Significance:Maximizing safe tumor resection remains a major challenge in brain tumor surgery due to the lack of reliable real-time intraoperative contrast between tumor and healthy brain tissue. Label-free optical methods based on endogenous tissue fluorescence could provide simple, noninvasive feedback to guide resection while avoiding the logistical and regulatory limitations of exogenous fluorophores. Aim:The study aims to investigate whether endogenous near-infrared (NIR) fluorescence can differentiate tumor from normal brain tissue in vivo and to develop a wide-field fluorescence and reflectance imaging system for label-free visualization of brain tumor specimens ex vivo. Approach:We analyzed in vivo data acquired with a hand-held near-infrared spectroscopy probe from 26 patients (430 measurements) to quantify endogenous fluorescence differences between glioblastoma or astrocytoma and normal brain. Based on these findings, we built a wide-field imaging prototype using 808-nm laser excitation, red-shifted fluorescence detection with an InGaAs short-wave infrared camera, and co-registered reflectance imaging for ratiometric normalization. The system was validated with ex vivo human surgical specimens (14 samples from five patients). Results:Endogenous fluorescence intensity measured in vivo was significantly higher in normal brain tissue compared with tumor, yielding positive predictive values of 85 to 88% for tumor and a negative predictive value of 96% for normal tissue. Ex vivo wide-field imaging reproduced this contrast: normalized fluorescence was lower in tumor regions than in histologically confirmed normal areas, consistent with metabolic differences observed in vivo. Conclusions:Endogenous NIR fluorescence provides intrinsic contrast between normal and tumoral brain tissue. The demonstrated corrected wide-field fluorescence imaging approach could offer a feasible, label-free method for real-time visualization of tumor margins, supporting its future clinical translation as an intraoperative guidance tool.

Significance:Maximizing brain tumor resection while preserving neurological function remains a neurosurgical challenge. Current intraoperative tools, including MRI, ultrasound, and frozen section, disrupt workflow and provide limited real-time molecular information. Aim:This review synthesizes evidence for Raman spectroscopy as an intraoperative tool in glioma and brain metastasis surgery, comparing it against established alternatives and framing findings within a diagnostic hierarchy from analytic validity through clinical utility to outcome benefit. Approach:Evidence is arranged by surgical setting: in vivo handheld fiber-optic probes and ex vivo platforms including Raman microscopy, stimulated Raman scattering (SRS) imaging, and visible resonance Raman. Results:In vivo multicenter studies report > 85 % per-measurement sensitivity and specificity for tumor versus normal classification, with nondestructive analysis in ∼ 2    s . Molecular marker detection, including IDH mutation status, has been demonstrated predominantly ex vivo. Ex vivo SRS generates histology-quality images and can classify selected molecular alterations within ∼ 90    s , though this molecular capability is not clinically validated. Conclusions:While multicenter studies support the analytic validity of Raman-based tissue detection, clinical adoption requires prospective outcome trials, standardized acquisition protocols, and regulatory advancement.

Significance:Early prediction of COVID-19 severity and mortality is crucial for optimizing clinical care and patient outcomes, but remains challenging. Aim:We aim to develop a screening tool combining label-free Raman spectroscopy and machine learning modeling to predict COVID-19 severity and mortality. Approach:Patients infected by SARS-CoV-2 ( N = 58 ) were recruited during the first wave of COVID-19 and stratified based on respiratory support. Blood samples were collected during hospitalization and analyzed using Raman spectroscopy and metabolomics. Machine learning models based on Raman spectra were developed to classify (1) survivors versus nonsurvivors, (2) critical patients with noninvasive versus invasive ventilation, and (3) noncritical (no respiratory support or oxygen via nasal cannula) versus critical patients. Results:Raman peaks assigned to proteins, glucose, lactic acid, fatty acids, urea, and lipids were extracted by the models. Area under the receiver operating characteristic curve ranged between 0.83 and 0.94, with sensitivities and specificities ranging between 80% and 83% and 75% and 92%, respectively. Accuracy for detecting mortality, invasive ventilation, and critical disease was 90%, 87%, and 78%. A complementary metabolomic analysis confirmed some molecular differences between groups. Conclusions:These results suggest the potential of Raman spectroscopy and machine learning modeling to stratify COVID-19 patients at admission, individualize care, and improve survival rates.

