Molecular pathways and cellular subsets associated with adverse clinical outcomes in overlapping immune-related myocarditis and myositis.

Immune checkpoint therapies (ICTs) can induce life-threatening immune-related adverse events, including myocarditis and myositis, which are rare but often concurrent. The molecular pathways and immune subsets underlying these toxicities remain poorly understood. To address this need, we obtained heart and skeletal muscle biopsies for single-cell RNA sequencing in living patients with cancers treated with ICTs admitted to the hospital with myocarditis and/or myositis (overlapping myocarditis plus myositis, n=10; myocarditis-only, n=1) compared to ICT-exposed patients ruled out for toxicity utilized as controls (n=9) within 96 hours of clinical presentation. Analyses of 58,523 cells revealed CD8+ T cells with a cytotoxic phenotype expressing activation/exhaustion markers in both myocarditis and myositis. Furthermore, the analyses identified a population of myeloid cells expressing tissue-resident signatures and FcγRIIIa (CD16a), which is known to bind IgG and regulate complement activation. Immunohistochemistry of affected cardiac and skeletal muscle tissues revealed protein expression of pan-IgG and complement product C4d that were associated with the presence of high-titer serum autoantibodies against muscle antigens in a subset of patients. We further identified a population of inflammatory IL-1B+TNF+ myeloid cells specifically enriched in myocarditis and associated with greater toxicity severity and poorer clinical outcomes. These results are the first to recognize these myeloid subsets in human immune-related myocarditis and myositis tissues and nominate new targets for investigation into rational treatments to overcome these high-mortality toxicities.

A 3D Sparse Autoencoder for Fully Automated Quality Control of Affine Registrations in Big Data Brain MRI Studies.

This paper presents a fully automated pipeline using a sparse convolutional autoencoder for quality control (QC) of affine registrations in large-scale T1-weighted (T1w) and T2-weighted (T2w) magnetic resonance imaging (MRI) studies. Here, a customized 3D convolutional encoder-decoder (autoencoder) framework is proposed and the network is trained in a fully unsupervised manner. For cross-validating the proposed model, we used 1000 correctly aligned MRI images of the human connectome project young adult (HCP-YA) dataset. We proposed that the quality of the registration is proportional to the reconstruction error of the autoencoder. Further, to make this method applicable to unseen datasets, we have proposed dataset-specific optimal threshold calculation (using the reconstruction error) from ROC analysis that requires a subset of the correctly aligned and artificially generated misalignments specific to that dataset. The calculated optimum threshold is used for testing the quality of remaining affine registrations from the corresponding datasets. The proposed framework was tested on four unseen datasets from autism brain imaging data exchange (ABIDE I, 215 subjects), information eXtraction from images (IXI, 577 subjects), Open Access Series of Imaging Studies (OASIS4, 646 subjects), and "Food and Brain" study (77 subjects). The framework has achieved excellent performance for T1w and T2w affine registrations with an accuracy of 100% for HCP-YA. Further, we evaluated the generality of the model on four unseen datasets and obtained accuracies of 81.81% for ABIDE I (only T1w), 93.45% (T1w) and 81.75% (T2w) for OASIS4, and 92.59% for "Food and Brain" study (only T1w) and in the range 88-97% for IXI (for both T1w and T2w and stratified concerning scanner vendor and magnetic field strengths). Moreover, the real failures from "Food and Brain" and OASIS4 datasets were detected with sensitivities of 100% and 80% for T1w and T2w, respectively. In addition, AUCs of > 0.88 in all scenarios were obtained during threshold calculation on the four test sets.

Intraoperative language mapping guided by real-time visualization of gamma band modulation electrocorticograms: Case report and proof of concept.

