Bakgrund Extern strålbehandling är en viktig del av processen för behandling av cervikal cancer. Målet med denna typ av behandling är att bestråla tumörområdet med så hög dos som möjligt och omkringliggande frisk vävnad med så lite dos som möjligt. Ett problem som upptäckts är att rörelser i tumörområdet under behandling kan påverka precisionen av strålfältet. Syfte Syftet med litteraturstudien var att beskriva omfattning av rörelse och volymförändring i tumörområdet under strålbehandling av cervikal cancer. Metod                  Nio artiklar med kvantitativ design kvalitetsgranskades och resultaten sammanställdes med inspiration av Fribergs modell. Resultat Omfattning av rörelser och volymförändringar upptäcktes variera och är något som skiljer sig mellan individer. Valet av bildtagnings- och mätmetod har stor betydelse gällande patientsäkerhet och stråldos.   Slutsats Hur mycket tumörområdet rör sig/förändras i volym var något som varierade mellan patienterna i studierna och det är därför viktigt att anpassa behandlingen efter dessa variationer. Olika metoder för bildtagning och mätning kan användas både för att upptäcka rörelser och volymförändringar i tumörområdet under extern strålbehandling. De olika metodernas fördelar bör noggrant vägas mot nackdelar innan användning för att hitta en optimal metod som främjar patientsäkerhet. / Background External radiation therapy is an important part when treating cervical cancer. The goal of this type of therapy is to irradiate the tumor area with the highest possible dose and minimize the dose given to the surrounding healthy tissue. One problem showed is that movement in the tumor area during treatment can cause problems regarding keeping the precision as high as possible. Aim The aim of this literature study was to describe the extent of movement and volume change in the tumor area during external radiotherapy treatment of cervical cancer. Method Nine articles with quantitative design were quality-reviewed and the results were compiled with inspiration from Friberg's model. Results The extent of movements and volume changes was discovered varying and is something that differs between individuals. The choice of imaging and measurement method is of great importance in terms of patient safety and radiation dose. Conclusions The extent of movement and change in volume varied between patients in the studies and it is therefore important to adjust the treatment according to these variations. Different methods of imaging and measurement can be used both to detect movements and volume changes in the tumor area during external radiation therapy. The benefits of the different methods should be carefully weighed against disadvantages before use to find an optimal method that promotes patient safety.

External beam radiation therapy is currently one of the most commonly used modalities for treating cancer. With the rise of new technologies and increasing computational power, machine learning, deep learning and artificial intelligence applications used for classification and regression problems have begun to find their way into the field of radiation oncology. One such application is the automated generation of radiotherapy treatment plans, which must be optimized for every single patient. The department of radiation physics in Lund, Sweden, has developed an autoplanning software, which in combination with a commercially available treatment planning system (TPS), can be used for automatic creation of clinical treatment plans. The parameters of a multivariable cost function are changed iteratively, making it possible to generate a great amount of different treatment plans for a single patient. The output leads to optimal, near-optimal, clinically acceptable or even non-acceptable treatment plans. In this thesis, the possibility of using machine and deep learning to minimize the amount of treatment plans generated by the autoplanning software as well as the possibility of finding cost function parameters that lead to clinically acceptable optimal or near-optimal plans is evaluated. Data augmentation is used to create matrices of optimal treatment plan parameters, which are stored in a training database.  Patient specific training features are extracted from the TPS, as well as from the bottleneck layer of a trained deep neural network autoencoder. The training features are then matched against the same features extracted for test patients, using a k-nearest neighbor algorithm. Finally, treatment plans for a new patient are generated using the output plan parameter matrices of its nearest neighbors. This allows for a reduction in computation time as well as for finding suitable cost function parameters for a new patient.

Submitted by Pedro Silva Filho (pfsilva@ipen.br) on 2017-11-17T17:35:26Z No. of bitstreams: 0 / Made available in DSpace on 2017-11-17T17:35:26Z (GMT). No. of bitstreams: 0 / A utilização da técnica de teleterapia para tratamento de câncer tem sido usada por anos com bons resultados clínicos. Em meados da década de 90, a remoção do filtro aplainador, item que compõe o cabeçote de um acelerador linear de uso clínico, tem sido objeto de estudos por demonstrar bons resultados no tratamento de alguns tipos de câncer. Técnicas utilizadas como Radioterapia de Intensidade Modulada (IMRT) e Radioterapia Estereotáxica (SRT), mostram-se mais eficazes quando não se utiliza o filtro aplainador. A empresa Varian Oncology lançou em 2012 um acelerador linear de uso clínico capaz de operar com o filtro aplainador (FF) e sem o filtro aplainador (FFF), o TrueBeam. Os objetivos desse trabalho são avaliar a homogeneidade de dois importantes parâmetros utilizados para o cálculo de dose nos pacientes submetidos ao tratamento com esse modelo de acelerador linear, a porcentagem de dose profunda (PDP) e índice de qualidade do feixe (TPR20/10). Os dados fornecidos para a análise foram cedidos pelo Hospital Israelita Albert Einstein (HIAE), Real Hospital Português (RHP) e 3 instituições norte-americanas. Através de uma análise estatística dos dados das instituições citadas pode-se observar melhor o comportamento desses parâmetro que demonstraram-se muito homogêneos e com erros menores que 1% na maioria dos casos, confirmando desse modo que os aceleradores lineares do modelo TrueBeam mantém na maioria das vezes uma boa concordância dos parâmetros analisados. / Dissertação (Mestrado em Tecnologia Nuclear) / IPEN/D / Instituto de Pesquisas Energéticas e Nucleares - IPEN-CNEN/SP

Jamaican males are a high-risk population for aggressive prostate cancer (PrCa) due to genetic influences, and identifying empirical data on treatments, which provide survival benefits is a prime challenge for clinicians who manage Jamaican PrCa patients. Thus, the purpose of this investigation was to elucidate treatment effects of brachytherapy and ERBT in the survival of a Jamaican PrCa cohort. Differences in survival outcomes of brachytherapy and ERBT treated Jamaican, and White U.S.-born PrCa patients with localized PrCa were compared. The mechanism of radiation programmed cell death in PrCa carcinogenesis explained in the oxidative stress theory, was the theoretical base for interpreting the research questions. A retrospective cohort design was used, and included survival analysis of secondary data from the Surveillance Epidemiology and End Results database. The sample size was 10,752 Jamaican and White U.S.-born prostate cancer patients diagnosed between 1992 and 2011. Kaplan-Meier and Cox proportional hazard regression models confirmed that brachytherapy resulted in enhanced survival benefits to the Jamaicans, HR 0.63, 95% CI [0.55, 0.73], p < .001, but ERBT did not, HR 1.6, 95% CI [1.38, 1.84] p < .001. Hence, brachytherapy may be an appropriate treatment option for Jamaican PrCa patients. Clinicians and health care planners can utilize the results for policy decisions aimed at increasing access to brachytherapy treatments to Jamaicans. Improving access to efficient PrCa treatments could reduce the morbidity and mortality rates of PrCa among Jamaicans, decrease years of potential life lost from PrCa, and enhance the life expectancy of the Jamaican male population.

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.