Intraoperative Raman spectroscopy of soft tissue sarcomas

Intraoperative use of high-speed Raman spectroscopy during soft tissue sarcoma resection

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%.

Label-free discrimination analysis of breast cancer tumor and adjacent tissues of patients after neoadjuvant treatment using Raman spectroscopy: a diagnostic study

BACKGROUND AND OBJECTIVE

Breast-conserving surgery (BCS) plays a crucial role in breast cancer treatment, with a primary focus on ensuring cancer-free surgical margins, particularly for patients undergoing neoadjuvant treatment. After neoadjuvant treatment, tumor regression can complicate the differentiation between breast cancer tumor and adjacent tissues. Raman spectroscopy, as a rapid and non-invasive optical technique, offers the advantage of providing detailed biochemical information and molecular signatures of internal molecular components in tissue samples. Despite its potential, there is currently no research on using label-free Raman spectroscopy to distinguish between breast cancer tumors and adjacent tissues after neoadjuvant treatment. This study intends to distinguish between tumor and adjacent tissues after neoadjuvant treatment in breast cancer through label-free Raman spectroscopy.

METHODS

In this study, the intraoperative frozen samples of breast cancer tumor and adjacent tissue were collected from patients who underwent neoadjuvant treatment during surgery. The samples were examined using Raman confocal microscopy, and Raman spectra were collected by LabSpec6 software. Spectra were preprocessed by Savitz-Golay filter, adaptive iterative reweighted penalized least squares and MinMax normalization method. The differences in Raman spectra between breast cancer tumor and adjacent tissues after neoadjuvant treatment were analyzed by Wilcoxon rank-sum test, with a Bonferroni correction for multiple comparisons. Based on the support vector machine (SVM) method in machine learning, a predictive model for classification was established in the total group and subgroups of different hormone receptor (HR) status, human epidermal growth factor receptor 2 (HER2) status and Ki-67 expression level. The independent test set was used to evaluate the performance of the model, and the area under curve (AUC) of the receiver operating characteristic (ROC) curve, sensitivity, specificity and accuracy of different models were obtained.

RESULT

This study comprised 4260 Raman spectra of breast cancer tumor and adjacent frozen tissue samples from 142 breast cancer patients treated with neoadjuvant treatment. The Raman peaks associated with nucleotides and their metabolites in the Raman spectra of breast cancer tumor tissues were higher in intensities than those of adjacent tissues after neoadjuvant therapy (676 cm -1 : Bonferroni adjusted P < 0.0001; 724 cm -1 : P < 0.0001; 754 cm -1 : P < 0.0001), and the Raman peaks from amide III bands were more intense (1271 cm -1 : P < 0.01). Multivariate curve resolution-alternating least squares (MCR-ALS) decomposition of Raman spectra revealed reduced lipid content and increased collagen and nucleic acid content in breast cancer tumor tissues compared to adjacent tissues following neoadjuvant therapy. The predictive model based on the Raman spectral signature of breast cancer tumor and adjacent tissues after neoadjuvant treatment achieved an AUC of 0.98, with accuracy, sensitivity, and specificity values of 0.89, 0.97, and 0.83, respectively. The AUC of subgroup analysis according to different status of molecular pathological biomarkers was stably around 99%.

CONCLUSION

This study demonstrated that label-free Raman spectroscopy can differentiate tumor and adjacent tissues of breast cancer patients treated with neoadjuvant therapy thorough getting the panoramic perspective of the biochemical compounds for the first time. Our study provided a novel technique for determining the margin status in BCS in breast cancer following neoadjuvant treatment rapidly and precisely.

