Classification of tissue biopsies by Raman spectroscopy guided by quantitative phase imaging and its application to bladder cancer

Taieb, Almog; Berkovic, Garry; Haifler, Miki; Cheshnovsky, Ori; Shaked, Natan T.

doi:10.1002/jbio.202200009

Cited by 2 publications

(3 citation statements)

References 39 publications

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“…A combination of label-free quantitative phase imaging of the entire unstained bladder tissue slices and localized Raman spectroscopy was performed. Sparse multinomial logistic regression of the Raman spectra classified the urothelium into benign and malignant bladder cancer tissues with 94.7% [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

Raman Spectroscopic Imaging of Human Bladder Resectates towards Intraoperative Cancer Assessment

Krafft

Popp

Bronsert

et al. 2023

Cancers

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Raman spectroscopy offers label-free assessment of bladder tissue for in vivo and ex vivo intraoperative applications. In a retrospective study, control and cancer specimens were prepared from ten human bladder resectates. Raman microspectroscopic images were collected from whole tissue samples in a closed chamber at 785 nm laser excitation using a 20× objective lens and 250 µm step size. Without further preprocessing, Raman images were decomposed by the hyperspectral unmixing algorithm vertex component analysis into endmember spectra and their abundancies. Hierarchical cluster analysis distinguished endmember Raman spectra that were assigned to normal bladder, bladder cancer, necrosis, epithelium and lipid inclusions. Interestingly, Raman spectra of microplastic particles, pigments or carotenoids were detected in 13 out of 20 specimens inside tissue and near tissue margins and their identity was confirmed by spectral library surveys. Hypotheses about the origin of these foreign materials are discussed. In conclusion, our Raman workflow and data processing protocol with minimal user interference offers advantages for future clinical translation such as intraoperative tumor detection and label-free material identification in complex matrices.

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Section: Introductionmentioning

confidence: 99%

Raman Spectroscopic Imaging of Human Bladder Resectates towards Intraoperative Cancer Assessment

Krafft

Popp

Bronsert

et al. 2023

Cancers

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“…This includes Raman imaging flow cytometry that captures Raman images of cells as they pass through the channel, thus providing spatial information about the distribution of Ramanactive molecules within each cell. [10][11][12] Another intrinsic property of a cell is its refractive index (RI), which is linked to various biophysical properties, including the cell's mechanical and electrical parameters, and can provide useful information about its internal morphological organization and concentration. [13] Several optical techniques can be used to measure the RI of cells noninvasively, with interferometric phase microscopy (IPM) being a prominent example.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Label‐Free Imaging Flow Cytometry for Cell Classification Based on Multiple Interferometric Projections Using Deep Learning

Cohen,

Dudaie,

Barnea

et al. 2023

Advanced Intelligent Systems

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A new label‐free imaging flow cytometry method for noninvasive and automated biological cell classification is presented. Each cell is rolled during flow, and its off‐axis holograms from multiple viewpoints are acquired. Using the reconstructed quantitative phase profiles of the cell projections, highly discriminating features, enabling cell detection, classification, and differentiation, are extracted via a modified ResNet‐18 deep convolutional neural network architecture. The model is first validated by classifying metastatic breast carcinoma cells (MCF‐7) and normal human mammary epithelial cells (MCF‐10A). An increase in classification accuracy by 1% is achieved when processing five interferometric projections versus processing a single interferometric projection. This model is further tested on four types of white blood cells and exhibits an accuracy increase of 5% when processing 12 interferometric projections versus processing a single interferometric projection. This approach is shown to be superior to that of using conventional 2D‐rotation augmentation, and can be used to decrease substantially the number of cell examples needed for training the classification model without impairing the results. This novel concept has great potential to be incorporated into label‐free imaging flow cytometry and improve cell classification, and be used to detect various types of medical conditions and diseases.

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Classification of tissue biopsies by Raman spectroscopy guided by quantitative phase imaging and its application to bladder cancer

Abstract: We present a multimodal label-free optical measurement approach for analyzing sliced tissue biopsies by a unique combination of quantitative phase imaging and localized Raman spectroscopy. First, label-free quantita-

Cited by 2 publications

References 39 publications

Raman Spectroscopic Imaging of Human Bladder Resectates towards Intraoperative Cancer Assessment

Raman Spectroscopic Imaging of Human Bladder Resectates towards Intraoperative Cancer Assessment

Label‐Free Imaging Flow Cytometry for Cell Classification Based on Multiple Interferometric Projections Using Deep Learning

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