Improving radiotherapy using image analysis and machine learning
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With ever increasing advancements in imaging, there is an increasing abundance of images being acquired in the clinical environment. However, this increase in information can be a burden as well as a blessing as it may require significant amounts of time to interpret the information contained in these images. Computer assisted evaluation is one way in which better use could be made of these images. This thesis presents the combination of texture analysis of images acquired during the treatment of cancer with machine learning in order to improve radiotherapy. The first application is to the prediction of radiation induced pneumonitis. In 13- 37% of cases, lung cancer patients treated with radiotherapy develop radiation induced lung disease, such as radiation induced pneumonitis. Three dimensional texture analysis, combined with patient-specific clinical parameters, were used to compute unique features. On radiotherapy planning CT data of 57 patients, (14 symptomatic, 43 asymptomatic), a Support Vector Machine (SVM) obtained an area under the receiver operator curve (AUROC) of 0.873 with sensitivity, specificity and accuracy of 92%, 72% and 87% respectively. Furthermore, it was demonstrated that a Decision Tree classifier was capable of a similar level of performance using sub-regions of the lung volume. The second application is related to prostate cancer identification. T2 MRI scans are used in the diagnosis of prostate cancer and in the identification of the primary cancer within the prostate gland. The manual identification of the cancer relies on the assessment of multiple scans and the integration of clinical information by a clinician. This requires considerable experience and time. As MRI becomes more integrated within the radiotherapy work flow and as adaptive radiotherapy (where the treatment plan is modified based on multi-modality image information acquired during or between RT fractions) develops it is timely to develop automatic segmentation techniques for reliably identifying cancerous regions. In this work a number of texture features were coupled with a supervised learning model for the automatic segmentation of the main cancerous focus in the prostate - the focal lesion. A mean AUROC of 0.713 was demonstrated with 10-fold stratified cross validation strategy on an aggregate data set. On a leave one case out basis a mean AUROC of 0.60 was achieved which resulted in a mean DICE coefficient of 0.710. These results showed that is was possible to delineate the focal lesion in the majority (11) of the 14 cases used in the study.