Near real-time intraoperative brain tumor diagnosis using stimulated Raman histology and deep neural networks

Deep learning-enabled realistic virtual histology with ultraviolet photoacoustic remote sensing microscopy

The goal of oncologic surgeries is complete tumor resection, yet positive margins are frequently found postoperatively using gold standard H&E-stained histology methods. Frozen section analysis is sometimes performed for rapid intraoperative margin evaluation, albeit with known inaccuracies. Here, we introduce a label-free histological imaging method based on an ultraviolet photoacoustic remote sensing and scattering microscope, combined with unsupervised deep learning using a cycle-consistent generative adversarial network for realistic virtual staining. Unstained tissues are scanned at rates of up to 7 mins/cm2, at resolution equivalent to 400x digital histopathology. Quantitative validation suggests strong concordance with conventional histology in benign and malignant prostate and breast tissues. In diagnostic utility studies we demonstrate a mean sensitivity and specificity of 0.96 and 0.91 in breast specimens, and respectively 0.87 and 0.94 in prostate specimens. We also find virtual stain quality is preferred (P = 0.03) compared to frozen section analysis in a blinded survey of pathologists.

Accelerating Cancer Histopathology Workflows with Chemical Imaging and Machine Learning

Histopathology has remained a cornerstone for biomedical tissue assessment for over a century, with a resource-intensive workflow involving biopsy or excision, gross examination, sampling, tissue processing to snap frozen or formalin-fixed paraffin-embedded blocks, sectioning, staining, optical imaging, and microscopic assessment. Emerging chemical imaging approaches, including stimulated Raman scattering (SRS) microscopy, can directly measure inherent molecular composition in tissue (thereby dispensing with the need for tissue processing, sectioning, and using dyes) and can use artificial intelligence (AI) algorithms to provide high-quality images. Here we show the integration of SRS microscopy in a pathology workflow to rapidly record chemical information from minimally processed fresh-frozen prostate tissue. Instead of using thin sections, we record data from intact thick tissues and use optical sectioning to generate images from multiple planes. We use a deep learning–based processing pipeline to generate virtual hematoxylin and eosin images. Next, we extend the computational method to generate archival-quality images in minutes, which are equivalent to those obtained from hours/days-long formalin-fixed, paraffin-embedded processing. We assessed the quality of images from the perspective of enabling pathologists to make decisions, demonstrating that the virtual stained image quality was diagnostically useful and the interpathologist agreement on prostate cancer grade was not impacted. Finally, because this method does not wash away lipids and small molecules, we assessed the utility of lipid chemical composition in determining grade. Together, the combination of chemical imaging and AI provides novel capabilities for rapid assessments in pathology by reducing the complexity and burden of current workflows.

SIGNIFICANCE

Archival-quality (formalin-fixed paraffin-embedded), thin-section diagnostic images are obtained from thick-cut, fresh-frozen prostate tissues without dyes or stains to expedite cancer histopathology by combining SRS microscopy and machine learning.

Deep-Learning-Based Screening and Ancillary Testing for Thyroid Cytopathology

Thyroid cancer is the most common malignant endocrine tumor. The key test to assess preoperative risk of malignancy is cytologic evaluation of fine-needle aspiration biopsies (FNABs). The evaluation findings can often be indeterminate, leading to unnecessary surgery for benign post-surgical diagnoses. We have developed a deep-learning algorithm to analyze thyroid FNAB whole-slide images (WSIs). We show, on the largest reported data set of thyroid FNAB WSIs, clinical-grade performance in the screening of determinate cases and indications for its use as an ancillary test to disambiguate indeterminate cases. The algorithm screened and definitively classified 45.1% (130/288) of the WSIs as either benign or malignant with risk of malignancy rates of 2.7% and 94.7%, respectively. It reduced the number of indeterminate cases (N = 108) by reclassifying 21.3% (N = 23) as benign with a resultant risk of malignancy rate of 1.8%. Similar results were reproduced using a data set of consecutive FNABs collected during an entire calendar year, achieving clinically acceptable margins of error for thyroid FNAB classification.

Novel approaches to targeting gliomas at the leading/cutting edge

Despite decades of clinical trials and surgical advances, the most common high-grade glioma, glioblastoma (GBM), remains an incurable disease with a dismal prognosis. Because of its infiltrative nature, GBM almost always recurs at the margin, or leading edge, where tumor cells invade the surrounding brain parenchyma. This region of GBMs is unique, or heterogeneous, with its own microenvironment that is different from the tumor bulk or core. The GBM microenvironment at the margin contains immunosuppressive constituents as well as invasive and therapy-resistant tumor cells that are difficult to treat. In addition, the blood-brain barrier remains essentially intact at the infiltrative margin of tumors; further limiting the effectiveness of therapies. The invasive margin creates the greatest challenge for neurosurgeons when managing these tumors. The current paradigm of resection of GBM tumors mainly focuses on resection of the contrast-enhancing component of tumors, while GBMs extend well beyond the contrast enhancement. The infiltrative margin represents a unique challenge and opportunity for solutions that may overcome current limitations in tumor treatments. In this review of the current literature, the authors discuss the current and developing advances focused on the detection and treatment of GBM at the infiltrative margin and how this could impact patient outcomes.

Brain tumor detection and screening using artificial intelligence techniques: Current trends and future perspectives

A brain tumor is an abnormal mass of tissue located inside the skull. In addition to putting pressure on the healthy parts of the brain, it can lead to significant health problems. Depending on the region of the brain tumor, it can cause a wide range of health issues. As malignant brain tumors grow rapidly, the mortality rate of individuals with this cancer can increase substantially with each passing week. Hence it is vital to detect these tumors early so that preventive measures can be taken at the initial stages. Computer-aided diagnostic (CAD) systems, in coordination with artificial intelligence (AI) techniques, have a vital role in the early detection of this disorder. In this review, we studied 124 research articles published from 2000 to 2022. Here, the challenges faced by CAD systems based on different modalities are highlighted along with the current requirements of this domain and future prospects in this area of research.

Clinical Validation of Stimulated Raman Histology for Rapid Intraoperative Diagnosis of Central Nervous System Tumors

Stimulated Raman histology (SRH) is an ex vivo optical imaging method that enables microscopic examination of fresh tissue intraoperatively. The conventional intraoperative method uses frozen section analysis, which is labor and time intensive, introduces artifacts that limit diagnostic accuracy, and consumes tissue. SRH imaging allows rapid microscopic imaging of fresh tissue, avoids tissue loss, and enables remote telepathology review. This improves access to expert neuropathology consultation in both low- and high-resource practices. We clinically validated SRH by performing a blinded, retrospective two-arm telepathology study to clinically validate SRH for telepathology at our institution. Using surgical specimens from 47 subjects, we generated a data set composed of 47 SRH images and 47 matched whole slide images (WSIs) of formalin-fixed, paraffin-embedded tissue stained with hematoxylin and eosin, with associated intraoperative clinicoradiologic information and structured diagnostic questions. We compared diagnostic concordance between WSI and SRH-rendered diagnoses. Also, we compared the 1-year median turnaround time (TAT) of intraoperative conventional neuropathology frozen sections with prospectively rendered SRH-telepathology TAT. All SRH images were of sufficient quality for diagnostic review. A review of SRH images showed high accuracy in distinguishing glial from nonglial tumors (96.5% SRH vs 98% WSIs) and predicting final diagnosis (85.9% SRH vs 93.1% WSIs). SRH-based diagnosis and WSI-permanent section diagnosis had high concordance (κ = 0.76). The median TAT for prospectively SRH-rendered diagnosis was 3.7 minutes, approximately 10-fold shorter than the median frozen section TAT (31 minutes). The SRH-imaging procedure did not affect ancillary studies. SRH generates diagnostic virtual histologic images with accuracy comparable to conventional hematoxylin and eosin-based methods in a rapid manner. Our study represents the largest and most rigorous clinical validation of SRH to date. It supports the feasibility of implementing SRH as a rapid method for intraoperative diagnosis complementary to conventional pathology laboratory methods.