PURPOSE: A major challenge in pituitary adenoma surgery is distinguishing adenoma from normal pituitary gland and adjacent structures. Accurate intraoperative identification is critical to optimize tumor resection, hormonal remission, and preservation of pituitary function. Building on our previous preclinical validation of a bayonet-style endonasal Raman probe, we evaluated a dual-modality Raman spectroscopy and tissue fluorescence system for ex vivo discrimination of pituitary adenoma, normal gland, and surrounding sellar tissues. METHODS: A prospective study was performed in 32 patients undergoing transsphenoidal resection of adenomas. During surgery, Raman spectroscopy and near-infrared fluorescence measurements were acquired ex vivo from excised tissues. A limited number of spectra were obtained from normal gland. Histopathology was the gold standard. Fluorescence intensity was normalized for integration time and compared statistically across tissue classes. Spectral angular mapping (SAM) was applied to standard-normal-variate (SNV)-normalized Raman spectra for tissue classification. RESULTS: 502 spectra were analyzed. Both pituitary gland and adenoma showed higher fluorescence than surrounding tissues. Fluorescence alone achieved an AUC of 0.90, with 86% accuracy, 88% sensitivity, and 86% specificity for discriminating adenomas from normal surrounding tissues. Raman spectroscopy analyzed using the SAM metric provided complementary biochemical contrast (87% accuracy, 76% sensitivity, 96% specificity). A two-stage approach, combining fluorescence preselection with Raman classification, improved discrimination between normal gland and adenoma: 94.5% sensitivity, 85.7% specificity. CONCLUSIONS: Raman spectroscopy and tissue fluorescence enable label-free discrimination of pituitary adenoma from normal gland and surrounding structures in ex vivo human tissue. These findings support the feasibility of optical guidance, to be further validated in ongoing in vivo clinical studies.

Myalgic encephalomyelitis (ME) is characterized by profound fatigue, post-exertional malaise (PEM), and cognitive dysfunction. Despite its clinical significance, the pathophysiology of PEM and disease heterogeneity remain unclear, and no validated biomarkers are available for rapid diagnosis or monitoring. We aimed to develop a screening approach combining label-free Raman spectroscopy (RS) and machine learning modeling (ML) to detect biomolecular changes in blood plasma and differentiate patients with ME from sedentary healthy controls. Blood plasma was collected from 115 patients with ME and 45 controls at rest (T0) and 90 min after a standardized, non-invasive stress test designed to induce PEM. Plasma samples were analyzed by RS, and ML models were developed independently at each time point to differentiate patients with ME and controls. The RS-ML models identified spectral features consistent with contributions from proteins, lipids, and low-molecular-weight metabolites. At T0 and T90, the area under the receiver operating characteristic curve, accuracy, specificity and sensitivity were 0.85 and 0.83, 79% and 84%, 82% and 90%, and 73% and 69%, respectively. RS-ML provides a rapid, low-cost approach to detect ME-associated biomolecular signatures in plasma and capture biochemical alterations associated with standardized stress.

Significance:Monitoring COVID-19 disease from acute infection to recovery is critical to understand biochemical dysregulation and COVID-19 heterogeneity over time. Aim:Our aim is to develop an approach combining label-free Raman spectroscopy and machine learning modeling to enable sensitive biomolecular detection of COVID-19 over time. Approach:Hospitalized patients infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) were recruited and stratified based on respiratory support (critical and non-critical). Controls had a negative SARS-CoV-2 test. Blood was collected in the acute and recovery phases and was analyzed with Raman spectroscopy. Four machine learning models based on Raman spectra were developed to differentiate critical and non-critical patients in the acute and recovery phases from controls. For each group of patients, two additional models also classified the patient status (acute versus recovery) using cross-sectional and longitudinal analyses. Results:Raman peaks assigned to proteins, glucose, fatty acids, lactic acid, vitamin A, and lipids were identified in models. Overall, area under the receiver operating characteristic curve values were between 0.83 and 1.00 with sensitivities, specificities, and accuracies between 73% and 100%, 77% and 100%, and 78% and 100%, respectively. Conclusions:These results highlight the capability of combined Raman spectroscopy and machine learning modeling to stratify patients at admission, monitor recovery after discharge, and support strategies to potentially reduce the risk of long-COVID.

We present a wide-field Raman spectroscopy system for glioma invasion detection based on an ML model from a 67 patient study. Full-text article not available; see video presentation

We present a low-cost lung cancer detection technique using a Raman spectroscopy biofluid platform. Our study is tested in 263 patients and may bring lung cancer screening to a wider population.

