Multispectral tissue mapping - a novel concept for the optical evaluation of liver malignancy

Abstract

Introduction: Malignancy alters the optical absorption behaviour of liver tissue. This process can be evaluated using multi spectral analysis, a method that has been proposed as an intraoperative diagnostic tool. Existing systems either require invasive probe insertion or have limited spectral sensitivity which reduces their diagnostic ability. This work describes the development and evaluation of a novel, imaging method called multispectral tissue mapping, which addresses these issues. Methods: The imaging system (spectral range 1100-1700nm) consists of a tuneable excitation light source and a near-infrared camera. Following ethics approval, absorption spectra from resected human liver specimen were acquired ex-vivo. Twenty repeat acquisitions of malignant and non-malignant tissues from the same patient were averaged to reduce variability and subsequently compared. Generalised linear mixed modelling was employed for statistical analysis of serial changes in pixel based absorption (pixel area≈0.5mm2). Subsequently absorption data was transformed into augmented colour images with the aim of identifying cancer tissue. Results: Multiple spectra from liver specimens with colorectal cancer liver metastasis (CRLM) (three patients, n=505.141 pixel values) and hepatocellular carcinoma (HCC) (two patients, n=674.477 pixel values) were examined. Malignant tissue had a heterogeneous appearance on multispectral imaging compared to non-malignant tissue. Whereas absorption was increased in CRLM (maxima 1410nm; OR 0.005, CI 0.004-0.007) it was reduced in HCC (minimum 1440nm; OR 5.4, CI 3.9-7.6). Data was randomised into two groups and subsequently a predictive algorithm was built and tested. On ROC analysis predictive accuracy was 0.6 (area under curve) for HCC and 0.7 for CRLM, respectively. Augmented colour processing empirically enhanced image contrast between malignant and non-malignant tissue. Conclusion: Multispectral tissue mapping enables identification of liver malignancy on a sub-millimetre level. Augmented colour representation may enhance image contrast between malignant and normal liver tissues. Studies on larger datasets are required to improve the accuracy of the predictive algorithm.