Intraoperative assessment of skull base tumors using stimulated Raman scattering microscopy

Label-free optical imaging for brain cancer assessment

Advances in label-free optical imaging offer a promising avenue for brain cancer assessment, providing high-resolution, real-time insights without the need for radiation or exogeneous agents. These cost-effective and intricately detailed techniques overcome the limitations inherent in magnetic resonance imaging (MRI), computed tomography (CT), and positron emission tomography (PET) scans by offering superior resolution and more readily accessible imaging options. This comprehensive review explores a variety of such methods, including photoacoustic imaging (PAI), optical coherence tomography (OCT), Raman imaging, and IR microscopy. It focuses on their roles in the detection, diagnosis, and management of brain tumors. By highlighting recent advances in these imaging techniques, the review aims to underscore the importance of label-free optical imaging in enhancing early detection and refining therapeutic strategies for brain cancer.

Virtual Staining of Nonfixed Tissue Histology

Surgical pathology workflow involves multiple labor-intensive steps, such as tissue removal, fixation, embedding, sectioning, staining, and microscopic examination. This process is time-consuming and costly and requires skilled technicians. In certain clinical scenarios, such as intraoperative consultations, there is a need for faster histologic evaluation to provide real-time surgical guidance. Currently, frozen section techniques involving hematoxylin and eosin (H&E) staining are used for intraoperative pathology consultations. However, these techniques have limitations, including a turnaround time of 20 to 30 minutes, staining artifacts, and potential tissue loss, negatively impacting accurate diagnosis. To address these challenges, researchers are exploring alternative optical imaging modalities for rapid microscopic tissue imaging. These modalities differ in optical characteristics, tissue preparation requirements, imaging equipment, and output image quality and format. Some of these imaging methods have been combined with computational algorithms to generate H&E-like images, which could greatly facilitate their adoption by pathologists. Here, we provide a comprehensive, organ-specific review of the latest advancements in emerging imaging modalities applied to nonfixed human tissue. We focused on studies that generated H&E-like images evaluated by pathologists. By presenting up-to-date research progress and clinical utility, this review serves as a valuable resource for scholars and clinicians, covering some of the major technical developments in this rapidly evolving field. It also offers insights into the potential benefits and drawbacks of alternative imaging modalities and their implications for improving patient care.

Seeing is Believing: Developing Multimodal Metabolic Insights at the Molecular Level

This outlook explores how two different molecular imaging approaches might be combined to gain insight into dynamic, subcellular metabolic processes. Specifically, we discuss how matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) and stimulated Raman scattering (SRS) microscopy, which have significantly pushed the boundaries of imaging metabolic and metabolomic analyses in their own right, could be combined to create comprehensive molecular images. We first briefly summarize the recent advances for each technique. We then explore how one might overcome the inherent limitations of each individual method, by envisioning orthogonal and interchangeable workflows. Additionally, we delve into the potential benefits of adopting a complementary approach that combines both MSI and SRS spectro-microscopy for informing on specific chemical structures through functional-group-specific targets. Ultimately, by integrating the strengths of both imaging modalities, researchers can achieve a more comprehensive understanding of biological and chemical systems, enabling precise metabolic investigations. This synergistic approach holds substantial promise to expand our toolkit for studying metabolites in complex environments.

Meningiomas with CNS invasion

CNS invasion has been included as an independent criterion for the diagnosis of a high-grade (WHO and CNS grade 2 and 3) meningioma in the 2016 and more recently in the 2021 WHO classification. However, the prognostic role of brain invasion has recently been questioned. Also, surgical treatment for brain invasive meningiomas may pose specific challenges. We conducted a systematic review of the 2016-2022 literature on brain invasive meningiomas in Pubmed, Scopus, Web of Science and the Cochrane Library. The prognostic relevance of brain invasion as a stand-alone criterion is still unclear. Additional and larger studies using robust definitions of histological brain invasion and addressing the issue of sampling errors are clearly warranted. Although the necessity of molecular profiling in meningioma grading, prognostication and decision making in the future is obvious, specific markers for brain invasion are lacking for the time being. Advanced neuroimaging may predict CNS invasion preoperatively. The extent of resection (e.g., the Simpson grading) is an important predictor of tumor recurrence especially in higher grade meningiomas, but also - although likely to a lesser degree - in benign tumors, and therefore also in brain invasive meningiomas with and without other histological features of atypia or malignancy. Hence, surgery for brain invasive meningiomas should follow the principles of maximal but safe resections. There are some data to suggest that safety and functional outcomes in such cases may benefit from the armamentarium of surgical adjuncts commonly used for surgery of eloquent gliomas such as intraoperative monitoring, awake craniotomy, DTI tractography and further advanced intraoperative brain tumor visualization.

