TU-F-CAMPUS-J-03: Elasticity functions based on 4DCT images to predict tumor and normal tissue response to radiation for patients with lung cancers
Zhong H, Li H, Gordon J, and Chetty I. TU-F-CAMPUS-J-03: Elasticity functions based on 4DCT images to predict tumor and normal tissue response to radiation for patients with lung cancers. Med Phys 2015; 42(6):3641.
Purpose: To investigate radiotherapy outcomes by incorporating 4DCT‐based physiological and tumor elasticity functions for lung cancer patients.
Methods: 4DCT images were acquired from 28 lung SBRT patients before radiation treatment. Deformable image registration (DIR) was performed from the end‐inhale to the end‐exhale using a B‐Spline‐based algorithm (Elastix, an open source software package). The resultant displacement vector fields (DVFs) were used to calculate a relative Jacobian function (RV) for each patient. The computed functions in the lung and tumor regions represent lung ventilation and tumor elasticity properties, respectively. The 28 patients were divided into two groups: 16 with two‐year tumor local control (LC) and 12 with local failure (LF). The ventilation and elasticity related RV functions were calculated for each of these patients.
Results: The LF patients have larger RV values than the LC patients. The mean RV value in the lung region was 1.15 (±0.67) for the LF patients, higher than 1.06 (±0.59) for the LC patients. In the tumor region, the elasticity‐related RV values are 1.2 (±0.97) and 0.86 (±0.64) for the LF and LC patients, respectively. Among the 16 LC patients, 3 have the mean RV values greater than 1.0 in the tumors. These tumors were located near the diaphragm, where the displacements are relatively large.. RV functions calculated in the tumor were better correlated with treatment outcomes than those calculated in the lung.
Conclusion: The ventilation and elasticity‐related RV functions in the lung and tumor regions were calculated from 4DCT image and the resultant values showed differences between the LC and LF patients. Further investigation of the impact of the displacements on the computed RV is warranted. Results suggest that the RV images might be useful for evaluation of treatment outcome for lung cancer patients.