Ms. Trang Tran Profile

Trang Tran

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Publications (8)

Proceedings Article | 13 March 2024 Presentation

Novel Raman spectroscopy fiber optics system design can enable real-time intraoperative tissue probing for focal cortical dysplasia in children

Hugo Le Moël, Trang Tran, Sandryne David, Guillaume Sheehy, Roy Dudley, Frédéric Leblond

Proceedings Volume PC12831, PC1283105 (2024) https://doi.org/10.1117/12.3001084

KEYWORDS: Raman spectroscopy, Tissues, Fiber optics, Design and modelling, Tissue optics, Surgery, Epilepsy, Brain tissue, Brain, Signal processing

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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 | 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 | 9 September 2023 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

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.

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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 | 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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