Rapid digital pathology of H&E-stained fresh human brain specimens as an alternative to frozen biopsy

BACKGROUND

Hematoxylin and Eosin (H&E)-based frozen section (FS) pathology is presently the global standard for intraoperative tumor assessment (ITA). Preparation of frozen section is labor intensive, which might consume up-to 30 minutes, and is susceptible to freezing artifacts. An FS-alternative technique is thus necessary, which is sectioning-free, artifact-free, fast, accurate, and reliably deployable without machine learning and/or additional interpretation training.

METHODS

We develop a training-free true-H&E Rapid Fresh digital-Pathology (the-RFP) technique which is 4 times faster than the conventional preparation of frozen sections. The-RFP is assisted by a mesoscale Nonlinear Optical Gigascope (mNLOG) platform with a streamlined rapid artifact-compensated 2D large-field mosaic-stitching (rac2D-LMS) approach. A sub-6-minute True-H&E Rapid whole-mount-Soft-Tissue Staining (the-RSTS) protocol is introduced for soft/frangible fresh brain specimens. The mNLOG platform utilizes third harmonic generation (THG) and two-photon excitation fluorescence (TPEF) signals from H and E dyes, respectively, to yield the-RFP images.

RESULTS

We demonstrate the-RFP technique on fresh excised human brain specimens. The-RFP enables optically-sectioned high-resolution 2D scanning and digital display of a 1 cm² area in <120 seconds with 3.6 Gigapixels at a sustained effective throughput of >700 M bits/sec, with zero post-acquisition data/image processing. Training-free blind tests considering 50 normal and tumor-specific brain specimens obtained from 8 participants reveal 100% match to the respective formalin-fixed paraffin-embedded (FFPE)-biopsy outcomes.

CONCLUSIONS

We provide a digital ITA solution: the-RFP, which is potentially a fast and reliable alternative to FS-pathology. With H&E-compatibility, the-RFP eliminates color- and morphology-specific additional interpretation training for a pathologist, and the-RFP-assessed specimen can reliably undergo FFPE-biopsy confirmation.

Surgical margin assessment of bone tumours: A systematic review of current and emerging technologies

Osteosarcoma is the most common malignant tumour of the bone. Complete surgical excision is critical to achieve optimal outcomes and lower recurrence rates. However, accurate assessment of tumour margins remains a challenge and multiple technologies are employed for this purpose. The aim of this study is to highlight current and emerging technologies and their efficacy in detecting clear bone margins intraoperatively, through a systematic review of the literature. The following databases were searched using the OVID platform: Medline, Embase, Global Health and Google Scholar. Studies were screened using predetermined eligibility criteria. Data was extracted based on study and patient characteristics, modes of detection, and commercial availability, followed by quality assessment. A total of 17 studies were included. The primary diagnosis varied, with osteosarcoma being reported by 9 studies. Three studies reported relapse, ranging between 17.6%-48%. Twelve studies reported non-invasive imaging as the mode of detection used, while 4 studies reported the use of frozen section. MRI and CT were found to have an accuracy of up to 93 %. Raman spectroscopy was reported to have an accuracy, sensitivity, and specificity of 69%, 58.8% and 83.3% respectively. CT had a sensitivity and specificity up to 83% and 100%, respectively. In conclusion, there seems to be high potential for the use of multimodal technologies to increase the accuracy of intraoperative margin assessment. Although imaging modalities possess a fair level of accuracy, they carry the risk of radiation exposure, are expensive, and cannot be used in-situ. Future clinical trials are needed to test the effectiveness of these technologies to measure the diagnostic accuracy and overall patient survival.

