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Abstract

Significance: Maximal 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.

Aim: We 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.

Approach: A 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.

Results: A 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.

Conclusions: Developing 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.

Leblond et al., “Quantitative assessment of the generalizability of a brain tumor Raman spectroscopy machine learning model to various tumor types including astrocytoma and oligodendroglioma”, Journal of Biomedical Optics, 30(1), 2025. doi: 10.1117/1.jbo.30.1.010501

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Abstract
Intraoperative Raman spectroscopy uses near-infrared laser light to gain molecular information without causing damage. It can be used in vivo or ex vivo without exogenous contrast agents. Clinically, the technique was primarily used with machine learning for in situ tumor detection with fiberoptics probes analyzing tissue at sub-millimeter scales one point at the time. Here we report the development of a whole-specimen spectroscopic imaging system designed to detect cancer cells at the margins of surgical specimens. The system has a field of view covering a square area of side one centimeter with a pixel size of a quarter of a millimeter . First, a tumor detection model was developed from data acquired using a point-probe in 24 glioblastoma patients that had a detection sensitivity of 90% and a specificity of 95%. That model was then used to produce cancer prediction maps of nine glioblastoma specimens from five patients with validation based on histopathology analyses. The results preliminarily demonstrate the instrument was able to detect tissue areas associated with cancer cells from the Raman peaks associated with the amino acids phenylalanine and tryptophan as well as the relative concentration of lipids and proteins linked with deformations of the CH₂ and CH₃ bonds.

Daoust et al., “Preliminary study demonstrating cancer cells detection at the margins of whole glioblastoma specimens with Raman spectroscopy imaging”, Scientific Reports 15, 2025. doi: 10.1038/s41598-025-87109-1

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Abstract
Safe and effective brain tumor surgery aims to remove tumor tissue, not non-tumoral brain. This is a challenge since tumor cells are often not visually distinguishable from peritumoral brain during surgery. To address this, we conducted a multicenter study testing whether the Sentry System could distinguish the three most common types of brain tumors from brain tissue in a label-free manner. The Sentry System is a new real time, in situ brain tumor detection device that merges Raman spectroscopy with machine learning tissue classifiers. Nine hundred and seventy-six in situ spectroscopy measurements and colocalized tissue specimens were acquired from 67 patients undergoing surgery for glioblastoma, brain metastases, or meningioma to assess tumor classification. The device achieved diagnostic accuracies of 91% for glioblastoma, 97% for brain metastases, and 96% for meningiomas. These data show that the Sentry System discriminated tumor containing tissue from non-tumoral brain in real time and prior to resection.

Ember et al., “In situ brain tumor detection using a Raman spectroscopy system—results of a multicenter study”, Scientific Reports 14, 2024. doi: 10.1038/s41598-024-62543-9

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Abstract
Here we introduce a Raman spectroscopy approach combining multi-spectral imaging and a new fluorescence background subtraction technique to image individual Raman peaks in less than 5 seconds over a square field-of-view of 1-centimeter sides with 350 micrometers resolution. First, human data is presented supporting the feasibility of achieving cancer detection with high sensitivity and specificity – in brain, breast, lung, and ovarian/endometrium tissue – using no more than three biochemically interpretable biomarkers associated with the inelastic scattering signal from specific Raman peaks. Second, a proof-of-principle study in biological tissue is presented demonstrating the feasibility of detecting a single Raman band – here the CH₂/CH₃ deformation bands from proteins and lipids – using a conventional multi-spectral imaging system in combination with the new background removal method. This study paves the way for the development of a new Raman imaging technique that is rapid, label-free, and wide field.

David et al., “Toward noncontact macroscopic imaging of multiple cancers using multi-spectral inelastic scattering detection”, Journal of Biophotonics, 2024. doi: 10.1002/jbio.202400087

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Abstract

Significance: Of patients with early-stage breast cancer, 60% to 75% undergo breast-conserving surgery. Of those, 20% or more need a second surgery because of an incomplete tumor resection only discovered days after surgery. An intraoperative imaging technology allowing cancer detection on the margins of breast specimens could reduce re-excision procedure rates and improve patient survival.