Rapid and Precise Diagnosis of Retroperitoneal Liposarcoma with Deep-Learned Label-Free Molecular Microscopy

The retroperitoneal liposarcoma (RLPS) is a rare malignancy whose only curative therapy is surgical resection. However, well-differentiated liposarcomas (WDLPSs), one of its most common types, can hardly be distinguished from normal fat during operation without an effective margin assessment method, jeopardizing the prognosis severely with a high recurrence risk. Here, we combined dual label-free nonlinear optical modalities, stimulated Raman scattering (SRS) microscopy and second harmonic generation (SHG) microscopy, to image two predominant tissue biomolecules, lipids and collagen fibers, in 35 RLPSs and 34 normal fat samples collected from 35 patients. The produced dual-modal tissue images were used for RLPS diagnosis based on deep learning. Dramatically decreasing lipids and increasing collagen fibers during tumor progression were reflected. A ResNeXt101-based model achieved 94.7% overall accuracy and 0.987 mean area under the ROC curve (AUC) in differentiating among normal fat, WDLPSs, and dedifferentiated liposarcomas (DDLPSs). In particular, WDLPSs were detected with 94.1% precision and 84.6% sensitivity superior to existing methods. The ablation experiment showed that such performance was attributed to both SRS and SHG microscopies, which increased the sensitivity of recognizing WDLPS by 16.0 and 3.6%, respectively. Furthermore, we utilized this model on RLPS margins to identify the tumor infiltration. Our method holds great potential for accurate intraoperative liposarcoma detection.

3D Specimen Scanning and Mapping in Musculoskeletal Oncology: A Feasibility Study

BACKGROUND

Surgical resection is the primary treatment for bone and soft tissue tumors. Negative margin status is a key factor in prognosis. Given the three-dimensional (3D) anatomic complexity of musculoskeletal tumor specimens, communication of margin results between surgeons and pathologists is challenging. We sought to perform ex vivo 3D scanning of musculoskeletal oncology specimens to enhance communication between surgeons and pathologists.

METHODS

Immediately after surgical resection, 3D scanning of the fresh specimen is performed prior to frozen section analysis. During pathologic grossing, whether frozen or permanent, margin sampling sites are annotated on the virtual 3D model using computer-aided design (CAD) software.

RESULTS

3D scanning was performed in seven cases (six soft tissue, one bone), with specimen mapping on six cases. Intraoperative 3D scanning and mapping was performed in one case in which the location of margin sampling was shown virtually in real-time to the operating surgeon to help achieve a negative margin. In six cases, the 3D model was used to communicate final permanent section analysis. Soft tissue, cartilage, and bone (including lytic lesions within bone) showed acceptable resolution.

CONCLUSIONS

Virtual 3D scanning and specimen mapping is feasible and may allow for enhanced documentation and communication. This protocol provides useful information for anatomically complex musculoskeletal tumor specimens. Future studies will evaluate the effect of the protocol on positive margin rates, likelihood that a re-resection contains additional malignancy, and exploration of targeted adjuvant radiation protocols using a patient-specific 3D specimen map.

Toward the use of diffuse reflection spectroscopy for intra-operative tissue discrimination during sarcoma surgery

Significance

Accurately distinguishing tumor tissue from normal tissue is crucial to achieve complete resections during soft tissue sarcoma (STS) surgery while preserving critical structures. Incomplete tumor resections are associated with an increased risk of local recurrence and worse patient prognosis.

Aim

We evaluate the performance of diffuse reflectance spectroscopy (DRS) to distinguish tumor tissue from healthy tissue in STSs.

Approach

DRS spectra were acquired from different tissue types on multiple locations in 20 freshly excised sarcoma specimens. A k -nearest neighbors classification model was trained to predict the tissue types of the measured locations, using binary and multiclass approaches.

Results

Tumor tissue could be distinguished from healthy tissue with a classification accuracy of 0.90, sensitivity of 0.88, and specificity of 0.93 when well-differentiated liposarcomas were included. Excluding this subtype, the classification performance increased to an accuracy of 0.93, sensitivity of 0.94, and specificity of 0.93. The developed model showed a consistent performance over different histological subtypes and tumor locations.

Conclusions

Automatic tissue discrimination using DRS enables real-time intra-operative guidance, contributing to more accurate STS resections.

