A population based statistical model for daily geometric variations in the thorax. Issue 1 (April 2017)

• Record Type: Journal Article
• Authors: Szeto, Yenny Z.; Witte, Marnix G.; van Herk, Marcel; Sonke, Jan-Jakob
• Is Part Of: Radiotherapy and oncology. Volume 123:Issue 1(2017:Apr.)
• Page Start: 99
• Page End: 105
• Publication Date: 2017-04
• Subjects: Principal component analysis -- Lung cancer -- Geometric variation; Oncology -- Periodicals; Radiotherapy -- Periodicals; Tumors -- Periodicals; Medical Oncology -- Periodicals; Neoplasms -- radiotherapy -- Periodicals; Radiotherapy -- Periodicals; Radiothérapie -- Périodiques; Cancérologie -- Périodiques; Tumeurs -- Périodiques; Electronic journals; 616.9940642
• DOI: 10.1016/j.radonc.2017.02.012
• Languages: English
• ISSNs: 0167-8140

Abstract

Abstract: Purpose: To develop a population based statistical model of the systematic interfraction geometric variations between the planning CT and first treatment week of lung cancer patients for inclusion as uncertainty term in future probabilistic planning. Materials and methods: Deformable image registrations between the planning CT and first week CBCTs of 235 lung cancer patients were used to generate deformation vector fields (DVFs) representing the geometric variations of lung cancer patients. Using a second deformable registration step, the average DVF per patient was mapped to an average patient CT. Subsequently, the dominant modes of systematic geometric variations were extracted using Principal Component Analysis (PCA). For evaluation a leave-one-out cross-validation was performed. Results: The first three PCA components mainly described cranial–caudal, anterior–posterior, and left–right variations, respectively. Fifty and 112 components were needed to describe correspondingly 75% and 90% of the variance. An overall systematic variation of 3.6 mm SD was observed and could be described with an accuracy of about 1.0 mm with the PCA model. Conclusions: A PCA based model for systematic geometric variations in the thorax was developed, and its accuracy determined. Such a model can serve as a basis for probability based treatment planning in lung cancer patients.