Dosimetric comparison of autocontouring techniques for online adaptive proton therapy.

Objective.Anatomical and daily set-up uncertainties impede high precision delivery of proton therapy. With online adaptation, the daily plan is reoptimized on an image taken shortly before the treatment, reducing these uncertainties and, hence, allowing a more accurate delivery. This reoptimization requires target and organs-at-risk (OAR) contours on the daily image, which need to be delineated automatically since manual contouring is too slow. Whereas multiple methods for autocontouring exist, none of them are fully accurate, which affects the daily dose. This work aims to quantify the magnitude of this dosimetric effect for four contouring techniques.Approach.Plans reoptimized on automatic contours are compared with plans reoptimized on manual contours. The methods include rigid and deformable registration (DIR), deep-learning based segmentation and patient-specific segmentation.Main results.It was found that independently of the contouring method, the dosimetric influence of usingautomaticOARcontoursis small (<5% prescribed dose in most cases), with DIR yielding the best results. Contrarily, the dosimetric effect of using theautomatic target contourwas larger (>5% prescribed dose in most cases), indicating that manual verification of that contour remains necessary. However, when compared to non-adaptive therapy, the dose differences caused by automatically contouring the target were small and target coverage was improved, especially for DIR.Significance.The results show that manual adjustment of OARs is rarely necessary and that several autocontouring techniques are directly usable. Contrarily, manual adjustment of the target is important. This allows prioritizing tasks during time-critical online adaptive proton therapy and therefore supports its further clinical implementation.

Prostaglandin-endoperoxide synthase genes COX1 and COX2 - novel modifiers of disease severity in cystic fibrosis patients.

Cystic fibrosis (CF) is one of the most common autosomal recessive diseases among Caucasians caused by a mutation in the CFTR gene. However, the clinical outcome of CF pulmonary disease varies remarkably even in patients with the same CFTR genotype. This has led to a search for genetic modifiers located outside the CFTR gene. The aim of this study was to evaluate the effect of functional variants in prostaglandin-endoperoxide synthase genes (COX1 and COX2) on the severity of lung disease in CF patients. To the best of our knowledge, it is the first time when analysis of COX1 and COX2 as potential CF modifiers is provided. The study included 94 CF patients homozygous for F508del mutation of CFTR. To compare their clinical condition, several parameters were recorded, e.g. a unique clinical score: disease severity status (DSS). To analyse the effect of non-CFTR genetic polymorphisms on the clinical course of CF patients, the whole coding region of COX1 and selected COX2 polymorphisms were analysed. Statistical analysis of genotype-phenotype associations revealed a relationship between the heterozygosity status of identified polymorphisms and better lung function. These results mainly concern COX2 polymorphisms: -765G>C and 8473T>C. The COX1 and COX2 polymorphisms reducing COX protein levels had a positive effect on all analysed clinical parameters. This suggests an important role of these genes as protective modifiers of pulmonary disease in CF patients, due to inhibition of arachidonic acid conversion into prostaglandins, which probably reduces the inflammatory process.