Background Four-dimensional computed tomography (4D-CT) ventilation is an emerging imaging modality. Functional avoidance of regions according to 4D-CT ventilation may reduce lung toxicity after radiation therapy. This study evaluated associations between 4D-CT ventilation-based dosimetric parameters and clinical outcomes. Methods Pre-treatment 4D-CT data were used to retrospectively construct ventilation images for 40 thoracic cancer patients retrospectively. Fifteen patients were treated with conventional radiation therapy, 6 patients with hyperfractionated radiation therapy and 19 patients with stereotactic body radiation therapy (SBRT). Ventilation images were calculated from 4D-CT data using a deformable image registration and Jacobian-based algorithm. Each ventilation map was normalized by converting it to percentile images. Ventilation-based dosimetric parameters (Mean Dose, V5 [percent lung volume receiving ≥5 Gy], and V20 [percent lung volume receiving ≥20 Gy]) were calculated for highly and poorly ventilated regions. To test whether the ventilation-based dosimetric parameters could be used predict radiation pneumonitis of ≥Grade 2, the area under the curve (AUC) was determined from the receiver operating characteristic analysis. Results For Mean Dose, poorly ventilated lung regions in the 0±30% range showed the highest AUC value (0.809; 95% confidence interval [CI], 0.663±0.955). For V20, poorly ventilated lung regions in the 0±20% range had the highest AUC value (0.774; 95% [CI], 0.598±0.915), and for V5, poorly ventilated lung regions in the 0±30% range had the highest AUC value (0.843; 95% [CI], 0.732±0.954). The highest AUC values for Mean Dose, V20, and V5 were obtained in poorly ventilated regions. There were significant differences in all dosimetric parameters between radiation pneumonitis of Grade 1 and Grade ≥2. Conclusions Poorly ventilated lung regions identified on 4D-CT had higher AUC values than highly ventilated regions, suggesting that functional planning based on poorly ventilated regions may reduce the risk of lung toxicity in radiation therapy.
CITATION STYLE
Otsuka, M., Monzen, H., Matsumoto, K., Tamura, M., Inada, M., Kadoya, N., & Nishimura, Y. (2018). Evaluation of lung toxicity risk with computed tomography ventilation image for thoracic cancer patients. PLoS ONE, 13(10). https://doi.org/10.1371/journal.pone.0204721
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