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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

机译：基于频谱分析和神经网络分类的2D图像用于活检指导，而3D图像用于治疗计划和监测前列腺癌

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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/spl plusmn/0.04 compared to an area of 0.64/spl plusmn/0.04 for B-mode methods, which implies significantly superior differentiation of cancerous from non-cancerous prostate tissue.
机译：超声回波信号的频谱分析已显示出将癌性组织与非癌性前列腺组织区分开的潜力。最近，使用神经网络从频谱分析结果中对组织进行分类，为成像，指导活检以及规划，执行和监视治疗提供了强大的基础。从神经网络分类器性能的一劳永逸评估中得出的ROC曲线的面积为0.87 / spl plusmn / 0.04，而B模式方法的面积为0.64 / spl plusmn / 0.04，这意味着癌性肿瘤的明显优越性来自非癌性前列腺组织。

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