Stimulated Raman Scattering Microscopy Enables Gleason Scoring of Prostate Core Needle Biopsy by a Convolutional Neural Network

Focal therapy (FT) has been proposed as an approach to eradicate clinically significant prostate cancer while preserving the normal surrounding tissues to minimize treatment-related toxicity. Rapid histology of core needle biopsies is essential to ensure the precise FT for localized lesions and to determine tumor grades. However, it is difficult to achieve both high accuracy and speed with currently available histopathology methods. Here, we demonstrated that stimulated Raman scattering (SRS) microscopy could reveal the largely heterogeneous histologic features of fresh prostatic biopsy tissues in a label-free and near real-time manner. A diagnostic convolutional neural network (CNN) built based on images from 61 patients could classify Gleason patterns of prostate cancer with an accuracy of 85.7%. An additional 22 independent cases introduced as external test dataset validated the CNN performance with 84.4% accuracy. Gleason scores of core needle biopsies from 21 cases were calculated using the deep learning SRS system and showed a 71% diagnostic consistency with grading from three pathologists. This study demonstrates the potential of a deep learning-assisted SRS platform in evaluating the tumor grade of prostate cancer, which could help simplify the diagnostic workflow and provide timely histopathology compatible with FT treatment.

SIGNIFICANCE

A platform combining stimulated Raman scattering microscopy and a convolutional neural network provides rapid histopathology and automated Gleason scoring on fresh prostate core needle biopsies without complex tissue processing.

Stimulated Raman Histology for Rapid Intra-Operative Diagnosis of Sinonasal and Skull Base Tumors

OBJECTIVE

Intra-operative stimulated Raman histology (SRH) is a novel technology that uses laser spectroscopy and color-matching algorithms to create images similar to the formalin-fixed paraffin-embedded (FFPE) section. We aim to assess the accuracy of SRH in a novel range of sinonasal and skull base tumors.

METHODS

Select patients undergoing sinonasal and skull base surgery using the Invenio Imaging™ Nio™ Laser Imaging SRH system between June 2020 and September 2021 were assessed. The SRH images were reviewed for pathologic features similar to frozen section (FS) and FFPE. Time taken for results and diagnostic concordance was assessed.

RESULTS

Sixty-seven SRH images from 7 tumor types in 12 patients were assessed. Pathologies included squamous cell carcinoma, rhabdomyosarcoma, inverted papilloma, adenoid cystic carcinoma, SMARCB1-deficient sinonasal carcinoma, mucosal melanoma, metastatic colonic adenocarcinoma, and meningioma. Tumor was identified in 100% of lesional specimens, with characteristic diagnostic features readily appreciable on SRH. Median time for diagnosis was significantly faster for SRH (4.3 min) versus FS (44.5 min; p = <.0001). Where SRH sample site matched precisely to FS (n = 32/67, 47.8%), the same diagnosis was confirmed in 93.8%. Sensitivity, specificity, precision, and overall accuracy of SRH were 93.3%, 94.1%, 93.8%, and 93.3%, respectively. Near-perfect concordance was seen between SRH and FS (Cohen's kappa [κ] = 0.89).

CONCLUSION

Stimulated Raman histology can rapidly produce images similar to FFPE H&E in sinonasal and skull base tumors. This technology has the potential to act as an adjunct or alternative to standard FS.

LEVEL OF EVIDENCE

4 Laryngoscope, 132:2142-2147, 2022.

Instant diagnosis of gastroscopic biopsy via deep-learned single-shot femtosecond stimulated Raman histology

Gastroscopic biopsy provides the only effective method for gastric cancer diagnosis, but the gold standard histopathology is time-consuming and incompatible with gastroscopy. Conventional stimulated Raman scattering (SRS) microscopy has shown promise in label-free diagnosis on human tissues, yet it requires the tuning of picosecond lasers to achieve chemical specificity at the cost of time and complexity. Here, we demonstrate that single-shot femtosecond SRS (femto-SRS) reaches the maximum speed and sensitivity with preserved chemical resolution by integrating with U-Net. Fresh gastroscopic biopsy is imaged in <60 s, revealing essential histoarchitectural hallmarks perfectly agreed with standard histopathology. Moreover, a diagnostic neural network (CNN) is constructed based on images from 279 patients that predicts gastric cancer with accuracy >96%. We further demonstrate semantic segmentation of intratumor heterogeneity and evaluation of resection margins of endoscopic submucosal dissection (ESD) tissues to simulate rapid and automated intraoperative diagnosis. Our method holds potential for synchronizing gastroscopy and histopathological diagnosis.

