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2-D images for biopsy guidance and 3-D images for treatment planning and monitoring of prostate cancer based upon spectrum analysis and neural-network classification

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11 Author(s)
Feleppa, E.J. ; Riverside Res. Inst., New York, NY, USA ; Liu, T. ; Kalisz, A. ; Manolakis, D.
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Spectrum analysis of ultrasonic echo signals has been showing potential for distinguishing cancerous from non-cancerous prostate tissues. Recently, using neural networks to classify tissue from spectrum analysis results has provided a powerful basis for imaging, guiding biopsies, and planning, executing, and monitoring therapy. ROC curves derived from leave-one-out evaluations of neural-network classifier performance have an area of 0.87±0.04 compared to an area of 0.64±0.04 for B-mode methods, which implies significantly superior differentiation of cancerous from non-cancerous prostate tissue

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Ultrasonics Symposium, 1999. Proceedings. 1999 IEEE  (Volume:2 )

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