Abstract
Significant numbers of liver biopsies fail to yield representative tissue samples. This study was conducted to evaluate the ability of LED-based diffuse reflectance spectroscopy to discriminate tumors from liver parenchyma. Ex vivo spectra were acquired from malignant lesions and liver parenchyma of 32 patients who underwent liver resection using a white light source and several LEDs. Integrated spectra of two combined LEDs with emission peaks at 470 nm and 515 nm were classified with 98.4% sensitivity and 99.2% specificity. The promising results could yield to a simple handheld and cost-efficient tool for real-time tissue differentiation implemented in a biopsy needle.
Highlights
Diagnosis and management of liver tumors play a significant role in modern cancer treatment
We present the ability of selected light emitting diodes (LEDs) in comparison with Diffuse reflectance spectroscopy (DRS) using a white light source (WS-based DRS) in order to discriminate liver parenchyma from tumorous tissue
These findings are similar to those that have been reported for different ex vivo studies on human liver tissue of tumor patients with primary cancer [14], as well as with liver metastases [17,18,19]
Summary
Diagnosis and management of liver tumors play a significant role in modern cancer treatment. Liver biopsy is a crucial component for initial diagnosis as well as molecular analysis during the course of treatment to define the best patient tailored treatment. Even with imaging guidance methods, up to 15% of liver biopsies are insufficient due to a sampling of non-informative tissues [2]. Such failures are caused by a lack of recognition of tumor tissue during biopsy, which results in a repetition of the biopsy increasing the risk of hemorrhage and possibly needle tract seeding. In combination with the standard imaging techniques the integration of an in vivo tissue differentiation device in the tip of a common biopsy needle yields an additional source of local information which can potentially achieve a higher biopsy success rate
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