Malignant tumours of the liver are one of the most common causes of cancer death worldwide. The presence and type of tumours can be verified only after histological and cytological analysis of a sample obtained during percutaneous needle biopsy. However, the relatively high probability of false-negative results caused by insufficient tissue samples remains a problem. The introduction of real-time methods to navigate the needle during the biopsy can serve as a solution that reduces the number of mistakes. The paper presents the results of measurements in the murine model of hepatocellular carcinoma by a system equipped with fluorescence lifetime and diffuse reflectance channels, as well as a novel needle optical probe compatible with 17.5G standard biopsy needles. The experimental setup developed allowed us to evaluate the parameters of short and slow fluorescence lifetimes, fluorescence intensities and blood oxygen saturation in tissues. We demonstrated that the set of recorded parameters allows us to distinguish the malignant tissue, control liver tissue and metabolically changed liver tissues near the tumour. We suppose that the proposed technique, supported by machine learning, can significantly decrease the rate of false-negative results obtained with percutaneous needle biopsy.
|Journal||Proceedings of SPIE - The International Society for Optical Engineering|
|Publication status||Published - 19 May 2022|
|Event||Tissue Optics and Photonics II 2022 - Virtual, Online|
Duration: 9 May 2022 → 20 May 2022
Bibliographical noteCopyright 2022 SPIE. One print or electronic copy may be made for personal use only. Systematic reproduction, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.
The study was supported by the Russian Science Foundation (project 21-15-00325).
- adenocarcinoma metastasis
- diffuse reflectance spectroscopy
- fluorescence lifetime
- hepatocellular carcinoma
- liver cancer
- optical biopsy
- reactive oxygen species