Recent Advances of Biosensors for Detection of Multiple Antibiotics

The abuse of antibiotics has caused a serious threat to human life and health. It is urgent to develop sensors that can detect multiple antibiotics quickly and efficiently. Biosensors are widely used in the field of antibiotic detection because of their high specificity. Advanced artificial intelligence/machine learning algorithms have allowed for remarkable achievements in image analysis and face recognition, but have not yet been widely used in the field of biosensors. Herein, this paper reviews the biosensors that have been widely used in the simultaneous detection of multiple antibiotics based on different detection mechanisms and biorecognition elements in recent years, and compares and analyzes their characteristics and specific applications. In particular, this review summarizes some AI/ML algorithms with excellent performance in the field of antibiotic detection, and which provide a platform for the intelligence of sensors and terminal apps portability. Furthermore, this review gives a short review of biosensors for the detection of multiple antibiotics.

Advances in artificial intelligence, robotics, augmented and virtual reality in neurosurgery

Neurosurgical practitioners undergo extensive and prolonged training to acquire diverse technical proficiencies, while neurosurgical procedures necessitate a substantial amount of pre-, post-, and intraoperative clinical data acquisition, making decisions, attention, and convalescence. The past decade witnessed an appreciable escalation in the significance of artificial intelligence (AI) in neurosurgery. AI holds significant potential in neurosurgery as it supplements the abilities of neurosurgeons to offer optimal interventional and non-interventional care to patients by improving prognostic and diagnostic outcomes in clinical therapy and assisting neurosurgeons in making decisions while surgical interventions to enhance patient outcomes. Other technologies including augmented reality, robotics, and virtual reality can assist and promote neurosurgical methods as well. Moreover, they play a significant role in generating, processing, as well as storing experimental and clinical data. Also, the usage of these technologies in neurosurgery is able to curtail the number of costs linked with surgical care and extend high-quality health care to a wider populace. This narrative review aims to integrate the results of articles that elucidate the role of the aforementioned technologies in neurosurgery.

Brain Tumor Detection Based on Deep Learning Approaches and Magnetic Resonance Imaging

The rapid development of abnormal brain cells that characterizes a brain tumor is a major health risk for adults since it can cause severe impairment of organ function and even death. These tumors come in a wide variety of sizes, textures, and locations. When trying to locate cancerous tumors, magnetic resonance imaging (MRI) is a crucial tool. However, detecting brain tumors manually is a difficult and time-consuming activity that might lead to inaccuracies. In order to solve this, we provide a refined You Only Look Once version 7 (YOLOv7) model for the accurate detection of meningioma, glioma, and pituitary gland tumors within an improved detection of brain tumors system. The visual representation of the MRI scans is enhanced by the use of image enhancement methods that apply different filters to the original pictures. To further improve the training of our proposed model, we apply data augmentation techniques to the openly accessible brain tumor dataset. The curated data include a wide variety of cases, such as 2548 images of gliomas, 2658 images of pituitary, 2582 images of meningioma, and 2500 images of non-tumors. We included the Convolutional Block Attention Module (CBAM) attention mechanism into YOLOv7 to further enhance its feature extraction capabilities, allowing for better emphasis on salient regions linked with brain malignancies. To further improve the model's sensitivity, we have added a Spatial Pyramid Pooling Fast+ (SPPF+) layer to the network's core infrastructure. YOLOv7 now includes decoupled heads, which allow it to efficiently glean useful insights from a wide variety of data. In addition, a Bi-directional Feature Pyramid Network (BiFPN) is used to speed up multi-scale feature fusion and to better collect features associated with tumors. The outcomes verify the efficiency of our suggested method, which achieves a higher overall accuracy in tumor detection than previous state-of-the-art models. As a result, this framework has a lot of potential as a helpful decision-making tool for experts in the field of diagnosing brain tumors.

Raman Spectroscopy: A Tool for Molecular Fingerprinting of Brain Cancer

Brain cancer is one of those few cancers with very high mortality and low five-year survival rate. First and foremost reason for the woes is the difficulty in diagnosing and monitoring the progression of brain tumors both benign and malignant, noninvasively and in real time. This raises a need in this hour for a tool to diagnose the tumors in the earliest possible time frame. On the other hand, Raman spectroscopy which is well-known for its ability to precisely represent the molecular markers available in any sample given, including biological ones, with great sensitivity and specificity. This has led to a number of studies where Raman spectroscopy has been used in brain tumors in various ways. This review article highlights the fundamentals of Raman spectroscopy and its types including conventional Raman, SERS, SORS, SRS, CARS, etc. are used in brain tumors for diagnostics, monitoring, and even theragnostics, collating all the major works in the area. Also, the review explores how Raman spectroscopy can be even more effectively used in theragnostics and the clinical level which would make them a one-stop solution for all brain cancer needs in the future.

Precision medicine in neurosurgery: The evolving role of theranostics

Theranostics in neurosurgery is a rapidly advancing field of precision medicine that combines diagnostic and therapeutic modalities to optimize patient outcomes. This approach has the potential to provide real-time feedback during therapy and diagnose a condition while simultaneously providing treatment. One such form of theranostics is focused ultrasound, which has been found to be effective in inducing neuroablation and neuromodulation and improving the efficacy of chemotherapy drugs by disrupting the blood-brain barrier. Targeted radionuclide therapy, which pairs positron emission tomography tracers with therapeutic effects and imaging modalities, is another promising form of theranostics for neurosurgery. Automated pathology analysis is yet another form of theranostics that can provide real-time feedback during the surgical resection of tumors. Electrical stimulation has also shown promise in optimizing therapies for patients with cerebral palsy. Overall, theranostics is a cost-effective way to optimize medical care for patients in neurosurgery. It is a relatively new field, but the advancements made so far show great promise for improving patient outcomes.

Optimizing maximum resection of glioblastoma: Raman spectroscopy versus 5-aminolevulinic acid

OBJECTIVE

The objective of this study was to assess and compare the potential of 5-aminolevulinic acid (5-ALA) and Raman spectroscopy (RS) in detecting tumor-infiltrated brain in patients with glioblastoma (GBM).

