Automated breast cancer diagnosis based on fine needle aspiration.

Objective: To design and analyze an automated diagnostic system for breast carcinoma based on fine needle aspiration (FNA).

Study Design: FNA is a noninvasive alternative to surgical biopsy for the diagnosis of breast carcinoma. Widespread clinical use of FNA is limited by the relatively poor interobserver reproducibility of the visual interpretation of FNA images. To overcome the reproducibility problem, past research has focused on the development of automated diagnosis systems that yield accurate, reproducible results. While automated diagnosis is, by definition, reproducible, it has yet to achieve diagnostic accuracy comparable to that of surgical biopsy. In this article we describe a sophisticated new diagnostic system in which the mean sensitivity (of FNA diagnosis) approaches that of surgical biopsy. The diagnostic system that we devised analyzes the digital FNA data extracted from FNA images. To achieve high sensitivity, the system needs to solve large, equality-constrained, integer nonlinear optimization problems repeatedly. Powerful techniques from the theory of Lie groups and a novel optimization technique are built into the system to solve the underlying optimization problems effectively. The system is trained using digital data from FNA samples with confirmed diagnosis. To analyze the diagnostic accuracy of the system > 8,000 computational experiments were performed using digital FNA data from the Wisconsin Breast Cancer Database.

Results: The system has a mean sensitivity of 99.62% and mean specificity of 93.31%. Statistical analysis shows that at the 95% confidence level, the system can be trusted to correctly diagnose new malignant FNA samples with an accuracy of 99.44-99.8% and new benign FNA samples with an accuracy of 92.43-93.93%.

Conclusion: The diagnostic system is robust and has higher sensitivity than do all the other systems reported in the literature. The specificity of the system needs to be improved.