Pre-clinical evaluation of an image-guided in-situ Raman spectroscopy navigation system for targeted prostate cancer interventions

Shams, Roozbeh; Picot, Fabien; Grajales, David; Sheehy, Guillaume; Dallaire, F.; Birlea, Mirela; Saad, Fred; Trudel, Dominique; Ménard, Cynthia; Leblond, Frédéric; Kadoury, Samuel

doi:10.1007/s11548-020-02136-9

Cited by 6 publications

(13 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“… Clinical setup: (a) ultrasound system; (b) near-infrared laser and spectrometer; (c) EM tracking system; (d) EM field generator; (e) 3D Slicer navigation system; (f) closeup of the RS probe fiber bundle next to cannula; (g) Raman excitation fiber; (h) Raman detection fibers; (i) EM sensor; and (k) schematic illustration of optical setup. 20 , 45 …”

Section: Methodsmentioning

confidence: 99%

“… 14 – 16 However, neither of these modalities allows in-situ , real-time tissue characterization, which could significantly impact the diagnosis and treatment efficacy, reducing false-negative rates and boosting personalized treatments for more than 1.4 million PCa new cases diagnosed every year worldwide. 17 – 20 …”

Section: Introductionmentioning

confidence: 99%

“… 25 – 28 Furthermore, with the development of optical fiber RS probes, this technique is moving to clinical applications 29 ; different optical probe designs have been used for in-vivo tissue characterization (in human and animal models) for targeting skin cancer in open surgeries, 30 minimally invasive diagnosis of lung cancers, 31 bladder cancer detection using a superficial and nonsuperficial Raman probes, 32 observation of skin changes after breast cancer treatment, 33 and others. 34 – 37 In prostate applications, it has been used for ex-vivo characterization and in-vivo margin detection, 19 , 20 , 23 , 38 , 39 but, to the best of our knowledge, so far, not for real-time in-vivo prostate tumor burden confirmation, which can provide great benefit for clinical procedures.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

et al. 2022

Self Cite

View full text Add to dashboard Cite

. 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 , and 21 as grade group , according to histopathological reports. The combination of the fingerprint region of the RS and radiomics showed an accuracy of 83% ( and a ), 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 ( ) or grade group ( ). 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.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

et al. 2022

Self Cite

View full text Add to dashboard Cite

show abstract

“…The technique was used to guide tumor resection where the objective was to use the vibrational spectroscopy information in combination with machine learning technology to maximize the volume of resected malignant tissue while preserving healthy tissue. Raman spectroscopy also showed promises in other clinical applications such as guided biopsy procedures in prostate surgery and neurosurgery [4, 5]. In 2011, Vargis et al presented a Raman probe used in an ex vivo human study [6] to discriminate between normal, benign and malignant areas of the cervix, leading the way towards in vivo diagnosis.…”

Section: Introductionmentioning

confidence: 99%

“…Innovative clinical applications of RS, with in vivo human surgery potential, are still in development. It is partially done by conducting new animal experiments [32] and by designing new hardware systems, including Raman widefield imaging systems [33,34] as well as fused navigation platforms with Raman probe [5]. Overall, RS has demonstrated its great potential as an assisting diagnosis tool in the clinical environment.…”

Section: Introductionmentioning

confidence: 99%

Data consistency and classification model transferability across biomedical Raman spectroscopy systems

Picot

Daoust

Sheehy

et al. 2020

Transl Biophotonics

Self Cite

View full text Add to dashboard Cite

Surgical guidance applications using Raman spectroscopy are being developed at a rapid pace in oncology to ensure safe and complete tumor resection during surgery. Clinical translation of these approaches relies on the acquisition of large spectral and histopathological data sets to train classification models. Data calibration must ensure compatibility across Raman systems and predictive model transferability to allow multi‐centric studies to be conducted. This paper addresses issues relating to Raman measurement standardization by first comparing Raman spectral measurements made on an optical phantom and acquired with nine distinct point probe systems and one wide‐field imaging instrument. Data standardization method led to normalized root‐mean‐square deviations between instruments of 2%. A classification model discriminating between white and gray matter was trained with one point probe system. When used to classify independent data sets acquired with the other systems, model predictions led to >95% accuracy, preliminarily demonstrating model transferability across different biomedical Raman spectroscopy instruments.

show abstract

Current Trends of Raman Spectroscopy in Clinic Settings: Opportunities and Challenges

Wang,

Fang,

Wang

et al. 2023

Advanced Science

View full text Add to dashboard Cite

Early clinical diagnosis, effective intraoperative guidance, and an accurate prognosis can lead to timely and effective medical treatment. The current conventional clinical methods have several limitations. Therefore, there is a need to develop faster and more reliable clinical detection, treatment, and monitoring methods to enhance their clinical applications. Raman spectroscopy is noninvasive and provides highly specific information about the molecular structure and biochemical composition of analytes in a rapid and accurate manner. It has a wide range of applications in biomedicine, materials, and clinical settings. This review primarily focuses on the application of Raman spectroscopy in clinical medicine. The advantages and limitations of Raman spectroscopy over traditional clinical methods are discussed. In addition, the advantages of combining Raman spectroscopy with machine learning, nanoparticles, and probes are demonstrated, thereby extending its applicability to different clinical phases. Examples of the clinical applications of Raman spectroscopy over the last 3 years are also integrated. Finally, various prospective approaches based on Raman spectroscopy in clinical studies are surveyed, and current challenges are discussed.

show abstract

Pre-clinical evaluation of an image-guided in-situ Raman spectroscopy navigation system for targeted prostate cancer interventions

Cited by 6 publications

References 30 publications

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

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

Data consistency and classification model transferability across biomedical Raman spectroscopy systems

Current Trends of Raman Spectroscopy in Clinic Settings: Opportunities and Challenges

Contact Info

Product

Resources

About