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

Machine learning can aid in prediction of IDH mutation from H&E-stained histology slides in infiltrating gliomas

While Machine Learning (ML) models have been increasingly applied to a range of histopathology tasks, there has been little emphasis on characterizing these models and contrasting them with human experts. We present a detailed empirical analysis comparing expert neuropathologists and ML models at predicting IDH mutation status in H&E-stained histology slides of infiltrating gliomas, both independently and synergistically. We find that errors made by neuropathologists and ML models trained using the TCGA dataset are distinct, representing modest agreement between predictions (human-vs.-human κ = 0.656; human-vs.-ML model κ = 0.598). While no ML model surpassed human performance on an independent institutional test dataset (human AUC = 0.901, max ML AUC = 0.881), a hybrid model aggregating human and ML predictions demonstrates predictive performance comparable to the consensus of two expert neuropathologists (hybrid classifier AUC = 0.921 vs. two-neuropathologist consensus AUC = 0.920). We also show that models trained at different levels of magnification exhibit different types of errors, supporting the value of aggregation across spatial scales in the ML approach. Finally, we present a detailed interpretation of our multi-scale ML ensemble model which reveals that predictions are driven by human-identifiable features at the patch-level.

Raman Spectroscopy on Brain Disorders: Transition from Fundamental Research to Clinical Applications

Brain disorders such as brain tumors and neurodegenerative diseases (NDs) are accompanied by chemical alterations in the tissues. Early diagnosis of these diseases will provide key benefits for patients and opportunities for preventive treatments. To detect these sophisticated diseases, various imaging modalities have been developed such as computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET). However, they provide inadequate molecule-specific information. In comparison, Raman spectroscopy (RS) is an analytical tool that provides rich information about molecular fingerprints. It is also inexpensive and rapid compared to CT, MRI, and PET. While intrinsic RS suffers from low yield, in recent years, through the adoption of Raman enhancement technologies and advanced data analysis approaches, RS has undergone significant advancements in its ability to probe biological tissues, including the brain. This review discusses recent clinical and biomedical applications of RS and related techniques applicable to brain tumors and NDs.

Deep Learning with a Dataset Created Using Kanno Saitama Macro, a Self-Made Automatic Foveal Avascular Zone Extraction Program

The extraction of the foveal avascular zone (FAZ) from optical coherence tomography angiography (OCTA) images has been used in many studies in recent years due to its association with various ophthalmic diseases. In this study, we investigated the utility of a dataset for deep learning created using Kanno Saitama Macro (KSM), a program that automatically extracts the FAZ using swept-source OCTA. The test data included 40 eyes of 20 healthy volunteers. For training and validation, we used 257 eyes from 257 patients. The FAZ of the retinal surface image was extracted using KSM, and a dataset for FAZ extraction was created. Based on that dataset, we conducted a training test using a typical U-Net. Two examiners manually extracted the FAZ of the test data, and the results were used as gold standards to compare the Jaccard coefficients between examiners, and between each examiner and the U-Net. The Jaccard coefficient was 0.931 between examiner 1 and examiner 2, 0.951 between examiner 1 and the U-Net, and 0.933 between examiner 2 and the U-Net. The Jaccard coefficients were significantly better between examiner 1 and the U-Net than between examiner 1 and examiner 2 (p < 0.001). These data indicated that the dataset generated by KSM was as good as, if not better than, the agreement between examiners using the manual method. KSM may contribute to reducing the burden of annotation in deep learning.

Innovation in Neurosurgery: Lessons Learned, Obstacles, and Potential Funding Sources

Innovation is central to neurosurgery and has dramatically increased over the last twenty years. Although the specialty innovates as a whole, only 3-4.7% of practicing neurosurgeons hold patents. Various roadblocks to innovation impede this process such as lack of understanding, increasing regulatory complexity, and lack of funding. Newly emerging technologies allow us to understand how to innovate and how to learn from other medical specialties. By further understanding the process of innovation, and the funding that supports it, Neurosurgery can continue to hold innovation as one of its's central tenets.

Deep learning-based age estimation from chest X-rays indicates cardiovascular prognosis

BACKGROUND

In recent years, there has been considerable research on the use of artificial intelligence to estimate age and disease status from medical images. However, age estimation from chest X-ray (CXR) images has not been well studied and the clinical significance of estimated age has not been fully determined.

METHODS

To address this, we trained a deep neural network (DNN) model using more than 100,000 CXRs to estimate the patients' age solely from CXRs. We applied our DNN to CXRs of 1562 consecutive hospitalized heart failure patients, and 3586 patients admitted to the intensive care unit with cardiovascular disease.

RESULTS

The DNN's estimated age (X-ray age) showed a strong significant correlation with chronological age on the hold-out test data and independent test data. Elevated X-ray age is associated with worse clinical outcomes (heart failure readmission and all-cause death) for heart failure. Additionally, elevated X-ray age was associated with a worse prognosis in 3586 patients admitted to the intensive care unit with cardiovascular disease.

CONCLUSIONS

Our results suggest that X-ray age can serve as a useful indicator of cardiovascular abnormalities, which will help clinicians to predict, prevent and manage cardiovascular diseases.

Advances, technological innovations, and future prospects in stereotactic brain biopsies

Stereotactic brain biopsy is one of the most frequently performed brain surgeries. This review aimed to expose the latest cutting-edge and updated technologies and innovations available to neurosurgeons to safely perform stereotactic brain biopsy by minimizing the risks of complications and ensuring that the procedure is successful, leading to a histological diagnosis. We also examined methods for improving preoperative, intraoperative, and postoperative workflows. We performed a comprehensive state-of-the-art literature review. Intraoperative histology, fluorescence, and imaging techniques appear as smart tools to improve the diagnostic yield of biopsy. Constant innovations such as optical methods and augmented reality are also being made to increase patient safety. Robotics and integrated imaging techniques provide an enhanced intraoperative workflow. Patients' management algorithms based on early discharge after biopsy optimize the patient's personal experience and make the most efficient possible use of the available hospital resources. Many new trends are emerging, constantly improving patient care and safety, as well as surgical workflow. A parameter that must be considered is the cost-effectiveness of these devices and the possibility of using them on a daily basis. The decision to implement a new instrument in the surgical workflow should also be dependent on the number of procedures per year, the existing stereotactic equipment, and the experience of each center. Research on patients' postbiopsy management is another mandatory approach to enhance the safety profile of stereotactic brain biopsy and patient satisfaction, as well as to reduce healthcare costs.

