Prof. Dominique Trudel Profile

Prof. Dominique Trudel

at CRCHUM

SPIE Involvement:

Author

Publications (13)

Proceedings Article | 13 March 2024 Presentation

Rapid detection of COVID-19 in liquid saliva using reagent-free Raman spectroscopy

Katherine Ember, Nassim Ksantini, Frédéric Dallaire, Guillaume Sheehy, Antoine Noé, Trang Tran, Madeleine Durand, Dominique Trudel, Frederic Leblond

Proceedings Volume PC12850, PC1285005 (2024) https://doi.org/10.1117/12.3001877

KEYWORDS: COVID 19, Artificial intelligence, Portability, Statistical modeling, Statistical analysis, Raman spectroscopy, Spectroscopy, Receivers, Point-of-care devices, Machine learning

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SPIE Journal Paper | 30 March 2023 Open Access

In situ Raman spectroscopy and machine learning unveil biomolecular alterations in invasive breast cancer

Sandryne David, Trang Tran, Frédérick Dallaire, Guillaume Sheehy, Feryel Azzi, Dominique Trudel, Francine Tremblay, Atilla Omeroglu, Frédéric Leblond, Sarkis Meterissian

JBO, Vol. 28, Issue 03, 036009, (March 2023) https://doi.org/10.1117/12.10.1117/1.JBO.28.3.036009

KEYWORDS: Cancer, Raman spectroscopy, Tissues, Tumor growth modeling, Machine learning, Breast, Surgery, Breast cancer, Biomolecules, Tumors

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SPIE Journal Paper | 21 February 2023 Open Access

Open-sourced Raman spectroscopy data processing package implementing a baseline removal algorithm validated from multiple datasets acquired in human tissue and biofluids

Guillaume Sheehy, Fabien Picot, Frédérick Dallaire, Katherine Ember, Tien Nguyen, Kevin Petrecca, Dominique Trudel, Frédéric Leblond

JBO, Vol. 28, Issue 02, 025002, (February 2023) https://doi.org/10.1117/12.10.1117/1.JBO.28.2.025002

KEYWORDS: Raman spectroscopy, Data processing, Tissues, Equipment, Bubbles, Biological samples, Raman scattering, Fluorescence, Aluminum, Spectroscopy

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SPIE Journal Paper | 9 September 2022 Open Access

Image-guided Raman spectroscopy navigation system to improve transperineal prostate cancer detection. Part 2: in-vivo tumor-targeting using a classification model combining spectral and MRI-radiomics features

David Grajales , Fabien Picot, Roozbeh Shams, Frédérick Dallaire, Guillaume Sheehy, Stephanie Alley, Maroie Barkati, Guila Delouya, Jean-Francois Carrier, Mirela Birlea, Dominique Trudel, Frédéric Leblond, Cynthia Ménard, Samuel Kadoury

JBO, Vol. 27, Issue 09, 095004, (September 2022) https://doi.org/10.1117/12.10.1117/1.JBO.27.9.095004

KEYWORDS: Raman spectroscopy, Navigation systems, Remote sensing, Tumors, Image segmentation, Data modeling, Principal component analysis, In vivo imaging, Biopsy, Tissue optics