METHODS

Between July 2020 and October 2021, the authors conducted a prospective clinical trial with 15 patients who underwent neurosurgical treatment of newly diagnosed and histologically verified GBM. A solid contrast-enhancing tumor core and peritumoral tissue were investigated intraoperatively for cancer cells by using 5-ALA and RS to achieve pathology-tailored maximum resection. In each case, a minimum of 10 biopsies were sampled from navigation-guided areas. Two neuropathologists examined the biopsies for the presence of neoplastic cells. The detection performance of 5-ALA and RS alone and in combination was assessed. Pre- and postoperative MRI, Karnofsky Performance Status (KPS), and National Institutes of Health Stroke Scale (NIHSS) scores were compared, and median progression-free survival (PFS) was evaluated.

RESULTS

A total of 185 biopsy samples were harvested from the contrast-enhancing tumor core (n = 19) and peritumoral tissue (n = 166). In the tumor core, 5-ALA and RS each showed a sensitivity of 100%. In the peritumoral tissue, 5-ALA was less sensitive than RS in detecting cancer (46% vs 69%) but showed higher specificity (81% vs 57%). When the two methods were combined, the accuracy of tumor detection was increased by about 10%. Pathology-tailored resection led to a 52% increase in resection volume comparing the volume of preoperative contrast enhancement with the postoperative resection cavity on MRI (p = 0.0123). Eloquent brain involvement prevented gross-total resection in 4 patients. Four weeks after surgery, mean KPS (p = 0.7637) and NIHSS scores (p = 0.3146) were not significantly different from preoperative values. Of the 13 patients who had received postoperative chemoradiotherapy, 4 did not show any progression after a median follow-up of 14 months. The remaining 9 patients had a median PFS of 8 months.

CONCLUSIONS

According to the study data, RS is capable of detecting tumor-infiltrated brain with higher sensitivity but lower specificity than the current standard of 5-ALA. With further technological and workflow advancements, RS in combination with protoporphyrin IX fluorescence may contribute to pathology-tailored glioma resection in the future.

Application of digital pathology-based advanced analytics of tumour microenvironment organisation to predict prognosis and therapeutic response

In recent years, the application of advanced analytics, especially artificial intelligence (AI), to digital H&E images, and other histological image types, has begun to radically change how histological images are used in the clinic. Alongside the recognition that the tumour microenvironment (TME) has a profound impact on tumour phenotype, the technical development of highly multiplexed immunofluorescence platforms has enhanced the biological complexity that can be captured in the TME with high precision. AI has an increasingly powerful role in the recognition and quantitation of image features and the association of such features with clinically important outcomes, as occurs in distinct stages in conventional machine learning. Deep-learning algorithms are able to elucidate TME patterns inherent in the input data with minimum levels of human intelligence and, hence, have the potential to achieve clinically relevant predictions and discovery of important TME features. Furthermore, the diverse repertoire of deep-learning algorithms able to interrogate TME patterns extends beyond convolutional neural networks to include attention-based models, graph neural networks, and multimodal models. To date, AI models have largely been evaluated retrospectively, outside the well-established rigour of prospective clinical trials, in part because traditional clinical trial methodology may not always be suitable for the assessment of AI technology. However, to enable digital pathology-based advanced analytics to meaningfully impact clinical care, specific measures of 'added benefit' to the current standard of care and validation in a prospective setting are important. This will need to be accompanied by adequate measures of explainability and interpretability. Despite such challenges, the combination of expanding datasets, increased computational power, and the possibility of integration of pre-clinical experimental insights into model development means there is exciting potential for the future progress of these AI applications. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Stimulated Raman histology for histological evaluation of oral squamous cell carcinoma

OBJECTIVES

To investigate whether in patients undergoing surgery for oral squamous cell carcinoma, stimulated Raman histology (SRH), in comparison with H&E-stained frozen sections, can provide accurate diagnoses regarding neoplastic tissue and sub-classification of non-neoplastic tissues.

MATERIALS AND METHODS

SRH, a technology based on Raman scattering, was applied to generate digital histopathologic images of 80 tissue samples obtained from 8 oral squamous cell carcinoma (OSCC) patients. Conventional H&E-stained frozen sections were then obtained from all 80 samples. All images/sections (SRH and H&E) were analyzed for squamous cell carcinoma, normal mucosa, connective tissue, muscle tissue, adipose tissue, salivary gland tissue, lymphatic tissue, and inflammatory cells. Agreement between SRH and H&E was evaluated by calculating Cohen's kappa. Accuracy of SRH compared to H&E was quantified by calculating sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) as well as area under the receiver operating characteristic curve (AUC).

RESULTS

Thirty-six of 80 samples were classified as OSCC by H&E-based diagnosis. Regarding the differentiation between neoplastic and non-neoplastic tissue, high agreement between H&E and SRH (kappa: 0.880) and high accuracy of SRH (sensitivity: 100%; specificity: 90.91%; PPV: 90.00%, NPV: 100%; AUC: 0.954) were demonstrated. For sub-classification of non-neoplastic tissues, SRH performance was dependent on the type of tissue, with high agreement and accuracy for normal mucosa, muscle tissue, and salivary glands.

CONCLUSION

SRH provides high accuracy in discriminating neoplastic and non-neoplastic tissues. Regarding sub-classification of non-neoplastic tissues in OSCC patients, accuracy varies depending on the type of tissue examined.

CLINICAL RELEVANCE

This study demonstrates the potential of SRH for intraoperative imaging of fresh, unprocessed tissue specimens from OSCC patients without the need for sectioning or staining.

Stimulated Raman histology as a method to determine the adequacy of renal mass biopsy and identify malignant subtypes of renal cell carcinoma

INTRODUCTION

Renal tumor biopsy requires adequate tissue sampling to aid in the investigation of small renal masses. In some centers the contemporary nondiagnostic renal mass biopsy rate may be as high as 22% and may be as high as 42% in challenging cases. Stimulated Raman Histology (SRH) is a novel microscopic technique which has created the possibility for rapid, label-free, high-resolution images of unprocessed tissue which may be viewed on standard radiology viewing platforms. The application of SRH to renal biopsy may provide the benefits of routine pathologic evaluation during the procedure, thereby reducing nondiagnostic results. We conducted a pilot feasibility study, to assess if renal cell carcinoma (RCC) subtypes may be imaged and to see if high-quality hematoxylin and eosin (H&E) could subsequently be generated.

METHODS/MATERIALS

An 18-gauge core needle biopsy was taken from a series of 25 ex vivo radical or partial nephrectomy specimens. Histologic images of the fresh, unstained biopsy samples were obtained using a SRH microscope using 2 Raman shifts: 2,845 cm-1 and 2,930 cm-1. The cores were then processed as per routine pathologic protocols. The SRH images and hematoxylin and eosin (H&E) slides were then viewed by a genitourinary pathologist.

RESULTS

The SRH microscope took 8 to 11 minutes to produce high-quality images of the renal biopsies. Total of 25 renal tumors including 1 oncocytoma, 3 chromophobe RCC, 16 clear cells RCC, 4 papillary RCC, and 1 medullary RCC were included. All renal tumor subtypes were captured, and the SRH images were easily differentiated from adjacent normal renal parenchyma. High quality H&E slides were produced from each of the renal biopsies after SRH was completed. Immunostains were performed on selected cases and the staining was not affected by the SRH image process.

