Identification of intraductal carcinoma of the prostate on tissue specimens using Raman micro-spectroscopy: A diagnostic accuracy case–control study with multicohort validation

Grosset, Andrée-Anne; Dallaire, F.; Nguyen, Tien Thanh; Birlea, Mirela; Wong, Jahg; Daoust, François; Roy, Noémi; Kougioumoutzakis, André; Azzi, Féryel; Aubertin, Kelly; Kadoury, Samuel; Latour, Mathieu; Albadine, Roula; Prendeville, Susan; Boutros, Paul C.; Fraser, Michael; Bristow, R.; Kwast, Theodorus van der; Orain, Michèle; Brisson, Hervé; Benzerdjeb, Nazim; Hovington, Hélène; Bergeron, Alain; Fradet, Yves; Têtu, Bernard; Saad, Fred; Leblond, Frédéric; Trudel, Dominique

doi:10.1371/journal.pmed.1003281

Cited by 26 publications

(35 citation statements)

References 53 publications

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“…In the following paragraphs a selection of investigations performed on pathological calcifications developed in breast [102][103][104][105][106][107][108][109][110][111][112][113], kidney , gallbladder [140][141][142][143][144][145][146][147][148][149], prostate [150][151][152][153][154][155][156][157][158][159][160][161], skin [162][163][164][165][166][167][168], testicular microlithiasis [169][170][171][172][173][174][175][176]…”

Section: Selected Applications Of Raman Spectroscopymentioning

confidence: 99%

Raman opportunities in the field of pathological calcifications

Lucas¹,

Bazin²,

Daudon³

2022

Comptes Rendus. Chimie

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The aim of this contribution is to present to the community the very unique diagnostic capabilities of Raman spectroscopy in the context of pathological calcifications. By offering the clinician the opportunity to determine within seconds the chemical composition of micron-sized crystallites, possibly in vivo, Raman analyses find no equivalent among the large set of analytical tools available. After a brief recap of some peculiar aspects of this spectrometry and a presentation of the latest instrumental developments, this review gathers the most recent literature on the use of Raman to diagnose various lithiases or microcrystalline pathologies.

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Section: Selected Applications Of Raman Spectroscopymentioning

confidence: 99%

Raman opportunities in the field of pathological calcifications

Lucas¹,

Bazin²,

Daudon³

2022

Comptes Rendus. Chimie

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“…Raman spectroscopy can identify biomolecular features (such as lipids, proteins, nucleic acids, and amino acids) within biological samples. In recent years, clinical applications of this light-based technique have gained traction in oncology, [19][20][21] inflammatory diseases, 22,23 transplantation 24 and virology. 25,26 For example, Camacho et al 25 developed surface enhanced Raman sensors for the detection of the Zika virus by functionalizing coreshell nanoparticles with Zika ZIKV NS1 antibodies and reported a sensibility of 10 ng/mL.…”

Section: Raman Spectroscopymentioning

confidence: 99%

“…The following data preprocessing steps were applied to every individual measurement: 1) removal of cosmic rays was imperative due to the long acquisition time, 2) smoothing using a Savitzky-Golay filter of order 3 with a window size of 11, 3) background subtraction of signals produced by the aluminum slides and autofluorescence using a custom adaptation of the rolling ball algorithm 41 , 4) cropping the region below 1100 cm -1 due to the large variances at lower wavenumber shifts, 5) averaging of the repeat measurements taken at a given spatial point, and 6) standard normal variate (SNV) normalization. 21…”

Section: Spectral Data Processingmentioning

confidence: 99%

Saliva-based detection of COVID-19 infection in a real-world setting using reagent-free Raman spectroscopy and machine learning

