@inproceedings{cfdf9810a34e40c39409edbb94570260,
title = "Time-resolved fluorescence spectroscopy in differential diagnosis of liver cancer in vivo",
abstract = "This work reports a machine-learning-based approach to interpret time-resolved fluorescence spectroscopy data acquired during optical biopsy of the liver. The approach allowed to differentiate between liver parenchyma and tumor with sensitivity and specificity above 0.91 and 0.79, respectively, providing differential diagnosis of liver cancer (primary malignant tumor, metastases, or benign) with sensitivity and specificity of at least 0.80 and 0.95.",
keywords = "liver cancer, machine learning, optical biopsy, time-resolved fluorescence spectroscopy",
author = "Potapova, {E. V.} and Shupletsov, {V. V.} and Dremin, {V. V.} and Mamoshin, {A. V.} and Dunaev, {A. V.}",
year = "2024",
month = aug,
day = "19",
doi = "10.1109/ICLO59702.2024.10624371",
language = "English",
series = "Proceedings of International Conference Laser Optics",
publisher = "IEEE",
pages = "493",
booktitle = "2024 International Conference Laser Optics (ICLO)",
address = "United States",
note = "2024 International Conference Laser Optics, ICLO 2024 ; Conference date: 01-07-2024 Through 05-07-2024",
}