CONCLUSION

SRH produces high quality images of all renal cell subtypes that can be rapidly produced and easily interpreted to determine renal mass biopsy adequacy, and on occasion, may allow renal tumor subtype identification. Renal biopsies remained available to produce high quality H&E slides and immunostains for confirmation of diagnosis. Procedural application has promise to decrease the known rate of renal mass nondiagnostic biopsies, and application of convolutional neural network methodology may further improve diagnostic capability and increase utilization of renal mass biopsy among urologists.

Ultrasonic Aspirator for Tissue Contact Detection: An Online Classification on Time-Series

The goal of neurosurgical tumor surgery is to remove the tumor completely without damaging healthy brain structures and thereby impairing the patient's neurological functions. This requires careful planning and execution of the operation by experienced neurosurgeons using the latest intraoperative technologies to achieve safe and rapid tumor reduction without harming the patient. To achieve this goal, a standard ultrasonic aspirator designed for tissue removal is equipped with additional intraoperative tissue detection using machine learning methods.Since decision-making in a clinical context must be fast, online contact detection is critical. Data are generated on three types of artificial tissue models in a CNC machine-controlled environment with four different ultrasonic aspirator settings. Contact classification on artificial tissue models is evaluated on four classification algorithms: change point detection (CPD), random forest (RF), recurrent neural network (RNN) and temporal convolutional network (TCN). Data preprocessing steps are applied, and their impacts are investigated. All methods are evaluated on five-fold cross-validation and provide generally good results with a performance of up to 0.977±0.007 in mean F1-score. Preprocessing the data has a positive effect on the classification processes for all methods and consistently improves the metrics. Thus, this work indicates in a first step that contact classification is feasible in an online context for an ultrasonic aspirator. Further research is necessary on different tissue types, as well as hand-held use to more closely resemble the intraoperative clinical conditions.

Machine Learning and AI in Cancer Prognosis, Prediction, and Treatment Selection: A Critical Approach

Cancer is a leading cause of morbidity and mortality worldwide. While progress has been made in the diagnosis, prognosis, and treatment of cancer patients, individualized and data-driven care remains a challenge. Artificial intelligence (AI), which is used to predict and automate many cancers, has emerged as a promising option for improving healthcare accuracy and patient outcomes. AI applications in oncology include risk assessment, early diagnosis, patient prognosis estimation, and treatment selection based on deep knowledge. Machine learning (ML), a subset of AI that enables computers to learn from training data, has been highly effective at predicting various types of cancer, including breast, brain, lung, liver, and prostate cancer. In fact, AI and ML have demonstrated greater accuracy in predicting cancer than clinicians. These technologies also have the potential to improve the diagnosis, prognosis, and quality of life of patients with various illnesses, not just cancer. Therefore, it is important to improve current AI and ML technologies and to develop new programs to benefit patients. This article examines the use of AI and ML algorithms in cancer prediction, including their current applications, limitations, and future prospects.

Motivation for using data-driven algorithms in research: A review of machine learning solutions for image analysis of micrographs in neuroscience

Machine learning is a powerful tool that is increasingly being used in many research areas, including neuroscience. The recent development of new algorithms and network architectures, especially in the field of deep learning, has made machine learning models more reliable and accurate and useful for the biomedical research sector. By minimizing the effort necessary to extract valuable features from datasets, they can be used to find trends in data automatically and make predictions about future data, thereby improving the reproducibility and efficiency of research. One application is the automatic evaluation of micrograph images, which is of great value in neuroscience research. While the development of novel models has enabled numerous new research applications, the barrier to use these new algorithms has also decreased by the integration of deep learning models into known applications such as microscopy image viewers. For researchers unfamiliar with machine learning algorithms, the steep learning curve can hinder the successful implementation of these methods into their workflows. This review explores the use of machine learning in neuroscience, including its potential applications and limitations, and provides some guidance on how to select a fitting framework to use in real-life research projects.

Performance of deep learning algorithms to distinguish high-grade glioma from low-grade glioma: A systematic review and meta-analysis

This study aims to evaluate deep learning (DL) performance in differentiating low- and high-grade glioma. Search online database for studies continuously published from 1st January 2015 until 16th August 2022. The random-effects model was used for synthesis, based on pooled sensitivity (SE), specificity (SP), and area under the curve (AUC). Heterogeneity was estimated using the Higgins inconsistency index (I²). 33 were ultimately included in the meta-analysis. The overall pooled SE and SP were 94% and 93%, with an AUC of 0.98. There was great heterogeneity in this field. Our evidence-based study shows DL achieves high accuracy in glioma grading. Subgroup analysis reveals several limitations in this field: 1) Diagnostic trials require standard method for data merging for AI; 2) small sample size; 3) poor-quality image preprocessing; 4) not standard algorithm development; 5) not standard data report; 6) different definition of HGG and LGG; and 7) poor extrapolation.

Artificial intelligence, machine learning, and deep learning in liver transplantation

Liver transplantation (LT) is a life-saving treatment for individuals with end-stage liver disease. The management of LT recipients is complex, predominantly because of the need to consider demographic, clinical, laboratory, pathology, imaging, and omics data in the development of an appropriate treatment plan. Current methods to collate clinical information are susceptible to some degree of subjectivity; thus, clinical decision-making in LT could benefit from the data-driven approach offered by artificial intelligence (AI). Machine learning and deep learning could be applied in both the pre- and post-LT settings. Some examples of AI applications pre-transplant include optimising transplant candidacy decision-making and donor-recipient matching to reduce waitlist mortality and improve post-transplant outcomes. In the post-LT setting, AI could help guide the management of LT recipients, particularly by predicting patient and graft survival, along with identifying risk factors for disease recurrence and other associated complications. Although AI shows promise in medicine, there are limitations to its clinical deployment which include dataset imbalances for model training, data privacy issues, and a lack of available research practices to benchmark model performance in the real world. Overall, AI tools have the potential to enhance personalised clinical decision-making, especially in the context of liver transplant medicine.

Converting Nanotoxicity Data to Information Using Artificial Intelligence and Simulation

Decades of nanotoxicology research have generated extensive and diverse data sets. However, data is not equal to information. The question is how to extract critical information buried in vast data streams. Here we show that artificial intelligence (AI) and molecular simulation play key roles in transforming nanotoxicity data into critical information, i.e., constructing the quantitative nanostructure (physicochemical properties)-toxicity relationships, and elucidating the toxicity-related molecular mechanisms. For AI and molecular simulation to realize their full impacts in this mission, several obstacles must be overcome. These include the paucity of high-quality nanomaterials (NMs) and standardized nanotoxicity data, the lack of model-friendly databases, the scarcity of specific and universal nanodescriptors, and the inability to simulate NMs at realistic spatial and temporal scales. This review provides a comprehensive and representative, but not exhaustive, summary of the current capability gaps and tools required to fill these formidable gaps. Specifically, we discuss the applications of AI and molecular simulation, which can address the large-scale data challenge for nanotoxicology research. The need for model-friendly nanotoxicity databases, powerful nanodescriptors, new modeling approaches, molecular mechanism analysis, and design of the next-generation NMs are also critically discussed. Finally, we provide a perspective on future trends and challenges.

The convergence of traditional and digital biomarkers through AI-assisted biosensing: A new era in translational diagnostics?