Diagnosis of gastric cancer currently requires gastroscopic biopsy, which requires time and expertize to perform. Here, the authors demonstrate a femto-SRS imaging method which showed high accuracy in diagnosing gastric cancer without the need for pathologistbased diagnosis.

Quantitative Stimulated Raman Scattering Microscopy: Promises and Pitfalls

Since its first demonstration, stimulated Raman scattering (SRS) microscopy has become a powerful chemical imaging tool that shows promise in numerous biological and biomedical applications. The spectroscopic capability of SRS enables identification and tracking of specific molecules or classes of molecules, often without labeling. SRS microscopy also has the hallmark advantage of signal strength that is directly proportional to molecular concentration, allowing for in situ quantitative analysis of chemical composition of heterogeneous samples with submicron spatial resolution and subminute temporal resolution. However, it is important to recognize that quantification through SRS microscopy requires assumptions regarding both system and sample. Such assumptions are often taken axiomatically, which may lead to erroneous conclusions without proper validation. In this review, we focus on the tacitly accepted, yet complex, quantitative aspect of SRS microscopy. We discuss the various approaches to quantitative analysis, examples of such approaches, challenges in different systems, and potential solutions. Through our examination of published literature, we conclude that a scrupulous approach to experimental design can further expand the powerful and incisive quantitative capabilities of SRS microscopy.

In vivo simultaneous nonlinear absorption Raman and fluorescence (SNARF) imaging of mouse brain cortical structures

Label-free multiphoton microscopy is a powerful platform for biomedical imaging. Recent advancements have demonstrated the capabilities of transient absorption microscopy (TAM) for label-free quantification of hemoglobin and stimulated Raman scattering (SRS) microscopy for pathological assessment of label-free virtual histochemical staining. We propose the combination of TAM and SRS with two-photon excited fluorescence (TPEF) to characterize, quantify, and compare hemodynamics, vessel structure, cell density, and cell identity in vivo between age groups. In this study, we construct a simultaneous nonlinear absorption, Raman, and fluorescence (SNARF) microscope with the highest reported in vivo imaging depth for SRS and TAM at 250–280 μm to enable these multimodal measurements. Using machine learning, we predict capillary-lining cell identities with 90% accuracy based on nuclear morphology and capillary relationship. The microscope and methodology outlined herein provides an exciting route to study several research topics, including neurovascular coupling, blood-brain barrier, and neurodegenerative diseases.

In this study a microscope is constructed that carries out simultaneous nonlinear absorption, Raman, and fluorescence (SNARF). Machine learning is then used to predict capillary-lining cell identities with 90% accuracy based on nuclear morphology and capillary relationship, which in combination with the developed microscope, can provide a means to study several fields such as neurovascular coupling.

Quantitative Chemical Imaging of Bone Tissue for Intraoperative and Diagnostic Applications

Bone is difficult to image using traditional histopathological methods, leading to challenges in intraoperative pathological evaluation that is critical in guiding surgical treatment, particularly in orthopedic oncology. In this study, we demonstrate that a multimodal quantitative imaging approach that combines stimulated Raman scattering (SRS) microscopy, two-photon fluorescence (TPF) microscopy, and second-harmonic generation (SHG) microscopy can provide useful diagnostic information regarding intact bone tissue fragments from surgical excision or biopsy specimens. We imaged bone samples from 17 patient cases and performed quantitative chemical and morphological analyses of both mineral and organic components of bone. Our main findings show that carbonate content combined with morphometric analysis of bone organic matrix can separate several major classes of bone cancer-associated diagnostic categories with an average accuracy of 92%. This proof-of-principle study demonstrates that quantitative multimodal imaging and machine learning-based analysis of bony tissue can provide crucial diagnostic information for guiding clinical decisions in orthopedic oncology. Moreover, the general methodology of morphological and chemical imaging combined with machine learning can be readily extended to other tissue types for tissue diagnosis in intraoperative and other clinical settings.