Impact of preprocessing methods on the Raman spectra of brain tissue

Delineating cancer tissue while leaving functional tissue intact is crucial in brain tumor resection. Despite several available aids, surgeons are limited by preoperative or subjective tools. Raman spectroscopy is a label-free optical technique with promising indications for tumor tissue identification. To allow direct comparisons between measurements preprocessing of the Raman signal is required. There are many recognized methods for preprocessing Raman spectra; however, there is no universal standard. In this paper, six different preprocessing methods were tested on Raman spectra (n > 900) from fresh brain tissue samples (n = 34). The sample cohort included both primary brain tumors, such as adult-type diffuse gliomas and meningiomas, as well as metastases of breast cancer. Each tissue sample was classified according to the CNS WHO 2021 guidelines. The six methods include both direct and iterative polynomial fitting, mathematical morphology, signal derivative, commercial software, and a neural network. Data exploration was performed using principal component analysis, t-distributed stochastic neighbor embedding, and k-means clustering. For each of the six methods, the parameter combination that explained the most variance in the data, i.e., resulting in the highest Gap-statistic, was chosen and compared to the other five methods. Depending on the preprocessing method, the resulting clusters varied in number, size, and associated spectral features. The detected features were associated with hemoglobin, neuroglobin, carotenoid, water, and protoporphyrin, as well as proteins and lipids. However, the spectral features seen in the Raman spectra could not be unambiguously assigned to tissue labels, regardless of preprocessing method. We have illustrated that depending on the chosen preprocessing method, the spectral appearance of Raman features from brain tumor tissue can change. Therefore, we argue both for caution in comparing spectral features from different Raman studies, as well as the importance of transparency of methodology and implementation of the preprocessing. As discussed in this study, Raman spectroscopy for in vivo guidance in neurosurgery requires fast and adaptive preprocessing. On this basis, a pre-trained neural network appears to be a promising approach for the operating room.

Early repeat resection for residual glioblastoma: decision-making among an international cohort of neurosurgeons

OBJECTIVE

The importance of extent of resection (EOR) in glioblastoma (GBM) has been thoroughly demonstrated. However, few studies have explored the practices and benefits of early repeat resection (ERR) when residual tumor deemed resectable is unintentionally left after an initial resection, and the survival benefit of ERR is still unknown. Herein, the authors aimed to internationally survey current practices regarding ERR and to analyze differences based on geographic location and practice setting.

METHODS

The authors distributed a survey to the American Association of Neurological Surgeons and Congress of Neurological Surgeons Tumor Section, Society of British Neurological Surgeons, European Association of Neurosurgical Society, and Latin American Federation of Neurosurgical Societies. Neurosurgeons responded to questions about their training, practice setting, and current ERR practices. They also reported the EOR threshold below which they would pursue ERR and their likelihood of performing ERR using a Likert scale of 1-5 (5 being the most likely) in two sets of 5 cases, the first set for a patient's initial hospitalization and the second for a referred patient who had undergone resection elsewhere. The resection likelihood index for each respondent was calculated as the mean Likert score across all cases.

RESULTS

Overall, 180 neurosurgeons from 25 countries responded to the survey. Neurosurgeons performed ERRs very rarely in their practices (< 1% of all GBM cases), with an EOR threshold of 80.2% (75%-95%). When presented with 10 cases, the case context (initial hospitalization vs referred patient) did not significantly change the surgeon ERR likelihood, although ERR likelihood did vary significantly on the basis of tumor location (p < 0.0001). Latin American neurosurgeons were more likely to pursue ERR in the provided cases. Neurosurgeons were more likely to pursue ERR when the tumor was MGMT methylated versus unmethylated, with a resection likelihood index of 3.78 and 3.21, respectively (p = 0.004); however, there was no significant difference between IDH mutant and IDH wild-type tumors.

CONCLUSIONS

Results of this survey reveal current practices regarding ERR, but they also demonstrate the variability in how neurosurgeons approach ERR. Standardized guidelines based on future studies incorporating tumor molecular characteristics are needed to guide neurosurgeons in their decision-making on this complicated issue.

Toward digital histopathological assessment in surgery for central nervous system tumors using stimulated Raman histology

OBJECTIVE

Intraoperative neuropathological assessment with conventional frozen sections supports the neurosurgeon in optimizing the surgical strategy. However, preparation and review of frozen sections can take as long as 45 minutes. Stimulated Raman histology (SRH) was introduced as a novel technique to provide rapid high-resolution digital images of unprocessed tissue samples directly in the operating room that are comparable to conventional histopathological images. Additionally, SRH images are simultaneously and easily accessible for neuropathological judgment. Recently, the first study showed promising results regarding the accuracy and feasibility of SRH compared with conventional histopathology. Thus, the aim of this study was to compare SRH with conventional H&E images and frozen sections in a large cohort of patients with different suspected central nervous system (CNS) tumors.

METHODS

The authors included patients who underwent resection or stereotactic biopsy of suspected CNS neoplasm, including brain and spinal tumors. Intraoperatively, tissue samples were safely collected and SRH analysis was performed directly in the operating room. To enable optimal comparison of SRH with H&E images and frozen sections, the authors created a digital databank that included images obtained with all 3 imaging modalities. Subsequently, 2 neuropathologists investigated the diagnostic accuracy, tumor cellularity, and presence of diagnostic histopathological characteristics (score 0 [not present] through 3 [excellent]) determined with SRH images and compared these data to those of H&E images and frozen sections, if available.

RESULTS

In total, 94 patients with various suspected CNS tumors were included, and the application of SRH directly in the operating room was feasible in all cases. The diagnostic accuracy based on SRH images was 99% when compared with the final histopathological diagnosis based on H&E images. Additionally, the same histopathological diagnosis was established in all SRH images (100%) when compared with that of the corresponding frozen sections. Moreover, the authors found a statistically significant correlation in tumor cellularity between SRH images and corresponding H&E images (p < 0.0005 and R = 0.867, Pearson correlation coefficient). Finally, excellent (score 3) or good (2) accordance between diagnostic histopathological characteristics and H&E images was present in 95% of cases.

CONCLUSIONS

The results of this retrospective analysis demonstrate the near-perfect diagnostic accuracy and capability of visualizing relevant histopathological characteristics with SRH compared with conventional H&E staining and frozen sections. Therefore, digital SRH histopathology seems especially useful for rapid intraoperative investigation to confirm the presence of diagnostic tumor tissue and the precise tumor entity, as well as to rapidly analyze multiple tissue biopsies from the suspected tumor margin. A real-time analysis comparing SRH images and conventional histological images at the time of surgery should be performed as the next step in future studies.

The diagnostic accuracy of intraoperative differentiation and delineation techniques in brain tumours

Brain tumour identification and delineation in a timeframe of seconds would significantly guide and support surgical decisions. Here, treatment is often complicated by the infiltration of gliomas in the surrounding brain parenchyma. Accurate delineation of the invasive margins is essential to increase the extent of resection and to avoid postoperative neurological deficits. Currently, histopathological annotation of brain biopsies and genetic phenotyping still define the first line treatment, where results become only available after surgery. Furthermore, adjuvant techniques to improve intraoperative visualisation of the tumour tissue have been developed and validated. In this review, we focused on the sensitivity and specificity of conventional techniques to characterise the tumour type and margin, specifically fluorescent-guided surgery, neuronavigation and intraoperative imaging as well as on more experimental techniques such as mass spectrometry-based diagnostics, Raman spectrometry and hyperspectral imaging. Based on our findings, all investigated methods had their advantages and limitations, guiding researchers towards the combined use of intraoperative imaging techniques. This can lead to an improved outcome in terms of extent of tumour resection and progression free survival while preserving neurological outcome of the patients.