Ember

Daoust

Mahfoud

et al. 2021

Preprint

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Significance: The primary method of COVID-19 detection is reverse transcription polymerase chain reaction (RT-PCR) testing. PCR test sensitivity may decrease as more variants of concern arise. Aim: We aimed to develop a reagent-free way to detect COVID-19 in a real-world setting with minimal constraints on sample acquisition. Approach: We present a workflow for collecting, preparing and imaging dried saliva supernatant droplets using a non-invasive, label-free technique known as Raman spectroscopy to detect changes in the molecular profile of saliva associated with COVID-19 infection. Results: Using machine learning and droplet segmentation, amongst all confounding factors, we discriminated between COVID-positive and negative individuals yielding receiver operating coefficient (ROC) curves with an area under curve (AUC) of 0.8 in both males (79% sensitivity, 75% specificity) and females (84% sensitivity, 64% specificity). Taking the sex of the saliva donor into account increased the AUC by 5%. Conclusion:These findings may pave the way for new rapid Raman spectroscopic screening tools for COVID-19 and other infectious diseases.

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“…Firstly, paraffin embedding, formalin fixation as well as dewaxing were widely used in tissue preparation. [17][18][19] They were usually accompanied with changes of tissue composition and loss of tissue fluid, resulting in amounts of untrue and incomplete information acquired. [20,21] Therefore, "zero processing" tissue specimens may be the best choice for Raman.…”

Section: Introductionmentioning

confidence: 99%

Fast Characteristic of Skin Lesions by Machine-Learning of Raman Spectrum

Zhang

Wang

et al. 2021

Preprint

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Background: The traditional diagnosis of skin lesions mainly relies on dermoscope and pathological biopsy, of which the former is non-objective and the latter is invasive and time-consuming. It is necessary to find an objective and non-invasive inspection method for the diagnosis of skin cancer which is the most common malignant tumor. Herein, we aimed to fast identify the skin cancers on ultrathin frozen fresh tissue sections by combining Raman spectroscopy detection and machine learning technology. Methods and material: 22 fresh frozen tissue sections including 3 squamous cell carcinomas, 11 basal cell carcinomas, 2 malignant melanomas, 3 seborrheic keratosis, and 3 melanocytic nevi, were included and performed Raman detection. To prevent the discrete Raman data distribution affecting the generalization ability of the learning model, a series of adaptive preprocessing algorithms were first applied to standardize the raw Raman data of five skin lesions. The processed Raman data were performed visualized cluster analysis by principal components analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE). And, using K-nearest Neighbor (KNN) and support vector machine (SVM) classifiers, two predictive models for diagnose were established and evaluated in the training set and test set by the confusion matrixes and receiver operating characteristic (ROC) curves.Results: The mean variance Raman spectrum graph of 5 skin lesion types were acquired after standardization procession and 4 peak positions with large differences were found. Through dimensionality reduction by PCA and t-SNE, the visual clustering results of Raman data showed heterogeneous intra-cluster homogeneity and inter-cluster dispersion. The test accuracies reached 94.56% and 98.94% in KNN and SVM classifiers respectively. The areas under the ROCs of the two classifiers, in the category dimension and the sample dimension, were all more than 0.99 which is close to the perfect classification effect. Conclusions: Raman spectroscopy is a competitive candidate for the fast and accurate diagnosis of skin lesions and the molecular information provided may be used in the pathological classification, predicting immunotherapy responsiveness and stratifying prognostic risk. Furthermore, the combination of Raman spectroscopy and machine learning methods showed great diagnostic capabilities with high accuracy is a promising tool for the diagnosis of skin lesions.

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Identification of intraductal carcinoma of the prostate on tissue specimens using Raman micro-spectroscopy: A diagnostic accuracy case–control study with multicohort validation

Cited by 26 publications

References 53 publications

Raman opportunities in the field of pathological calcifications

Raman opportunities in the field of pathological calcifications

Saliva-based detection of COVID-19 infection in a real-world setting using reagent-free Raman spectroscopy and machine learning

Fast Characteristic of Skin Lesions by Machine-Learning of Raman Spectrum

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