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Title: |
Hybrid Methods for Automated Diagnosis of Breast Tumors | |||||||||||||||||||
| Authors: | Casey Diekman, B.S., Wei He, Ph.D., Nagabhushana Prabhu, Ph.D., Ph.D., and Harvey Cramer, M.D. | |||||||||||||||||||
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Objective: To design and analyze a new family of hybrid methods for the diagnosis of breast tumors using fine needle aspirates.
Study Design: We present a radically new approach to the design of diagnosis systems. In the new approach, a nonlinear classifier with high sensitivity but low specificity is hybridized with a linear classifier having low sensitivity but high specificity. Data from the Wisconsin Breast Cancer Database are used to evaluate, computationally, the performance of the hybrid classifiers. Results: The diagnosis scheme obtained by hybridizing the nonlinear classifier ellipsoidal multisurface method (EMSM) with the linear classifier proximal support vector machine (PSVM) was found to have a mean sensitivity of 97.36% and a mean specificity of 95.14% and was found to yield a 2.44% improvement in the reliability of positive diagnosis over that of EMSM at the expense of 0.4% degradation in the reliability of negative diagnosis, again compared to EMSM. At the 95% confidence level we can trust the hybrid method to be 96.19-98.53% correct in its malignant diagnosis of new tumors and 93.57-96.71% correct in its benign diagnosis. Conclusion: Hybrid diagnosis schemes represent a significant paradigm shift and provide a promising new technique to improve the specificity of nonlinear classifiers without seriously affecting the high sensitivity of nonlinear classifiers. (Analyt Quant Cytol Histol 2003;25:183-190) |
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| Keywords: | breast cancer; aspiration biopsy; diagnosis, computer-assisted | |||||||||||||||||||
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