Background: Electrocorticography (ECoG) language mapping is often performed extraoperatively, frequently involves offline processing, and relationships with direct cortical stimulation (DCS) remain variable. We sought to determine the feasibility and preliminary utility of an intraoperative language mapping approach guided by real-time visualization of electrocorticograms. Methods: A patient with astrocytoma underwent awake craniotomy with intraoperative language mapping, utilizing a dual iPad stimulus presentation system coupled to a real-time neural signal processing platform capable of both ECoG recording and delivery of DCS. Gamma band modulations in response to 4 language tasks at each electrode were visualized in real-time. Next, DCS was conducted for each neighboring electrode pair during language tasks. Results: All language tasks resulted in strongest heat map activation at an electrode pair in the anterior to mid superior temporal gyrus. Consistent speech arrest during DCS was observed for Object and Action naming tasks at these same electrodes, indicating good correspondence with ECoG heat map recordings. This region corresponded well with posterior language representation via preoperative functional MRI. Conclusions: Intraoperative real-time visualization of language task-based ECoG gamma band modulation is feasible and may help identify targets for DCS. If validated, this may improve the efficiency and accuracy of intraoperative language mapping.

Decoding the clinical effects of low-grade glioma-induced cortical excitability.

OBJECTIVE: Patients with low-grade glioma (LGG) in eloquent regions often present with seizures, and findings on detailed neuropsychological testing are often abnormal. This study evaluated the association between cortical excitability, seizures, and cognitive function in patients with LGG. METHODS: LGG patients who underwent transcranial magnetic stimulation (TMS) from January 2021 to December 2022 were studied. Cortical excitability was measured using the resting motor thresholds (RMTs) of the upper and lower extremities. Early postoperative seizures served as the seizure endpoint. Neuropsychological assessment was completed prior to surgery contemporaneous with the TMS studies. RESULTS: A total of 31 patients were analyzed for seizure outcome. Median (interquartile range [IQR]) upper-extremity RMT was 39% (34%-46%) of maximum stimulator output, and the median (IQR) lower-extremity RMT was 69% (51%-79%). Lower-extremity RMT was higher in patients with early postoperative seizures, especially in those with motor region tumors (p = 0.02); however, RMT was not associated with seizures at presentation or long-term seizure control. A total of 26 patients completed neuropsychological assessment. There were significant negative correlations between upper-extremity RMT and psychomotor processing speed (Wechsler Adult Intelligence Scale-Fourth Edition [WAIS-IV] Processing Speed Index r = -0.42, p = 0.031; WAIS-IV Coding r = -0.41, p = 0.036; WAIS-IV Symbol Search r = -0.39, p = 0.048), executive function (Trail Making Test Part B r = -0.41, p = 0.036), and hand dexterity (Grooved Pegboard Test r = -0.50, p = 0.047). CONCLUSIONS: RMT was positively correlated with early postoperative seizure risk and negatively correlated with psychomotor processing speed, executive function, and hand dexterity. These findings support the theory of local and regional resting oscillatory network dysfunction from a glioma-brain network.

Management of respiratory failure in immune checkpoint inhibitors-induced overlap syndrome: a case series and review of the literature.

BACKGROUND: Checkpoint inhibitor-induced overlap syndrome ([OS] myocarditis, and myositis with or without myasthenia gravis) is rare but life-threatening. CASES PRESENTATION: Here we present a case series of four cancer patients that developed OS. High troponinemia raised the concern for myocarditis in all the cases. However, the predominant clinical feature differed among the cases. Two patients showed marked myocarditis with a shorter hospital stay. The other two patients had a prolonged ICU stay due to severe neuromuscular involvement secondary to myositis and myasthenia gravis. Treatment was based on steroids, plasmapheresis, intravenous immunoglobulin, and immunosuppressive biological agents. CONCLUSION: The management of respiratory failure is challenging, particularly in those patients with predominant MG. Along with intensive clinical monitoring, bedside respiratory mechanics can guide the decision-making process of selecting a respiratory support method, the timing of elective intubation and extubation.

An Explainable Classification Method Based on Complex Scaling in Histopathology Images for Lung and Colon Cancer.

