Classificação de nódulos pulmonares em imagens tomográficas utilizando redes neurais artificiais em cascata

Lung cancer is the most common of malignant tumors, with 1.59 million new cases worldwide in 2012. Early detection is the main factor to determine the survival of patients affected by this disease. Furthermore, the correct classification is important to define the most appropriate therapeutic approach as well as suggest the prognosis and the clinical disease evolution. Among the exams used to detect lung cancer, computed tomography have been the most indicated. However, CT images are naturally complex and even experts medical are subject to fault detection or classification. In order to assist the detection of malignant tumors, computer-aided diagnosis systems have been developed to aid reduce the amount of false positives biopsies. In this work it was developed an automatic classification system of pulmonary nodules on CT images by using Artificial Neural Networks. Morphological, texture and intensity attributes were extracted from lung nodules cut tomographic images using elliptical regions of interest that they were subsequently segmented by Otsu method. These features were selected through statistical tests that compare populations (T test of Student and U test of Mann-Whitney); from which it originated a ranking. The features after selected, were inserted in Artificial Neural Networks (backpropagation) to compose two types of classification; one to classify nodules in malignant and benign (network 1); and another to classify two types of malignancies (network 2); featuring a cascade classifier. The best networks were associated and its performance was measured by the area under the ROC curve, where the network 1 and network 2 achieved performance equal to 0.901 and 0.892 respectively.

Tratamento tópico para xerostomia pós-radioterapia para câncer de cabeça e pescoço – Revisão Sistemática

The purpose of this systematic review was to compare the effectiveness of topical treatments to minimize post-radiotherapy xerostomia. PubMed, Cochrane Library (CENTRAL) and LILACS databases were searched without restriction on date or language until the 6thAugust, 2015. Key-wordsused for searching were radiotherapy, xerostomia and saliva. Two independent reviewers screened titles and abstracts, carried out data extraction and assessed risk of bias. The first search identified 429 articles. From these, 117 studies were selected for full-text reading, from which 18 were included in the qualitative synthesis. From the eighteen articles included, seven were non- controlled clinical trial, one article was controlled clinical trial and ten studies were randomized clinical trials (three clinical trials were placebo controlled and seven were crossover). By the assessment of the quality of the studies included, ten showed high risk of bias, four showed moderate risk of bias and four presented low risk of bias. All interventions were considered effective in treating xerostomia (mucin, polysaccharides, aloe vera, rape oil, linseed oil, carboxymethylcellulose, polyethylene oxide, pilocarpine and systems of care for xerostomia - gel, paste and mouthwash). Meta-analysis could not be performed due to heterogeneity between thestudiesand interventions. This systematic review showed that a single and general protocol for topical treatment of xerostomia post-radiotherapy does not exist and that follow-up visits should be performed to validate the individualized treatment plan.