Thesis (M.Med. (Radiation Oncology)) -- University of Limpopo, 2022 / Background Hypothyroidism in head and neck cancer patients after radiotherapy is known to occur, yet thyroid function tests are not routinely monitored in all patients post radiation therapy. Routine post radiation therapy thyroid function testing is currently not part of the follow-up protocol in these patients at Pietersburg Hospital. The aim of this study is to evaluate post radiation therapy hypothyroidism among head and neck cancer patients treated with radiotherapy at Pietersburg Hospital Methods A prospective (cohort) observational study was carried out among head and neck cancer patients receiving radiotherapy at the radiation oncology department in Pietersburg Hospital. Sample size of n=37 was calculated using Statistica V13.0. Thyroid function tests were performed at the start of radiation therapy and repeated on the first day of follow up, 6 weeks after completing radiation therapy. During follow up, participants were also interviewed for the presence of symptoms of hypothyroidism such as dry skin, dry hair, fatigue, cold intolerance, or weight gain. Data analysis was done with STATA version 16. Descriptive statistics were used to characterise variables, and summarised in tables, graphs and charts. Changes in thyroid function tests and other variables were analysed. A p-value of 0.05 was deemed statistically significant. Results Thirty-seven patients were enrolled in the study, 26 males and 11 females. The mean age of the patients was 53.1 ±12.3 standard deviation [SD]) with a range of 40.8 to 65.4 years. The most common diagnoses were cancer of the larynx and hypopharynx, forming 29.7% and oral cavity cancer, 29.7%. Only three patients (8%) had an early stage cancer (Stages 1 and 2), 11 patients (29.7%) moderately advanced cancer (Stage 3) while the majority (62%; n =23) had locally advanced cancer (Stage 4). Majority of the patients received 70Gy in 35 daily fractions, five fractions per week via 3-D conformal radiotherapy. Only 29 patients who had complete pre- and post radiotherapy thyroid function tests were included in the final analysis. Of these, none had clinical hypothyroidism at 3 months. Two patients (6.8%) had sub-clinical hypothyroidism, with post radiation therapy TSH values greater than 3.5mIU/ml. The mean post radiation therapy TSH values increased by 8.3% and the mean fT4 values decreased by 2.05% compared to the pre-radiation therapy values. Both changes were not statistically significant (p=0.99 and p=0.82 respectively). There was no statistically significant correlation between changes in TSH and fT4 versus age (p=0.88 and p=0.92 respectively), sex (p=0.55 and p=0.15 respectively), cancer stage (p=0.21 and p=0.78 respectively), and cancer site (p=0.17 and p=0.74 respectively). The most common post radiotherapy symptom was fatigue (62%) followed by cold intolerance (54%), weight gain (43%) and dry skin or dry hair (43% each). Conclusion The results of the study suggest that sub-clinical hypothyroidism is detectable early post radiation therapy presenting as clinical symptoms.

