Liquid saliva-based Raman spectroscopy device with on-board machine learning detects COVID-19 infection in real-time.

With greater population density, the likelihood of viral outbreaks achieving pandemic status is increasing. However, current viral screening techniques use specific reagents, and as viruses mutate, test accuracy decreases. Here, we present the first real-time, reagent-free, portable analysis platform for viral detection in liquid saliva, using COVID-19 as a proof-of-concept. We show that vibrational molecular spectroscopy and machine learning (ML) detect biomolecular changes consistent with the presence of viral infection. Saliva samples were collected from 470 individuals, including 65 that were infected with COVID-19 (28 from hospitalized patients and 37 from a walk-in testing clinic) and 251 that had a negative polymerase chain reaction (PCR) test. A further 154 were collected from healthy volunteers. Saliva measurements were achieved in 6 minutes or less and led to machine learning models predicting COVID-19 infection with sensitivity and specificity reaching 90%, depending on volunteer symptoms and disease severity. Machine learning models were based on linear support vector machines (SVM). This platform could be deployed to manage future pandemics using the same hardware but using a tunable machine learning model that could be rapidly updated as new viral strains emerge.

Spectral effects and enhancement quantification in healthy human saliva with surface-enhanced Raman spectroscopy using silver nanopillar substrates.

OBJECTIVES: Raman spectroscopy as a diagnostic tool for biofluid applications is limited by low inelastic scattering contributions compared to the fluorescence background from biomolecules. Surface-enhanced Raman spectroscopy (SERS) can increase Raman scattering signals, thereby offering the potential to reduce imaging times. We aimed to evaluate the enhancement related to the plasmonic effect and quantify the improvements in terms of spectral quality associated with SERS measurements in human saliva. METHODS: Dried human saliva was characterized using spontaneous Raman spectroscopy and SERS. A fabrication protocol was implemented leading to the production of silver (Ag) nanopillar substrates by glancing angle deposition. Two different imaging systems were used to interrogate saliva from 161 healthy donors: a custom single-point macroscopic system and a Raman micro-spectroscopy instrument. Quantitative metrics were established to compare spontaneous RS and SERS measurements: the Raman spectroscopy quality factor (QF), the photonic count rate (PR), the signal-to-background ratio (SBR). RESULTS: SERS measurements acquired with an excitation energy four times smaller than with spontaneous RS resulted in improved QF, PR values an order of magnitude larger and a SBR twice as large. The SERS enhancement reached 100×, depending on which Raman bands were considered. CONCLUSIONS: Single-point measurement of dried saliva with silver nanopillars substrates led to reproducible SERS measurements, paving the way to real-time tools of diagnosis in human biofluids.

A clinical Raman spectroscopy imaging system and safety requirements for in situ intraoperative tissue characterization.

Raman spectroscopy imaging is a technique that can be adapted for intraoperative tissue characterization to be used for surgical guidance. Here we present a macroscopic line scanning Raman imaging system that has been modified to ensure suitability for intraoperative use. The imaging system has a field of view of 1 × 1 cm2 and acquires Raman fingerprint images of 40 × 42 pixels, typically in less than 5 minutes. The system is mounted on a mobile cart, it is equiped with a passive support arm and possesses a removable and sterilizable probe muzzle. The results of a proof of concept study are presented in porcine adipose and muscle tissue. Supervised machine learning models (support vector machines and random forests) were trained and they were tested on a holdout dataset consisting of 7 Raman images (10 080 spectra) acquired in different animal tissues. This led to a detection accuracy >96% and prediction confidence maps providing a quantitative detection assessment for tissue border visualization. Further testing was accomplished on a dataset acquired with the imaging probe's contact muzzle and tailored classification models showed robust classifications capabilities with specificity, sensitivity and accuracy all surpassing 95% with a support vector machine classifier. Finally, laser safety, biosafety and sterilization of the system was assest. The safety assessment showed that the system's laser can be operated safetly according to the American National Standards Institute's standard for maximum permissible exposures for eyes and skin. It was further shown that during tissue interrogation, the temperature-history in cumulative equivalent minutes at 43 °C (CEM43 °C) never exceeded a safe threshold of 5 min.

Identification of intraductal carcinoma of the prostate on tissue specimens using Raman micro-spectroscopy: A diagnostic accuracy case-control study with multicohort validation.