Advancing Brain Research through Surface-Enhanced Raman Spectroscopy (SERS): Current Applications and Future Prospects

Surface-enhanced Raman spectroscopy (SERS) has recently emerged as a potent analytical technique with significant potential in the field of brain research. This review explores the applications and innovations of SERS in understanding the pathophysiological basis and diagnosis of brain disorders. SERS holds significant advantages over conventional Raman spectroscopy, particularly in terms of sensitivity and stability. The integration of label-free SERS presents promising opportunities for the rapid, reliable, and non-invasive diagnosis of brain-associated diseases, particularly when combined with advanced computational methods such as machine learning. SERS has potential to deepen our understanding of brain diseases, enhancing diagnosis, monitoring, and therapeutic interventions. Such advancements could significantly enhance the accuracy of clinical diagnosis and further our understanding of brain-related processes and diseases. This review assesses the utility of SERS in diagnosing and understanding the pathophysiological basis of brain disorders such as Alzheimer's and Parkinson's diseases, stroke, and brain cancer. Recent technological advances in SERS instrumentation and techniques are discussed, including innovations in nanoparticle design, substrate materials, and imaging technologies. We also explore prospects and emerging trends, offering insights into new technologies, while also addressing various challenges and limitations associated with SERS in brain research.

Precise diagnosis of breast phyllodes tumors using Raman spectroscopy: Biochemical fingerprint, tumor metabolism and possible mechanism

BACKGROUND

Breast fibroadenomas and phyllodes tumors are both fibroepithelial tumors with comparable histological characteristics. However, rapid and precise differential diagnosis is a tough point in clinical pathology. Given the tendency of phyllodes tumors to recur, the difficulty in differential diagnosis with fibroadenomas leads to the difficulty in optimal management for these patients.

METHOD

In this study, we used Raman spectroscopy to differentiate phyllodes tumors from breast fibroadenomas based on the biochemical and metabolic composition and develop a classification model. The model was validated by 5-fold cross-validation in the training set and tested in an independent test set. The potential metabolic differences between the two types of tumors observed in Raman spectroscopy were confirmed by targeted metabolomic analysis using liquid chromatography-tandem mass spectrometry (LC-MS/MS).

RESULTS

A total of 204 patients with formalin-fixed paraffin-embedded (FFPE) tissue samples, including 100 fibroadenomas and 104 phyllodes tumors were recruited from April 2014 to August 2021. All patients were randomly divided into the training cohort (n = 153) and the test cohort (n = 51). The Raman classification model could differentiate phyllodes tumor versus fibroadenoma with cross-validation accuracy, sensitivity, precision, and area under curve (AUC) of 85.58 % ± 1.77 %, 83.82 % ± 1.01 %, 87.65 % ± 4.22 %, and 93.18 % ± 1.98 %, respectively. When tested in the independent test set, it performed well with the test accuracy, sensitivity, specificity, and AUC of 83.50 %, 86.54 %, 80.39 %, and 90.71 %. Furthermore, the AUC was significantly higher for the Raman model than that for ultrasound (P = 0.0017) and frozen section diagnosis (P < 0.0001). When it came to much more difficult diagnosis between fibroadenoma and benign or small-size phyllodes tumor for pathological examination, the Raman model was capable of differentiating with AUC up to 97.45 % and 95.61 %, respectively. On the other hand, targeted metabolomic analysis, based on fresh-frozen tissue samples, confirmed the differential metabolites (including thymine, dihydrothymine, trans-4-hydroxy-l-proline, etc.) identified from Raman spectra between phyllodes tumor and fibroadenoma.

SIGNIFICANCE AND NOVELTY

In this study, we obtained the molecular information map of breast phyllodes tumors provided by Raman spectroscopy for the first time. We identified a novel Raman fingerprint signature with the potential to precisely characterize and distinguish phyllodes tumors from fibroadenoma as a quick and accurate diagnostic tool. Raman spectroscopy is expected to further guide the precise diagnosis and optimal treatment of breast fibroepithelial tumors in the future.

