Abstract
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.