Abstract
Abdominal cancer is a widely prevalent group of tumours with a high level of mortality if diagnosed at a late stage. Although the cancer death rates have in general declined over the past few decades, the mortality from tumours in the hepatoduodenal area has significantly increased in recent years. The broader use of minimal access surgery (MAS) for diagnostics and treatment can significantly improve the survival rate and quality of life of patients after surgery. This work aims to develop and characterise an appropriate technical implementation for tissue endogenous fluorescence (TEF) and assess the efficiency of machine learning methods for the real-time diagnosis of tumours in the hepatoduodenal area. In this paper, we present the results of the machine learning approach applied to the optically guided MAS. We have elaborated tissue fluorescence approach with a fibre-optic probe to record the TEF and blood perfusion parameters during MAS in patients with cancers in the hepatoduodenal area. The measurements from the laser Doppler flowmetry (LDF) channel were used as a sensor of the tissue vitality to reduce variability in TEF data. Also, we evaluated how the blood perfusion oscillations are changed in the tumour tissue. The evaluated amplitudes of the cardiac (0.6–1.6 Hz) and respiratory (0.2–0.6 Hz) oscillations was significantly higher in intact tissues (p < 0.001) compared to the cancerous ones, while the myogenic (0.2–0.06 Hz) oscillation did not demonstrate any statistically significant difference. Our results demonstrate that a fibre-optic TEF probe accompanied with ML algorithms such as k-Nearest Neighbours or AdaBoost is highly promising for the real-time in situ differentiation between cancerous and healthy tissues by detecting the information about the tissue type that is encoded in the fluorescence spectrum. Also, we show that the detection can be supplemented and enhanced by parallel collection and classification of blood perfusion oscillations.
Highlights
Abdominal cancer is a widely prevalent group of tumours with a high level of mortality if diagnosed at a late stage
machine learning (ML) models underwent tuning in terms of was the best accuracy to library
Classifier had 4depth neighbours distance weighting forpossible both channels; for both the best maximum of the with decision tree weighting for both channels; for both channels, the best maximum depth of the decision turned out to be only 1; the Support Vector Machines (SVM) model had a regularisation parameter of Z = 0.1 and a linear kerneltree for turned be 450 onlynm; 1; the model had a regularisation parameter
Summary
Abdominal cancer is a widely prevalent group of tumours with a high level of mortality if diagnosed at a late stage. Invasive techniques require the use of reliable tools for real-time feedback to assist the surgeon. Most imaging approaches still need to address major issues concerning the motion artefacts, real time imaging processing and the trade-off between the parameters of resolution, frame rate and aperture diameter [6]. This set of limitations affects the minimally invasive surgical interventions as one deals with the tools of low outer diameters being applied in confined space of the abdominal cavity [7]
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