Defining the relevance of surgical margins. Part two: Strategies to improve prediction of recurrence risk

Due to the complex nature of tumour biology and the integration between host tissues and molecular processes of the tumour cells, a continued reliance on the status of the microscopic cellular margin should not remain our only determinant of the success of a curative-intent surgery for patients with cancer. Based on current evidence, relying on a purely cellular focus to provide a binary indication of treatment success can provide an incomplete interpretation of potential outcome. A more holistic analysis of the cancer margin may be required. If we are to move ahead from our current situation - and allow treatment plans to be more intelligently tailored to meet the requirements of each individual tumour - we need to improve our utilisation of techniques that either improve recognition of residual tumour cells within the surgical field or enable a more comprehensive interrogation of tumour biology that identifies a risk of recurrence. In the second article in this series on defining the relevance of surgical margins, the authors discuss possible alternative strategies for margin assessment and evaluation in the canine and feline cancer patient. These strategies include considering adoption of the residual tumour classification scheme; intra-operative imaging systems including fluorescence-guided surgery, optical coherence tomography and Raman spectroscopy; molecular analysis and whole transcriptome analysis of tissues; and the development of a biologic index (nomogram). These techniques may allow evaluation of individual tumour biology and the status of the resection margin in ways that are different to our current techniques. Ultimately, these techniques seek to better define the risk of tumour recurrence following surgery and provide the surgeon and patient with more confidence in margin assessment.

Intraoperative imaging in pathology-assisted surgery

The pathological assessment of surgical specimens during surgery can reduce the incidence of positive resection margins, which otherwise can result in additional surgeries or aggressive therapeutic regimens. To improve patient outcomes, intraoperative spectroscopic, fluorescence-based, structural, optoacoustic and radiological imaging techniques are being tested on freshly excised tissue. The specific clinical setting and tumour type largely determine whether endogenous or exogenous contrast is to be detected and whether the tumour specificity of the detected biomarker, image resolution, image-acquisition times or penetration depth are to be prioritized. In this Perspective, we describe current clinical standards for intraoperative tissue analysis and discuss how intraoperative imaging is being implemented. We also discuss potential implementations of intraoperative pathology-assisted surgery for clinical decision-making.

Development of deep learning algorithms to discriminate giant cell tumors of bone from adjacent normal tissues by confocal Raman spectroscopy

Raman spectroscopy is a non-destructive analysis technique that provides detailed information about the chemical structure of tumors. Raman spectra of 52 giant cell tumors of bone (GCTB) and 21 adjacent normal tissues of formalin-fixed paraffin embedded (FFPE) and frozen specimens were obtained using a confocal Raman spectrometer and analyzed with machine learning and deep learning algorithms. We discovered characteristic Raman shifts in the GCTB specimens. They were assigned to phenylalanine and tyrosine. Based on the spectroscopic data, classification algorithms including support vector machine, k-nearest neighbors and long short-term memory (LSTM) were successfully applied to discriminate GCTB from adjacent normal tissues of both the FFPE and frozen specimens, with the accuracy ranging from 82.8% to 94.5%. Importantly, our LSTM algorithm showed the best performance in the discrimination of the frozen specimens, with a sensitivity and specificity of 93.9% and 95.1% respectively, and the AUC was 0.97. The results of our study suggest that confocal Raman spectroscopy accomplished by the LSTM network could non-destructively evaluate a tumor margin by its inherent biochemical specificity which may allow intraoperative assessment of the adequacy of tumor clearance.

Classification of Soft Tissue Sarcoma Specimens with Raman Spectroscopy as Smart Sensing Technology

Intraoperative confirmation of negative resection margins is an essential component of soft tissue sarcoma surgery. Frozen section examination of samples from the resection bed after excision of sarcomas is the gold standard for intraoperative assessment of margin status. However, it takes time to complete histologic examination of these samples, and the technique does not provide real-time diagnosis in the operating room (OR), which delays completion of the operation. This paper presents a study and development of sensing technology using Raman spectroscopy that could be used for detection and classification of the tumor after resection with negative sarcoma margins in real time. We acquired Raman spectra from samples of sarcoma and surrounding benign muscle, fat, and dermis during surgery and developed (i) a quantitative method (QM) and (ii) a machine learning method (MLM) to assess the spectral patterns and determine if they could accurately identify these tissue types when compared to findings in adjacent H&E-stained frozen sections. High classification accuracy (>85%) was achieved with both methods, indicating that these four types of tissue can be identified using the analytical methodology. A hand-held Raman probe could be employed to further develop the methodology to obtain spectra in the OR to provide real-time in vivo capability for the assessment of sarcoma resection margin status.