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Significance: The diagnosis and treatment of prostate cancer (PCa) are limited by a lack of intraoperative information to accurately target tumors with needles for biopsy and brachytherapy. An innovative image-guidance technique using optical devices could improve the diagnostic yield of biopsy and efficacy of radiotherapy.Aim: To evaluate the performance of multimodal PCa detection using biomolecular features from in-situ Raman spectroscopy (RS) combined with image-based (radiomics) features from multiparametric magnetic resonance images (mpMRI).Approach: In a prospective pilot clinical study, 18 patients were recruited and underwent high-dose-rate brachytherapy. Multimodality image fusion (preoperative mpMRI with intraoperative transrectal ultrasound) combined with electromagnetic tracking was used to navigate an RS needle in the prostate prior to brachytherapy. This resulting dataset consisted of Raman spectra and co-located radiomics features from mpMRI. Feature selection was performed with the constraint that no more than 10 features were retained overall from a combination of inelastic scattering spectra and radiomics. These features were used to train support vector machine classifiers for PCa detection based on leave-one-patient-out cross-validation.Results: RS along with biopsy samples were acquired from 47 sites along the insertion trajectory of the fiber-optics needle: 26 were confirmed as benign or grade group = 1, and 21 as grade group >1, according to histopathological reports. The combination of the fingerprint region of the RS and radiomics showed an accuracy of 83% (sensitivity = 81 % and a specificity = 85 % ), outperforming by more than 9% models trained with either spectroscopic or mpMRI data alone. An optimal number of features was identified between 6 and 8 features, which have good potential for discriminating grade group ≥1 / grade group <1 (accuracy = 87 % ) or grade group >1 / grade group ≤1 (accuracy = 91 % ).Conclusions: In-situ Raman spectroscopy combined with mpMRI radiomics features can lead to highly accurate PCa detection for improved in-vivo targeting of biopsy sample collection and radiotherapy seed placement.

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SPIE Journal Paper | 1 September 2022 Open Access

Image-guided Raman spectroscopy navigation system to improve transperineal prostate cancer detection. Part 1: Raman spectroscopy fiber-optics system and in situ tissue characterization

Fabien Picot, Roozbeh Shams, Frédérick Dallaire, Guillaume Sheehy, Tran Trang, David Grajales, Mirela Birlea, Dominique Trudel, Cynthia Ménard, Samuel Kadoury, Frédéric Leblond

JBO, Vol. 27, Issue 09, 095003, (September 2022) https://doi.org/10.1117/12.10.1117/1.JBO.27.9.095003

KEYWORDS: Raman spectroscopy, Prostate, Cancer, In vivo imaging, Tissues, Principal component analysis, Biopsy, Remote sensing, Navigation systems, Tumors

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Significance: The diagnosis of prostate cancer (PCa) and focal treatment by brachytherapy are limited by the lack of precise intraoperative information to target tumors during biopsy collection and radiation seed placement. Image-guidance techniques could improve the safety and diagnostic yield of biopsy collection as well as increase the efficacy of radiotherapy.Aim: To estimate the accuracy of PCa detection using in situ Raman spectroscopy (RS) in a pilot in-human clinical study and assess biochemical differences between in vivo and ex vivo measurements.Approach: A new miniature RS fiber-optics system equipped with an electromagnetic (EM) tracker was guided by trans-rectal ultrasound-guided imaging, fused with preoperative magnetic resonance imaging to acquire 49 spectra in situ (in vivo) from 18 PCa patients. In addition, 179 spectra were acquired ex vivo in fresh prostate samples from 14 patients who underwent radical prostatectomy. Two machine-learning models were trained to discriminate cancer from normal prostate tissue from both in situ and ex vivo datasets.Results: A support vector machine (SVM) model was trained on the in situ dataset and its performance was evaluated using leave-one-patient-out cross validation from 28 normal prostate measurements and 21 in-tumor measurements. The model performed at 86% sensitivity and 72% specificity. Similarly, an SVM model was trained with the ex vivo dataset from 152 normal prostate measurements and 27 tumor measurements showing reduced cancer detection performance mostly attributable to spatial registration inaccuracies between probe measurements and histology assessment. A qualitative comparison between in situ and ex vivo measurements demonstrated a one-to-one correspondence and similar ratios between the main Raman bands (e.g., amide I-II bands, phenylalanine).Conclusions: PCa detection can be achieved using RS and machine learning models for image-guidance applications using in situ measurements during prostate biopsy procedures.

