Prediction of response to hormonal treatment in metastatic breast cancer

Prediction of outcome and individualization of therapeutic strategies are challenging problems in oncology. Predictive parameters for response to hormonal treatment include the expression of hormone receptor, the extent and location of metastatic spread, disease-free interval, patient age, response to prior hormonal therapy, grading, and more recently, some molecular markers like the expression of HER-2/neu. The use of conventional statistics for prediction of response to hormonal treatment is limited by non-linearities and complex interactions between predictive factors. Modern computational mathematical models like artificial neural networks, entropy-based inductive algorithms or chi(2) interaction detection algorithms can describe these interactions and generate classification models and decision structures. They can be used to predict the clinical outcome for individual patients. In contrast to conventional methods, the level of confidence for the predictions can reach 90% and more. This might be an important step towards further individualization of therapeutic strategies.