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Effects of Optimization Cost Function On Ventilation in SBRT of NSCLC

I Mihaylov

I Mihaylov1*, K Latifi2 , M De Ornelas-Couto1*, E Moros2 , G Zhang2 , (1) University Miami, Miami, FL, (2) H. Lee Moffitt Cancer Center, Tampa, FL,


SU-F-J-90 (Sunday, July 31, 2016) 3:00 PM - 6:00 PM Room: Exhibit Hall

To explore the effects of dose-volume (Dvh) and dose-mass (Dmh) inverse optimization approaches on the lung ventilation for SBRT in NSCLC.

Six cases were studied. For each case ventilation was computed on voxel-by-voxel basis, derived from the time-resolved (4D) CT scans. Ventilation is defined as voxel volume change between full inspiration and full expiration, divided by voxel volume. The ventilation volume was mapped to full expiration phase. For each patient case two IMRT plans were created - one with Dvh and one with Dmh quadratic objective function, applied to the OARs. The OARs used as dose limiting structures were lung, spinal cord, esophagus, and heart. After obtaining the IMRT solutions average doses as well as 2000 cGy isovolumes and isomases were extracted for ventilation volumes of 0.5, 0.9, 1.1, 1.2, and 1.5. Ventilation volume of 0.5 incorporates all CT voxels having a voxel volume change (exhale-to-inhale) 50% and more. Similarly, ventilation volume of 1.5 encompasses all CT voxels with volume change of 150% and more. Thereby, ventilation volume of 0.5 encompasses almost the entire lung, while ventilation volume of 1.5 represents ~1% of ventilation 0.5 volume. The doses, the isovolumes, and the isomasses derived from the Dvh optimization were used as references.

The results indicate that with decreasing ventilation (from 1.5 to 0.5) the differences between Dvh and Dmh derived tallied quantities decrease. For some cases the differences between the average doses for the different ventilation volumes are up to 20%. The changes in the isovolumes and the isomasses for the different ventilations are much more dramatic and can differ by a factor of 3.5.

This is the first investigation on the effects of different optimization schemes on lung ventilation. The presented results indicate that the larger the ventilation the more dramatic the effects.

Funding Support, Disclosures, and Conflict of Interest: Supported in part by NIH R01 CA163360

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