BACKGROUND: Prostate cancer (PC) is the most frequently diagnosed cancer in North American men. Pathologists are in critical need of accurate biomarkers to characterize PC, particularly to confirm the presence of intraductal carcinoma of the prostate (IDC-P), an aggressive histopathological variant for which therapeutic options are now available. Our aim was to identify IDC-P with Raman micro-spectroscopy (RµS) and machine learning technology following a protocol suitable for routine clinical histopathology laboratories. METHODS AND FINDINGS: We used RµS to differentiate IDC-P from PC, as well as PC and IDC-P from benign tissue on formalin-fixed paraffin-embedded first-line radical prostatectomy specimens (embedded in tissue microarrays [TMAs]) from 483 patients treated in 3 Canadian institutions between 1993 and 2013. The main measures were the presence or absence of IDC-P and of PC, regardless of the clinical outcomes. The median age at radical prostatectomy was 62 years. Most of the specimens from the first cohort (Centre hospitalier de l'Université de Montréal) were of Gleason score 3 + 3 = 6 (51%) while most of the specimens from the 2 other cohorts (University Health Network and Centre hospitalier universitaire de Québec-Université Laval) were of Gleason score 3 + 4 = 7 (51% and 52%, respectively). Most of the 483 patients were pT2 stage (44%-69%), and pT3a (22%-49%) was more frequent than pT3b (9%-12%). To investigate the prostate tissue of each patient, 2 consecutive sections of each TMA block were cut. The first section was transferred onto a glass slide to perform immunohistochemistry with H&E counterstaining for cell identification. The second section was placed on an aluminum slide, dewaxed, and then used to acquire an average of 7 Raman spectra per specimen (between 4 and 24 Raman spectra, 4 acquisitions/TMA core). Raman spectra of each cell type were then analyzed to retrieve tissue-specific molecular information and to generate classification models using machine learning technology. Models were trained and cross-validated using data from 1 institution. Accuracy, sensitivity, and specificity were 87% ± 5%, 86% ± 6%, and 89% ± 8%, respectively, to differentiate PC from benign tissue, and 95% ± 2%, 96% ± 4%, and 94% ± 2%, respectively, to differentiate IDC-P from PC. The trained models were then tested on Raman spectra from 2 independent institutions, reaching accuracies, sensitivities, and specificities of 84% and 86%, 84% and 87%, and 81% and 82%, respectively, to diagnose PC, and of 85% and 91%, 85% and 88%, and 86% and 93%, respectively, for the identification of IDC-P. IDC-P could further be differentiated from high-grade prostatic intraepithelial neoplasia (HGPIN), a pre-malignant intraductal proliferation that can be mistaken as IDC-P, with accuracies, sensitivities, and specificities > 95% in both training and testing cohorts. As we used stringent criteria to diagnose IDC-P, the main limitation of our study is the exclusion of borderline, difficult-to-classify lesions from our datasets. CONCLUSIONS: In this study, we developed classification models for the analysis of RµS data to differentiate IDC-P, PC, and benign tissue, including HGPIN. RµS could be a next-generation histopathological technique used to reinforce the identification of high-risk PC patients and lead to more precise diagnosis of IDC-P.

Feature engineering applied to intraoperative in vivo Raman spectroscopy sheds light on molecular processes in brain cancer: a retrospective study of 65 patients.

Raman spectroscopy is a promising tool for neurosurgical guidance and cancer research. Quantitative analysis of the Raman signal from living tissues is, however, limited. Their molecular composition is convoluted and influenced by clinical factors, and access to data is limited. To ensure acceptance of this technology by clinicians and cancer scientists, we need to adapt the analytical methods to more closely model the Raman-generating process. Our objective is to use feature engineering to develop a new representation for spectral data specifically tailored for brain diagnosis that improves interpretability of the Raman signal while retaining enough information to accurately predict tissue content. The method consists of band fitting of Raman bands which consistently appear in the brain Raman literature, and the generation of new features representing the pairwise interaction between bands and the interaction between bands and patient age. Our technique was applied to a dataset of 547 in situ Raman spectra from 65 patients undergoing glioma resection. It showed superior predictive capacities to a principal component analysis dimensionality reduction. After analysis through a Bayesian framework, we were able to identify the oncogenic processes that characterize glioma: increased nucleic acid content, overexpression of type IV collagen and shift in the primary metabolic engine. Our results demonstrate how this mathematical transformation of the Raman signal allows the first biological, statistically robust analysis of in vivo Raman spectra from brain tissue.

