Modelling and Bayesian adaptive prediction of individual patients’ tumour volume change during radiotherapy

2016/02/23 by Imran Tariq, Tao Chen, Norman F Kirkby +2 · 6 citations
Physics and Astronomy · Medicine · #Advanced Radiotherapy Techniques #Medical Imaging Techniques and Applications #Lung Cancer Diagnosis and Treatment

paper · doi:10.1088/0031-9155/61/5/2145

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.

Cited by

• Proliferation saturation index in an adaptive Bayesian approach to predict patient-specific radiotherapy responses. [europepmc]
• Personalized Radiotherapy Design for Glioblastoma: Integrating Mathematical Tumor Models, Multimodal Scans, and Bayesian Inference. [europepmc]
• Hybrid modeling frameworks of tumor development and treatment. [europepmc]
• Predicting Radiotherapy Patient Outcomes with Real-Time Clinical Data Using Mathematical Modelling. [europepmc]
• Predicting and monitoring response to head and neck cancer radiotherapy using multimodality imaging and radiobiological digital twin simulations. [europepmc]
• Forecasting Chemoradiation Response Midtreatment for High-Grade Gliomas Through Patient-Specific Biology-Based Modeling. [europepmc]

Related

• Predicting radiotherapy patient outcomes with real-time clinical data using mathematical modelling
• A mathematical aid decision tool for RT planning
• Bayesian information-theoretic calibration of patient-specific radiotherapy sensitivity parameters for informing effective scanning protocols in cancer
• Novel Radiomic Feature for Survival Prediction of Lung Cancer Patients using Low-Dose CBCT Images
• An organ deformation model using Bayesian inference to combine population and patient-specific data