RESUMO
PURPOSE: The dosimetric parameters used clinically to reduce the likelihood of radiation pneumonitis (RP) for lung cancer radiation therapy have traditionally been V20Gy ≤ 30% to 35% and mean lung dose ≤ 20 to 23 Gy; however, these parameters are derived based on studies from photon therapy. The purpose of this study is to evaluate whether such dosimetric predictors for RP are applicable for locally advanced non-small cell lung cancer (LA-NSCLC) patients treated with proton therapy. METHODS AND MATERIALS: In the study, 160 (78 photon, 82 proton) patients with LA-NSCLC treated with chemoradiotherapy between 2011 and 2016 were retrospectively identified. Forty (20 photon, 20 proton) patients exhibited grade ≥2 RP after therapy. Dose volume histograms for the uninvolved lung were extracted for each patient. The percent lung volumes receiving above various dose levels were obtained in addition to V20Gy and Dmean. These dosimetric parameters and patient characteristics were evaluated with univariate and multivariate logistic regression tests. Receiver operating characteristic curves were generated to obtain the optimal dosimetric constraints through analyzing RP and non-RP sensitivity and specificity values. RESULTS: The multivariate analysis showed V40Gy and Dmean to be statistically significant for proton and photon patients, respectively. V35Gy to V50Gy were strongly correlated to V40Gy for proton patients. Based on the receiver operating characteristic curves, V35Gy to V50Gy had the highest area under the curve compared with other dose levels for proton patients. A potential dosimetric constraint for RP predictor in proton patients is V40Gy ≤ 23%. CONCLUSIONS: In addition to V20Gy and Dmean, the lung volume receiving higher doses, such as V40Gy, may be used as an additional indicator for RP in LA-NSCLC patients treated with proton therapy.
RESUMO
AIM: During cancer radiotherapy treatment, on-board four-dimensional-cone beam computed tomography (4D-CBCT) provides important patient 4D volumetric information for tumor target verification. Reconstruction of 4D-CBCT images requires sorting of acquired projections into different respiratory phases. Traditional phase sorting methods are either based on external surrogates, which might miscorrelate with internal structures; or on 2D internal structures, which require specific organ presence or slow gantry rotations. The aim of this study is to investigate the feasibility of a 3D motion modeling-based method for markerless 4D-CBCT projection-phase sorting. METHODS: Patient 4D-CT images acquired during simulation are used as prior images. Principal component analysis (PCA) is used to extract three major respiratory deformation patterns. On-board patient image volume is considered as a deformation of the prior CT at the end-expiration phase. Coefficients of the principal deformation patterns are solved for each on-board projection by matching it with the digitally reconstructed radiograph (DRR) of the deformed prior CT. The primary PCA coefficients are used for the projection-phase sorting. RESULTS: PCA coefficients solved in nine digital phantoms (XCATs) showed the same pattern as the breathing motions in both the anteroposterior and superoinferior directions. The mean phase sorting differences were below 2% and percentages of phase difference < 10% were 100% for all the nine XCAT phantoms. Five lung cancer patient results showed mean phase difference ranging from 1.62% to 2.23%. The percentage of projections within 10% phase difference ranged from 98.4% to 100% and those within 5% phase difference ranged from 88.9% to 99.8%. CONCLUSION: The study demonstrated the feasibility of using PCA coefficients for 4D-CBCT projection-phase sorting. High sorting accuracy in both digital phantoms and patient cases was achieved. This method provides an accurate and robust tool for automatic 4D-CBCT projection sorting using 3D motion modeling without the need of external surrogate or internal markers.
