Tumor fibroso pleural ¿son realmente benignos? ¿Cuándo deben operarse? / Pleural fibrous tumor, are they really benign? When should they have operation?

El tumor fibroso pleural es un tumor habitualmente asintomático, benigno y de lento crecimiento, que en un pequeño porcentaje de los casos tiene un comportamiento más agresivo. Para definir los criterios de malignidad es necesario un análisis inmunohistoquímico. El tratamiento de elección es la resección quirúrgica completa con márgenes de seguridad ya sea por toracotomía o por videotoracoscopia en función del tamaño. Se encuentra en estudio la utilización de terapia adyuvante radioterápica o quimioterápica que en el momento actual no cuenta con resultados significativos. Presentamos dos casos que fueron tratados de forma quirúrgica, con extirpación completa siendo el primero definido como maligno según los criterios histológicos y el segundo benigno, pero de alto riesgo. Solo uno de ellos recibió terapia adyuvante. (AU)

Solitary fibrous pleural tumor is a usually asymptomatic, benign and slow-growing tumor, which in a small percentage of cases has a more aggressive behavior. To define the malignancy criteria, an immunohistochemical analysis is necessary. The gold standard treatment is a complete surgical resection with safety margins either by thoracotomy or by videothoracoscopy depending on the size. The use of radiotherapy or chemotherapy as an adjuvant therapy is under study, which at the present time does not have significant results. We present two cases that were treated surgically, with complete removal, one being classified as malignant according to histological criteria and the other benign but high risk. Only one of them received adjuvant therapy. (AU)

Feature selection in pathology detection using hybrid multidimensional analysis.

Heuristical algorithms can reduce the computational complexity. Such methods require of some stopping criteria (cost function). Some of these cost functions are based on statistics like univariate and multivariate methods of analysis. Dimensional reduction techniques such as principal component analysis (PCA) allow to find a lower dimension transformed space based on data variance, but this procedure does not take into account information about classes separability, the direction of maximum variance does not necessarily correspond to the direction of maximum separability. In this work, we propose a feature selection algorithm with heuristic search that uses multivariate analysis of variance (MANOVA) as the cost function. This technique is put to test by classifying hypernasal from normal voices of CLP (Cleft Lip and/or Palate) patients. The classification performance, computational time and reduction ratio are also considered by the comparison with an alternate feature selection method founded on unfolding the multivariate analysis into univariate and bivariate analysis.

[Tooth retention in the anterior region]. / Retenciones dentarias en región anterior.

Bibliography was researched in pursuit of an improved classification of retained canine teeth that may, ideally, prove to be simpler, easy to apply in any individual case, and uncomplicated to remember. Desirably, such classification would allow establishing the location of each retention in the maxilla, mandible, or both, and its relationship with adjacent structures. Additionally, a proposal was put forth for a retained canine teeth classification that may clearly and simply outline the retention, be easily understood and remembered, and may also apply to retained dentary organs with some similarity to canines as to their shape and root number.