in mean intensity over all phases in 4DCT, for a subgroup of 19 subjects.

Results: Reproducibility of tumor intensity between phases as characterized by the root mean square of standard deviation across 19 subjects was 1.8 HU. Subjects had a mean initial tumor intensity of 16.5 ± 11.6 HU and an overall reduction in HU by 10.3 ± 8.5 HU. Evaluation of the changes in tumor intensity during treatment showed a decrease of 0.3 ± 0.3 HU/day for all subjects, except three. No significant correlation was found between change in HU/day and initial HU intensity (p=0.53), initial PET SUVmax (p=0.69), or initial tumor volume (p=0.70). The rate of tumor volume change was weakly correlated (R^2=0.05) with HU change (p=0.01).

Conclusion: Most lung cancer subjects showed a marked trend of decreasing mean tumor CT intensity throughout radiotherapy, including early in the treatment course. Change in HU/day is not correlated with other potential early predictors for response, such as SUV and tumor volume change. This result supports future studies to evaluate change in tumor intensity on CT as an early predictor of response.

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