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SPIE Journal Paper | 9 February 2022 Open Access

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

Katherine Ember, François Daoust, Myriam Mahfoud, Frédérick Dallaire, Esmat Zamani, Trang Tran, Arthur Plante, Mame-Kany Diop, Tien Nguyen, Amélie St-Georges-Robillard, Nassim Ksantini, Julie Lanthier, Antoine Filiatrault, Guillaume Sheehy, Gabriel Beaudoin, Caroline Quach, Dominique Trudel, Frédéric Leblond

JBO, Vol. 27, Issue 02, 025002, (February 2022) https://doi.org/10.1117/12.10.1117/1.JBO.27.2.025002

KEYWORDS: Raman spectroscopy, Crystals, Proteins, Statistical modeling, Spectroscopy, Data modeling, Molecules, Statistical analysis, Feature selection, Aluminum

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SPIE Journal Paper | 6 November 2021 Open Access

Dimensional reduction based on peak fitting of Raman micro spectroscopy data improves detection of prostate cancer in tissue specimens

Arthur Plante, Frédérick Dallaire, Andrée-Anne Grosset, Tien Nguyen, Mirela Birlea, Jahg Wong, François Daoust, Noémi Roy, André Kougioumoutzakis, Feryel Azzi, Kelly Aubertin, Samuel Kadoury, Mathieu Latour, Roula Albadine, Susan Prendeville, Paul Boutros, Michael Fraser, Rob Bristow, Theodorus Van Der Kwast, Michele Orain, Hervé Brisson, Nazim Benzerdjeb, Hélène Hovington, Alain Bergeron, Yves Fradet, Bernard Têtu, Fred Saad, Dominique Trudel, Frédéric Leblond

JBO, Vol. 26, Issue 11, 116501, (November 2021) https://doi.org/10.1117/12.10.1117/1.JBO.26.11.116501

KEYWORDS: Raman spectroscopy, Cancer, Tissues, Proteins, Tumor growth modeling, Spectroscopy, Micro raman spectroscopy, Prostate, Prostate cancer, Machine learning

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Significance: Prostate cancer is the most common cancer among men. An accurate diagnosis of its severity at detection plays a major role in improving their survival. Recently, machine learning models using biomarkers identified from Raman micro-spectroscopy discriminated intraductal carcinoma of the prostate (IDC-P) from cancer tissue with a ≥85 % detection accuracy and differentiated high-grade prostatic intraepithelial neoplasia (HGPIN) from IDC-P with a ≥97.8 % accuracy. Aim: To improve the classification performance of machine learning models identifying different types of prostate cancer tissue using a new dimensional reduction technique. Approach: A radial basis function (RBF) kernel support vector machine (SVM) model was trained on Raman spectra of prostate tissue from a 272-patient cohort (Centre hospitalier de l’Université de Montréal, CHUM) and tested on two independent cohorts of 76 patients [University Health Network (UHN)] and 135 patients (Centre hospitalier universitaire de Québec-Université Laval, CHUQc-UL). Two types of engineered features were used. Individual intensity features, i.e., Raman signal intensity measured at particular wavelengths and novel Raman spectra fitted peak features consisting of peak heights and widths. Results: Combining engineered features improved classification performance for the three aforementioned classification tasks. The improvements for IDC-P/cancer classification for the UHN and CHUQc-UL testing sets in accuracy, sensitivity, specificity, and area under the curve (AUC) are (numbers in parenthesis are associated with the CHUQc-UL testing set): +4 % (+8 % ), +7 % (+9 % ), +2 % (6%), +9 (+9) with respect to the current best models. Discrimination between HGPIN and IDC-P was also improved in both testing cohorts: +2.2 % (+1.7 % ), +4.5 % (+3.6 % ), +0 % (+0 % ), +2.3 (+0). While no global improvements were obtained for the normal versus cancer classification task [+0 % (−2 % ), +0 % (−3 % ), +2 % (−2 % ), +4 (+3)], the AUC was improved in both testing sets. Conclusions: Combining individual intensity features and novel Raman fitted peak features, improved the classification performance on two independent and multicenter testing sets in comparison to using only individual intensity features.