Aim: We aimed to develop an experimental protocol using hyperspectral line-scanning Raman spectroscopy to image fresh breast specimens from cancer patients. Our objective was to determine whether macroscopic specimen images could be produced to distinguish invasive breast cancer from normal tissue structures.

Approach: A hyperspectral inelastic scattering imaging instrument was used to interrogate eight specimens from six patients undergoing breast cancer surgery. Machine learning models trained with a different system to distinguish cancer from normal breast structures were used to produce tissue maps with a field-of-view of 1 cm² classifying each pixel as either cancer, adipose, or other normal tissues. The predictive model results were compared with spatially correlated histology maps of the specimens.

Results: A total of eight specimens from six patients were imaged. Four of the hyperspectral images were associated with specimens containing cancer cells that were correctly identified by the new ex vivo pathology technique. The images associated with the remaining four specimens had no histologically detectable cancer cells, and this was also correctly predicted by the instrument.

Conclusions: We showed the potential of hyperspectral Raman imaging as an intraoperative breast cancer margin assessment technique that could help surgeons improve cosmesis and reduce the number of repeat procedures in breast cancer surgery.

David et al., “Macroscopic inelastic scattering imaging using a hyperspectral line-scanning system identifies invasive breast cancer in lumpectomy and mastectomy specimens”, Journal of Biomedical Optics, 29(6), 2024. 10.1117/1.jbo.29.6.065004

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Abstract
Significance: Lung cancer is the most frequently diagnosed cancer overall and the deadliest cancer in North America. Early diagnosis through current bronchoscopy techniques is limited by poor diagnostic yield and low specificity, especially for lesions located in peripheral pulmonary locations. Even with the emergence of robotic-assisted platforms, bronchoscopy diagnostic yields remain below 80%.

Aim: The aim of this study was to determine whether in situ single-point fingerprint (800 to 1700  cm⁻¹) Raman spectroscopy coupled with machine learning could detect lung cancer within an otherwise heterogenous background composed of normal tissue and tissue associated with benign conditions, including emphysema and bronchiolitis.

Approach: A Raman spectroscopy probe was used to measure the spectral fingerprint of normal, benign, and cancer lung tissue in 10 patients. Each interrogated specimen was characterized by histology to determine cancer type, i.e., small cell carcinoma or non-small cell carcinoma (adenocarcinoma and squamous cell carcinoma). Biomolecular information was extracted from the fingerprint spectra to identify biomolecular features that can be used for cancer detection.

Results: Supervised machine learning models were trained using leave-one-patient-out cross-validation, showing lung cancer could be detected with a sensitivity of 94% and a specificity of 80%.

Conclusions: This proof of concept demonstrates fingerprint Raman spectroscopy is a promising tool for the detection of lung cancer during diagnostic procedures and can capture biomolecular changes associated with the presence of cancer among a complex heterogeneous background within less than 1 s.

Leblond et al., “Subsecond lung cancer detection within a heterogeneous background of normal and benign tissue using single-point Raman spectroscopy”, Journal of Biomedical Optics, 28(9), 2023. doi: 10.1117/1.JBO.28.9.090501

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Abstract
Significance: As many as 60% of patients with early stage breast cancer undergo breast-conserving surgery. Of those, 20% to 35% need a second surgery because of incomplete resection of the lesions. A technology allowing in situ detection of cancer could reduce re-excision procedure rates and improve patient survival.

Aim: Raman spectroscopy was used to measure the spectral fingerprint of normal breast and cancer tissue ex-vivo. The aim was to build a machine learning model and to identify the biomolecular bands that allow one to detect invasive breast cancer.