Mesoscopic characterization of prostate cancer using Raman spectroscopy: potential for diagnostics and therapeutics.

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.

Fluorescence hyperspectral imaging for live monitoring of multiple spheroids in microfluidic chips.

Tumor spheroids represent a realistic 3D in vitro cancer model because they provide a missing link between monolayer cell culture and live tissues. While microfluidic chips can easily form and assay thousands of spheroids simultaneously, few commercial instruments are available to analyze this massive amount of data. Available techniques to measure spheroid response to external stimuli, such as confocal imaging and flow cytometry, are either not appropriate for 3D cultures, or destructive. We designed a wide-field hyperspectral imaging system to analyze multiple spheroids trapped in a microfluidic chip in a single acquisition. The system and its fluorescence quantification algorithm were assessed using liquid phantoms mimicking spheroid optical properties. Spectral unmixing was tested on three overlapping spectral entities. Hyperspectral images of co-culture spheroids expressing two fluorophores were compared with confocal microscopy and spheroid growth was measured over time. The system can spectrally analyze multiple fluorescent markers simultaneously and allows multiple time-points assays, providing a fast and versatile solution for analyzing lab on a chip devices.

Raman spectroscopy in microsurgery: impact of operating microscope illumination sources on data quality and tissue classification.

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.

Sub-diffuse interstitial optical tomography to improve the safety of brain needle biopsies: a proof-of-concept study.

The extraction of tissue samples during brain needle biopsy can cause life-threatening hemorrhage because of significant blood vessel injury during the procedure. Vessel rupture can have significant consequences for patient health, ranging from transient neurological deficits to death. Here, we present a sub-diffuse optical tomography technique that can be integrated into neurosurgical workflow to detect the presence of blood vessels. A proof-of-concept study performed on a realistic brain tissue phantom is presented and demonstrates that interstitial optical tomography (iOT) can detect several 1 mm diameter high-contrast absorbing objects located <2 mm from the needle.

Toward noncontact macroscopic imaging of multiple cancers using multi-spectral inelastic scattering detection.

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.

Robot-assisted biopsy sampling for online Raman spectroscopy cancer confirmation in the operating room.

PURPOSE: Cancer confirmation in the operating room (OR) is crucial to improve local control in cancer therapies. Histopathological analysis remains the gold standard, but there is a lack of real-time in situ cancer confirmation to support margin confirmation or remnant tissue. Raman spectroscopy (RS), as a label-free optical technique, has proven its power in cancer detection and, when integrated into a robotic assistance system, can positively impact the efficiency of procedures and the quality of life of patients, avoiding potential recurrence. METHODS: A workflow is proposed where a 6-DOF robotic system (optical camera + MECA500 robotic arm) assists the characterization of fresh tissue samples using RS. Three calibration methods are compared for the robot, and the temporal efficiency is compared with standard hand-held analysis. For healthy/cancerous tissue discrimination, a 1D-convolutional neural network is proposed and tested on three ex vivo datasets (brain, breast, and prostate) containing processed RS and histopathology ground truth. RESULTS: The robot achieves a minimum error of 0.20 mm (0.12) on a set of 30 test landmarks and demonstrates significant time reduction in 4 of the 5 proposed tasks. The proposed classification model can identify brain, breast, and prostate cancer with an accuracy of 0.83 (0.02), 0.93 (0.01), and 0.71 (0.01), respectively. CONCLUSION: Automated RS analysis with deep learning demonstrates promising classification performance compared to commonly used support vector machines. Robotic assistance in tissue characterization can contribute to highly accurate, rapid, and robust biopsy analysis in the OR. These two elements are an important step toward real-time cancer confirmation using RS and OR integration.

Macroscopic inelastic scattering imaging using a hyperspectral line-scanning system identifies invasive breast cancer in lumpectomy and mastectomy specimens.

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 cm 2 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.

In situ brain tumor detection using a Raman spectroscopy system-results of a multicenter study.

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.

System and methods for wide-field quantitative fluorescence imaging during neurosurgery.