Real-Time, Two-Color Stimulated Raman Scattering Imaging of Mouse Brain for Tissue Diagnosis

Stimulated Raman scattering (SRS) microscopy has emerged as a powerful optical imaging technique for tissue diagnosis. In recent years, two-color SRS has been shown to be able to provide hematoxylin and eosin (H&E)-equivalent images that allow fast and reliable diagnosis of brain cancer. Such capability has enabled exciting intraoperative cancer diagnosis applications. Two-color SRS imaging of tissue can be done with either a picosecond or femtosecond laser source. Femtosecond lasers have the advantage of enabling flexible imaging modes, including fast hyperspectral imaging and real-time, two-color SRS imaging. A spectral-focusing approach with chirped laser pulses is typically used with femtosecond lasers to achieve high spectral resolution. Two-color SRS acquisition can be realized with orthogonal modulation and lock-in detection. The complexity of pulse chirping, modulation, and characterization is a bottleneck for the widespread adoption of this method. This article provides a detailed protocol to demonstrate the implementation and optimization of spectral-focusing SRS and real-time, two-color imaging of mouse brain tissue in the epi-mode. This protocol can be used for a broad range of SRS imaging applications that leverage the high speed and spectroscopic imaging capability of SRS.

Raman Scattering-Based Biosensing: New Prospects and Opportunities

The growing interest in the development of new platforms for the application of Raman spectroscopy techniques in biosensor technologies is driven by the potential of these techniques in identifying chemical compounds, as well as structural and functional features of biomolecules. The effect of Raman scattering is a result of inelastic light scattering processes, which lead to the emission of scattered light with a different frequency associated with molecular vibrations of the identified molecule. Spontaneous Raman scattering is usually weak, resulting in complexities with the separation of weak inelastically scattered light and intense Rayleigh scattering. These limitations have led to the development of various techniques for enhancing Raman scattering, including resonance Raman spectroscopy (RRS) and nonlinear Raman spectroscopy (coherent anti-Stokes Raman spectroscopy and stimulated Raman spectroscopy). Furthermore, the discovery of the phenomenon of enhanced Raman scattering near metallic nanostructures gave impetus to the development of the surface-enhanced Raman spectroscopy (SERS) as well as its combination with resonance Raman spectroscopy and nonlinear Raman spectroscopic techniques. The combination of nonlinear and resonant optical effects with metal substrates or nanoparticles can be used to increase speed, spatial resolution, and signal amplification in Raman spectroscopy, making these techniques promising for the analysis and characterization of biological samples. This review provides the main provisions of the listed Raman techniques and the advantages and limitations present when applied to life sciences research. The recent advances in SERS and SERS-combined techniques are summarized, such as SERRS, SE-CARS, and SE-SRS for bioimaging and the biosensing of molecules, which form the basis for potential future applications of these techniques in biosensor technology. In addition, an overview is given of the main tools for success in the development of biosensors based on Raman spectroscopy techniques, which can be achieved by choosing one or a combination of the following approaches: (i) fabrication of a reproducible SERS substrate, (ii) synthesis of the SERS nanotag, and (iii) implementation of new platforms for on-site testing.

Advances in nonlinear optical microscopy techniques for in vivo and in vitro neuroimaging

Understanding the mechanism of the brain via optical microscopy is one of the challenges in neuroimaging, considering the complex structures. Advanced neuroimaging techniques provide a more comprehensive insight into patho-mechanisms of brain disorders, which is useful in the early diagnosis of the pathological and physiological changes associated with various neurodegenerative diseases. Recent advances in optical microscopy techniques have evolved powerful tools to overcome scattering of light and provide improved in vivo neuroimaging with sub-cellular resolution, endogenous contrast specificity, pinhole less optical sectioning capability, high penetration depth, and so on. The following article reviews the developments in various optical imaging techniques including two-photon and three-photon fluorescence, second-harmonic generation, third-harmonic generation, coherent anti-Stokes Raman scattering, and stimulated Raman scattering in neuroimaging. We have outlined the potentials and drawbacks of these techniques and their possible applications in the investigation of neurodegenerative diseases.

