基于反向传播神经网络的前列腺癌诊断系统的诊断价值

目的 探讨基于反向传播（BP）神经网络的前列腺癌诊断系统的诊断价值。方法 收集2008年1月至2011年9月四川大学华西医院收治的941例经直肠超声检查并行穿刺活检的前列腺疾病患者的临床病理资料，在MATLAB软件中采用年龄、经直肠超声检查指标和前列腺特异性抗原（prostate specific antigen, PSA）构建基于BP神经网络的前列腺癌诊断系统，以穿刺活检结果为“金标准”，分析该诊断系统对前列腺癌的诊断价值。结果 941例前列腺疾病患者中，前列腺癌358例（38.04%），非前列腺癌583例（61.96%）。BP神经网络对前列腺癌预测的灵敏度、特异度、准确性、阳性预测值、阴性预测值分别为78.57%、92.94%、87.23%、88.00%、86.81%。结论 基于BP神经网络的年龄、经直肠超声检查联合血清PSA对前列腺癌的诊断价值高，可作为临床辅助诊断前列腺癌的重要手段。

Diagnosis Values of Back Propagation Neural Network Integrating Age, Transrectal Ultrasound Characteristics and Serum PSA for Prostate Cancer

Objective To explore the diagnosis value of back propagation (BP) neural network integrating age, transrectal ultrasound characteristics and serum prostate specific antigen (PSA) for prostate cancer. Methods The data of age, PSA, and transrectal ultrasound characteristics were collected from 941 patients who received color doppler transrectal ultrasound scan and systemic biopsies of prostates. A prostate cancer diagnosis system of BP neural network with age, transrectal ultrasound characteristics and serum PSA was developed in MATLAB software, and its diagnostic value for prostate cancer was analyzed based on the pathological results of prostatic biopsy. Results The biopsy results confirmed 358 cases of prostate cancer (38.04%) and 583 cases noncancerous prostate diseases (61.96%). The sensitivity, specificity, accuracy, positive value and negative predictive value of BP neural networks for prostate cancer diagnosis were 78.57%, 92.94%, 87.23%, 88.00% and 86.81% respectively. Conclusion Back propagation neural network with age, transrectal ultrasound characteristics and PSA shows good diagnosis value for prostate cancer.