Lung and colon cancers are among the leading causes of human mortality and morbidity. Early diagnostic work up of these diseases include radiography, ultrasound, magnetic resonance imaging, and computed tomography. Certain blood tumor markers for carcinoma lung and colon also aid in the diagnosis. Despite the lab and diagnostic imaging, histopathology remains the gold standard, which provides cell-level images of tissue under examination. To read these images, a histopathologist spends a large amount of time. Furthermore, using conventional diagnostic methods involve high-end equipment as well. This leads to limited number of patients getting final diagnosis and early treatment. In addition, there are chances of inter-observer errors. In recent years, deep learning has shown promising results in the medical field. This has helped in early diagnosis and treatment according to severity of disease. With the help of EffcientNetV2 models that have been cross-validated and tested fivefold, we propose an automated method for detecting lung (lung adenocarcinoma, lung benign, and lung squamous cell carcinoma) and colon (colon adenocarcinoma and colon benign) cancer subtypes from LC25000 histopathology images. A state-of-the-art deep learning architecture based on the principles of compound scaling and progressive learning, EffcientNetV2 large, medium, and small models. An accuracy of 99.97%, AUC of 99.99%, F1-score of 99.97%, balanced accuracy of 99.97%, and Matthew's correlation coefficient of 99.96% were obtained on the test set using the EffcientNetV2-L model for the 5-class classification of lung and colon cancers, outperforming the existing methods. Using gradCAM, we created visual saliency maps to precisely locate the vital regions in the histopathology images from the test set where the models put more attention during cancer subtype predictions. This visual saliency maps may potentially assist pathologists to design better treatment strategies. Therefore, it is possible to use the proposed pipeline in clinical settings for fully automated lung and colon cancer detection from histopathology images with explainability.

Concurrent intrathecal and intravenous nivolumab in leptomeningeal disease: phase 1 trial interim results.

There is a critical need for effective treatments for leptomeningeal disease (LMD). Here, we report the interim analysis results of an ongoing single-arm, first-in-human phase 1/1b study of concurrent intrathecal (IT) and intravenous (IV) nivolumab in patients with melanoma and LMD. The primary endpoints are determination of safety and the recommended IT nivolumab dose. The secondary endpoint is overall survival (OS). Patients are treated with IT nivolumab alone in cycle 1 and IV nivolumab is included in subsequent cycles. We treated 25 patients with metastatic melanoma using 5, 10, 20 and 50 mg of IT nivolumab. There were no dose-limiting toxicities at any dose level. The recommended IT dose of nivolumab is 50 mg (with IV nivolumab 240 mg) every 2 weeks. Median OS was 4.9 months, with 44% and 26% OS rates at 26 and 52 weeks, respectively. These initial results suggest that concurrent IT and IV nivolumab is safe and feasible with potential efficacy in patients with melanoma LMD, including in patients who had previously received anti-PD1 therapy. Accrual to the study continues, including in patients with lung cancer. ClinicalTrials.gov registration: NCT03025256 .

Few-shot learning using explainable Siamese twin network for the automated classification of blood cells.

Automated classification of blood cells from microscopic images is an interesting research area owing to advancements of efficient neural network models. The existing deep learning methods rely on large data for network training and generating such large data could be time-consuming. Further, explainability is required via class activation mapping for better understanding of the model predictions. Therefore, we developed a Siamese twin network (STN) model based on contrastive learning that trains on relatively few images for the classification of healthy peripheral blood cells using EfficientNet-B3 as the base model. Hence, in this study, a total of 17,092 publicly accessible cell histology images were analyzed from which 6% were used for STN training, 6% for few-shot validation, and the rest 88% for few-shot testing. The proposed architecture demonstrates percent accuracies of 97.00, 98.78, 94.59, 95.70, 98.86, 97.09, 99.71, and 96.30 during 8-way 5-shot testing for the classification of basophils, eosinophils, immature granulocytes, erythroblasts, lymphocytes, monocytes, platelets, and neutrophils, respectively. Further, we propose a novel class activation mapping scheme that highlights the important regions in the test image for the STN model interpretability. Overall, the proposed framework could be used for a fully automated self-exploratory classification of healthy peripheral blood cells. The whole proposed framework demonstrates the Siamese twin network training and 8-way k-shot testing. The values indicate the amount of dissimilarity.