Ultrahigh-speed multiplex coherent anti-Stokes Raman scattering microspectroscopy using scanning elliptical focal spot

We present a beam-scanning multiplex coherent anti-Stokes Raman scattering (CARS) microspectroscopy system using parallel excitation and parallel detection schemes based on an elliptical focal spot, which enables highly efficient signal acquisition even for short exposures. The elliptical focal spot was used to simultaneously observe the CARS signals of an enlarged region and reduce the peak irradiance. The developed system realized an acquisition rate of 34 139 spectra/s and enabled ultrahigh-speed acquisition of a vibrational spectroscopic image, covering the fingerprint region of 930-1 830 cm^-1 with 256_(x) × 256_(y) × 512_(spectrum) pixels in 1.92 s or with 128_(x) × 128_(y) × 256_(spectrum) pixels in 0.54 s. We demonstrated ultrahigh-speed hyperspectral imaging of a mixture of polymer beads in liquid linoleic acid and living adipocytes using the developed system. All of the present demonstrations were performed with a low-peak irradiance excitation of ∼19 GW/cm², which has been reported in previous studies to cause less photodamage to living cells. The label-free and ultrahigh-speed identification and visualization of various molecules made possible by the present system will accelerate the development of practical live-cell investigation.

Nonresonant CARS Imaging of Porous and Solid Silicon Nanoparticles in Human Cells

Coherent anti-Stokes Raman scattering (CARS), a nonlinear optical method for rapid visualization of biological objects, represents a progressive tool in biology and medicine to explore cells and tissue structures in living systems and biopsies. In this study, we report efficient nonresonant CARS imaging of silicon nanoparticles (SiNPs) in human cells as a proof of concept. As both bulk and porous silicon exhibit a high third-order nonlinear susceptibility, χ⁽³⁾, which is responsible for the CARS intensity, it is possible to visualize the SiNPs without specific labels. Porous and solid SiNPs were obtained from layers of porous and nonporous silicon nanowires and mesoporous silicon. Electron microscopy and Raman spectroscopy showed that porous SiNPs consisted of ∼3 nm silicon nanocrystals (nc-Si) and pores, whereas solid nanoparticles comprised ∼30 nm nc-Si. All types of SiNPs were nontoxic at concentrations up to 500 μg/mL after 24 h of incubation with cells. We demonstrated that although nc-Si possesses a distinguished narrow Raman band of about 520 cm^-1, it is possible to detect a high CARS signal from SiNPs in the epi-direction even in a nonresonant regime. 3D CARS images showed that all types of studied SiNPs were visualized as bright spots inside the cytoplasm of cells after 3-6 h of incubation because of the contrast provided by the high third-order nonlinear susceptibility of SiNPs, which is 1 × 10⁴ to 1 × 10⁵ times higher than that of water and typical biological media. Overall, CARS microscopy can provide localization of SiNPs within biological structures at the cellular level and can be a powerful tool for in vitro monitoring of silicon-based drug delivery systems or use SiNPs as labels to monitor various bioprocesses inside living cells.

Time-encoded stimulated Raman scattering microscopy of tumorous human pharynx tissue in the fingerprint region from 1500-1800 cm-1

Stimulated Raman scattering (SRS) microscopy for biomedical analysis can provide a molecular localization map to infer pathological tissue changes. Compared to spontaneous Raman, SRS achieves much faster imaging speeds at reduced spectral coverage. By targeting spectral features in the information dense fingerprint region, SRS allows fast and reliable imaging. We present time-encoded (TICO) SRS microscopy of unstained head-and-neck biopsies in the fingerprint region with molecular contrast. We combine a Fourier-domain mode-locked (FDML) laser with a master oscillator power amplifier (MOPA) to cover Raman transitions from 1500-1800cm^-1. Both lasers are fiber-based and electronically programmable making this fingerprint TICO system robust and reliable. The results of our TICO approach were cross-checked with a spontaneous Raman micro-spectrometer and show good agreement, paving the way toward clinical applications.