Advances in consumer electronics, alongside the fields of microfluidics and nanotechnology have brought to the fore low-cost wearable/portable smart devices. Although numerous smart devices that track digital biomarkers have been successfully translated from bench-to-bedside, only a few follow the same fate when it comes to track traditional biomarkers. Current practices still involve laboratory-based tests, followed by blood collection, conducted in a clinical setting as they require trained personnel and specialized equipment. In fact, real-time, passive/active and robust sensing of physiological and behavioural data from patients that can feed artificial intelligence (AI)-based models can significantly improve decision-making, diagnosis and treatment at the point-of-procedure, by circumventing conventional methods of sampling, and in person investigation by expert pathologists, who are scarce in developing countries. This review brings together conventional and digital biomarker sensing through portable and autonomous miniaturized devices. We first summarise the technological advances in each field vs the current clinical practices and we conclude by merging the two worlds of traditional and digital biomarkers through AI/ML technologies to improve patient diagnosis and treatment. The fundamental role, limitations and prospects of AI in realizing this potential and enhancing the existing technologies to facilitate the development and clinical translation of "point-of-care" (POC) diagnostics is finally showcased.

Intraoperative microscopic autofluorescence detection and characterization in brain tumors using stimulated Raman histology and two-photon fluorescence

Introduction

The intrinsic autofluorescence of biological tissues interferes with the detection of fluorophores administered for fluorescence guidance, an emerging auxiliary technique in oncological surgery. Yet, autofluorescence of the human brain and its neoplasia is sparsely examined. This study aims to assess autofluorescence of the brain and its neoplasia on a microscopic level by stimulated Raman histology (SRH) combined with two-photon fluorescence.

Methods

With this experimentally established label-free microscopy technique unprocessed tissue can be imaged and analyzed within minutes and the process is easily incorporated in the surgical workflow. In a prospective observational study, we analyzed 397 SRH and corresponding autofluorescence images of 162 samples from 81 consecutive patients that underwent brain tumor surgery. Small tissue samples were squashed on a slide for imaging. SRH and fluorescence images were acquired with a dual wavelength laser (790 nm and 1020 nm) for excitation. In these images tumor and non-tumor regions were identified by a convolutional neural network that reliably differentiates between tumor, healthy brain tissue and low quality SRH images. The identified areas were used to define regions.of- interests (ROIs) and the mean fluorescence intensity was measured.

Results

In healthy brain tissue, we found an increased mean autofluorescence signal in the gray (11.86, SD 2.61, n=29) compared to the white matter (5.99, SD 5.14, n=11, p<0.01) and in the cerebrum (11.83, SD 3.29, n=33) versus the cerebellum (2.82, SD 0.93, n=7, p<0.001), respectively. The signal of carcinoma metastases, meningiomas, gliomas and pituitary adenomas was significantly lower (each p<0.05) compared to the autofluorescence in the cerebrum and dura, and significantly higher (each p<0.05) compared to the cerebellum. Melanoma metastases were found to have a higher fluorescent signal (p<0.01) compared to cerebrum and cerebellum.

Discussion

In conclusion we found that autofluorescence in the brain varies depending on the tissue type and localization and differs significantly among various brain tumors. This needs to be considered for interpreting photon signal during fluorescence-guided brain tumor surgery.

In vivo Raman spectroscopy in the diagnostics of colon cancer

Early detection and accurate diagnosis of colorectal carcinoma are crucial for successful treatment, yet current methods can be invasive and even inaccurate in some cases. In this work, we present a novel approach for in vivo tissue diagnostics of colorectal carcinoma using Raman spectroscopy. This almost non-invasive technique allows for fast and accurate detection of colorectal carcinoma and its precursors, adenomatous polyps, enabling timely intervention and improved patient outcomes. Using several methods of supervised machine learning, we were able to achieve over 91% accuracy in distinguishing colorectal lesions from healthy epithelial tissue and more than 90% classification accuracy for premalignant adenomatous polyps. Moreover, our models enabled the discrimination of cancerous and precancerous lesions with a mean accuracy of almost 92%. Such results demonstrate the potential of in vivo Raman spectroscopy to become a valuable tool in the fight against colon cancer.

Stimulated Raman histology, a novel method to allow for rapid pathologic examination of unprocessed, fresh prostate biopsies

INTRODUCTION

Delay between targeted prostate biopsy (PB) and pathologic diagnosis can lead to a concern of inadequate sampling and repeated biopsy. Stimulated Raman histology (SRH) is a novel microscopic technique allowing real-time, label-free, high-resolution microscopic images of unprocessed, unsectioned tissue. This technology holds potential to decrease the time for PB diagnosis from days to minutes. We evaluated the concordance of pathologist interpretation of PB SRH as compared with traditional hematoxylin and eosin (H&E) stained slides.

METHODS

Men undergoing prostatectomy were included in an IRB-approved prospective study. Ex vivo 18-gauge PB cores, taken from prostatectomy specimen, were scanned in an SRH microscope (NIO; Invenio Imaging) at 20 microns depth using two Raman shifts: 2845 and 2930 cm^-1 , to create SRH images. The cores were then processed as per normal pathologic protocols. Sixteen PB containing a mix of benign and malignant histology were used as an SRH training cohort for four genitourinary pathologists, who were then tested on a set of 32 PBs imaged by SRH and processed by traditional H&E. Sensitivity, specificity, accuracy, and concordance for prostate cancer (PCa) detection on SRH relative to H&E were assessed.

RESULTS

The mean pathologist accuracy for the identification of any PCa on PB SRH was 95.7%. In identifying any PCa or ISUP grade group 2-5 PCa, a pathologist was independently able to achieve good and very good concordance (κ: 0.769 and 0.845, respectively; p < 0.001). After individual assessment was completed a pathology consensus conference was held for the interpretation of the PB SRH; after the consensus conference the pathologists' concordance in identifying any PCa was also very good (κ: 0.925, p < 0.001; sensitivity 95.6%; specificity 100%).

CONCLUSION

SRH produces high-quality microscopic images that allow for accurate identification of PCa in real-time without need for sectioning or tissue processing. The pathologist performance improved through progressive training, showing that ultimately high accuracy can be obtained. Ongoing SRH evaluation in the diagnostic and treatment setting hold promise to reduce time to tissue diagnosis, while interpretation by convolutional neural network may further improve diagnostic characteristics and broaden use.

Technical report: surgical preparation of human brain tissue for clinical and basic research

BACKGROUND

The study of the distinct structure and function of the human central nervous system, both in healthy and diseased states, is becoming increasingly significant in the field of neuroscience. Typically, cortical and subcortical tissue is discarded during surgeries for tumors and epilepsy. Yet, there is a strong encouragement to utilize this tissue for clinical and basic research in humans. Here, we describe the technical aspects of the microdissection and immediate handling of viable human cortical access tissue for basic and clinical research, highlighting the measures needed to be taken in the operating room to ensure standardized procedures and optimal experimental results.

METHODS

In multiple rounds of experiments (n = 36), we developed and refined surgical principles for the removal of cortical access tissue. The specimens were immediately immersed in cold carbogenated N-methyl-D-glucamine-based artificial cerebrospinal fluid for electrophysiology and electron microscopy experiments or specialized hibernation medium for organotypic slice cultures.