EfficientNetV2 Based Ensemble Model for Quality Estimation of Diabetic Retinopathy Images from DeepDRiD.

Diabetic retinopathy (DR) is one of the major complications caused by diabetes and is usually identified from retinal fundus images. Screening of DR from digital fundus images could be time-consuming and error-prone for ophthalmologists. For efficient DR screening, good quality of the fundus image is essential and thereby reduces diagnostic errors. Hence, in this work, an automated method for quality estimation (QE) of digital fundus images using an ensemble of recent state-of-the-art EfficientNetV2 deep neural network models is proposed. The ensemble method was cross-validated and tested on one of the largest openly available datasets, the Deep Diabetic Retinopathy Image Dataset (DeepDRiD). We obtained a test accuracy of 75% for the QE, outperforming the existing methods on the DeepDRiD. Hence, the proposed ensemble method may be a potential tool for automated QE of fundus images and could be handy to ophthalmologists.

Ulnar fascicle to brachialis branch of musculocutaneous nerve for restoration of elbow flexion associated with spinal cord tumor and radiation-induced lower motor neuron disease.

Transfer of the ulnar fascicle to the biceps branch of the musculocutaneous nerve, or Oberlin transfer, has been widely used for the treatment of elbow flexion weakness in the setting of upper trunk brachial plexus palsy. The authors present a modified application of this technique for restoration of functional elbow flexion in a 30-year-old woman with a history of recurrent upper cervical spinal cord pilocytic astrocytoma, complex spinal deformity, and radiation-induced lower motor neuron disease. The video can be found here: https://stream.cadmore.media/r10.3171/2022.10.FOCVID2299.

Comparative study of preoperative functional imaging combined with tractography for prediction of iatrogenic motor deficits.

OBJECTIVE: Robust preoperative imaging can improve the extent of resection in patients with brain tumors while minimizing postoperative neurological morbidity. Both structural and functional imaging techniques can provide helpful preoperative information. A recent study found that transcranial magnetic stimulation (TMS) tractography has significant predictive value for permanent deficits. The present study directly compares the predictive value of TMS tractography and task-based functional MRI (fMRI) tractography in the same cohort of glioma patients. METHODS: Clinical outcome data were collected from charts of patients with motor eloquent glioma and preoperative fMRI and TMS studies. The primary outcome was a new or worsened motor deficit present at the 3-month postoperative follow-up, which was termed a "permanent deficit." Postoperative MR images were overlaid onto preoperative plans to determine which imaging features were resected. Multiple fractional anisotropic thresholds (FATs) were screened for both TMS and fMRI tractography. The predictive value of the various thresholds was modeled using receiver operating characteristic curve analysis. RESULTS: Forty patients were included in this study. Six patients (15%) sustained permanent postoperative motor deficits. A significantly greater predictive value was found for TMS tractography than for fMRI tractography regardless of the FAT. Despite 35% of patients showing clinically relevant neuroplasticity captured by TMS, only 2.5% of patients showed a blood oxygen level-dependent signal displaced from the precentral gyrus. Comparing the best-performing FAT for both modalities, TMS seeded tractography showed superior predictive value across all metrics: sensitivity, specificity, positive predictive value, and negative predictive value. CONCLUSIONS: The results from this study indicate that the prediction of permanent deficits with TMS tractography is superior to that with fMRI tractography, possibly because TMS tractography captures clinically relevant neuroplasticity. However, future large-scale prospective studies are needed to fully illuminate the proper role of each modality in comprehensive presurgical workups for patients with motor-eloquent tumors.

Long-latency gamma modulation after median nerve stimulation delineates the central sulcus and contrasts the states of consciousness.

