Mr. Roozbeh Shams Profile

Roozbeh Shams

Software Developer

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Publications (3)

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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Proceedings Article | 1 April 2016 Paper

Dynamic programming on a tree for ultrasound elastography

Roozbeh Shams, Mathieu Boily, Paul Martineau, Hassan Rivaz

Proceedings Volume 9790, 97901F (2016) https://doi.org/10.1117/12.2217345

KEYWORDS: Statistical analysis, Ultrasonography, Elastography, Computer programming, Tissues, In vivo imaging, Image analysis, Real time image processing, Lithium, Data acquisition, Liver cancer

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