Mr. François Daoust Profile

François Daoust

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Publications (8)

SPIE Journal Paper | 6 June 2024 Open Access

Macroscopic inelastic scattering imaging using a hyperspectral line-scanning system identifies invasive breast cancer in lumpectomy and mastectomy specimens

Sandryne David, Hugo Tavera, Tran Trang, Frédérick Dallaire, François Daoust, Francine Tremblay, Lara Richer, Sarkis Meterissian, Frédéric Leblond

JBO, Vol. 29, Issue 06, 065004, (June 2024) https://doi.org/10.1117/12.10.1117/1.JBO.29.6.065004

KEYWORDS: Cancer, Raman spectroscopy, Hyperspectral imaging, Tissues, Surgery, Breast, Tumor growth modeling, Breast cancer, Adipose tissue, Biological samples

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Proceedings Article | 13 March 2024 Presentation

Line-scanning Raman spectroscopy imaging system for cancer margin detection with spatial-offset depth-resolved imaging capabilities

Hugo Tavera, Sandryne David, François Daoust, Guillaume Sheehy, Trang Tran, Sarkis Meterissian, Frédéric Leblond

Proceedings Volume PC12836, PC1283609 (2024) https://doi.org/10.1117/12.3003091

KEYWORDS: Imaging systems, Raman spectroscopy, Cancer detection, Tissues, Cancer, Visible radiation, RGB color model, Near infrared, Imaging devices, Beam path

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Proceedings Article | 15 March 2023 Poster + Paper

Raman macroscopic imaging system for intraoperative brain cancer detection

Patrick Orsini, Joannie Desroches, François Daoust, Hugo Tavera, Frédérick Dallaire, Keven Savard, Jacques Bismuth, Philippe Mckoy, Israel Veilleux, Kevin Petrecca, Frédéric Leblond

Proceedings Volume 12361, 123610H (2023) https://doi.org/10.1117/12.2668370

KEYWORDS: Imaging systems, Raman spectroscopy, Adipose tissue, Tissues, Biological imaging, Tissue optics, Muscles, Design and modelling, Helium, Classification systems

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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 | 12 February 2021 Open Access

Handheld macroscopic Raman spectroscopy imaging instrument for machine-learning-based molecular tissue margins characterization

François Daoust, Tien Nguyen, Patrick Orsini, Jacques Bismuth, Marie-Maude de Denus-Baillargeon, Israel Veilleux, Alexandre Wetter, Philippe McKoy, Isabelle Dicaire, Maroun Massabki, Kevin Petrecca, Frédéric Leblond

JBO, Vol. 26, Issue 02, 022911, (February 2021) https://doi.org/10.1117/12.10.1117/1.JBO.26.2.022911

KEYWORDS: Tissues, Raman spectroscopy, Imaging systems, Imaging spectroscopy, Spatial resolution, Cancer, Surgery, Tumors, Tissue optics, Fiber lasers

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Proceedings Article | 1 April 2020 Presentation

Data compatibility across point probe Raman spectroscopy systems and its impact on statistical models portability (Conference Presentation)

Fabien Picot, Guillaume Sheehy, Frédéric Dallaire, Jean-Phillipe Tremblay, François Daoust, Émile Lemoine, Layal Chaikho, Théophile Bégin, Kevin Petrecca, Samuel Kadoury, Frédéric Leblond

Proceedings Volume 11362, 113620F (2020) https://doi.org/10.1117/12.2555848

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Proceedings Article | 9 March 2020 Presentation

Large field of view macroscopic Raman line-scanning imaging system for neuro-oncology applications (Conference Presentation)

Francois Daoust, Patrick Orsini, Jacques Bismuth, Marie-Maude de Denus-Baillargeon, Israel Veilleux, Alexandre Wetter, Philippe McKoy, Isabelle Dicaire, Maroun Massabki, Kevin Petrecca, Frédéric Leblond

Proceedings Volume 11236, 112360E (2020) https://doi.org/10.1117/12.2543047

KEYWORDS: Imaging systems, Raman spectroscopy, Neuro-oncology, Spatial resolution, In vivo imaging, Real time imaging, Coherence imaging, Endoscopes, RGB color model, Cameras

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Showing 5 of 8 publications

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