Toward Raman Subcellular Imaging of Endothelial Dysfunction

Multiple diseases are at some point associated with altered endothelial function, and endothelial dysfunction (ED) contributes to their pathophysiology. Biochemical changes of the dysfunctional endothelium are linked to various cellular organelles, including the mitochondria, endoplasmic reticulum, and nucleus, so organelle-specific insight is needed for better understanding of endothelial pathobiology. Raman imaging, which combines chemical specificity with microscopic resolution, has proved to be useful in detecting biochemical changes in ED at the cellular level. However, the detection of spectroscopic markers associated with specific cell organelles, while desirable, cannot easily be achieved by Raman imaging without labeling. This critical review summarizes the current advances in Raman-based analysis of ED, with a focus on a new approach involving molecular Raman reporters that could facilitate the study of biochemical changes in cellular organelles. Finally, imaging techniques based on both conventional spontaneous Raman scattering and the emerging technique of stimulated Raman scattering are discussed.

Advances in stimulated Raman scattering imaging for tissues and animals

Stimulated Raman scattering (SRS) microscopy has emerged in the last decade as a powerful optical imaging technology with high chemical selectivity, speed, and subcellular resolution. Since the invention of SRS microscopy, it has been extensively employed in life science to study composition, structure, metabolism, development, and disease in biological systems. Applications of SRS in research and the clinic have generated new insights in many fields including neurobiology, tumor biology, developmental biology, metabolomics, pharmacokinetics, and more. Herein we review the advances and applications of SRS microscopy imaging in tissues and animals, as well as envision future applications and development of SRS imaging in life science and medicine.

Recent advances in the use of stimulated Raman scattering in histopathology

Stimulated Raman histopathology (SRH) utilises the intrinsic vibrational properties of lipids, proteins and nucleic acids to generate contrast providing rapid image acquisition that allows visualisation of histopathological features. It is currently being trialled in the intraoperative setting, where the ability to image unprocessed samples rapidly and with high resolution offers several potential advantages over the use of conventional haematoxylin and eosin stained images. Here we review recent advances in the field including new updates in instrumentation and computer aided diagnosis. We also discuss how other non-linear modalities can be used to provide additional diagnostic contrast which together pave the way for enhanced histopathology and open up possibilities for in vivo pathology.

Review of Stimulated Raman Scattering Microscopy Techniques and Applications in the Biosciences

Stimulated Raman scattering (SRS) microscopy is a nonlinear optical imaging method for visualizing chemical content based on molecular vibrational bonds. Featuring high speed, high resolution, high sensitivity, high accuracy, and 3D sectioning, SRS microscopy has made tremendous progress toward biochemical information acquisition, cellular function investigation, and label-free medical diagnosis in the biosciences. In this review, the principle of SRS, system design, and data analysis are introduced, and the current innovations of the SRS system are reviewed. In particular, combined with various bio-orthogonal Raman tags, the applications of SRS microscopy in cell metabolism, tumor diagnosis, neuroscience, drug tracking, and microbial detection are briefly examined. The future prospects for SRS microscopy are also shared.

The Future of Skull Base Surgery: A View Through Tinted Glasses

In the present report, we have broadly outlined the potential advances in the field of skull base surgery, which might occur within the next 20 years based on the many areas of current research in biology and technology. Many of these advances will also be broadly applicable to other areas of neurosurgery. We have grounded our predictions for future developments in an exploration of what patients and surgeons most desire as outcomes for care. We next examined the recent developments in the field and outlined several promising areas of future improvement in skull base surgery, per se, as well as identifying the new hospital support systems needed to accommodate these changes. These include, but are not limited to, advances in imaging, Raman spectroscopy and microscopy, 3-dimensional printing and rapid prototyping, master-slave and semiautonomous robots, artificial intelligence applications in all areas of medicine, telemedicine, and green technologies in hospitals. In addition, we have reviewed the therapeutic approaches using nanotechnology, genetic engineering, antitumor antibodies, and stem cell technologies to repair damage caused by traumatic injuries, tumors, and iatrogenic injuries to the brain and cranial nerves. Additionally, we have discussed the training requirements for future skull base surgeons and stressed the need for adaptability and change. However, the essential requirements for skull base surgeons will remain unchanged, including knowledge, attention to detail, technical skill, innovation, judgment, and compassion. We believe that active involvement in these rapidly evolving technologies will enable us to shape some of the future of our discipline to address the needs of both patients and our profession.