A deep learning model designed for Raman spectroscopy with a novel hyperparameter optimization method

Raman spectroscopy is a spectroscopic technique typically used to determine vibrational modes of molecules and is commonly used in chemistry to provide a structural fingerprint by which molecules can be identified. With the help of deep learning, Raman spectroscopy can be analyzed more efficiently and thus provide more accurate molecular information. However, no general neural network is designed for one-dimensional Raman spectral data so far. Furthermore, different combinations of hyperparameters of neural networks lead to results with significant differences, so the optimization of hyperparameters is a crucial issue in deep learning modeling. In this work, we propose a deep learning model designed for Raman spectral data and a hyperparameter optimization method to achieve its best performance, i.e., a method based on the simulated annealing algorithm to optimize the hyperparameters of the model. The proposed model and optimization method have been fully validated in a glioma Raman spectroscopy dataset. Compared with other published methods including linear regression, support vector regression, long short-term memory, VGG and ResNet, the mean squared error is reduced by 0.1557 while the coefficient determination is increased by 0.1195 on average.

Updates in intraoperative strategies for enhancing intra-axial brain tumor control

To ensure excellent postoperative clinical outcomes while preserving critical neurologic function, neurosurgeons who manage patients with intra-axial brain tumors can use intraoperative technologies and tools to achieve maximal safe resection. Neurosurgical oncology revolves around safe and optimal extent of resection, which further dictates subsequent treatment regimens and patient outcomes. Various methods can be adapted for treating both primary and secondary intra-axial brain lesions. We present a review of recent advances and published research centered on different innovative tools and techniques, including fluorescence-guided surgery, new methods of drug delivery, and minimally invasive procedural options.

Novel intraoperative strategies for enhancing tumor control: Future directions

Maximal safe surgical resection plays a key role in the care of patients with gliomas. A range of technologies have been developed to aid surgeons in distinguishing tumor from normal tissue, with the goal of increasing tumor resection and limiting postoperative neurological deficits. Technologies that are currently being investigated to aid in improving tumor control include intraoperative imaging modalities, fluorescent tumor makers, intraoperative cell and molecular profiling of tumors, improved microscopic imaging, intraoperative mapping, augmented and virtual reality, intraoperative drug and radiation delivery, and ablative technologies. In this review, we summarize the aforementioned advancements in neurosurgical oncology and implications for improving patient outcomes.

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.

Investigation of the Spatial Generation of Stimulated Raman Scattering Using Computer Simulation and Experimentation

Stimulated Raman scattering is a phenomenon with potential use in providing real-time molecular information in three-dimensions (3D) of a sample using imaging. For precise imaging, the knowledge about the spatial generation of stimulated Raman scattering is essential. To investigate the spatial behavior in an idealized case, computer simulations and experiments were performed. For the computer simulations, diffraction theory was used for the beam propagation complemented with nonlinear phase modulation describing the interaction between the light and matter. For the experiments, a volume of ethanol was illuminated by an expanded light beam and a plane inside the volume was imaged in transmission. For generating stimulated Raman scattering, a pump beam was focused into this volume and led to a beam dump after passing the volume. The pulse duration of the two beams were 6 ns and the pump beam energy ranged from 1 to 27 mJ. The effect of increasing pump power on the spatial distribution of the Raman gain and the spatial growth of the signal at different interaction lengths between the beam and the sample was investigated. The spatial width of the region where the stimulated Raman scattering signal was generated for experiments and simulation was 0.21 and 0.09 mm, respectively. The experimental and simulation results showed that most of the stimulated Raman scattering is generated close to the pump beam focus and the maximum peak of the Stokes intensity spatially comes shortly after the peak of the pump intensity.

Evaluation of an Integrated Spectroscopy and Classification Platform for Point-of-Care Core Needle Biopsy Assessment: Performance Characteristics from Ex Vivo Renal Mass Biopsies

PURPOSE

To evaluate a transmission optical spectroscopy instrument for rapid ex vivo assessment of core needle cancer biopsies (CNBs) at the point of care.

MATERIALS AND METHODS

CNBs from surgically resected renal tumors and nontumor regions were scanned on their sampling trays with a custom spectroscopy instrument. After extracting principal spectral components, machine learning was used to train logistic regression, support vector machines, and random decision forest (RF) classifiers on 80% of randomized and stratified data. The algorithms were evaluated on the remaining 20% of the data set held out during training. Binary classification (tumor/nontumor) was performed based on a decision threshold. Multinomial classification was also performed to differentiate between the subtypes of renal cell carcinoma (RCC) and account for potential confounding effects from fat, blood, and necrotic tissue. Classifiers were compared based on sensitivity, specificity, and positive predictive value (PPV) relative to a histopathologic standard.

RESULTS

A total of 545 CNBs from 102 patients were analyzed, yielding 5,583 spectra after outlier exclusion. At the individual spectra level, the best performing algorithm was RF with sensitivities of 96% and 92% and specificities of 90% and 89%, for the binary and multiclass analyses, respectively. At the full CNB level, RF algorithm also showed the highest sensitivity and specificity (93% and 91%, respectively). For RCC subtypes, the highest sensitivity and PPV were attained for clear cell (93.5%) and chromophobe (98.2%) subtypes, respectively.

CONCLUSIONS

Ex vivo spectroscopy imaging paired with machine learning can accurately characterize renal mass CNB at the time of tissue acquisition.

Radiomics in neuro-oncological clinical trials

The development of clinical trials has led to substantial improvements in the prevention and treatment of many diseases, including brain cancer. Advances in medicine, such as improved surgical techniques, the development of new drugs and devices, the use of statistical methods in research, and the development of codes of ethics, have considerably influenced the way clinical trials are conducted today. In addition, methods from the broad field of artificial intelligence, such as radiomics, have the potential to considerably affect clinical trials and clinical practice in the future. Radiomics is a method to extract undiscovered features from routinely acquired imaging data that can neither be captured by means of human perception nor conventional image analysis. In patients with brain cancer, radiomics has shown its potential for the non-invasive identification of prognostic biomarkers, automated response assessment, and differentiation between treatment-related changes from tumour progression. Despite promising results, radiomics is not yet established in routine clinical practice nor in clinical trials. In this Viewpoint, the European Organization for Research and Treatment of Cancer Brain Tumour Group summarises the current status of radiomics, discusses its potential and limitations, envisions its future role in clinical trials in neuro-oncology, and provides guidance on how to address the challenges in radiomics.