RESULTS

The surgical principles of brain tissue microdissection were (1) rapid preparation (<1 min), (2) maintenance of the cortical axis, (3) minimization of mechanical trauma to sample, (4) use of pointed scalpel blade, (5) avoidance of cauterization and blunt preparation, (6) constant irrigation, and (7) retrieval of the sample without the use of forceps or suction. After a single round of introduction to these principles, multiple surgeons adopted the technique for samples with a minimal dimension of 5 mm spanning all cortical layers and subcortical white matter. Small samples (5-7 mm) were ideal for acute slice preparation and electrophysiology. No adverse events from sample resection were observed.

CONCLUSION

The microdissection technique of human cortical access tissue is safe and easily adoptable into the routine of neurosurgical procedures. The standardized and reliable surgical extraction of human brain tissue lays the foundation for human-to-human translational research on human brain tissue.

In vivo characterization of the human glioblastoma infiltrative edge with label-free intraoperative fluorescence lifetime imaging

Challenges in identifying a glioblastoma's infiltrative edge during neurosurgical procedures result in rapid recurrence. A label-free fluorescence lifetime imaging (FLIm) device was used to evaluate glioblastoma's infiltrative edge in vivo in 15 patients (89 samples). FLIm data were analyzed according to tumor cell density, infiltrating tissue type (gray and white matter), and diagnosis history (new or recurrent). Infiltrations in white matter from new glioblastomas showed decreasing lifetimes and a spectral red shift with increasing tumor cell density. Areas of high versus low tumor cell density were separated through a linear discriminant analysis with a ROC-AUC=0.74. Current results support the feasibility of intraoperative FLIm for real-time in vivo brain measurements and encourage refinement to predict glioblastoma infiltrative edge, underscoring the ability of FLIm to optimize neurosurgical outcomes.

Accurate and Rapid Detection of Peritoneal Metastasis from Gastric Cancer by AI-Assisted Stimulated Raman Molecular Cytology

Peritoneal metastasis (PM) is the mostcommon form of distant metastasis and one of the leading causes of death in gastriccancer (GC). For locally advanced GC, clinical guidelines recommend peritoneal lavage cytology for intraoperative PM detection. Unfortunately, current peritoneal lavage cytology is limited by low sensitivity (<60%). Here the authors established the stimulated Raman molecular cytology (SRMC), a chemical microscopy-based intelligent cytology. The authors firstly imaged 53 951 exfoliated cells in ascites obtained from 80 GC patients (27 PM positive, 53 PM negative). Then, the authors revealed 12 single cell features of morphology and composition that are significantly different between PM positive and negative specimens, including cellular area, lipid protein ratio, etc. Importantly, the authors developed a single cell phenotyping algorithm to further transform the above raw features to feature matrix. Such matrix is crucial to identify the significant marker cell cluster, the divergence of which is finally used to differentiate the PM positive and negative. Compared with histopathology, the gold standard of PM detection, their SRMC method could reach 81.5% sensitivity, 84.9% specificity, and the AUC of 0.85, within 20 minutes for each patient. Together, their SRMC method shows great potential for accurate and rapid detection of PM from GC.

Artificial-intelligence-based molecular classification of diffuse gliomas using rapid, label-free optical imaging

Molecular classification has transformed the management of brain tumors by enabling more accurate prognostication and personalized treatment. However, timely molecular diagnostic testing for patients with brain tumors is limited, complicating surgical and adjuvant treatment and obstructing clinical trial enrollment. In this study, we developed DeepGlioma, a rapid (<90 seconds), artificial-intelligence-based diagnostic screening system to streamline the molecular diagnosis of diffuse gliomas. DeepGlioma is trained using a multimodal dataset that includes stimulated Raman histology (SRH); a rapid, label-free, non-consumptive, optical imaging method; and large-scale, public genomic data. In a prospective, multicenter, international testing cohort of patients with diffuse glioma (n = 153) who underwent real-time SRH imaging, we demonstrate that DeepGlioma can predict the molecular alterations used by the World Health Organization to define the adult-type diffuse glioma taxonomy (IDH mutation, 1p19q co-deletion and ATRX mutation), achieving a mean molecular classification accuracy of 93.3 ± 1.6%. Our results represent how artificial intelligence and optical histology can be used to provide a rapid and scalable adjunct to wet lab methods for the molecular screening of patients with diffuse glioma.

Rapid ultraviolet photoacoustic remote sensing microscopy using voice-coil stage scanning

There is an unmet need for fast virtual histology technologies that exhibit histological realism and can scan large sections of fresh tissue within intraoperative time-frames. Ultraviolet photoacoustic remote sensing microscopy (UV-PARS) is an emerging imaging modality capable of producing virtual histology images that show good concordance to conventional histology stains. However, a UV-PARS scanning system that can perform rapid intraoperative imaging over mm-scale fields-of-view at fine resolution (<500 nm) has yet to be demonstrated. In this work, we present a UV-PARS system which utilizes voice-coil stage scanning to demonstrate finely resolved images for 2×2 mm² areas at 500 nm sampling resolution in 1.33 minutes and coarsely resolved images for 4×4 mm² areas at 900 nm sampling resolution in 2.5 minutes. The results of this work demonstrate the speed and resolution capabilities of the UV-PARS voice-coil system and further develop the potential for UV-PARS microscopy to be employed in a clinical setting.

Refining the Intraoperative Identification of Suspected High-Grade Glioma Using a Surgical Fluorescence Biomarker: GALA BIDD Study Report

Background. Improving intraoperative accuracy with a validated surgical biomarker is important because identifying high-grade areas within a glioma will aid neurosurgical decision-making and sampling. Methods. We designed a multicentre, prospective surgical cohort study (GALA-BIDD) to validate the presence of visible fluorescence as a pragmatic intraoperative surgical biomarker of suspected high-grade disease within a tumour mass in patients undergoing 5-aminolevulinic acid (5-ALA) fluorescence-guided cytoreductive surgery. Results. A total of 106 patients with a suspected high-grade glioma or malignant transformation of a low-grade glioma were enrolled. Among the 99 patients who received 5-ALA, 89 patients were eligible to assess the correlation of fluorescence with diagnosis as per protocol. Of these 89, 81 patients had visible fluorescence at surgery, and 8 patients had no fluorescence. A total of 80 out of 81 fluorescent patients were diagnosed as high-grade gliomas on postoperative central review with 1 low-grade glioma case. Among the eight patients given 5-ALA who did not show any visible fluorescence, none were high-grade gliomas, and all were low-grade gliomas. Of the seven patients suspected radiologically of malignant transformation of low-grade gliomas and with visible fluorescence at surgery, six were diagnosed with high-grade gliomas, and one had no tissue collected. Conclusion. In patients where there is clinical suspicion, visible 5-ALA fluorescence has clinical utility as an intraoperative surgical biomarker of high-grade gliomas and can aid surgical decision-making and sampling. Further studies assessing the use of 5-ALA to assess malignant transformation in all diffuse gliomas may be valuable.

