Clear cell renal cell carcinoma detection by multimodal photoacoustic tomography

Lin Li; Hanbo Wu; Shuai Hu; Yanfei Yu; Zhicong Chen; Pu Wang; Liqun Zhou; Rui Li; Lin Yao; Shuhua Yue

doi:10.1016/j.pacs.2020.100221

Clear cell renal cell carcinoma detection by multimodal photoacoustic tomography

Photoacoustics. 2020 Nov 10:21:100221. doi: 10.1016/j.pacs.2020.100221. eCollection 2021 Mar.

Authors

Lin Li¹, Hanbo Wu¹, Shuai Hu², Yanfei Yu², Zhicong Chen², Pu Wang^{1

3}, Liqun Zhou², Rui Li³, Lin Yao², Shuhua Yue¹

Affiliations

¹ Institute of Medical Photonics, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing 100083, China.
² Department of Urology, Peking University First Hospital, Beijing 100034, China.
³ Vibronix Inc., West Lafayette, IN, USA.

Abstract

There is a need for accurate and rapid detection of renal cancer in clinic. Here, we integrated photoacoustic tomography (PAT) with ultrasound imaging in a single system, which achieved tissue imaging depth about 3 mm and imaging speed about 3.5 cm²/min. We used the wavelength at 1197 nm to map lipid distribution in normal renal tissues and clear cell renal cell carcinoma (ccRCC) tissues collected from 19 patients undergone nephrectomy. Our results indicated that the photoacoustic signal from lipids was significantly higher in ccRCC tissues than that in normal tissues. Moreover, based on the quantification of lipid area ratio, we were able to differentiate normal and ccRCC with 100 % sensitivity, 80 % specificity, and area under receiver operating characteristic curve of 0.95. Our findings demonstrate that multimodal PAT can differentiate normal and ccRCC by integrating the morphologic information from ultrasound and lipid amount information from vibrational PAT.

Keywords: Clear cell renal cell carcinoma; Lipid accumulation; Photoacoustic tomography.