ESS - RADT - Livro, parte de livro ou capítulo de livro
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Browsing ESS - RADT - Livro, parte de livro ou capítulo de livro by Author "Lopes, Maria do Carmo"
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- Advantage of beam angle optimization in head-and-neck IMRT: Patient specific analysisPublication . Ventura, Tiago; Lopes, Maria do Carmo; Rocha, Humberto; Costa Ferreira, Brígida; Dias, JoanaRadiation therapy (RT) main purpose is to eliminate, in a controlled way, all tumor cells sparing as much as possible the normal tissues. Intensity Modulated Radiation Therapy (IMRT) is becoming the standard treatment technique in RT. Beam angle optimization (BAO) has potential to confer more quality to IMRT inverse planning process compared to manual trial and error approaches. In this study, the BAO advantages in head-and-neck patients are highlighted, using a patient specific analysis. Fluence optimization was done with Erasmus-iCycle multicriterial engine and BAO optimization was performed using two different algorithms: a combinatorial iterative algorithm and an algorithm based on a pattern search method. Plan assessment and comparison was performed with the graphical tool SPIDERplan. Among a set of forty studied nasopharynx cancer cases, three patients have been select for the specific analysis presented in this work. BAO presented plan quality improvements when beam angular optimized plans were compared with the equidistant beam angle solution and when plans based on non-coplanar beams geometries were compared with coplanar arrangements. Improvement in plan quality with a reduced number of beams was also achieved, in one case. For all cases, BAO generated plans with higher target coverage and better sparing of the normal tissues
- Automated Radiotherapy Treatment Planning Using Fuzzy Inference SystemsPublication . Dias, Joana; Rocha, Humberto; Ventura, Tiago; Costa Ferreira, Brigida; Lopes, Maria do CarmoRadiotherapy is one of the treatments available for cancer patients, aiming to irradiate the tumor while preserving healthy structures. The planning of a treatment is a lengthy trial and error procedure, where treatment parameters are iteratively changed and the delivered dose is calculated to see whether it complies with the desired medical prescription. In this paper, a procedure based on fuzzy inference systems (FIS) for automated treatment planning is developed, allowing the calculation of high quality treatment plans without requiring human intervention. The procedure is structured in two different phases, incorporating the automatic selection of the best set of equidistant beam irradiation directions by an enumeration procedure. The developed method is extensively tested using ten head-and-neck cancer cases.
- Feature selection in small databases: A medical-case studyPublication . Soares, Inês; Dias, Joana; Rocha, Humberto; Lopes, Maria do Carmo; Costa Ferreira, BrigidaPredictions made by using machine learning classification models are recurrent in many research fields for a variety of reasons. In some cases, feature selection can effi- ciently improve the accuracy of classifications, while reducing the computational requirements. However, some predictive studies are characterized by a high dimensionality or based on small datasets.
- A heuristic based on fuzzy inference systems for multiobjective IMRT treatment planningPublication . Dias, Joana; Rocha, Humberto; Ventura, Tiago; Costa Ferreira, Brigida; Lopes, Maria do CarmoRadiotherapy is one of the treatments used against cancer. Each treatment has to be planned considering the medical prescription for each specific patient and the information contained in the patient’s medical images. The medical prescription usually is composed by a set of dosimetry constraints, imposing maximum or minimum radiation doses that should be satisfied. Treatment planning is a trial-and-error time consuming process, where the planner has to tune several parameters (like weights and bounds) until an admissible plan is found. Radiotherapy treatment planning can be interpreted as a multiobjective optimization problem, because besides the set of dosimetry constraints there are also several conflicting objectives: maximizing the dose deposited in the volumes to treat and, at the same time, minimizing the dose delivered to healthy cells. In this paper we present a new multiobjective optimization procedure that will, in an automated way, calculate a set of potential non-dominated treatment plans. It is also possible to consider an interactive procedure whenever the planner wants to explore new regions in the non-dominated frontier. The optimization procedure is based on fuzzy inference systems. The new methodology is described and it is applied to a head-and-neck cancer case.
- Semi-supervised Self-training Approaches in Small and Unbalanced Datasets: Application to Xerostomia Radiation Side-EffectPublication . Soares, Inês; Dias, Joana; Rocha, Humberto; Khouri, Leila; Lopes, Maria do Carmo; Costa Ferreira, BrigidaSupervised learning algorithms have been widely used as predictors and applied in a myriad of studies. The accuracy of the classification algorithms is strongly dependent on the existence of large and balanced training sets. The existence of a reduced number of labeled data can deeply affect the use of supervised approaches. In these cases, semi-supervised learning algorithms can be a way to circumvent the problem.