We report an accurate, precise and sensitive method and system for quantitative fluorescence image-guided neurosurgery. With a low-noise, high-dynamic-range CMOS array, we perform rapid (integration times as low as 50 ms per wavelength) hyperspectral fluorescence and diffuse reflectance detection and apply a correction algorithm to compensate for the distorting effects of tissue absorption and scattering. Using this approach, we generated quantitative wide-field images of fluorescence in tissue-simulating phantoms for the fluorophore PpIX, having concentrations and optical absorption and scattering variations over clinically relevant ranges. The imaging system was tested in a rodent model of glioma, detecting quantitative levels down to 20 ng/ml. The resulting performance is a significant advance on existing wide-field quantitative imaging techniques, and provides performance comparable to a point-spectroscopy probe that has previously demonstrated significant potential for improved detection of malignant brain tumors during surgical resection.

Methodology to optimize detector geometry in fluorescence tomography of tissue using the minimized curvature of the summed diffuse sensitivity projections.

The dependence of the sensitivity function in fluorescence tomography on the geometry of the excitation source and detection locations can severely influence an imaging system's ability to recover fluorescent distributions. Here a methodology for choosing imaging configuration based on the uniformity of the sensitivity function is presented. The uniformity of detection sensitivity is correlated with reconstruction accuracy in silico, and reconstructions in a murine head model show that a detector configuration optimized using Nelder-Mead minimization improves recovery over uniformly sampled tomography.

Preclinical evaluation of Raman spectroscopy for pedicular screw insertion surgical guidance in a porcine spine model.

Significance: Orthopedic 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. Aim: The 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. Approach: A 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. Results: The 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. Conclusions: We 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.

Detection of SARS-CoV-2 in saliva by a low-cost LSPR-based sensor.

The SARS-CoV-2 pandemic started more than 3 years ago, but the containment of the spread is still a challenge. Screening is imperative for informed decision making by government authorities to contain the spread of the virus locally. The access to screening tests is disproportional, due to the lack of access to reagents, equipment, finances or because of supply chain disruptions. Low and middle-income countries have especially suffered with the lack of these resources. Here, we propose a low cost and easily constructed biosensor device based on localized surface plasmon resonance, or LSPR, for the screening of SARS-CoV-2. The biosensor device, dubbed "sensor" for simplicity, was constructed in two modalities: (1) viral detection in saliva and (2) antibody against COVID in saliva. Saliva collected from 18 patients were tested in triplicates. Both sensors successfully classified all COVID positive patients (among hospitalized and non-hospitalized). From the COVID negative patients 7/8 patients were correctly classified. For both sensors, sensitivity was determined as 100% (95% CI 79.5-100) and specificity as 87.5% (95% CI 80.5-100). The reagents and equipment used for the construction and deployment of this sensor are ubiquitous and low-cost. This sensor technology can then add to the potential solution for challenges related to screening tests in underserved communities.

Open-sourced Raman spectroscopy data processing package implementing a baseline removal algorithm validated from multiple datasets acquired in human tissue and biofluids.

Significance: Standardized data processing approaches are required in the field of bio-Raman spectroscopy to ensure information associated with spectral data acquired by different research groups, and with different systems, can be compared on an equal footing. Aim: An open-sourced data processing software package was developed, implementing algorithms associated with all steps required to isolate the inelastic scattering component from signals acquired using Raman spectroscopy devices. The package includes a novel morphological baseline removal technique (BubbleFill) that provides increased adaptability to complex baseline shapes compared to current gold standard techniques. Also incorporated in the package is a versatile tool simulating spectroscopic data with varying levels of Raman signal-to-background ratios, baselines with different morphologies, and varying levels of stochastic noise. Results: Application of the BubbleFill technique to simulated data demonstrated superior baseline removal performance compared to standard algorithms, including iModPoly and MorphBR. The data processing workflow of the open-sourced package was validated in four independent in-human datasets, demonstrating it leads to inter-systems data compatibility. Conclusions: A new open-sourced spectroscopic data pre-processing package was validated on simulated and real-world in-human data and is now available to researchers and clinicians for the development of new clinical applications using Raman spectroscopy.

Subsecond lung cancer detection within a heterogeneous background of normal and benign tissue using single-point Raman spectroscopy.

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.

In situ Raman spectroscopy and machine learning unveil biomolecular alterations in invasive breast cancer.

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.