Artificial intelligence for multimodal data integration in oncology

In oncology, the patient state is characterized by a whole spectrum of modalities, ranging from radiology, histology, and genomics to electronic health records. Current artificial intelligence (AI) models operate mainly in the realm of a single modality, neglecting the broader clinical context, which inevitably diminishes their potential. Integration of different data modalities provides opportunities to increase robustness and accuracy of diagnostic and prognostic models, bringing AI closer to clinical practice. AI models are also capable of discovering novel patterns within and across modalities suitable for explaining differences in patient outcomes or treatment resistance. The insights gleaned from such models can guide exploration studies and contribute to the discovery of novel biomarkers and therapeutic targets. To support these advances, here we present a synopsis of AI methods and strategies for multimodal data fusion and association discovery. We outline approaches for AI interpretability and directions for AI-driven exploration through multimodal data interconnections. We examine challenges in clinical adoption and discuss emerging solutions.

Innovations in the Diagnosis and Surgical Management of Low-Grade Gliomas

Low-grade gliomas are a broad category of tumors that can manifest at different stages of life. As a group, their prognosis has historically been considered to be favorable, and surgery is a mainstay of treatment. Advances in the molecular characterization of individual lesions has led to newer classification systems, a better understanding of the biological behavior of different neoplasms, and the identification of previously unrecognized entities. New prospective genetic and molecular data will help delineate better treatment paradigms and will continue to change the taxonomy of central nervous system tumors in the coming years. Advances in the field of radiomics will help predict the molecular profile of a particular tumor through noninvasive testing. Similarly, more precise methods of intraoperative tumor tissue analysis will aid surgical planning. Improved surgical outcomes propelled by novel surgical techniques and intraoperative adjuncts and emerging forms of medical treatment in the field of immunotherapy have enriched the management of these lesions. We review the contemporary management and innovations in the treatment of low-grade gliomas.

Challenges in the Diagnosis and Management of Low-Grade Gliomas

Low-grade gliomas are clinically challenging entities. Patients with these tumors tend to be relatively young at presentation, and lesions are often incidental findings or are identified because the patient presents with a seizure. Rapidly emerging and evolving molecular classifications of gliomas have influenced treatment paradigms. Importantly, low-grade gliomas can be classified on the basis of IDH mutation status, whereby low-grade astrocytomas harbor the IDH mutation, while oligodendrogliomas are defined by both IDH mutant status and 1p/19q co-deletion. Given the importance of molecular classification for diagnosis, treatment planning, and prognostication, tissue samples are necessary for proper management. Literature supports improved overall survival and outcomes with increased extent of resection for low-grade glioma. Awake craniotomies and resection of insular low-grade gliomas both have been demonstrated as safe and improve outcomes for patients with lesions located in eloquent areas. Given the younger age at diagnosis of these lesions compared with higher-grade gliomas, fertility, fertility preservation, and potential malignant transformation should be discussed with patients of childbearing age.

Intraoperative Glioma Grading Using Neural Architecture Search and Multi-Modal Imaging

Glioma grading during surgery can help clinical treatment planning and prognosis, but intraoperative pathological examination of frozen sections is limited by the long processing time and complex procedures. Near-infrared fluorescence imaging provides chances for fast and accurate real-time diagnosis. Recently, deep learning techniques have been actively explored for medical image analysis and disease diagnosis. However, issues of near-infrared fluorescence images, including small-scale, noise, and low-resolution, increase the difficulty of training a satisfying network. Multi-modal imaging can provide complementary information to boost model performance, but simultaneously designing a proper network and utilizing the information of multi-modal data is challenging. In this work, we propose a novel neural architecture search method DLS-DARTS to automatically search for network architectures to handle these issues. DLS-DARTS has two learnable stems for multi-modal low-level feature fusion and uses a modified perturbation-based derivation strategy to improve the performance on the area under the curve and accuracy. White light imaging and fluorescence imaging in the first near-infrared window (650-900 nm) and the second near-infrared window (1,000-1,700 nm) are applied to provide multi-modal information on glioma tissues. In the experiments on 1,115 surgical glioma specimens, DLS-DARTS achieved an area under the curve of 0.843 and an accuracy of 0.634, which outperformed manually designed convolutional neural networks including ResNet, PyramidNet, and EfficientNet, and a state-of-the-art neural architecture search method for multi-modal medical image classification. Our study demonstrates that DLS-DARTS has the potential to help neurosurgeons during surgery, showing high prospects in medical image analysis.

Multistrategy Improved Sparrow Search Algorithm Optimized Deep Neural Network for Esophageal Cancer

Deep neural network is a complex pattern recognition network system. It is widely favored by scholars for its strong nonlinear fitting ability. However, training deep neural network models on small datasets typically realizes worse performance than shallow neural network. In this study, a strategy to improve the sparrow search algorithm based on the iterative map, iterative perturbation, and Gaussian mutation is developed. This optimized strategy improved the sparrow search algorithm validated by fourteen benchmark functions, and the algorithm has the best search accuracy and the fastest convergence speed. An algorithm based on the iterative map, iterative perturbation, and Gaussian mutation improved sparrow search algorithm is designed to optimize deep neural networks. The modified sparrow algorithm is exploited to search for the optimal connection weights of deep neural network. This algorithm is implemented for the esophageal cancer dataset along with the other six algorithms. The proposed model is able to achieve 0.92 under all the eight scoring criteria, which is better than the performance of the other six algorithms. Therefore, an optimized deep neural network based on an improved sparrow search algorithm with iterative map, iterative perturbation, and Gaussian mutation is an effective approach to predict the survival rate of esophageal cancer.

Deep-Learning to Predict BRCA Mutation and Survival from Digital H&E Slides of Epithelial Ovarian Cancer

BRCA 1/2 genes mutation status can already determine the therapeutic algorithm of high grade serous ovarian cancer patients. Nevertheless, its assessment is not sufficient to identify all patients with genomic instability, since BRCA 1/2 mutations are only the most well-known mechanisms of homologous recombination deficiency (HR-d) pathway, and patients displaying HR-d behave similarly to BRCA mutated patients. HRd assessment can be challenging and is progressively overcoming BRCA testing not only for prognostic information but more importantly for drugs prescriptions. However, HR testing is not already integrated in clinical practice, it is quite expensive and it is not refundable in many countries. Selecting patients who are more likely to benefit from this assessment (BRCA 1/2 WT patients) at an early stage of the diagnostic process, would allow an optimization of genomic profiling resources. In this study, we sought to explore whether somatic BRCA1/2 genes status can be predicted using computational pathology from standard hematoxylin and eosin histology. In detail, we adopted a publicly available, deep-learning-based weakly supervised method that uses attention-based learning to automatically identify sub regions of high diagnostic value to accurately classify the whole slide (CLAM). The same model was also tested for progression free survival (PFS) prediction. The model was tested on a cohort of 664 (training set: n = 464, testing set: n = 132) ovarian cancer patients, of whom 233 (35.1%) had a somatic BRCA 1/2 mutation. An area under the curve of 0.7 and 0.55 was achieved in the training and testing set respectively. The model was then further refined by manually identifying areas of interest in half of the cases. 198 images were used for training (126/72) and 87 images for validation (55/32). The model reached a zero classification error on the training set, but the performance was 0.59 in terms of validation ROC AUC, with a 0.57 validation accuracy. Finally, when applied to predict PFS, the model achieved an AUC of 0.71, with a negative predictive value of 0.69, and a positive predictive value of 0.75. Based on these analyses, we have planned further steps of development such as proving a reference classification performance, exploring the hyperparameters space for training optimization, eventually tweaking the learning algorithms and the neural networks architecture for better suiting this specific task. These actions may allow the model to improve performances for all the considered outcomes.