Perspective of Future SERS Clinical Application Based on Current Status of Raman Spectroscopy Clinical Trials

Raman spectroscopy has emerged as a promising tool in biomedical analysis and clinical diagnosis. The development of surface-enhanced Raman scattering spectroscopy (SERS) improved the detection limit with ultrahigh sensitivity and simplicity. More and more Raman spectroscopy clinical trials (R-PCT) have been conducted recently. However, there is a lack of an up-to-date review summarizing the current status of Raman clinical trials performed until now. Hence, the clinical trials for Raman were retrieved from the International Clinical Trials Registration Platform. We summarized the clinical characteristics of 55 registered Raman spectroscopy clinical trials (R-RSCTs) and 44 published Raman spectroscopy clinical trials (P-RSCTs). This review could assist researchers and clinicians to understand the current status of Raman spectroscopy clinical research and perhaps could benefit the reasonable and accurate design of future SERS studies.

Repurposing Molecular Imaging and Sensing for Cancer Image-Guided Surgery

Gone are the days when medical imaging was used primarily to visualize anatomic structures. The emergence of molecular imaging (MI), championed by radiolabeled ¹⁸F-FDG PET, has expanded the information content derived from imaging to include pathophysiologic and molecular processes. Cancer imaging, in particular, has leveraged advances in MI agents and technology to improve the accuracy of tumor detection, interrogate tumor heterogeneity, monitor treatment response, focus surgical resection, and enable image-guided biopsy. Surgeons are actively latching on to the incredible opportunities provided by medical imaging for preoperative planning, intraoperative guidance, and postoperative monitoring. From label-free techniques to enabling cancer-selective imaging agents, image-guided surgery provides surgical oncologists and interventional radiologists both macroscopic and microscopic views of cancer in the operating room. This review highlights the current state of MI and sensing approaches available for surgical guidance. Salient features of nuclear, optical, and multimodal approaches will be discussed, including their strengths, limitations, and clinical applications. To address the increasing complexity and diversity of methods available today, this review provides a framework to identify a contrast mechanism, suitable modality, and device. Emerging low-cost, portable, and user-friendly imaging systems make the case for adopting some of these technologies as the global standard of care in surgical practice.

Raman spectroscopy for cancer detection and cancer surgery guidance: translation to the clinics

Oncological applications of Raman spectroscopy have been contemplated, pursued, and developed at academic level for at least 25 years. Published studies aim to detect pre-malignant lesions, detect cancer in less invasive stages, reduce the number of unnecessary biopsies and guide surgery towards the complete removal of the tumour with adequate tumour resection margins. This review summarizes actual clinical needs in oncology that can be addressed by spontaneous Raman spectroscopy and it provides an overview over the results that have been published between 2007 and 2017. An analysis is made of the current status of translation of these results into clinical practice. Despite many promising results, most of the applications addressed in scientific studies are still far from clinical adoption and commercialization. The main hurdles are identified, which need to be overcome to ensure that in the near future we will see the first Raman spectroscopy-based solutions being used in routine oncologic diagnostic and surgical procedures.

Diagnosis of immunomarkers in vivo via multiplexed surface enhanced Raman spectroscopy with gold nanostars

