Abstract

Abstract

Abstract
CT-guide Percutaneous Transthoracic Biopsies (PTB) performed in the Radiology Department of Garcia de Orta Hospital between 2002 and 2004 to evaluate undetermined pulmonary lesions were retrospectively analysed. 89 fine needle aspiration biopsies (FNAB) and 13 core needle biopsies (CNB) were performed on 92 patients (67 men, mean age: 64.4 years). 82 lesions (89%) were nodular lesions (mean diameter: 3.8±1.7 cm, 65 peripheral). We did not observe complications among patients who underwent CNB; minor complications and pneumothorax requiring drainage occurred in 11 FNAB. 72 FNAB were considered adequate for cytology diagnosis; 72% of them positive for malignancy. All CNB were adequate and conclusive. From the 7 CNB performed on patients with previous FNAB, 3 allowed a better histological characterization and in 3 cases of inadequate FNAB, CNB was conclusive. All malignant lesions were nodules: 20 adenocarcinoma, 13 non-small cell lung cancer (SCLC), 10 epidermoid tumours, 5 small-cell lung cancer, 2 carcinoids, 1 bronchiolo alveolar carcinoma, 1 malignant mesothelioma and 8 metastasis. Unspecific/ inflammatory lesions (n=5) were the most frequent benign lesions. Malignant lesions were more prevalent in older patients (p=0.007) and were larger (p=0.006). Spiculated and lobulated contour (p=0.05) were more prevalent in malignant lesions while regular contour was more frequent among benign lesions (p=0.0001). Gender, smoking, location, pleural tag, homogenous attenuation, cavitation, calcification, necrosis and air bronchogram did not differ significantly between benign and malignant nodules. This study shows that CT-guided PTB is a safe and effective procedure in the evaluation of undetermined pulmonary lesions.

Abstract
This paper presents the AFRANCI tool for the development of Multi-Strategy learning systems. Designing a Multi-Strategy system using AFRANCI is a two step process. The use interactively designs the structure of the system and then chooses the learning strategies for each module. After providing the datasets all modules as automatically trained. The system is aware and takes into consideration the inter-dependency of the modules. The tool has built-in learning algorithms but can use external programs implementing the learning algorithms. The tool has the following facilities. It allows any user to design in an interactive and easy fashion the structure of the target system. The structure of the target system is a collection of interconnected modules. The user may then choose the different learning algorithms to construct each module. The tool has several built-in Machine Learning algorithms has has interfaces that enables it to use external learning tools like WEKA and CN2. AFRANCI uses the interdependency of the modules to determine the sequence of training. For each module the system uses a wrapper to tune automatically the parameters of the learning algorithm. In the final step of the design sequence AFRANCI generates a compact and legible ready-to-use ANSI C open-source code for the final system.

Abstract

Abstract
In this paper a Branch-and-Bound (BB) algorithm is developed to obtain an optimal solution to the single source uncapacitated minimum cost Network Flow Problem (NFP) with general concave costs. Concave NFPs are NP-Hard, even for the simplest version therefore, there is a scarcity of exact methods to address them in their full generality. The BB algorithm presented here can be used to solve optimally single source uncapacitated minimum cost NFPs with any kind of concave arc costs. The bounding is based on the computation of lower bounds derived from state space relaxations of a dynamic programming formulation. The relaxations, which are the subject of the paper (Fontes et al., 2005b) and also briefly discussed here, involve the use of non-injective mapping functions, which guarantee a reduction on the cardinality of the state space. Branching is performed by either fixing an arc as part of the final solution or by removing it from the final solution. Computational results are reported and compared to available alternative methods for addressing the same type of problems. It could be concluded that our BB algorithm has better performance and the results have also shown evidence that it has a sub-exponential time growth.