Photoacoustic spectroscopy can generate abundant chemical and physical information about biological tissues. However, this abundance of information makes it difficult to compare these tissues directly. Data mining methods can circumvent this problem. We describe the application of machine-learning methods (including unsupervised hierarchical clustering and supervised classification) to the diagnosis of prostate cancer by photoacoustic spectrum analysis. We focus on the content and distribution of hemoglobin, collagen, and lipids, because these molecules change during the development of prostate cancer. A higher correlation among the ultrasonic power spectra of these chemical components is observed in cancerous than in normal tissues, indicating that the microstructural distributions in cancerous tissues are more consistent. Different classifiers applied in cancer-tissue diagnoses achieved an accuracy of 82 % (better than that of standard clinical methods). The technique thus exhibits great potential for painless early diagnosis of aggressive prostate cancer.

光声光谱可以产生有关生物组织的丰富的化学和物理信息。然而，如此丰富的信息使得直接比较这些组织变得困难。数据挖掘方法可以规避这个问题。我们描述了机器学习方法（包括无监督层次聚类和监督分类）在通过光声光谱分析诊断前列腺癌中的应用。我们关注血红蛋白、胶原蛋白和脂质的含量和分布，因为这些分子在前列腺癌的发展过程中会发生变化。在癌组织中观察到这些化学成分的超声功率谱之间的相关性高于正常组织，表明癌组织中的微观结构分布更加一致。应用于癌症组织诊断的不同分类器的准确率达到 82%（优于标准临床方法）。因此，该技术在侵袭性前列腺癌的无痛早期诊断方面展现出巨大的潜力。