Simultaneous stimulated Raman gain and loss detection (SRGAL)

The fidelity of stimulated Raman scattering (SRS) microscopy images is impaired by artifacts such as thermal lensing, cross-phase modulation and multi-photon absorption. These artifacts affect differently the stimulated Raman loss (SRL) and stimulated Raman gain (SRG) channels making SRL and SRG image comparisons attractive to identify and correct SRS image artifacts. To provide answer to the question: "Can I trust my SRS images?", we designed a novel, but straightforward SRS scheme that enables the dectection of the stimulated Raman gain and loss (SRGAL) simultaneously at the same pixel level. As an advantage over the conventional SRS imaging scheme, SRGAL doubles the SRS signal by acquiring both SRL as well as SRG and allows for the identification of SRS artifacts and their reduction via a balanced summation of the SRL and SRG images.

Using deep learning to predict the hand-foot-and-mouth disease of enterovirus A71 subtype in Beijing from 2011 to 2018

Hand-foot-and-month disease (HFMD), especially the enterovirus A71 (EV-A71) subtype, is a major health problem in Beijing, China. Previous studies mainly used regressive models to forecast the prevalence of HFMD, ignoring its intrinsic age groups. This study aims to predict HFMD of EV-A71 subtype in three age groups (0–3, 3–6 and > 6 years old) from 2011 to 2018 using residual-convolutional-recurrent neural network (CNNRNN-Res), convolutional-recurrent neural network (CNNRNN) and recurrent neural network (RNN). They were compared with auto-regressio, global auto-regression and vector auto-regression on both short-term and long-term prediction. Results showed that CNNRNN-Res and RNN had higher accuracies on point forecast tasks, as well as robust performances in long-term prediction. Three deep learning models also had better skills in peak intensity forecast, and CNNRNN-Res achieved the best results in the peak month forecast. We also found that three age groups had consistent outbreak trends and similar patterns of prediction errors. These results highlight the superior performance of deep learning models in HFMD prediction and can assist the decision-makers to refine the HFMD control measures according to age groups.

Quantitative chemical imaging of breast calcifications in association with neoplastic processes

Calcifications play an essential role in early breast cancer detection and diagnosis. However, information regarding the chemical composition of calcifications identified on mammography and histology is limited. Detailed spectroscopy reveals an association between the chemical composition of calcifications and breast cancer, warranting the development of novel analytical tools to better define calcification types. Previous investigations average calcification composition across broad tissue sections with no spatially resolved information or provide qualitative visualization, which prevents a robust linking of specific spatially resolved changes in calcification chemistry with the pathologic process.

Method: To visualize breast calcification chemical composition at high spatial resolution, we apply hyperspectral stimulated Raman scattering (SRS) microscopy to study breast calcifications associated with a spectrum of breast changes ranging from benign to neoplastic processes, including atypical ductal hyperplasia, ductal carcinoma in situ, and invasive ductal carcinoma. The carbonate content of individual breast calcifications is quantified using a simple ratiometric analysis.

Results: Our findings reveal that intra-sample calcification carbonate content is closely associated with local pathological processes. Single calcification analysis supports previous studies demonstrating decreasing average carbonate level with increasing malignant potential. Sensitivity and specificity reach >85% when carbonate content level is used as the single differentiator in separating benign from neoplastic processes. However, the average carbonate content is limiting when trying to separate specific diagnostic categories, such as fibroadenoma and invasive ductal carcinoma. Second harmonic generation (SHG) data can provide critical information to bridge this gap.

Conclusion: SRS, combined with SHG, can be a valuable tool in better understanding calcifications in carcinogenesis, diagnosis, and possible prognosis. This study not only reveals previously unknown large variations of breast microcalcifications in association with local malignancy but also corroborates the clinical value of linking microcalcification chemistry to breast malignancy. More importantly, it represents an important step in the development of a label-free imaging strategy for breast cancer diagnosis with tremendous potential to address major challenges in diagnostic discordance in pathology.