Pre-radiotherapy FDG PET predicts radiation pneumonitis in lung cancer.

BACKGROUND: A retrospective analysis is performed to determine if pre-treatment [18 F]-2-fluoro-2-deoxyglucose positron emission tomography/computed tomography (FDG PET/CT) image derived parameters can predict radiation pneumonitis (RP) clinical symptoms in lung cancer patients. METHODS AND MATERIALS: We retrospectively studied 100 non-small cell lung cancer (NSCLC) patients who underwent FDG PET/CT imaging before initiation of radiotherapy (RT). Pneumonitis symptoms were evaluated using the Common Terminology Criteria for Adverse Events version 4.0 (CTCAEv4) from the consensus of 5 clinicians. Using the cumulative distribution of pre-treatment standard uptake values (SUV) within the lungs, the 80th to 95th percentile SUV values (SUV(80) to SUV(95) were determined. The effect of pre-RT FDG uptake, dose, patient and treatment characteristics on pulmonary toxicity was studied using multiple logistic regression. RESULTS: The study subjects were treated with 3D conformal RT (n=23), intensity modulated RT (n=64), and proton therapy (n=13). Multiple logistic regression analysis demonstrated that elevated pre-RT lung FDG uptake on staging FDG PET was related to development of RP symptoms after RT. A patient of average age and V(30) with SUV(95)=1.5 was an estimated 6.9 times more likely to develop grade ≥ 2 radiation pneumonitis when compared to a patient with SUV(95)=0.5 of the same age and identical V(30). Receiver operating characteristic curve analysis showed the area under the curve was 0.78 (95% CI=0.69 - 0.87). The CT imaging and dosimetry parameters were found to be poor predictors of RP symptoms. CONCLUSIONS: The pretreatment pulmonary FDG uptake, as quantified by the SUV(95), predicted symptoms of RP in this study. Elevation in this pre-treatment biomarker identifies a patient group at high risk for post-treatment symptomatic RP.

Deformable image registration for temporal subtraction of chest radiographs.

PURPOSE: Temporal subtraction images constructed from image registration can facilitate the visualization of pathologic changes. In this study, we propose a deformable image registration (DIR) framework for creating temporal subtraction images of chest radiographs. METHODS: We developed a DIR methodology using two different image similarity metrics, varying flow (VF) and compressible flow (CF). The proposed registration method consists of block matching, filtering, and interpolation. Specifically, corresponding point pairs between reference and target images are initially determined by minimizing a nonlinear least squares formulation using grid-searching optimization. A two-step filtering process, including least median of squares filtering and backward matching filtering, is then applied to the estimated point matches in order to remove erroneous matches. Finally, moving least squares is used to generate a full displacement field from the filtered point pairs. RESULTS: We applied the proposed DIR method to 10 pairs of clinical chest radiographs and compared it with the demons and B-spline algorithms using the five-point rating score method. The average quality scores were 2.7 and 3 for the demons and B-spline methods, but 3.5 and 4.1 for the VF and CF methods. In addition, subtraction images improved the visual perception of abnormalities in the lungs by using the proposed method. CONCLUSION: The VF and CF models achieved a higher accuracy than the demons and the B-spline methods. Furthermore, the proposed methodology demonstrated the ability to create clinically acceptable temporal subtraction chest radiographs that enhance interval changes and can be used to detect abnormalities such as non-small cell lung cancer.