Optical percutaneous needle biopsy of the liver: a pilot animal and clinical study

Dremin, Viktor; Potapova, Elena; Zherebtsov, Evgeny; Kandurova, Ksenia; Shupletsov, Valery; Alekseyev, Alexander; Mamoshin, Andrian; Dunaev, Andrey V.

doi:10.1038/s41598-020-71089-5

Cited by 35 publications

(24 citation statements)

References 61 publications

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“…The latter could be utilized to mimic human-like intellect when handling large and complex datasets, images, etc. Being part of AI, the vastly expanding field of machine learning (ML) covers a wide spectrum of applications for solving multiple scientific problems [47][48][49][50][51][52][53] as well as for cancer classification [54][55][56][57][58][59][60][61][62]. Since conventional programming processes an input data by means of particular syntax and semantics to produce a desired output, such a method is prone to multiple errors repetition.…”

Section: Introductionmentioning

confidence: 99%

Polarization-Based Histopathology Classification of Ex Vivo Colon Samples Supported by Machine Learning

et al. 2022

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In biophotonics, novel techniques and approaches are being constantly sought to assist medical doctors and to increase both sensitivity and specificity of the existing diagnostic methods. In such context, tissue polarimetry holds promise to become a valuable optical diagnostic technique as it is sensitive to tissue alterations caused by different benign and malignant formations. In our studies, multiple Mueller matrices were recorded for formalin-fixed, human, ex vivo colon specimens containing healthy and tumor zones. The available data were pre-processed to filter noise and experimental errors, and then all Mueller matrices were decomposed to derive polarimetric quantities sensitive to malignant formations in tissues. In addition, the Poincaré sphere representation of the experimental results was implemented. We also used the canonical and natural indices of polarimetric purity depolarization spaces for plotting our experimental data. A feature selection was used to perform a statistical analysis and normalization procedure on the available data, in order to create a polarimetric model for colon cancer assessment with strong predictors. Both unsupervised (principal component analysis) and supervised (logistic regression, random forest, and support vector machines) machine learning algorithms were used to extract particular features from the model and for classification purposes. The results from logistic regression allowed to evaluate the best polarimetric quantities for tumor detection, while the use of random forest yielded the highest accuracy values. Attention was paid to the correlation between the predictors in the model as well as both losses and relative risk of misclassification. Apart from the mathematical interpretation of the polarimetric quantities, the presented polarimetric model was able to support the physical interpretation of the results from previous studies and relate the latter to the samples’ health condition, respectively.

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Section: Introductionmentioning

confidence: 99%

Polarization-Based Histopathology Classification of Ex Vivo Colon Samples Supported by Machine Learning

et al. 2022

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“…The functional state of the liver can be estimated by various optical technologies capable of providing new opportunities and diagnostic criteria. Spectroscopy methods are especially promising for researchers, as they allow one to assess the perfusion-metabolic state of tissue in real time and can be used through standard biopsy tools for biopsies of different organs [5][6][7] .…”

Section: Introductionmentioning

confidence: 99%

Optical needle biopsy for multimodal detection of the malignant liver tumours

Zherebtsov

Mamoshin

Shupletsov

et al. 2022

Tissue Optics and Photonics II

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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.

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“…Optical spectroscopy has been thoroughly studied over the past years to characterize its diagnostic accuracy in diagnosing microcirculation disorders [19] or cancer on a wide variety of organs such as liver [20,21], breast [13], colon [9,22], skin [3,23,24], cervix [25,26], esophagus [27] or bronchi [28]. Those research teams decided either to use off-the shelf devices, or to develop their own devices.…”

Section: Introductionmentioning

confidence: 99%

Spatially-Resolved Multiply-Excited Autofluorescence and Diffuse Reflectance Spectroscopy: SpectroLive Medical Device for Skin In Vivo Optical Biopsy

et al. 2021

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This contribution presents the development of an optical spectroscopy device, called SpectroLive, that allows spatially-resolved multiply-excited autofluorescence and diffuse reflectance measurements. Besides describing the device, this study aims at presenting the metrological and safety regulation validations performed towards its aimed application to skin carcinoma in vivo diagnosis. This device is made of six light sources and four spectrometers for detection of the back-scattered intensity spectra collected through an optical probe (made of several optical fibers) featuring four source-to-detector separations (from 400 to 1000 µm). In order to be allowed by the French authorities to be evaluated in clinics, the SpectroLive device was successfully checked for electromagnetic compatibility and electrical and photobiological safety. In order to process spectra, spectral correction and metrological calibration were implemented in the post-processing software. Finally, we characterized the device’s sensitivity to autofluorescence detection: excitation light irradiance at the optical probe tip in contact with skin surface ranges from 2 to 11 W/m², depending on the light source. Such irradiances combined to sensitive detectors allow the device to acquire a full spectroscopic sequence within 6 s which is a short enough duration to be compatible with optical-guided surgery. All these results about sensitivity and safety make the SpectroLive device mature enough to be evaluated through a clinical trial that aims at evaluating its diagnostic accuracy for skin carcinoma diagnosis.

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Optical percutaneous needle biopsy of the liver: a pilot animal and clinical study

Cited by 35 publications

References 61 publications

Polarization-Based Histopathology Classification of Ex Vivo Colon Samples Supported by Machine Learning

Polarization-Based Histopathology Classification of Ex Vivo Colon Samples Supported by Machine Learning

Optical needle biopsy for multimodal detection of the malignant liver tumours

Spatially-Resolved Multiply-Excited Autofluorescence and Diffuse Reflectance Spectroscopy: SpectroLive Medical Device for Skin In Vivo Optical Biopsy

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