Dr. Frédérick Dallaire Profile

Dr. Frédérick Dallaire

at Polytechnique Montréal

SPIE Involvement:

Author

Publications (16)

SPIE Journal Paper | 24 January 2025 Open Access

Open Access

Quantitative assessment of the generalizability of a brain tumor Raman spectroscopy machine learning model to various tumor types including astrocytoma and oligodendroglioma

Frédéric Leblond, Frédérick Dallaire, Katherine Ember, Alice Le Moël, Victor Blanquez-Yeste, Hugo Tavera, Guillaume Sheehy, Trang Tran, Marie-Christine Guiot, Alexander Weil, Roy Dudley, Costas Hadjipanayis, Kevin Petrecca

JBO, Vol. 30, Issue 01, 010501, (January 2025) https://doi.org/10.1117/12.10.1117/1.JBO.30.1.010501

KEYWORDS: Tumors, Brain, Raman spectroscopy, Cancer detection, Data modeling, Machine learning, Brain tissue, Education and training, Signal to noise ratio, Surgery

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SignificanceMaximal safe resection of brain tumors can be performed by neurosurgeons through the use of accurate and practical guidance tools that provide real-time information during surgery. Current established adjuvant intraoperative technologies include neuronavigation guidance, intraoperative imaging (MRI and ultrasound), and 5-ALA for fluorescence-guided surgery.AimWe have developed intraoperative Raman spectroscopy as a real-time decision support system for neurosurgical guidance in brain tumors. Using a machine learning model, trained on data from a multicenter clinical study involving 67 patients, the device achieved diagnostic accuracies of 91% for glioblastoma, 97% for brain metastases, and 96% for meningiomas. Here, the aim is to assess the generalizability of a predictive model trained with data from this study to other types of brain tumors.ApproachA method was developed to assess the generalizability of the model, quantifying performance for tumors including astrocytoma, oligodendroglioma and ependymoma, pediatric glioblastoma, and classification of glioblastoma data acquired in the presence of 5-ALA induced fluorescence. Statistical analyses were conducted to assess the impact of vibrational bands beyond contributors identified in our previous research.ResultsA machine learning brain tumor detection model showed a positive predictive value (PPV) of 70% for astrocytoma, 74% for oligodendroglioma, and 100% for ependymoma. Furthermore, the PPV was 100% in classifying spectra from a pediatric glioblastoma and 90% for detecting adult glioblastoma labeled with 5-ALA-induced fluorescence. Univariate statistical analyses applied to individual vibrational bands demonstrated that the inclusion of Raman biomarkers unexploited to date had the potential to improve detectability, setting the stage for future advances.ConclusionsDeveloping predictive models relying on the inelastic scattering contrast from a wider pool of Raman bands may improve detection accuracy for astrocytoma and oligodendroglioma. To do so, larger tumor datasets and a higher Raman photon signal-to-noise ratio may be required.

SPIE Journal Paper | 6 June 2024 Open Access

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

Presentation

Towards non-contact and real-time large field-of-view single-band Raman spectroscopy imaging in brain cancer surgery

Frédéric Leblond, Sandryne David, Samaneh Pahlavani, Nassim Ksantini, Frédérick Dallaire, Guillaume Sheehy, Costas Hadjapanayis, Kevin Petrecca, Brian Wilson

Proceedings Volume PC12825, PC1282505 (2024) https://doi.org/10.1117/12.3001248

KEYWORDS: Imaging spectroscopy, Raman spectroscopy, Cancer detection, Brain, Surgery, Brain cancer, Tumors, Imaging systems, Cancer, Vibration

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Proceedings Article | 13 March 2024 Presentation

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 | 9 September 2023 Open Access

Open Access

Subsecond lung cancer detection within a heterogeneous background of normal and benign tissue using single-point Raman spectroscopy

Frédéric Leblond, Frédérick Dallaire, Trang Tran, Rajeev Yadav, Kelly Aubertin, Eric Goudie, Philippe Romeo, Christopher Kent, Charles Leduc, Moishe Liberman

JBO, Vol. 28, Issue 09, 090501, (September 2023) https://doi.org/10.1117/12.10.1117/1.JBO.28.9.090501

KEYWORDS: Tissues, Lung cancer, Raman spectroscopy, Cancer detection, Cancer, Tumor growth modeling, Lung, Diagnostics, Data modeling, Education and training

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SPIE Journal Paper | 31 May 2023 Open Access

Open Access

Preclinical evaluation of Raman spectroscopy for pedicular screw insertion surgical guidance in a porcine spine model

Ivan Kosik, Frédérick Dallaire, Layla Pires, Trang Tran, Frédéric Leblond, Brian Wilson

JBO, Vol. 28, Issue 05, 057003, (May 2023) https://doi.org/10.1117/12.10.1117/1.JBO.28.5.057003

KEYWORDS: Tissues, Bone, Raman spectroscopy, Data modeling, Spinal cord, In vivo imaging, Surgery, Education and training, Muscles, Cartilage

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SignificanceOrthopedic surgery is frequently performed but currently lacks consensus and availability of ideal guidance methods, resulting in high variability of outcomes. Misdirected insertion of surgical instruments can lead to weak anchorage and unreliable fixation along with risk to critical structures including the spinal cord. Current methods for surgical guidance using conventional medical imaging are indirect and time-consuming with unclear advantages.AimThe purpose of this study was to investigate the potential of intraoperative in situ near-infrared Raman spectroscopy (RS) combined with machine learning in guiding pedicular screw insertion in the spine.ApproachA portable system equipped with a hand-held RS probe was used to make fingerprint measurements on freshly excised porcine vertebrae, identifying six tissue types: bone, spinal cord, fat, cartilage, ligament, and muscle. Supervised machine learning techniques were used to train—and test on independent hold-out data subsets—a six-class model as well as two-class models engineered to distinguish bone from soft tissue. The two-class models were further tested using in vivo spectral fingerprint measurements made during intra-pedicular drilling in a porcine spine model.ResultsThe five-class model achieved >96 % accuracy in distinguish all six tissue classes when applied onto a hold-out testing data subset. The binary classifier detecting bone versus soft tissue (all soft tissue or spinal cord only) yielded 100% accuracy. When applied onto in vivo measurements performed during interpedicular drilling, the soft tissue detection models correctly detected all spinal canal breaches.ConclusionsWe provide a foundation for RS in the orthopedic surgical guidance field. It shows that RS combined with machine learning is a rapid and accurate modality capable of discriminating tissues that are typically encountered in orthopedic procedures, including pedicle screw placement. Future development of integrated RS probes and surgical instruments promises better guidance options for the orthopedic surgeon and better patient outcomes.

SPIE Journal Paper | 30 March 2023 Open Access

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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Proceedings Article | 15 March 2023 Poster + Paper

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 | 21 February 2023 Open Access

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

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.

SPIE Journal Paper | 1 September 2022 Open Access

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.

SPIE Journal Paper | 9 February 2022 Open Access

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

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.

SPIE Journal Paper | 21 April 2020 Open Access

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 | 1 April 2020 Presentation

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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SPIE Journal Paper | 14 February 2019 Open Access

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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Showing 5 of 16 publications

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