Neural Networks-Based On-Site Dermatologic Diagnosis through Hyperspectral Epidermal Images

Cancer originates from the uncontrolled growth of healthy cells into a mass. Chromophores, such as hemoglobin and melanin, characterize skin spectral properties, allowing the classification of lesions into different etiologies. Hyperspectral imaging systems gather skin-reflected and transmitted light into several wavelength ranges of the electromagnetic spectrum, enabling potential skin-lesion differentiation through machine learning algorithms. Challenged by data availability and tiny inter and intra-tumoral variability, here we introduce a pipeline based on deep neural networks to diagnose hyperspectral skin cancer images, targeting a handheld device equipped with a low-power graphical processing unit for routine clinical testing. Enhanced by data augmentation, transfer learning, and hyperparameter tuning, the proposed architectures aim to meet and improve the well-known dermatologist-level detection performances concerning both benign-malignant and multiclass classification tasks, being able to diagnose hyperspectral data considering real-time constraints. Experiments show 87% sensitivity and 88% specificity for benign-malignant classification and specificity above 80% for the multiclass scenario. AUC measurements suggest classification performance improvement above 90% with adequate thresholding. Concerning binary segmentation, we measured skin DICE and IOU higher than 90%. We estimated 1.21 s, at most, consuming 5 Watts to segment the epidermal lesions with the U-Net++ architecture, meeting the imposed time limit. Hence, we can diagnose hyperspectral epidermal data assuming real-time constraints.

Diagnostics and theranostics of central nervous system diseases based on aggregation-induced emission luminogens

Central nervous system (CNS) diseases include Alzheimer's disease (AD), Parkinson's disease (PD), brain tumors, strokes, and other important diseases that are harmful and fatal to human beings. CNS diseases have the characteristics of high fatality rates, difficult diagnosis, and costly treatment. The diagnosis and treatment of CNS diseases by molecular imaging are usually limited by the depth of tissue penetration and the blood-brain barrier (BBB). Therefore, it is still a huge challenge to distinguish between the lesion and the surrounding parenchymal boundary with high sensitivity and specificity. Compared with traditional fluorophores with aggregation-caused quenching effect, luminogens with aggregation-induced emission (AIE) characteristics have strong near-infrared deep penetration, large Stokes shift, excellent biocompatibility, light stability, and desirable BBB permeability. In view of this, developing novel AIE-based materials for diagnostics and theranostics of CNS diseases is promising and of great significance. Herein, we highlight the recent research progress in this field with a special focus on near-infrared imaging and AIE nanorobots for CNS diseases. The design principle of AIE probes is discussed in detail, and the outlook is presented as well.

Next Generation Digital Pathology: Emerging Trends and Measurement Challenges for Molecular Pathology

Digital pathology is revolutionising the analysis of histological features and is becoming more and more widespread in both the clinic and research. Molecular pathology extends the tissue morphology information provided by conventional histopathology by providing spatially resolved molecular information to complement the structural information provided by histopathology. The multidimensional nature of the molecular data poses significant challenge for data processing, mining, and analysis. One of the key challenges faced by new and existing pathology practitioners is how to choose the most suitable molecular pathology technique for a given diagnosis. By providing a comparison of different methods, this narrative review aims to introduce the field of molecular pathology, providing a high-level overview of many different methods. Since each pixel of an image contains a wealth of molecular information, data processing in molecular pathology is more complex. The key data processing steps and variables, and their effect on the data, are also discussed.

PDBL: Improving Histopathological Tissue Classification With Plug-and-Play Pyramidal Deep-Broad Learning

Histopathological tissue classification is a simpler way to achieve semantic segmentation for the whole slide images, which can alleviate the requirement of pixel-level dense annotations. Existing works mostly leverage the popular CNN classification backbones in computer vision to achieve histopathological tissue classification. In this paper, we propose a super lightweight plug-and-play module, named Pyramidal Deep-Broad Learning (PDBL), for any well-trained classification backbone to improve the classification performance without a re-training burden. For each patch, we construct a multi-resolution image pyramid to obtain the pyramidal contextual information. For each level in the pyramid, we extract the multi-scale deep-broad features by our proposed Deep-Broad block (DB-block). We equip PDBL in three popular classification backbones, ShuffLeNetV2, EfficientNetb0, and ResNet50 to evaluate the effectiveness and efficiency of our proposed module on two datasets (Kather Multiclass Dataset and the LC25000 Dataset). Experimental results demonstrate the proposed PDBL can steadily improve the tissue-level classification performance for any CNN backbones, especially for the lightweight models when given a small among of training samples (less than 10%). It greatly saves the computational resources and annotation efforts. The source code is available at: https://github.com/linjiatai/PDBL.

Identification of hepatocellular carcinoma and paracancerous tissue based on the peak area in FTIR microspectroscopy

Hepatocellular carcinoma (HCC) is one of the most common primary hepatic malignancies across the world. The annual incidence and death rates have increased at the highest rate of all cancers in recent years. Surgical resection is a potentially curative option for solitary HCC or unilobar disease without evidence of metastases or vascular invasion. This study focuses on the molecular differences between the HCC foci and paracancerous tissues and provides some valuable biomarkers based on the vibrational spectrum. Fourier transform infrared (FTIR) spectroscopy is a non-invasive and qualitative and semi-quantitative analysis technique that has been widely applied for the identification of macromolecular changes in biological tissues. In this study, the FTIR spectra of the HCC foci and the paracancerous tissues were recorded separately, and ten areas under the absorption peaks of all the specimens were calculated. The result demonstrates that the areas of protein-related absorption peaks at 1398 cm^-1, 1548 cm^-1, 1654 cm^-1 and 3070 cm^-1 may be the key indicators of the two different regions. After coupling with the classification algorithms of k-nearest neighbor (KNN), random forest (RF) and support vector machine (SVM), it was found that SVM with an RBF kernel performed best with the AUC (area under the ROC curve) reaching 0.997, and the performance was better than the feature based on the full spectrum. This reveals that the peak area-based FTIR spectra combined with the SVM algorithm may be a promising tool in identifying the HCC foci and the paracancerous tissues.