Significance:Surgery is a common intervention for patients with pituitary adenomas, particularly those experiencing endocrine symptoms or mass effect. Persistent challenges in pituitary surgery include the detection of small microadenomas, difficulty in discerning residual tumor from normal gland, and infiltrative adenomas. Although standard perioperative diagnostics include magnetic resonance imaging (MRI), computed tomography, ultrasound imaging, and neuronavigation, some centers employ intraoperative MRI, ultrasound, and fluorescence-guided endoscopy to increase the rate of gross total resection and preserve pituitary function. However, these techniques are often limited by availability, time requirements, cost, and inability to provide histological diagnosis. Aim:This review addresses opportunities to optimize both the extent of resection and gland preservation in pituitary adenoma procedures. We discuss the existing constraints faced in pituitary surgery and showcase the current and emerging detection techniques employed in clinical practice, as well as their limitations. We also discuss newer probing approaches such as elastography and Raman spectroscopy. Approach:We outline key attributes for an ideal optical tool, considering surgical theater functionality, ergonomics, and result reliability and accuracy. Results:A case study is presented describing the recent development of a fiber-optics instrument specifically designed for endonasal applications based on clinical requirements, along with preliminary data supporting the feasibility of intraoperative implementation. Conclusions:Current imaging and navigation tools, although invaluable, have inherent limitations in resolution, integration, and molecular specificity. Raman spectroscopy offers a promising, label-free method for real-time tissue identification, especially when integrated into fiber-optic probes for endonasal use. As a complementary tool, it could enhance intraoperative decision-making and surgical precision. Further clinical validation is needed to support its integration into standard workflows.

Significance:For most patients with pituitary adenomas, surgical resection represents a viable therapeutic option, particularly in cases with endocrine symptoms or local mass effects. Diagnostic imaging, including MRI and computed tomography, is employed clinically to plan pituitary adenoma surgery. However, these methods cannot provide surgical guidance information in real time to improve resection rates and reduce risks of damage to normal tissue during tumor debulking. Aim:Here, we present the development of a handheld Raman spectroscopy system that can be seamlessly integrated with transsphenoidal surgery workflows to allow live discrimination of all normal intracranial anatomical structures, including the pituitary gland, and potentially tissue abnormalities such as adenomas. Approach:A fiber-optic probe was developed with a form factor compatible with endoscopic systems for endonasal surgeries. The instrument was evaluated in an ex vivo experimental protocol designed to assess its ability to distinguish normal intracranial structures. A total of 274 in situ spectroscopic measurements were acquired from six lamb heads, targeting key anatomical structures encountered in surgery. Support vector machine models were developed to classify tissue types based on their spectral signatures. Results:Binary classification models successfully distinguished the pituitary gland from other tissue structures with a sensitivity and a specificity of 100%. In addition, a four-class predictive model enabled > 95 % accuracy in situ discrimination of four structures of most importance during pituitary adenoma tumor resection, i.e., the pituitary gland, the sella turcica (ST) bone, the optic chiasm, and the ST dura mater. Conclusions:This work sets the stage for the clinical deployment of Raman spectroscopy as an intraoperative real-time decision support system during transsphenoidal surgery, with future work focused on clinical integration and the generalization of the approach to include the detection of tissue abnormalities, such as pituitary adenomas.

Significance:The relationship between spatial offset and tissue sensing depth is not well understood in spatial offset Raman spectroscopy (SORS). Detection of the subsurface biochemical composition could improve clinical translation of SORS-based methods, including for lumpectomy margin characterization in breast cancer surgery. Aim:We aimed at developing an experimental method to establish a relationship between spatial offset in SORS and sampling depth. The technique was developed using a custom hyperspectral line-scanning imaging system optimized for Raman spectroscopy detection. Approach:Bilayer phantoms were produced with top and bottom layers made of material with different Raman spectroscopy signatures, i.e., poly(dimethylsiloxane) polymer (PDMS) and Nylon. The top layer of PDMS had different values of absorption and reduced elastic scattering coefficients, as well as a thickness up to ∼ 3    mm . A metric was used, called spectral angle mapper, that allowed for comparing SORS measurements with reference spectra of pure PDMS and Nylon. That metric was used to develop a technique predicting sensing depth for different values of spatial offset. A proof-of-concept study was performed to assess the performance of the method in biological tissue, demonstrating detectability of protein-rich tissue across layers of Intralipid and porcine fat to simulate the optical properties of human adipose tissue. Results:A total of 60 optical phantoms with varying optical properties and top layer thicknesses were imaged and processed to estimate sampling depth as a function of spatial offset. The study demonstrated the detectability of the underlying Nylon layer across a PDMS layer up to 3 mm in thickness. Similarly, the detectability of protein-rich tissue was demonstrated across layers of Intralipid up to 3 mm thick and < 2    mm for porcine fat. Conclusions:We showed the feasibility of using bilayer solid optical phantoms to create correlation curves between the optimal spatial offset for a desired probed depth given the optical properties of the top layer. The technique could facilitate the clinical translation of SORS measurements for tumor detection and margins assessment.