Approach: The system was used to interrogate specimens from 20 patients undergoing lumpectomy, mastectomy, or breast reduction surgery. This resulted in 238 ex-vivo measurements spatially registered with standard histology classifying tissue as cancer, normal, or fat. A technique based on support vector machines led to the development of predictive models, and their performance was quantified using a receiver-operating-characteristic analysis.

Results: Raman spectroscopy combined with machine learning detected normal breast from ductal or lobular invasive cancer with a sensitivity of 93% and a specificity of 95%. This was achieved using a model based on only two spectral bands, including the peaks associated with C-C stretching of proteins around 940 cm⁻¹ and the symmetric ring breathing at 1004 cm⁻¹ associated with phenylalanine.

Conclusions: Detection of cancer on the margins of surgically resected breast specimen is feasible with Raman spectroscopy.

David et al., “In situ Raman spectroscopy and machine learning unveil biomolecular alterations in invasive breast cancer”, Journal of Biomedical Optics, 28(3), 2023. doi: 10.1117/1.jbo.28.3.036009

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Abstract
Up to 70% of ovarian cancer patients are diagnosed with advanced-stage disease and the degree of cytoreduction is an important survival prognostic factor. The aim of this study was to evaluate if Raman spectroscopy could detect cancer from different organs within the abdominopelvic region, including the ovaries. A Raman spectroscopy probe was used to interrogate specimens from a cohort of nine patients undergoing cytoreductive surgery, including four ovarian cancer patients and three patients with endometrial cancer. A feature-selection algorithm was developed to determine which spectral bands contributed to cancer detection and a machine-learning model was trained. The model could detect cancer using only eight spectral bands. The receiver-operating-characteristic curve had an area-under-the-curve of 0.96, corresponding to an accuracy, a sensitivity and a specificity of 90%, 93% and 88%, respectively. These results provide evidence multispectral Raman spectroscopy could be developed to detect ovarian cancer intraoperatively.

David et al. “Multispectral label-free Raman spectroscopy can detect ovarian and endometrial cancer with high accuracy”, Journal of Biophotonics, 2021. doi: 10.1002/jbio.202100198

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Abstract
Surgical excision of the whole prostate through a radical prostatectomy procedure is part of the standard of care for prostate cancer. Positive surgical margins (cancer cells having spread into surrounding nonresected tissue) occur in as many as 1 in 5 cases and strongly correlate with disease recurrence and the requirement of adjuvant treatment. Margin assessment is currently only performed by pathologists hours to days following surgery and the integration of a real-time surgical readout would benefit current prostatectomy procedures. Raman spectroscopy is a promising technology to assess surgical margins: its in vivo use during radical prostatectomy could help ensure the extent of resected prostate and cancerous tissue is maximized. We thus present the design and development of a dual excitation Raman spectroscopy system (680- and 785-nm excitations) integrated to the robotic da Vinci surgical platform for in vivo use. Following validation in phantoms, spectroscopic data from 20 whole human prostates immediately following radical prostatectomy are obtained using the system. With this dataset, we are able to distinguish prostate from extra prostatic tissue with an accuracy, sensitivity, and specificity of 91%, 90.5%, and 96%, respectively. Finally, the integrated Raman spectroscopy system is used to collect preliminary spectroscopic data at the surgical margin in vivo in four patients.

Pinto et al. “Integration of a Raman spectroscopy system to a robotic-assisted surgical system for real-time tissue characterization during radical prostatectomy procedures.” Journal of biomedical optics, 24(2), 2019. doi: 10.1117/1.JBO.24.2.025001

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Abstract

Objective: To test if Raman spectroscopy (RS) is an appropriate tool for the diagnosis and possibly grading of prostate cancer (PCa).

Patients and methods: Between 20 and 50 Raman spectra were acquired from 32 fresh and non-processed post-prostatectomy specimens using a macroscopic handheld RS probe. Each measured area was characterized and categorized according to histopathological criteria: tissue type (extraprostatic or prostatic); tissue malignancy (benign or malignant); cancer grade (Grade Groups [GGs] 1-5); and tissue glandular level. The data were analysed using machine-learning classification with neural network.

