-Martínez, jlfm@uniovi.es, 0034 985 103 199. AbstractPurpose: The cure rate in Hodgkin Lymphoma is high, but the response along the treatment is still unpredictable and is highly variable among patients. Detecting those patients that do not respond to the treatment at early stages could bring improvements in their treatment. This research tries to identify the main biological prognostic variables currently gathered at diagnosis, and designing a simple machine learning methodology to help physicians improving the treatment response assessment. Methods: We carried out a retrospective analysis of the response to treatment for a cohort of 263 Caucasians who were diagnosed with Hodgkin Lymphoma in Asturias (Spain). For that purpose, we used a list of 35 clinical and biological variables that are currently measured at diagnosis, before any treatment begins. To establish the list of most discriminatory prognostic variables for the treatment response we designed a machine learning approach based on two different feature selection methods (Fisher's ratio and Maximum Percentile Distance) and recursive feature elimination using a nearest-neighbor classifier (k-NN). The weights of the k-NN classifier are optimized using different terms of the confusion matrix (true and false positive rates) in order to minimize risk in the decisions. Results and conclusions: We found that the optimum strategy to predict treatment response in Hodgkin lymphoma consists in solving two

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The complications of the transrectal biopsy of the prostate are frequent, autolimited and they rarely suppose a health hazard for the patients. The most frequent are haematuria and hemospermia, specially in younger patients, whereas infectious complications are rarer and in our study are more frequent in patients with smaller prostates. We must take into account that the information to the patient is very important after a prostate biopsy, so we can avoid useless consultations (for example with haematuria) and it will enable to identify important signs like fever earlier.

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