We present a new method for lung pathology detection in blood plasma, including lung cancer staging. Raman spectroscopy uses inelastically scattered laser light to obtain molecular information in a reagent-free manner. Obtaining Raman spectral data from liquid samples has long proven challenging, but we have developed a novel tool for obtaining spectra from 60 μl liquid samples within two minutes: Raman of Well-based Samples (ROWS). With a low-cost ROWS device, we analyzed 372 blood plasma samples from a national biobank, including controls (n=92), patients with stage I-II lung cancer (n=99), stage III-IV cancer (n=46), benign tumours (n=36) and other lung conditions (n=99). Machine learning models were built to assess lung cancer stage and lung pathology presence. ROWS achieves up to 94% sensitivity, 90% specificity and 93% accuracy depending on classification. ROWS proves a robust method for rapid, low-cost, user-friendly, point-of-care lung pathology analysis in small quantities of blood plasma. ### Competing Interest Statement Frederic Leblond, Francois Daoust and Nassim Ksantini are shareholders of Reveal Life Science. Francois Daoust, Juliette Selb and Nassim Ksantini are employees of Reveal Life Science. ### Funding Statement This research was funded by a National Science and Engineering Research Council (NSERC) Alliance Grant in collaboration with Reveal Life Science (previously Exclaro-Tridan) (FL). ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The Research Ethics Board of the Centre Hospitalier de l'universite de Montreal (CHUM) gave ethical approval for this work. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Data cannot be shared due to patient privacy and agreements with the AIRS Network Tissue Biobank. Data processing code is available through Github. Machine learning models are described in detail in Ember et al. Scientific Reports 2024. Code repository for model training, analysis and validation is publicly available in the paper "Open-sourced Raman spectroscopy data processing package implementing a novel baseline removal algorithm validated from multiple datasets acquired in human tissue and biofluids" Sheehy et al., Journal of Biomedical Optics, (2023) and also on Github (https://github.com/mr-sheg/orpl).

Retroperitoneal soft tissue sarcoma (RSTS) is a rare type of cancer with limited treatment options. Achieving complete resection with negative margins is one of the most significant prognostic factors for RSTS survival. The UltraProbe is a handheld point probe Raman spectroscopy system that significantly decreases the imaging time compared to the probe systems currently used. This study aims to determine the performance of the UltraProbe in detecting STS in an in vivo environment during their resection. Thirty patients were recruited at Maisonneuve-Rosemont Hospital, Montreal, Canada. Raman spectra were acquired during STS resection using the instrument. A machine learning random forest classification algorithm was developed to predict the diagnosis associated with new Raman spectra: STS or healthy tissue. The classification of Raman spectra as well-differentiated liposarcomas or normal adipose tissue was performed with a sensitivity of 94%, specificity of 95%, and accuracy of 94%. The classification of spectra as well-differentiated and dedifferentiated liposarcomas or normal adipose tissue was performed with a sensitivity of 90%, specificity of 93%, and accuracy of 90%. The classification of spectra as non-liposarcoma STS or protein-rich non-adipose tissue was performed with a sensitivity of 87%, specificity of 81%, and accuracy of 87%.

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.

Significance:Focal cortical dysplasia (FCD) type II is the leading cause of drug-resistant focal epilepsy in children. While surgical resection offers the only definitive cure, its success is hindered by the challenge of precisely identifying the lesion and its boundaries. Despite advancements in neuroimaging, FCD type II often remains elusive, complicating surgical planning and outcome optimization. Enhanced detection methods are crucial to improving the precision of resection and, ultimately, achieving seizure freedom in affected patients. Aim:Advanced techniques for detecting FCD type II margins during surgery are critically needed to enhance postoperative outcomes. Spontaneous Raman spectroscopy is a label-free optical method that allows the characterization of the tissue's biochemical composition. The goal of this proof-of-concept study was to compare-in pediatric patients-the spectral signature of abnormal cells in FCD tissue with cells associated with the normal cortex. Approach:A Raman microspectroscopy imaging workflow was developed and applied to 70 surgical specimens from 30 focal epilepsy patients diagnosed with FCD type II. Raman spectra from individual cells were recorded from FCD type II specimens (dysmorphic neurons and balloon cells) and normal brains (neurons). Machine learning models (support vector machines) were trained, validated, and tested to distinguish FCD tissue from the normal brain as well as to distinguish between two disease subtypes, i.e., FCD types IIa and IIb. Results:A total of 1420 single-cell spectra were acquired and spectral differences determined between FCD type II and normal cortex, as well as between FCD type IIa and type IIb. Machine learning distinguished FCD type II from the normal cortex with 96% accuracy, 100% sensitivity, and 95% specificity. FCD types IIa and IIb specimens were distinguished with 92% accuracy, 100% sensitivity, and 86% specificity. Conclusions:The Raman spectroscopy signature of single cells associated with FCD tissue was established. This provides credence to the hypothesis that Raman spectroscopy as a technique-if implemented using a fiber optics system-has the potential for safely optimizing the extent of FCD type II resection in pediatric focal epilepsy surgery. In addition, this technique provides insights into multiple biochemical alterations within dysplastic tissues, which may contribute to the underlying mechanisms of epileptogenesis.