Results: The RS technique was able to distinguish prostate from extraprostatic tissue with a sensitivity of 82% and a specificity of 83% and benign from malignant tissue with a sensitivity of 87% and a specificity of 86%. In an exploratory fashion, RS differentiated benign from GG1 in 726/801 spectra (91%; sensitivity 80%, specificity 91%), from GG2 in 588/805 spectra (73%; sensitivity 76%, specificity 73%), from GG3 in 670/797 spectra (84%; sensitivity 86%, specificity 84%), from GG4 in 711/802 spectra (88%; sensitivity 77%, specificity 89%) and from GG5 in 729/818 spectra (89%; sensitivity 90%, specificity 89%).

Conclusion: Current diagnostic approaches of PCa using needle biopsies have suboptimal cancer detection rates and a significant risk of infection. Standard non-targeted random sampling results in false-negative biopsies in 15-30% of patients, which affects clinical management. RS, a non-destructive tissue interrogation technique providing vibrational molecular information, resolved the highly complex architecture of the prostate and detect cancer with high accuracy using a fibre optic probe to interrogate radical prostatectomy (RP) specimens from 32 patients (947 spectra). This proof-of-principle paves the way for the development of in vivo tumour targeting spectroscopy tools for informed biopsy collection to address the clinical need for accurate PCa diagnosis and possibly to improve surgical resection during RP as a complement to histopathological analysis.

Aubertin et al. “Mesoscopic characterization of prostate cancer using Raman spectroscopy: potential for diagnostics and therapeutics.” BJU international, vol. 122, 2, pp. 326-336, 2018. doi: 10.1111/bju.14199

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Abstract
Raman scattering has long been used to analyze chemical compositions in biological systems. Owing to its high chemical specificity and non-invasive detection capability, Raman scattering has been widely employed in cancer screening, diagnosis, and intraoperative surgical guidance in the past ten years. In order to overcome the weak signal of spontaneous Raman scattering, coherent Raman scattering and surface-enhanced Raman scattering have been developed and recently applied in the field of cancer research. This review focuses on innovative studies of the use of Raman scattering in cancer diagnosis and their potential to transition from bench to bedside.

Cui, S. Zhang, and S. Yue, “Raman Spectroscopy and Imaging for Cancer Diagnosis,” J. Healthc. Eng., pp. 1–11, 2018. doi: 10.1155/2018/8619342

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Abstract
Currently, the most sensitive method for localizing lung cancers in central airways is autofluorescence bronchoscopy (AFB) in combination with white light bronchoscopy (WLB). The diagnostic accuracy of WLB + AFB for high-grade dysplasia (HGD) and carcinoma in situ is variable depending on physician’s experience. When WLB + AFB are operated at high diagnostic sensitivity, the associated diagnostic specificity is low. Raman spectroscopy probes molecular vibrations and gives highly specific, fingerprint-like spectral features and has high accuracy for tissue pathology classification. In this study we present the use of a real-time endoscopy Raman spectroscopy system to improve the specificity. A spectrum is acquired within 1 second and clinical data are obtained from 280 tissue sites (72 HGDs/malignant lesions, 208 benign lesions/normal sites) in 80 patients. Using multivariate analyses and waveband selection methods on the Raman spectra, we have demonstrated that HGD and malignant lung lesions can be detected with high sensitivity (90%) and good specificity (65%).

