Photoacoustic classification of tumor malignancy based on support vector machine

Abstract

Accurate diagnosis of malignancy tumor in early stage is great significance to achieve high curability, which could improve survival rate in this stage. Precise classification to differentiate malignancy of tumors is favourable to reduce cost in treatment when there is no obvious features in radiology diagnose in early phase. Photoacoustic tomography (PAT) is a burgeoning new imaging modality, which combines optical contrast and ultrasound penetrating in deep medium. However, it has not been fully exploited on the capability of PAT to discriminate tumor’s malignancy. In this paper, a multistatic classification approach in PAT is proposed, which could discriminate malignant/benign tumors based on its morphological feature in clinical diagnosis that tumors usually show different shape irregularity compared with healthy tissue. The multistatic photoacoustic waves were used to extract two different features to differentiate the two types of tumors with high accuracy (<90%) in three different scenarios using Support Vector Machines (SVM). In addition, two conventional PAT image reconstructing algorithms are also performed to reconstruct images as a comparative study, which unfortunately cannot differentiate their malignancy precisely because of limited detector bandwidth and severe acoustic distortion. We performed the feasibility study in this paper with both simulation and experimental results, which shows that the proposed multistatic photoacoustic classification method to distinguish between malignant and benign tumors works well, and could be easily applied for state-of-art array-based PAT system to ameliorate the diagnostic accuracy.