UNASSIGNED: 7280 Background: A variety of clinical and dosimetric parameters extracted from the 3D dose distribution have been associated with the risk of @entity1207 in retrospective studies. Such parameters include the @entity358 ( @entity358 ), the radiation dose distribution relative to functioning perfused lung (assessed by single photon emission computed tomography-SPECT), and the pre- @entity138 lung function (i.e. PFT's). Based on our work and others, we defined pre- @entity138 criteria that we hypothesized would be predictive for the later development of @entity55 . We herein prospectively test the predictive abilities of these dosimetric/functional parameters. This study was performed on a new cohort of @entity1 , who were not included in our analyses that defined the predictive models. METHODS: Between 1999-2003, 63 @entity1 receiving thoracic @entity138 for @entity2 were enrolled onto a prospective IRB-approved study to monitor @entity138 -induced @entity651 . 37 @entity1 without evidence of recurrent @entity5 after >6 months post- @entity138 were included in this analysis. @entity1 at high and low risk for @entity55 were defined based on: a) the @entity358 , b) the 3D dose distribution relative to the pre- @entity138 SPECT lung perfusion scan, and c) the pre- @entity138 DLCO. The rates of @entity55 in the high and low risk groups were compared using @entity16043 's exact test. The areas under receiver operating characteristic (ROC) curves were used to compare alternative predictive models. RESULTS: @entity55 rates in the @entity1 predicted to be at high vs. low risk were similar. For the overall @entity1 population, the ROC areas provided by the different models were similar and suboptimal (<0.6). No model provided a statistically significant segregation of the @entity1 . CONCLUSIONS: Segregating @entity1 into high and low risk groups for @entity55 is challenging. While the models do have some predictive value, their ROC areas are far from ideal. Additional work is needed to better identify predictors of @entity138 -induced @entity651 . Supported by NIH R01 Grant CA69579. Thanks to UNC for @entity41710 software. No significant financial relationships to disclose.

Prospective assessment of dosimetric/physiologic models to predict XXXX .

@entity1 :: ('9606', 'Species') :: ['Patients', 'patients', 'patient'] @entity55 :: ('MESH:D011014', 'Disease') :: ['pneumonitis', 'Pneumonitis'] @entity138 :: ('MESH:C563738', 'Disease') :: ['RT'] @entity5 :: ('MESH:D009369', 'Disease') :: ['intra-thoracic tumor'] @entity2 :: ('MESH:D008175', 'Disease') :: ['lung cancer'] @entity41710 :: ('51297', 'Gene') :: ['PLUNC'] @entity651 :: ('MESH:D055370', 'Disease') :: ['lung injury'] @entity1207 :: ('MESH:D017564', 'Disease') :: ['symptomatic radiation pneumonitis'] @entity358 :: ('MESH:D008171', 'Disease') :: ['mean lung dose', 'MLD'] @entity16043 :: ('76720', 'Species') :: ['Fisher']
@entity1207 :: (MESH:D017564,Disease) :: ['radiation pneumonitis']