Novel rapid intraoperative qualitative tumor detection by a residual convolutional neural network using label-free stimulated Raman scattering microscopy

Determining the presence of tumor in biopsies and the decision-making during resections is often dependent on intraoperative rapid frozen-section histopathology. Recently, stimulated Raman scattering microscopy has been introduced to rapidly generate digital hematoxylin-and-eosin-stained-like images (stimulated Raman histology) for intraoperative analysis. To enable intraoperative prediction of tumor presence, we aimed to develop a new deep residual convolutional neural network in an automated pipeline and tested its validity. In a monocentric prospective clinical study with 94 patients undergoing biopsy, brain or spinal tumor resection, Stimulated Raman histology images of intraoperative tissue samples were obtained using a fiber-laser-based stimulated Raman scattering microscope. A residual network was established and trained in ResNetV50 to predict three classes for each image: (1) tumor, (2) non-tumor, and (3) low-quality. The residual network was validated on images obtained in three small random areas within the tissue samples and were blindly independently reviewed by a neuropathologist as ground truth. 402 images derived from 132 tissue samples were analyzed representing the entire spectrum of neurooncological surgery. The automated workflow took in a mean of 240 s per case, and the residual network correctly classified tumor (305/326), non-tumorous tissue (49/67), and low-quality (6/9) images with an inter-rater agreement of 89.6% (κ = 0.671). An excellent internal consistency was found among the random areas with 90.2% (Cα = 0.942) accuracy. In conclusion, the novel stimulated Raman histology-based residual network can reliably detect the microscopic presence of tumor and differentiate from non-tumorous brain tissue in resection and biopsy samples within 4 min and may pave a promising way for an alternative rapid intraoperative histopathological decision-making tool.

Toward intraoperative tissue classification: exploiting signal feedback from an ultrasonic aspirator for brain tissue differentiation

PURPOSE

During brain tumor surgery, care must be taken to accurately differentiate between tumorous and healthy tissue, as inadvertent resection of functional brain areas can cause severe consequences. Since visual assessment can be difficult during tissue resection, neurosurgeons have to rely on the mechanical perception of tissue, which in itself is inherently challenging. A commonly used instrument for tumor resection is the ultrasonic aspirator, whose system behavior is already dependent on tissue properties. Using data recorded during tissue fragmentation, machine learning-based tissue differentiation is investigated for the first time utilizing ultrasonic aspirators.

METHODS

Artificial tissue model with two different mechanical properties is synthesized to represent healthy and tumorous tissue. 40,000 temporal measurement points of electrical data are recorded in a laboratory environment using a CNC machine. Three different machine learning approaches are applied: a random forest (RF), a fully connected neural network (NN) and a 1D convolutional neural network (CNN). Additionally, different preprocessing steps are investigated.

RESULTS

Fivefold cross-validation is conducted over the data and evaluated with the metrics F1, accuracy, positive predictive value, true positive rate and area under the receiver operating characteristic. Results show a generally good performance with a mean F1 of up to 0.900 ± 0.096 using a NN approach. Temporal information indicates low impact on classification performance, while a low-pass filter preprocessing step leads to superior results.

CONCLUSION

This work demonstrates the first steps to successfully differentiate healthy brain and tumor tissue using an ultrasonic aspirator during tissue fragmentation. Evaluation shows that both neural network-based classifiers outperform the RF. In addition, the effects of temporal dependencies are found to be reduced when adequate data preprocessing is performed. To ensure subsequent implementation in the clinic, handheld ultrasonic aspirator use needs to be investigated in the future as well as the addition of data to reflect tissue diversity during neurosurgical operations.

Intraoperative tissue classification methods in orthopedic and neurological surgeries: A systematic review

Accurate tissue differentiation during orthopedic and neurological surgeries is critical, given that such surgeries involve operations on or in the vicinity of vital neurovascular structures and erroneous surgical maneuvers can lead to surgical complications. By now, the number of emerging technologies tackling the problem of intraoperative tissue classification methods is increasing. Therefore, this systematic review paper intends to give a general overview of existing technologies. The review was done based on the PRISMA principle and two databases: PubMed and IEEE Xplore. The screening process resulted in 60 full-text papers. The general characteristics of the methodology from extracted papers included data processing pipeline, machine learning methods if applicable, types of tissues that can be identified with them, phantom used to conduct the experiment, and evaluation results. This paper can be useful in identifying the problems in the current status of the state-of-the-art intraoperative tissue classification methods and designing new enhanced techniques.

Accurate Brain Tumor Detection Using Deep Convolutional Neural Network

Detection and Classification of a brain tumor is an important step to better understanding its mechanism. Magnetic Reasoning Imaging (MRI) is an experimental medical imaging technique that helps the radiologist find the tumor region. However, it is a time taking process and requires expertise to test the MRI images, manually. Nowadays, the advancement of Computer-assisted Diagnosis (CAD), machine learning, and deep learning in specific allow the radiologist to more reliably identify brain tumors. The traditional machine learning methods used to tackle this problem require a handcrafted feature for classification purposes. Whereas deep learning methods can be designed in a way to not require any handcrafted feature extraction while achieving accurate classification results. This paper proposes two deep learning models to identify both binary (normal and abnormal) and multiclass (meningioma, glioma, and pituitary) brain tumors. We use two publicly available datasets that include 3064 and 152 MRI images, respectively. To build our models, we first apply a 23-layers convolution neural network (CNN) to the first dataset since there is a large number of MRI images for the training purpose. However, when dealing with limited volumes of data, which is the case in the second dataset, our proposed “23-layers CNN” architecture faces overfitting problem. To address this issue, we use transfer learning and combine VGG16 architecture along with the reflection of our proposed “23 layers CNN” architecture. Finally, we compare our proposed models with those reported in the literature. Our experimental results indicate that our models achieve up to 97.8% and 100% classification accuracy for our employed datasets, respectively, exceeding all other state-of-the-art models. Our proposed models, employed datasets, and all the source codes are publicly available at: (https://github.com/saikat15010/Brain-Tumor-Detection).

The Application of Artificial Intelligence in the Diagnosis and Drug Resistance Prediction of Pulmonary Tuberculosis

With the increasing incidence and mortality of pulmonary tuberculosis, in addition to tough and controversial disease management, time-wasting and resource-limited conventional approaches to the diagnosis and differential diagnosis of tuberculosis are still awkward issues, especially in countries with high tuberculosis burden and backwardness. In the meantime, the climbing proportion of drug-resistant tuberculosis poses a significant hazard to public health. Thus, auxiliary diagnostic tools with higher efficiency and accuracy are urgently required. Artificial intelligence (AI), which is not new but has recently grown in popularity, provides researchers with opportunities and technical underpinnings to develop novel, precise, rapid, and automated implements for pulmonary tuberculosis care, including but not limited to tuberculosis detection. In this review, we aimed to introduce representative AI methods, focusing on deep learning and radiomics, followed by definite descriptions of the state-of-the-art AI models developed using medical images and genetic data to detect pulmonary tuberculosis, distinguish the infection from other pulmonary diseases, and identify drug resistance of tuberculosis, with the purpose of assisting physicians in deciding the appropriate therapeutic schedule in the early stage of the disease. We also enumerated the challenges in maximizing the impact of AI in this field such as generalization and clinical utility of the deep learning models.