Hydrogel Microneedles Extracting Exosomes for Early Detection of Colorectal Cancer

There are several methods for early diagnosis of tumors, such as detecting circulating tumor DNAs, detecting circulating tumor cells, or imaging with tumor-targeting contrast agents. However, these assays are time-consuming and may cause patient discomfort during the biopsy collecting process. Here, we develop a facile method for early diagnosis of tumors by extracting exosomes from interstitial fluid (ISF) using hydrogel microneedles (MNs). The hydrogel MNs expand in the skin to absorb the ISF, and the tumor exosomes contained in the ISF bind with the glypican-1 antibodies inside the hydrogel of MNs. After removing the hydrogel on the MNs, exosomes are separately purified from the ISF to analyze tumor-related biomarkers. Finally, colorectal cancer can be diagnosed by ELISA for the colorectal cancer-induced model mice. This noninvasive hydrogel MN system to obtain the exosome samples would play an important role in early cancer diagnosis.

High-throughput line-illumination Raman microscopy with multislit detection

Raman microscopy is an emerging tool for molecular imaging and analysis of living samples. Use of Raman microscopy in life sciences is, however, still limited because of its slow measurement speed for spectral imaging and analysis. We developed a multiline-illumination Raman microscope to achieve ultrafast Raman spectral imaging. A spectrophotometer equipped with a periodic array of confocal slits detects Raman spectra from a sample irradiated by multiple line illuminations. A comb-like Raman hyperspectral image is formed on a two-dimensional detector in the spectrophotometer, and a hyperspectral Raman image is acquired by scanning the sample with multiline illumination array. By irradiating a sample with 21 simultaneous illumination lines, we achieved high-throughput Raman hyperspectral imaging of mouse brain tissue, acquiring 1108800 spectra in 11.4 min. We also measured mouse kidney and liver tissue as well as conducted label-free live-cell molecular imaging. The ultrafast Raman hyperspectral imaging enabled by the presented technique will expand the possible applications of Raman microscopy in biological and medical fields.

Multiplex Identification of Post-Translational Modifications at Point-of-Care by Deep Learning-Assisted Hydrogel Sensors

Multiplex detection of protein post-translational modifications (PTMs), especially at point-of-care, is of great significance in cancer diagnosis. Herein, we report a machine learning-assisted photonic crystal hydrogel (PCH) sensor for multiplex detection of PTMs. With closely-related PCH sensors microfabricated on a single chip, our design achieved not only rapid screening of PTMs at specific protein sites by using only naked eyes/cellphone, but also the feasibility of real-time monitoring of phosphorylation reactions. By taking advantage of multiplex sensor chips and a neural network algorithm, accurate prediction of PTMs by both their types and concentrations was enabled. This approach was ultimately used to detect and differentiate up/down regulation of different phosphorylation sites within the same protein in live mammalian cells. Our developed method thus holds potential for POC identification of various PTMs in early-stage diagnosis of protein-related diseases.

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.

A deep learning model for detection of leukocytes under various interference factors

The accurate detection of leukocytes is the basis for the diagnosis of blood system diseases. However, diagnosing leukocyte disorders by doctors is time-consuming and requires extensive experience. Automated detection methods with high accuracy can improve detection efficiency and provide recommendations to inexperienced doctors. Current methods and instruments either fail to automate the identification process fully or have low performance and need suitable leukocyte data sets for further study. To improve the current status, we need to develop more intelligent strategies. This paper investigates fulfilling high-performance automatic detection for leukocytes using a deep learning-based method. We established a new dataset more suitable for leukocyte detection, containing 6273 images (8595 leukocytes) and considering nine common clinical interference factors. Based on the dataset, the performance evaluation of six mainstream detection models is carried out, and a more robust ensemble model is proposed. The mean of average precision (mAP) @IoU = 0.50:0.95 and mean of average recall (mAR)@IoU = 0.50:0.95 of the ensemble model on the test set are 0.853 and 0.922, respectively. The detection performance of poor-quality images is robust. For the first time, it is found that the ensemble model yields an accuracy of 98.84% for detecting incomplete leukocytes. In addition, we also compared the test results of different models and found multiple identical false detections of the models, then provided correct suggestions for the clinic.

Label-free intraoperative histology of bone tissue via deep-learning-assisted ultraviolet photoacoustic microscopy

Obtaining frozen sections of bone tissue for intraoperative examination is challenging. To identify the bony edge of resection, orthopaedic oncologists therefore rely on pre-operative X-ray computed tomography or magnetic resonance imaging. However, these techniques do not allow for accurate diagnosis or for intraoperative confirmation of the tumour margins, and in bony sarcomas, they can lead to bone margins up to 10-fold wider (1,000-fold volumetrically) than necessary. Here, we show that real-time three-dimensional contour-scanning of tissue via ultraviolet photoacoustic microscopy in reflection mode can be used to intraoperatively evaluate undecalcified and decalcified thick bone specimens, without the need for tissue sectioning. We validate the technique with gold-standard haematoxylin-and-eosin histology images acquired via a traditional optical microscope, and also show that an unsupervised generative adversarial network can virtually stain the ultraviolet-photoacoustic-microscopy images, allowing pathologists to readily identify cancerous features. Label-free and slide-free histology via ultraviolet photoacoustic microscopy may allow for rapid diagnoses of bone-tissue pathologies and aid the intraoperative determination of tumour margins.

Advances in the intraoperative delineation of malignant glioma margin

Surgery plays a critical role in the treatment of malignant glioma. However, due to the infiltrative growth and brain shift, it is difficult for neurosurgeons to distinguish malignant glioma margins with the naked eye and with preoperative examinations. Therefore, several technologies were developed to determine precise tumor margins intraoperatively. Here, we introduced four intraoperative technologies to delineate malignant glioma margin, namely, magnetic resonance imaging, fluorescence-guided surgery, Raman histology, and mass spectrometry. By tracing their detecting principles and developments, we reviewed their advantages and disadvantages respectively and imagined future trends.

Polysulfide Serves as a Hallmark of Desmoplastic Reaction to Differentially Diagnose Ductal Carcinoma In Situ and Invasive Breast Cancer by SERS Imaging

Pathological examination of formalin-fixed paraffin-embedded (FFPE) needle-biopsied samples by certified pathologists represents the gold standard for differential diagnosis between ductal carcinoma in situ (DCIS) and invasive breast cancers (IBC), while information of marker metabolites in the samples is lost in the samples. Infrared laser-scanning large-area surface-enhanced Raman spectroscopy (SERS) equipped with gold-nanoparticle-based SERS substrate enables us to visualize metabolites in fresh-frozen needle-biopsied samples with spatial matching between SERS and HE staining images with pathological annotations. DCIS (n = 14) and IBC (n = 32) samples generated many different SERS peaks in finger-print regions of SERS spectra among pathologically annotated lesions including cancer cell nests and the surrounding stroma. The results showed that SERS peaks in IBC stroma exhibit significantly increased polysulfide that coincides with decreased hypotaurine as compared with DCIS, suggesting that alterations of these redox metabolites account for fingerprints of desmoplastic reactions to distinguish IBC from DCIS. Furthermore, the application of supervised machine learning to the stroma-specific multiple SERS signals enables us to support automated differential diagnosis with high accuracy. The results suggest that SERS-derived biochemical fingerprints derived from redox metabolites account for a hallmark of desmoplastic reaction of IBC that is absent in DCIS, and thus, they serve as a useful method for precision diagnosis in breast cancer.