In this work, we demonstrate the targeted diagnosis of immunomarker programmed death ligand 1 (PD-L1) and simultaneous detection of epidermal growth factor receptor (EGFR) in breast cancer tumors in vivo using gold nanostars (AuNS) with multiplexed surface enhanced Raman spectroscopy (SERS). Real-time longitudinal tracking with SERS demonstrated maximum accumulation of AuNS occurred 6 h post intravenous (IV) delivery, enabling detection of both biomarkers simultaneously. Raman signal correlating to both PD-L1 and EGFR decreased by ∼30% in control tumors where receptors were pre-blocked prior to AuNS delivery, indicating both the sensitivity and specificity of SERS in distinguishing tumors with different levels of PD-L1 and EGFR expression. Our in vivo study was combined with the first demonstration of ex vivo SERS spatial maps of whole tumor lesions that provided both a qualitative and quantitative assessment of biomarker status with near cellular-level resolution. High resolution SERS maps also provided an overview of AuNS distribution in tumors which correlated well with the vascular density. Mass spectrometry showed AuNS accumulation in tumor and liver, and clearance via spleen, and electron microscopy revealed AuNS were endocytosed in tumors, Kupffer cells in the liver, and macrophages in the spleen. This study demonstrates that SERS-based diagnosis mediated by AuNS provides an accurate measure of multiple biomarkers both in vivo and ex vivo, which will ultimately enable a clinically-translatable platform for patient-tailored immunotherapies and combination treatments.

In-vivo Raman spectroscopy: from basics to applications

For more than two decades, Raman spectroscopy has found widespread use in biological and medical applications. The instrumentation and the statistical evaluation procedures have matured, enabling the lengthy transition from ex-vivo demonstration to in-vivo examinations. This transition goes hand-in-hand with many technological developments and tightly bound requirements for a successful implementation in a clinical environment, which are often difficult to assess for novice scientists in the field. This review outlines the required instrumentation and instrumentation parameters, designs, and developments of fiber optic probes for the in-vivo applications in a clinical setting. It aims at providing an overview of contemporary technology and clinical trials and attempts to identify future developments necessary to bring the emerging technology to the clinical end users. A comprehensive overview of in-vivo applications of fiber optic Raman probes to characterize different tissue and disease types is also given.

Development of a modular fluorescence overlay tissue imaging system for wide-field intraoperative surgical guidance

Fluorescence imaging is a well-established optical modality that has been used to localize and track fluorophores in vivo and has demonstrated great potential for surgical guidance. Despite the variety of fluorophores currently being researched, many existing intraoperative fluorescence imaging systems are specifically designed for a limited number of applications. We present a modular wide-field fluorescence overlay tissue imaging system for intraoperative surgical guidance that is comprised of commercially available standardized components. Its modular layout allows for the accommodation of a broad range of fluorophores, fields of view (FOV), and spatial resolutions while maintaining an integrated portable design for intraoperative use. Measurements are automatic and feature a real-time projection overlay technique that intuitively displays fluorescence maps directly onto a [Formula: see text] FOV from a working distance of 35 cm. At a 20-ms exposure time, [Formula: see text] samples of indocyanine green could be measured with high signal-to-noise ratio and was later tested in an in vivo mouse model before finally being demonstrated for intraoperative autofluorescence imaging of human soft tissue sarcoma margins. The system's modular design and ability to enable naked-eye visualization of wide-field fluorescence allow for the flexibility to adapt to numerous clinical applications and can potentially extend the adoption of fluorescence imaging for intraoperative use.

Proof-of-concept Raman spectroscopy study aimed to differentiate thyroid follicular patterned lesions

Inter-observer variability and cancer over-diagnosis are emerging clinical problems, especially for follicular patterned thyroid lesions. This challenge strongly calls for a new clinical tool to reliably identify neoplastic lesions and to improve the efficiency of differentiation between benign and malignant neoplasms, especially considering the increased diagnosis of small carcinomas and the growing number of thyroid nodules. In this study, we employed a Raman spectroscopy (RS) microscope to investigate frozen thyroid tissues from fourteen patients with thyroid nodules. To generate tissue classification models, a supervised statistical analysis of the Raman spectra was performed. The results obtained demonstrate an accuracy of 78% for RS based diagnosis to discriminate between normal parenchyma and follicular patterned thyroid nodules, and 89% accuracy - for very challenging follicular lesions (carcinoma versus adenoma). RS translation into intraoperative diagnosis of frozen sections and in preoperative analysis of biopsies can be very helpful to reduce unnecessary surgery in patients with indeterminate cytological reports.