Zherebtsov Evgeny, Zajnulina Marina, Kandurova Ksenia, Potapova Elena, Dremin Viktor, Mamoshin Andrian, Sokolovski Sergei, Dunaev Andrey, Rafailov Edik U
Research and Development Center of Biomedical Photonics, Orel State University, 302026 Orel, Russia.
Faculty of Information Technology and Electrical Engineering, University of Oulu, Optoelectronics and Measurement Techniques Unit, 90570 Oulu, Finland.
Diagnostics (Basel). 2020 Oct 27;10(11):873. doi: 10.3390/diagnostics10110873.
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 ( < 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.
腹部癌症是一类广泛流行的肿瘤，如果在晚期被诊断出来，死亡率很高。尽管在过去几十年里癌症死亡率总体上有所下降，但近年来肝十二指肠区域肿瘤的死亡率显著上升。更广泛地使用微创外科手术（MAS）进行诊断和治疗，可以显著提高患者术后的生存率和生活质量。这项工作旨在开发并表征一种适用于组织内源性荧光（TEF）的技术实施方案，并评估机器学习方法对肝十二指肠区域肿瘤进行实时诊断的效率。在本文中，我们展示了应用于光学引导MAS的机器学习方法的结果。我们精心设计了一种组织荧光方法，使用光纤探头在肝十二指肠区域癌症患者的MAS过程中记录TEF和血流灌注参数。激光多普勒血流仪（LDF）通道的测量结果被用作组织活力的传感器，以减少TEF数据的变异性。此外，我们还评估了肿瘤组织中血流灌注振荡是如何变化的。与癌组织相比，完整组织中心脏（0.6 - 1.6赫兹）和呼吸（0.2 - 0.6赫兹）振荡的评估幅度显著更高（<0.001），而肌源性（0.2 - 0.06赫兹）振荡没有显示出任何统计学上的显著差异。我们的结果表明，配备k近邻或AdaBoost等机器学习算法的光纤TEF探头，通过检测荧光光谱中编码的组织类型信息，在实时原位区分癌组织和健康组织方面具有很大的前景。此外，我们还表明，通过并行收集和分类血流灌注振荡，可以补充和增强检测效果。