Bringing Vibrational Imaging to Chemical Biology with Molecular Probes

As an emerging optical imaging modality, stimulated Raman scattering (SRS) microscopy provides invaluable opportunities for chemical biology studies using its rich chemical information. Through rapid progress over the past decade, the development of Raman probes harnessing the chemical biology toolbox has proven to play a key role in advancing SRS microscopy and expanding biological applications. In this perspective, we first discuss the development of biorthogonal SRS imaging using small tagging of triple bonds or isotopes and highlight their unique advantages for metabolic pathway analysis and microbiology investigations. Potential opportunities for chemical biology studies integrating small tagging with SRS imaging are also proposed. We next summarize the current designs of highly sensitive and super-multiplexed SRS probes, as well as provide future directions and considerations for next-generation functional probe design. These rationally designed SRS probes are envisioned to bridge the gap between SRS microscopy and chemical biology research and should benefit their mutual development.

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.

Fast intraoperative histology-based diagnosis of gliomas with third harmonic generation microscopy and deep learning

Management of gliomas requires an invasive treatment strategy, including extensive surgical resection. The objective of the neurosurgeon is to maximize tumor removal while preserving healthy brain tissue. However, the lack of a clear tumor boundary hampers the neurosurgeon's ability to accurately detect and resect infiltrating tumor tissue. Nonlinear multiphoton microscopy, in particular higher harmonic generation, enables label-free imaging of excised brain tissue, revealing histological hallmarks within seconds. Here, we demonstrate a real-time deep learning-based pipeline for automated glioma image analysis, matching video-rate image acquisition. We used a custom noise detection scheme, and a fully-convolutional classification network, to achieve on average 79% binary accuracy, 0.77 AUC and 0.83 mean average precision compared to the consensus of three pathologists, on a preliminary dataset. We conclude that the combination of real-time imaging and image analysis shows great potential for intraoperative assessment of brain tissue during tumor surgery.

Metabolic detection of malignant brain gliomas through plasma lipidomic analysis and support vector machine-based machine learning

Background

Most malignant brain gliomas (MBGs) are associated with dismal outcomes, mainly due to their late diagnosis. Current diagnostic methods for MBGs are based on imaging and histological examination, which limits their early detection. Here, we aimed to identify reliable plasma lipid biomarkers for non-invasive diagnosis for MBGs.

Methods

Untargeted lipidomic analysis was firstly performed using a discovery cohort (n=107). The data were processed by a support vector machine (SVM)-based discriminating model to retrieve a panel of candidate biomarkers. Then, a targeted quantification method was developed, and the SVM-based diagnostic model was constructed using a training cohort (n=750) and tested using a test cohort (n=225). Finally, the performance of the diagnostic model was further evaluated in an independent validation cohort (n=920) enrolled from multiple medical centers.

Findings

A panel of 11 plasma lipids was identified as candidate biomarkers with an accuracy of 0.999. The diagnostic model developed achieved a high performance in distinguishing MBGs patients from normal controls with an area under the receiver-operating characteristic curve (AUC) of 0.9877 and 0.9869 in the training and test cohorts, respectively. In the validation cohort, the 11 lipid panel still achieved an accuracy of 0.9641 and an AUC of 0.9866.

Interpretation

The present study demonstrates the applicability and robustness of utilizing a machine learning algorithm to analyze lipidomic data for efficient and reliable biomarker screening. The 11 lipid biomarkers show great potential for the non-invasive diagnosis of MBGs with high throughput.

Funding

A full list of funding bodies that contributed to this study can be found in the Acknowledgments section.

Machine learning in neuro-oncology: toward novel development fields

PURPOSE

Artificial Intelligence (AI) involves several and different techniques able to elaborate a large amount of data responding to a specific planned outcome. There are several possible applications of this technology in neuro-oncology.

METHODS

We reviewed, according to PRISMA guidelines, available studies adopting AI in different fields of neuro-oncology including neuro-radiology, pathology, surgery, radiation therapy, and systemic treatments.

RESULTS

Neuro-radiology presented the major number of studies assessing AI. However, this technology is being successfully tested also in other operative settings including surgery and radiation therapy. In this context, AI shows to significantly reduce resources and costs maintaining an elevated qualitative standard. Pathological diagnosis and development of novel systemic treatments are other two fields in which AI showed promising preliminary data.

CONCLUSION

It is likely that AI will be quickly included in some aspects of daily clinical practice. Possible applications of these techniques are impressive and cover all aspects of neuro-oncology.

Stimulated Raman histology facilitates accurate diagnosis in neurosurgical patients: a one-to-one noninferiority study

OBJECTIVE

Stimulated Raman histology (SRH) offers efficient and accurate intraoperative neuropathological tissue analysis without procedural alteration to the diagnostic specimen. However, there are limited data demonstrating one-to-one tissue comparisons between SRH and traditional frozen sectioning. This study explores the non-inferiority of SRH as compared to frozen section on the same piece of tissue in neurosurgical patients.

METHODS

Tissue was collected over a 1-month period from 18 patients who underwent resection of central nervous system lesions. SRH and frozen section analyses were compared for diagnostic capabilities as well as assessed for quality and condition of tissue via a survey completed by pathologists.

RESULTS

SRH was sufficient for diagnosis in 78% of specimens as compared to 94% of specimens by frozen section of the same specimen. A Fisher's exact test determined there was no significant difference in diagnostic capability between the two groups. Additionally, both quality of SRH and condition of tissue after SRH were deemed to be non-inferior to frozen section.

CONCLUSIONS

This study provides further evidence for the non-inferiority of SRH techniques. It is also the first study to demonstrate SRH accuracy using one-to-one tissue analysis in neuropathological specimens.

Spatiotemporal analysis of glioma heterogeneity reveals COL1A1 as an actionable target to disrupt tumor progression

Intra-tumoral heterogeneity is a hallmark of glioblastoma that challenges treatment efficacy. However, the mechanisms that set up tumor heterogeneity and tumor cell migration remain poorly understood. Herein, we present a comprehensive spatiotemporal study that aligns distinctive intra-tumoral histopathological structures, oncostreams, with dynamic properties and a specific, actionable, spatial transcriptomic signature. Oncostreams are dynamic multicellular fascicles of spindle-like and aligned cells with mesenchymal properties, detected using ex vivo explants and in vivo intravital imaging. Their density correlates with tumor aggressiveness in genetically engineered mouse glioma models, and high grade human gliomas. Oncostreams facilitate the intra-tumoral distribution of tumoral and non-tumoral cells, and potentially the collective invasion of the normal brain. These fascicles are defined by a specific molecular signature that regulates their organization and function. Oncostreams structure and function depend on overexpression of COL1A1. Col1a1 is a central gene in the dynamic organization of glioma mesenchymal transformation, and a powerful regulator of glioma malignant behavior. Inhibition of Col1a1 eliminates oncostreams, reprograms the malignant histopathological phenotype, reduces expression of the mesenchymal associated genes, induces changes in the tumor microenvironment and prolongs animal survival. Oncostreams represent a pathological marker of potential value for diagnosis, prognosis, and treatment.

It is essential to improve our understanding of the features that influence aggressiveness and invasion in high grade gliomas (HGG). Here, the authors characterize dynamic anatomical structures in HGG called oncostreams, which are associated with tumor growth and are regulated by COL1A1.