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This thesis addresses three problems related to image registration, prediction and tracking, applied to Angiography and Oncology. For image analysis, various probabilistic models have been employed to characterize the image deformations, target motions and state estimations. (i) In Digital Subtraction Angiography (DSA), having a high quality visualization of the blood motion in the vessels is essential both in diagnostic and interventional applications. In order to reduce the inherent movement artifacts in DSA, non-rigid image registration is used before subtracting the mask from the contrast image. DSA image registration is a challenging problem, as it requires non-rigid matching across spatially non-uniform control points, at high speed. We model the problem of sub-pixel matching, as a labeling problem on a non-uniform Markov Random Field (MRF). We use quad-trees in a novel way to generate the non uniform grid structure and optimize the registration cost using graph-cuts technique. The MRF formulation produces a smooth displacement field which results in better artifact reduction than with the conventional approach of independently registering the control points. The above approach is further improved using two models. First, we introduce the concept of pivotal and non-pivotal control points. `Pivotal control points' are nodes in the Markov network that are close to the edges in the mask image, while 'non-pivotal control points' are identified in soft tissue regions. This model leads to a novel MRF framework and energy formulation. Next, we propose a Gaussian MRF model and solve the energy minimization problem for sub-pixel DSA registration using Random Walker (RW). An incremental registration approach is developed using quad-tree based MRF structure and RW, wherein the density of control points is hierarchically increased at each level M depending of the features to be used and the required accuracy. A novel numbering scheme of the control points allows us to reuse the computations done at level M in M + 1. Both the models result in an accelerated performance without compromising on the artifact reduction. We have also provided a CUDA based design of the algorithm, and shown performance acceleration on a GPU. We have tested the approach using 25 clinical data sets, and have presented the results of quantitative analysis and clinical assessment. (ii) In External Beam Radiation Therapy (EBRT), in order to monitor the intra fraction motion of thoracic and abdominal tumors, the lung diaphragm apex can be used as an internal marker. However, tracking the position of the apex from image based observations is a challenging problem, as it undergoes both position and shape variation. We propose a novel approach for tracking the ipsilateral hemidiaphragm apex (IHDA) position on CBCT projection images. We model the diaphragm state as a spatiotemporal MRF, and obtain the trace of the apex by solving an energy minimization problem through graph-cuts. We have tested the approach using 15 clinical data sets and found that this approach outperforms the conventional full search method in terms of accuracy. We have provided a GPU based heterogeneous implementation of the algorithm using CUDA to increase the viability of the approach for clinical use. (iii) In an adaptive radiotherapy system, irrespective of the methods used for target observations there is an inherent latency in the beam control as they involve mechanical movement and processing delays. Hence predicting the target position during `beam on target' is essential to increase the control precision. We propose a novel prediction model (called o set sine model) for the breathing pattern. We use IHDA positions (from CBCT images) as measurements and an Unscented Kalman Filter (UKF) for state estimation. The results based on 15 clinical datasets show that, o set sine model outperforms the state of the art LCM model in terms of prediction accuracy.

Les quantités physiques des tissus imagés par tomodensitométrie peuvent être calculées à l’aide d’algorithmes de caractérisation de tissus. Le développement de nouvelles technologies d’imagerie par tomodensitométrie spectrale a stimulé le domaine de la caractérisation de tissus à un point tel qu’il est maintenant difficile de comparer les performances des multiples algorithmes de caractérisation de tissus publiés dans les dernières années. De même, la difficulté à comparer les performances des algorithmes de caractérisation de tissus rend leur utilisation dans des projets de recherche clinique difficile. Ce projet a comme but de créer un environnement de simulation robuste et fidèle à la réalité dans lequel des techniques de caractérisation de tissus pourront être développées et comparées. De plus, la librairie de calcul servira comme tremplin pour facilement appliquer des méthodes de caractérisation de tissus dans des collaborations cliniques. En particulier, une des méthodes de caractérisation de tissus incluse dans la librairie de calcul sera appliquée sur des données cliniques pour produire des cartes de concentration d’iode dans le cadre d’un projet de recherche sur la récurrence de cancers otorhinolaryngologiques. De surcroît, deux autres techniques de caractérisation de tissus et un algorithme de correction d’artefacts de durcissement de faisceau seront implémentés dans la librairie de calcul scientifique. Conjointement, un module pour la simulation de patients virtuels sera dévelopé et intégré à la librairie de calcul. / The physical quantities of tissues imaged by computed tomography can be calculated using tissue characterization algorithms. The development of new spectral computed tomography scanners stimulated the field of tissue characterization to such an extent that it is now difficult to compare the performances of the multiple tissue characterization algorithms available in the literature. In addition, the difficulty in comparing the tissue characterization algorithms’ performances makes it difficult to include them in clinical research projects. The goal of this project is to create a robust and physically accurate simulation environment in which tissue characterization algorithms can be developed and compared. Furthermore, the scientific computing library will serve as a springboard to easily apply tissue characterization methods in clinical collaborations. In particular, one of the tissue characterization methods included in the scientific computing library will be applied on clinical data to produce iodine concentration maps for a clinical research project on head and neck cancer recurrence. Moreover, two additional tissue characterization algorithms and a technique for the correction of beam hardening artefacts will be implemented in the scientific computing library. Coincidentally, the virtual patient simulation environment will be developed.