Prognostic and predictive factors in early breast cancer

Abstract

RESUMENSon muchos los factores pronósticos y predictivos potenciales en el cáncer de mama y su número aumenta continuamente. Las aportaciones a este incremento de las nuevas tecnologías para el estudio de los materiales genéticos, capaces de medir miles de genes simultáneamente en un único espécimen tumoral, son fundamentales; aunque su incorporación a la práctica clínica dependerá del adecuado diseño y análisis de las investigaciones.Sin embargo, los factores pronósticos validados son pocos y no han aumentado en la última década. Se mantienen como tales el estado de los ganglios axilares, el tamaño tumoral, el grado histológico y nuclear, la edad y los receptores de estrógeno (RE) y de progesterona (RP). Los factores predictivos validados aún son menos: sólo se consideran significativos los RE y RP. Con estudios adicionales, y la estandarización de los métodos de laboratorio, el c-erbB-2 podría añadirse pronto a los factores pronósticos y predictivos validados.Palabras clave: Cáncer de mama. Factor pronóstico. Factor predictivo. ABSTRACTThere are many potential prognostic and predictive factors in breast cancer and their number is growing every day. The contributions of the novel technologies for the study of genetic materials, that already have the capability of measuring thousands of genes simultaneously on a single tumor especimen, are of primary importance for this increment, but their integration into clinical practice will depend on the proper design and analysis of clinical investigations.However, the validated prognostic factors are few and has been unchanged for more than a decade. Accepted prognostic factors include axillary node status, tumor size, histological and nuclear grade, age and hormonal receptor status (ER and PR). Predective validated factors still are less: only ER and PR. With additional studies and the estandarización of the assays, the c-erbB-2 may soon be added to the list of prognostic and predictive validated factors.

Breast Cancer Detection Using Automated Segmentation and Genetic Algorithms

“…It implement the genetic algorithm using Galgo [ 23 ]. This software is an object-oriented programming (OOP) implementation in R. Further, it includes the code to develop models using Random Forest [ 28 , 45 ].…”

“…Other research, which is based on texture description, spectral clustering, and Support Vector Machine (SVM) for the detection of breast masses [ 27 ], also aims to obtain more informative features. Other multivariate analysis approaches have demonstrated that prognostic information and predictive factors can be obtained to identify breast cancer in its early stages [ 28 ]. Among the different techniques of digital image processing and pattern recognition that have been applied in breast cancer, the use of mutual information and a greedy selection are used for this diagnosis when the information is uniformly distributed [ 29 ].…”