McGregor et al., “Real-time endoscopic Raman spectroscopy for in vivo early lung cancer detection,” J. Biophotonics, 10(1), pp. 98–110, 2017. doi: 10.1002/jbio.201500204

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Abstract
Effectiveness of surgery as a cancer treatment is reduced when all cancer cells are not detected during surgery, leading to recurrences that negatively impact survival. To maximize cancer cell detection during cancer surgery, we designed an in situ intraoperative, label-free, optical cancer detection system that combines intrinsic fluorescence spectroscopy, diffuse reflectance spectroscopy, and Raman spectroscopy. Using this multimodal optical cancer detection system, we found that brain, lung, colon, and skin cancers could be detected in situ during surgery with an accuracy, sensitivity, and specificity of 97%, 100%, and 93%, respectively. This highly sensitive optical molecular imaging approach can profoundly impact a wide range of surgical and noninvasive interventional oncology procedures by improving cancer detection capabilities, thereby reducing cancer burden and improving survival and quality of life. Cancer Res; 77(14); 3942–50. ©2017 AACR.

Jermyn et al. “Highly Accurate Detection of Cancer In Situ with Intraoperative, Label- Free, Multimodal Optical Spectroscopy.” Cancer research, 77(14), pp.3942-3950, 2017. doi:10.1158/0008-5472.CAN-17-0668

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Abstract
Ambient light artifacts can confound Raman spectroscopy measurements performed in a clinical setting such as during open surgery. However, requiring light sources to be turned off during intraoperative spectral acquisition can be impractical because it can slow down the procedure by requiring surgeons to acquire data under light conditions different from the routine clinical practice. Here a filter system is introduced allowing in vivo Raman spectroscopy measurements to be performed with the light source of a neurosurgical microscope turned on, without interfering with the standard procedure. Ex vivo and in vivo results on calf and human brain, respectively, show that when the new filter system is used there is no significant difference between Raman spectra acquired under pitch dark conditions or with the microscope light source turned on. This is important for the clinical translation of Raman spectroscopy because of the resulting decrease in total imaging time for each measurement and because the surgeon can now acquire spectroscopic data with no disruption of the surgical workflow.

Desroches et al., “Raman spectroscopy in microsurgery: impact of operating microscope illumination sources on data quality and tissue classification,” Analyst, 142(8), pp. 1185–1191, 2017. doi: 10.1039/c6an02061e

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Abstract
Surgical treatment of brain cancer is limited by the inability of current imaging capabilities such as magnetic resonance imaging (MRI) to detect the entirety of this locally invasive cancer. This results in residual cancer cells remaining following surgery, leading to recurrence and death. We demonstrate that intraoperative Raman spectroscopy can detect invasive cancer cells centimeters beyond pathological T1-contrast-enhanced and T2-weighted MRI signals. This intraoperative optical guide can be used to detect invasive cancer cells and minimize post-surgical cancer burden. The detection of distant invasive cancer cells beyond MRI signal has the potential to increase the effectiveness of surgery and directly lengthen patient survival.

Jermyn et al., “Raman spectroscopy detects distant invasive brain cancer cells centimeters beyond MRI capability in humans,” Biomed. Opt. Express 7, pp. 5129-5137, 2016. doi: 10.1364/boe.7.005129

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Abstract
Cancers are often impossible to visually distinguish from normal tissue. This is critical for brain cancer where residual invasive cancer cells frequently remain after surgery, leading to disease recurrence and a negative impact on overall survival. No preoperative or intraoperative technology exists to identify all cancer cells that have invaded normal brain. To address this problem, we developed a handheld contact Raman spectroscopy probe technique for live, local detection of cancer cells in the human brain. Using this probe intraoperatively, we were able to accurately differentiate normal brain from dense cancer and normal brain invaded by cancer cells, with a sensitivity of 93% and a specificity of 91%. This Raman-based probe enabled detection of the previously undetectable diffusely invasive brain cancer cells at cellular resolution in patients with grade 2 to 4 gliomas. This intraoperative technology may therefore be able to classify cell populations in real-time, making it an ideal guide for surgical resection and decision-making.

Jermyn et al., “Intraoperative brain cancer detection with Raman spectroscopy in humans,” Sci Transl Med. 2015, 7(274), 2015. doi: 10.1126/scitranslmed.aaa2384.

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