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SPIE Journal Paper | 21 April 2020 Open Access

Quantitative spectral quality assessment technique validated using intraoperative in vivo Raman spectroscopy measurements

Frédérick Dallaire, Fabien Picot, Jean-Philippe Tremblay, Guillaume Sheehy, Émile Lemoine, Rajeev Agarwal, Samuel Kadoury, Dominique Trudel, Frédéric Lesage, Kevin Petrecca, Frédéric Leblond

JBO, Vol. 25, Issue 04, 040501, (April 2020) https://doi.org/10.1117/12.10.1117/1.JBO.25.4.040501

KEYWORDS: Raman spectroscopy, Tissues, Signal to noise ratio, In vivo imaging, Cancer, Brain, Data acquisition, Luminescence, Tissue optics, Visualization

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Proceedings Article | 11 July 2019 Paper

Multimodal imaging platform for surgical guidance during epilepsy surgery

Audrey Laurence, Emile Beaulieu, Dang Nguyen, Alain Bouthillier, Dominique Trudel, Frederic Leblond

Proceedings Volume 11074, 1107425 (2019) https://doi.org/10.1117/12.2527232

KEYWORDS: Epilepsy, Auto-fluorescence imaging, Imaging systems, Surgery, Hemodynamics, Microscopes, Multimodal imaging, Hyperspectral imaging, Optical properties, Luminescence

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Proceedings Article | 4 March 2019 Presentation

Post-prostatectomy spatial frequency domain imaging for positive margins identification using endogenous tissue fluorescence, absorption and scattering (Conference Presentation)

Emile Beaulieu, Audrey Laurence, Mathieu Latour, Roula Albadine, Dominique Trudel, Frédéric Leblond

Proceedings Volume 10871, 108710B (2019) https://doi.org/10.1117/12.2510067

KEYWORDS: Luminescence, Tissues, Absorption, Scattering, Prostate, Imaging systems, Cancer, Light scattering, Laser scattering, Laser tissue interaction

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SPIE Journal Paper | 14 February 2019 Open Access

Integration of a Raman spectroscopy system to a robotic-assisted surgical system for real-time tissue characterization during radical prostatectomy procedures

Michael Pinto, Kevin Zorn, Jean-Philippe Tremblay, Joannie Desroches, Frédérick Dallaire, Kelly Aubertin, Eric Marple, Chris Kent, Frederic Leblond, Dominique Trudel, Frederic Lesage

JBO, Vol. 24, Issue 02, 025001, (February 2019) https://doi.org/10.1117/12.10.1117/1.JBO.24.2.025001

KEYWORDS: Raman spectroscopy, Prostate, Robotic systems, Tissue optics, Tissues, Surgery, Spectroscopy, In vivo imaging, Robotics, Prostate cancer

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SPIE Journal Paper | 12 October 2017

Dimension reduction technique using a multilayered descriptor for high-precision classification of ovarian cancer tissue using optical coherence tomography: a feasibility study

Catherine St. Pierre, Wendy-Julie Madore, Étienne De Montigny, Dominique Trudel, Caroline Boudoux, Nicolas Godbout, Anne-Marie Mes-Masson, Kurosh Rahimi, Frédéric Leblond

JMI, Vol. 4, Issue 04, 041306, (October 2017) https://doi.org/10.1117/12.10.1117/1.JMI.4.4.041306

KEYWORDS: Optical coherence tomography, Tissues, Image classification, Cancer, Ovarian cancer, Tissue optics, Multilayers, Binary data, Dimension reduction, Image segmentation

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Proceedings Article | 2 May 2017 Presentation

Raman spectroscopy for prostate cancer detection and characterization (Conference Presentation)

Kelly Aubertin, Quoc-Huy Trinh, Michael Jermyn, Catherine St-Pierre, Maria-Claudia Vladoiu, Andrée-Anne Grosset, Fred Saad, Dominique Trudel, Frédéric Leblond

Proceedings Volume 10038, 100380K (2017) https://doi.org/10.1117/12.2253117

KEYWORDS: Tumors, Laser therapeutics, Prostate cancer, Raman spectroscopy, Prostate, Tissues, Cancer, Microscopes, Machine learning, Collagen

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