title: Modelling and Bayesian adaptive prediction of individual patients' tumour volume change during radiotherapy.
abstract: The aim of this study is to develop a mathematical modelling method that can predict individual patients’ response to radiotherapy, in terms of tumour volume change during the treatment. The main concept is to start from a population-average model, which is subsequently updated from an individual’s tumour volume measurement. The model becomes increasingly personalized and so too does the prediction it produces. This idea of adaptive prediction was realised by using a Bayesian approach for updating the model parameters. The feasibility of the developed method was demonstrated on the data from 25 non-small cell lung cancer patients treated with helical tomotherapy, during which tumour volume was measured from daily imaging as part of the image-guided radiotherapy. The method could provide useful information for adaptive treatment planning and dose scheduling based on the patient’s personalised response.
authors.forename: Imran
authors.initials: I
authors.lastname: Tariq
authors.forename: Tao
authors.initials: T
authors.lastname: Chen
authors.forename: Norman F
authors.initials: NF
authors.lastname: Kirkby
authors.forename: Rajesh
authors.initials: R
authors.lastname: Jena
mesh_terms: D002289:Carcinoma, Non-Small-Cell Lung / Q000473:pathology / Q000532:radiotherapy*; D006801:Humans; D008175:Lung Neoplasms / Q000473:pathology / Q000532:radiotherapy*; D066230:Patient-Specific Modeling; D011880:Radiotherapy Planning, Computer-Assisted / Q000379:methods*; D050397:Radiotherapy, Intensity-Modulated / Q000379:methods*; D047368:Tumor Burden