Rapidly growing knowledge about the nature and behaviour of breast cancer has led to many treatment modalities. Consequently, the possibilities of individualizing the treatment of breast cancer increase. The major tool for the determination of an optimal treatment plan is the estimation of the extent of the disease: in other words, staging. As a consequence, together with the expected result of the treatment, the stage of the disease gives information on the prognosis of the patient. Current staging systems insufficiently describe the clinically important features of breast cancer with respect to management and outcome: local and regional extent, invasiveness, aggressiveness, the state of dissemination, and the effectiveness of different treatment modalities. For staging of the local and regional extent, histology plays a prominent role and should be incorporated in future staging systems. Histological workup therefore needs standardisation. Histological parameters as tumour size, grade, nodal status, and vascular invasion are also the most important prognostic factors. Many so-called biological prognostic factors are related to the invasiveness and aggressiveness (metastatic potential) of the tumour, and therefore to the prognosis of the patient. However, these factors do not necessarily predict the effectiveness of certain systemic treatments. Only if the biological foundation of a prognostic factor is completely clarified can treatment be based on this knowledge, and the factor will become a predictor for the treatment effect. Many "biological" prognostic factors do not fulfil this main criterion and are therefore not useful for clinical decision making. A clinically useful staging system covers three primary aims:
(1) to guide locoregional treatment, (2) to prognosticate the chance of survival, and (3) to indicate who needs what kind of adjuvant treatment. For the conception of a new staging system the following steps should be taken: standardization of all aspects of histology, identification of regional nodal involvement, and validation of prognostic factors with respect to their predictive value to treatment outcome.