Raman spectroscopy: A prospective intraoperative visualization technique for gliomas

The infiltrative growth and malignant biological behavior of glioma make it one of the most challenging malignant tumors in the brain, and how to maximize the extent of resection (EOR) while minimizing the impact on normal brain tissue is the pursuit of neurosurgeons. The current intraoperative visualization assistance techniques applied in clinical practice suffer from low specificity, slow detection speed and low accuracy, while Raman spectroscopy (RS) is a novel spectroscopy technique gradually developed and applied to clinical practice in recent years, which has the advantages of being non-destructive, rapid and accurate at the same time, allowing excellent intraoperative identification of gliomas. In the present work, the latest research on Raman spectroscopy in glioma is summarized to explore the prospect of Raman spectroscopy in glioma surgery.

Rapid, label-free histopathological diagnosis of liver cancer based on Raman spectroscopy and deep learning

Biopsy is the recommended standard for pathological diagnosis of liver carcinoma. However, this method usually requires sectioning and staining, and well-trained pathologists to interpret tissue images. Here, we utilize Raman spectroscopy to study human hepatic tissue samples, developing and validating a workflow for in vitro and intraoperative pathological diagnosis of liver cancer. We distinguish carcinoma tissues from adjacent non-tumour tissues in a rapid, non-disruptive, and label-free manner by using Raman spectroscopy combined with deep learning, which is validated by tissue metabolomics. This technique allows for detailed pathological identification of the cancer tissues, including subtype, differentiation grade, and tumour stage. 2D/3D Raman images of unprocessed human tissue slices with submicrometric resolution are also acquired based on visualization of molecular composition, which could assist in tumour boundary recognition and clinicopathologic diagnosis. Lastly, the potential for a portable handheld Raman system is illustrated during surgery for real-time intraoperative human liver cancer diagnosis.

Advanced intraoperative MRI in pediatric brain tumor surgery

Introduction: In the pediatric brain tumor surgery setting, intraoperative MRI (ioMRI) provides "real-time" imaging, allowing for evaluation of the extent of resection and detection of complications. The use of advanced MRI sequences could potentially provide additional physiological information that may aid in the preservation of healthy brain regions. This review aims to determine the added value of advanced imaging in ioMRI for pediatric brain tumor surgery compared to conventional imaging. Methods: Our systematic literature search identified relevant articles on PubMed using keywords associated with pediatrics, ioMRI, and brain tumors. The literature search was extended using the snowball technique to gather more information on advanced MRI techniques, their technical background, their use in adult ioMRI, and their use in routine pediatric brain tumor care. Results: The available literature was sparse and demonstrated that advanced sequences were used to reconstruct fibers to prevent damage to important structures, provide information on relative cerebral blood flow or abnormal metabolites, or to indicate the onset of hemorrhage or ischemic infarcts. The explorative literature search revealed developments within each advanced MRI field, such as multi-shell diffusion MRI, arterial spin labeling, and amide-proton transfer-weighted imaging, that have been studied in adult ioMRI but have not yet been applied in pediatrics. These techniques could have the potential to provide more accurate fiber tractography, information on intraoperative cerebral perfusion, and to match gadolinium-based T1w images without using a contrast agent. Conclusion: The potential added value of advanced MRI in the intraoperative setting for pediatric brain tumors is to prevent damage to important structures, to provide additional physiological or metabolic information, or to indicate the onset of postoperative changes. Current developments within various advanced ioMRI sequences are promising with regard to providing in-depth tissue information.

Neurooncology: 2023 update

This article presents some of the author's neuropathological highlights in the field on neuro-oncology research encountered in 2022. Major advances were made in the development of more precise, faster, easier, less invasive and unbiased diagnostic tools ranging from immunohistochemical prediction of 1p/19q loss in diffuse glioma, methylation analyses in CSF samples, molecular profiling for CNS lymphoma, proteomic analyses of recurrent glioblastoma, integrated molecular diagnostics for better stratification in meningioma, intraoperative profiling making use of Raman effect or methylation analysis, to finally, the assessment of histological slides by means of machine learning for the prediction of molecular tumor features. In addition, as the discovery of a new tumor entity may also be a highlight for the neuropathology community, the newly described high-grade glioma with pleomorphic and pseudopapillary features (HPAP) has been selected for this article. Regarding new innovative treatment approaches, a drug screening platform for brain metastasis is presented. Although diagnostic speed and precision is steadily increasing, clinical prognosis for patients with malignant tumors affecting the nervous system remains largely unchanged over the last decade, therefore future neuro-oncological research focus should be put on how the amazing developments presented in this article can be more sustainably applied to positively impact patient prognosis.

Deep learning-based high-speed, large-field, and high-resolution multiphoton imaging

Multiphoton microscopy is a formidable tool for the pathological analysis of tumors. The physical limitations of imaging systems and the low efficiencies inherent in nonlinear processes have prevented the simultaneous achievement of high imaging speed and high resolution. We demonstrate a self-alignment dual-attention-guided residual-in-residual generative adversarial network trained with various multiphoton images. The network enhances image contrast and spatial resolution, suppresses noise, and scanning fringe artifacts, and eliminates the mutual exclusion between field of view, image quality, and imaging speed. The network may be integrated into commercial microscopes for large-scale, high-resolution, and low photobleaching studies of tumor environments.

Histological diagnosis of unprocessed breast core-needle biopsy via stimulated Raman scattering microscopy and multi-instance learning

Core-needle biopsy (CNB) plays a vital role in the initial diagnosis of breast cancer. However, the complex tissue processing and global shortage of pathologists have hindered traditional histopathology from timely diagnosis on fresh biopsies. In this work, we developed a full digital platform by integrating label-free stimulated Raman scattering (SRS) microscopy with weakly-supervised learning for rapid and automated cancer diagnosis on un-labelled breast CNB. Methods: We first compared the results of SRS imaging with standard hematoxylin and eosin (H&E) staining on adjacent frozen tissue sections. Then fresh unprocessed biopsy tissues were imaged by SRS to reveal diagnostic histoarchitectures. Next, weakly-supervised learning, i.e., the multi-instance learning (MIL) model was conducted to evaluate the ability to differentiate between benign and malignant cases, and compared with the performance of supervised learning model. Finally, gradient-weighted class activation mapping (Grad-CAM) and semantic segmentation were performed to spatially resolve benign/malignant areas with high efficiency. Results: We verified the ability of SRS in revealing essential histological hallmarks of breast cancer in both thin frozen sections and fresh unprocessed biopsy, generating histoarchitectures well correlated with H&E staining. Moreover, we demonstrated that weakly-supervised MIL model could achieve superior classification performance to supervised learnings, reaching diagnostic accuracy of 95% on 61 biopsy specimens. Furthermore, Grad-CAM allowed the trained MIL model to visualize the histological heterogeneity within the CNB. Conclusion: Our results indicate that MIL-assisted SRS microscopy provides rapid and accurate diagnosis on histologically heterogeneous breast CNB, and could potentially help the subsequent management of patients.