Accumulating evidence suggests that characteristics of pre-treatment FDG-PET could be used as prognostic factors to predict outcomes in different cancer sites. Current risk analyses are limited to visual assessment or direct uptake value measurements. We are investigating intensity-volume histogram metrics and shape and texture features extracted from PET images to predict patient's response to treatment. These approaches were demonstrated using datasets from cervix and head and neck cancers, where AUC of 0.76 and 1.0 were achieved, respectively. The preliminary results suggest that the proposed approaches could potentially provide better tools and discriminant power for utilizing functional imaging in clinical prognosis.

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This paper proposes an algorithm for the set covering problem based on the metaheuristic Ant Colony Optimization (ACO) called Ant-Set, which uses a lineoriented approach and a novelty pheromone manipulation based on the connections between components of the construction graph, while also applying a local search. The algorithm is compared with other ACO-based approaches. The results obtained show the effectiveness of the algorithm and the impact of the pheromone manipulation.

Stream-based recommender systems are an active research field, relying on incremental algorithms to update models by incorporating new data on a single pass, discarding such data after processing. A limitation of solely including new data is the accumulation of obsolete concepts, which eventually raises accuracy and scalability concerns. In this work, we propose a gradual forgetting technique for incremental neighborhood-based methods that locally forgets items based on recency and popularity, by decreasing importance of neighborhood of items for every incoming observation to emphasize more recent and reinforced ones. The technique includes parameters to increase diversity, by retaining less popular yet relevant items, and scalability, by pruning obsolete connections not reinforced by new data. Experiments conducted by extending a recent incremental graph-based approach highlight the effectiveness of the proposed technique, as its application improved scalability and diversity, outperforming baselines.