Computer-aided classification of breast cancer nuclei.

Breast cancer is the most common malignancy affecting the female population in industrialized countries. Prognostic factors, such as steroid receptors visualized in biopsy slides, provide critical information to oncologists regarding the hormonal status of the individual tumors. These factors influence the choice of treatment and help in predicting patient survival and probability of recurrence. The objective of this paper is to introduce a new computer-aided system for the classification of breast cancer nuclei based on neural networks. Currently, medical experts assess steroid receptors in breast cancer biopsy slides mostly manually using four- or five-level grading schemes. These schemes are based on the assessment of two parameters: number of nuclei positive and their staining intensity. Available computerized systems define their own grading schemes based on automated measurements of low-level features, such as optical density, texture, area, and others. However, the findings produced by these systems may not be readily comprehensible by the majority of medical experts who have been accustomed to manual assessment schemes. Moreover, findings from one system cannot be directly compared to findings obtained from other computerized systems. To date, no standardized assessment scheme exists for computerized systems, while interobserver and intraobserver variabilities limit the utility of the routinely used manual assessment schemes. In this paper a new system for computer-aided biopsy analysis is introduced. Here, we focus on the system's nuclear classification module. The input to this module consists of a set of six local and global features: optical density, two chromaticity indices, a variance based texture measure, global nuclei density mean, and variance. The output of the nuclei classification module consists of a membership label in a zero to four grading scheme for each detected nucleus. The classification module is based on a feedforward neural network trained in a supervised fashion to classify the nuclear feature vectors. The sample data comprises 3015 nuclei from 28 images that were classified by a human expert. A Sammon plot visualization of the six dimensional input feature space shows that the classification problem is quite difficult. The neural network used in the classification module achieved 72% accuracy. Our result indicate that by using a nuclear classification module such as the one introduced in this paper it is possible to translate low-level system measurements into a vocabulary that is familiar to medical experts. Thus, a contribution is made to the standardization of grading schemes in addition to improving the accuracy in grading breast cancer nuclei.