A model that predicts a real-time tumour surface using intra-treatment skin surface and end-of-expiration and end-of-inhalation planning CT images.

OBJECTIVES: To develop a mapping model between skin surface motion and internal tumour motion and deformation using end-of-exhalation (EOE) and end-of-inhalation (EOI) 3D CT images for tracking lung tumours during respiration. METHODS: Before treatment, skin and tumour surfaces were segmented and reconstructed from the EOE and the EOI 3D CT images. A non-rigid registration algorithm was used to register the EOE skin and tumour surfaces to the EOI, resulting in a displacement vector field that was then used to construct a mapping model. During treatment, the EOE skin surface was registered to the real-time, yielding a real-time skin surface displacement vector field. Using the mapping model generated, the input of a real-time skin surface can be used to calculate the real-time tumour surface. The proposed method was validated with and without simulated noise on 4D CT images from 15 patients at Léon Bérard Cancer Center and the 4D-lung dataset. RESULTS: The average centre position error, dice similarity coefficient (DSC), 95%-Hausdorff distance and mean distance to agreement of the tumour surfaces were 1.29 mm, 0.924, 2.76 mm, and 1.13 mm without simulated noise, respectively. With simulated noise, these values were 1.33 mm, 0.920, 2.79 mm, and 1.15 mm, respectively. CONCLUSIONS: A patient-specific model was proposed and validated that was constructed using only EOE and EOI 3D CT images and real-time skin surface images to predict internal tumour motion and deformation during respiratory motion. ADVANCES IN KNOWLEDGE: The proposed method achieves comparable accuracy to state-of-the-art methods with fewer pre-treatment planning CT images, which holds potential for application in precise image-guided radiation therapy.

Cytochrome P450 2A6 deletion polymorphism and risk of lung cancer: a meta-analysis.

Previous studies concerning the association between cytochrome P450 2A6 (CYP2A6) deletion polymorphism and lung cancer risk provided controversial results. To clarify the precise association, a meta-analysis was performed. The electronic databases PubMed, Chinese Biomedical Database and Chinese National Knowledge Infrastructure Database were searched for case-control studies last updated on June 3, 2012 that investigated CYP2A6 deletion polymorphism and lung cancer risk. The odds ratio (OR) and its respective 95 % confidence interval (95 % CI) were used to measure the strength of association by means of a genetic model free approach. A total of 8 studies including 2,607 cases and 2,595 controls met the inclusion criteria and were subjected to the final analysis. The most appropriate co-dominant model was adopted. Overall, we found that CYP2A6 *1/*1 genotype was associated with an increased risk of lung cancer relative to *4/*4 genotype (OR = 2.65, 95 % CI: 1.84-3.81, P < 0.001). Significant association was also detected among Asians. Publication bias was absent in this meta-analysis. Therefore, our data suggested that the presence of the CYP2A6 *1/*1 might be associated with an increased lung cancer risk, especially for Asians. Further studies well-designed among different ethnicity populations are required.

Methylenetetrahydrofolate reductase 677TT genotype might be associated with an increased lung cancer risk in Asians.

BACKGROUND: The association between methylenetetrahydrofolate reductase (MTHFR) 677C>T polymorphism and lung cancer risk has been studied in various populations with conflicting results. The aim of this study was to assess the association strength by a meta-analysis of published studies. METHODS: We searched PubMed and Chinese Biomedical (CBM) databases for relevant literatures published by July 18, 2012. Pooled odds ratio (OR) with 95% confidence interval (CI) was calculated to assess the strength of the association. RESULTS: A total of 20 studies comprising 11,653 cases and 12,032 controls were included in the final meta-analysis. Using the random effect model, we found that MTHFR 677TT variant genotype was associated with an increased lung cancer risk (OR=1.26, 95% CI=1.05-1.50, P=0.011 for TT vs. CC; OR=1.19, 95% CI=1.03-1.37, P<0.001 for TT vs. CC+CT; OR=1.11, 95% CI=1.02-1.22, P=0.017 for T allele vs. C allele). In the further stratified analyses, the increased lung cancer risk was found in Asian subjects (OR=1.31, 95% CI=1.01-1.71, P=0.045 for TT vs. CC; OR=1.17, 95% CI=1.00-1.38, P=0.048 for TT vs. CC+CT). There were no evidences for obvious publication bias in the overall meta-analysis and Asian subjects. CONCLUSIONS: MTHFR 677TT genotype might increase the susceptibility of lung cancer, especially in Asians.