Neurosurgical Clinical Trials for Glioblastoma: Current and Future Directions

The mainstays of glioblastoma treatment, maximal safe resection, radiotherapy preserving neurological function, and temozolomide (TMZ) chemotherapy have not changed for the past 17 years despite significant advances in the understanding of the genetics and molecular biology of glioblastoma. This review highlights the neurosurgical foundation for glioblastoma therapy. Here, we review the neurosurgeon’s role in several new and clinically-approved treatments for glioblastoma. We describe delivery techniques such as blood–brain barrier disruption and convection-enhanced delivery (CED) that may be used to deliver therapeutic agents to tumor tissue in higher concentrations than oral or intravenous delivery. We mention pivotal clinical trials of immunotherapy for glioblastoma and explain their outcomes. Finally, we take a glimpse at ongoing clinical trials and promising translational studies to predict ways that new therapies may improve the prognosis of patients with glioblastoma.

Prospective intraoperative and histologic evaluation of cavernous sinus medial wall invasion by pituitary adenomas and its implications for acromegaly remission outcomes

Recurrence and biochemical remission rates vary widely among different histological subtypes of pituitary adenoma. In this prospective study, we evaluated 107 consecutive primary pituitary adenomas operated on by a single neurosurgeon including 28 corticotroph, 27 gonadotroph, 24 somatotroph, 17 lactotroph, 5 null-cell and 6 plurihormonal. In each case, we performed direct endoscopic intraoperative inspection of the medial wall of the cavernous sinus, which was surgically removed when invasion was visualized. This was performed irrespective of tumor functional status. Medial wall resection was performed in 47% of pituitary adenomas, and 39/50 walls confirmed pathologic evidence of invasion, rendering a positive predictive value of intraoperative evaluation of medial wall invasion of 78%. We show for the first-time dramatic disparities in the frequency of medial wall invasion among pathological subtypes. Somatotroph tumors invaded the medial wall much more often than other adenoma subtypes, 81% intraoperatively and 69% histologically, followed by plurihormonal tumors (40%) and gonadotroph cell tumors (33%), both with intraoperative positive predictive value of 100%. The least likely to invade were corticotroph adenomas, at a rate of 32% intraoperatively and 21% histologically, and null-cell adenomas at 0%. Removal of the cavernous sinus medial wall was not associated with permanent cranial nerve morbidity nor carotid artery injury, although 4 patients (all Knosp 3-4) experienced transient diplopia. Medial wall resection in acromegaly resulted in the highest potential for biochemical remission ever reported, with an average postoperative day 1 GH levels of 0.96 ug/L and surgical remission rates of 92% based on normalization of IGF-1 levels after surgery (mean = 15.56 months; range 3-30 months). Our findings suggest that tumor invasion of the medial wall of the cavernous sinus may explain the relatively low biochemical remission rates currently seen for acromegaly and illustrate the relevance of advanced intradural surgical approaches for successful and durable outcomes in endonasal pituitary surgery for functional adenomas.

Stimulated Raman scattering microscopy in chemistry and life science - Development, innovation, perspectives

In this review, we present a summary of the basics of the Stimulated Raman Scattering (SRS) phenomenon, methods of detecting the signal, and collection of the SRS images. We demonstrate the advantages of SRS imaging, and recent developments, but also the limitations, especially in image capture speeds and spatial resolution. We also compare the use of SRS microscopy in biological system studies with other techniques such as fluorescence microscopy, second-harmonic generation (SHG)-based microscopy, coherent anti-Stokes Raman scattering (CARS), and spontaneous Raman, and we show the compatibility of SRS-based systems with other discussed methods. The review is also focused on indicating innovations in SRS microscopy, on the background of which we present the layout and performance of our homemade setup built from commercially available elements enabling for imaging of the molecular structure of single cells over the spectral range of 800-3600 cm^-1. Methods of image analysis are discussed, including machine learning methods for obtaining images of the distribution of selected molecules and for the detection of pathological lesions in tissues or malignant cells in the context of clinical diagnosis of a wide range of diseases with the use of SRS microscopy. Finally, perspectives for the development of SRS microscopy are proposed.

Modeling and simulation of diffusion and reaction processes during the staining of tissue sections on slides

Histological slides are an important tool in the diagnosis of tumors as well as of other diseases that affect cell shapes and distributions. Until now, the research concerning an optimal staining time has been mainly done empirically. In experimental investigations, it is often not possible to stain an already-stained slide with another stain to receive further information. To overcome these challenges, in the present paper a continuum-based model was developed for conducting a virtual (re-)staining of a scanned histological slide. This model is capable of simulating the staining of cell nuclei with the dye hematoxylin (C.I. 75,290). The transport and binding of the dye are modeled (i) along with the resulting RGB intensities (ii). For (i), a coupled diffusion–reaction equation is used and for (ii) Beer–Lambert’s law. For the spatial discretization an approach based on the finite element method (FEM) is used and for the time discretization a finite difference method (FDM). For the validation of the proposed model, frozen sections from human liver biopsies stained with hemalum were used. The staining times were varied so that the development of the staining intensity could be observed over time. The results show that the model is capable of predicting the staining process. The model can therefore be used to perform a virtual (re-)staining of a histological sample. This allows a change of the staining parameters without the need of acquiring an additional sample. The virtual standardization of the staining is the first step towards universal cross-site comparability of histological slides.

Rapid Automated Analysis of Skull Base Tumor Specimens Using Intraoperative Optical Imaging and Artificial Intelligence

BACKGROUND

Accurate specimen analysis of skull base tumors is essential for providing personalized surgical treatment strategies. Intraoperative specimen interpretation can be challenging because of the wide range of skull base pathologies and lack of intraoperative pathology resources.

OBJECTIVE

To develop an independent and parallel intraoperative workflow that can provide rapid and accurate skull base tumor specimen analysis using label-free optical imaging and artificial intelligence.

METHODS

We used a fiber laser-based, label-free, nonconsumptive, high-resolution microscopy method (<60 seconds per 1 × 1 mm2), called stimulated Raman histology (SRH), to image a consecutive, multicenter cohort of patients with skull base tumor. SRH images were then used to train a convolutional neural network model using 3 representation learning strategies: cross-entropy, self-supervised contrastive learning, and supervised contrastive learning. Our trained convolutional neural network models were tested on a held-out, multicenter SRH data set.

RESULTS

SRH was able to image the diagnostic features of both benign and malignant skull base tumors. Of the 3 representation learning strategies, supervised contrastive learning most effectively learned the distinctive and diagnostic SRH image features for each of the skull base tumor types. In our multicenter testing set, cross-entropy achieved an overall diagnostic accuracy of 91.5%, self-supervised contrastive learning 83.9%, and supervised contrastive learning 96.6%. Our trained model was able to segment tumor-normal margins and detect regions of microscopic tumor infiltration in meningioma SRH images.

CONCLUSION

SRH with trained artificial intelligence models can provide rapid and accurate intraoperative analysis of skull base tumor specimens to inform surgical decision-making.