OBJECTIVE: To evaluate the functional use of sub-band modulations in somatosensory evoked potentials (SSEPs) to discriminate between the primary somatosensory (S1) and motor (M1) areas and contrast the states of consciousness. METHODS: During routine intraoperative cortical mapping, SSEPs were recorded with electrocorticography (ECoG) grids from the sensorimotor cortex of eight patients in the anesthetized and awake states. We conducted a time-frequency analysis on the SSEP trace to extract the spectral modulations in each state and visualize their spatial topography. RESULTS: We observed late gamma modulation (60-250 Hz) in all subjects approximately 50 ms after stimulation onset, extending up to 250 ms in each state. The late gamma activity enhancement was predominant in S1 in the awake state, where it discriminated S1 from M1 at a higher accuracy (92 %) than in the anesthetized state (accuracy = 70 %). CONCLUSIONS: These results showed that sensorimotor mapping does not need to rely on only SSEP phase reversal. The long latency gamma modulation can serve as a biomarker for primary sensorimotor localization and monitor the level of consciousness in neurosurgical practice. SIGNIFICANCE: While the intraoperative assessment of SSEP phase reversal with ECoG is widely employed to delineate the central sulcus, the median nerve stimulation-induced spatio-spectral patterns can distinctly localize it and distinguish between conscious states.

Classification of Brain Tumor from Magnetic Resonance Imaging Using Vision Transformers Ensembling.

The automated classification of brain tumors plays an important role in supporting radiologists in decision making. Recently, vision transformer (ViT)-based deep neural network architectures have gained attention in the computer vision research domain owing to the tremendous success of transformer models in natural language processing. Hence, in this study, the ability of an ensemble of standard ViT models for the diagnosis of brain tumors from T1-weighted (T1w) magnetic resonance imaging (MRI) is investigated. Pretrained and finetuned ViT models (B/16, B/32, L/16, and L/32) on ImageNet were adopted for the classification task. A brain tumor dataset from figshare, consisting of 3064 T1w contrast-enhanced (CE) MRI slices with meningiomas, gliomas, and pituitary tumors, was used for the cross-validation and testing of the ensemble ViT model's ability to perform a three-class classification task. The best individual model was L/32, with an overall test accuracy of 98.2% at 384 × 384 resolution. The ensemble of all four ViT models demonstrated an overall testing accuracy of 98.7% at the same resolution, outperforming individual model's ability at both resolutions and their ensembling at 224 × 224 resolution. In conclusion, an ensemble of ViT models could be deployed for the computer-aided diagnosis of brain tumors based on T1w CE MRI, leading to radiologist relief.

Transcranial magnetic stimulation tractography and the facilitation of gross total resection in a patient with a motor eloquent glioblastoma: illustrative case.

BACKGROUND: In patients with perieloquent tumors, neurosurgeons must use a variety of techniques to maximize survival while minimizing postoperative neurological morbidity. Recent publications have shown that conventional anatomical features may not always predict postoperative deficits. Additionally, scientific conceptualizations of complex brain function have shifted toward more dynamic, neuroplastic theories instead of traditional static, localizationist models. Functional imaging techniques have emerged as potential tools to incorporate these advances into modern neurosurgical care. In this case report, we describe our observations using preoperative transcranial magnetic stimulation data combined with tractography to guide a nontraditional surgical approach in a patient with a motor eloquent glioblastoma. OBSERVATIONS: The authors detail the use of preoperative functional and structural imaging to perform a gross total resection despite tumor infiltration of conventionally eloquent anatomical structures. The authors resected the precentral gyrus, specifically the paracentral lobule, localized using intraoperative mapping techniques. The patient demonstrated mild transient postoperative weakness and made a full neurological recovery by discharge 1 week later. LESSONS: Preoperative functional and structural imaging has potential to not only optimize patient selection and surgical planning, but also facilitate important intraoperative decisions. Innovative preoperative imaging techniques should be optimized and used to identify safely resectable structures.

Real-Time Delineation of the Central Sulcus with the Spatial Profile of SSEPs Captured with High-Density Ecog Grid.

Cortical mapping is widely employed to define the sensorimotor area and delineate the central sulcus (CS) during awake craniotomies. The approach involves the gold standard somatosensory evoked potentials (SSEPs) recorded with electrocorticogram (ECoG) strip electrodes. However, the evoked response can be misconstrued from the manual peak interpretation due to the poor spatial resolution of the strip electrode or when the electrode does not precisely cover the desired cortical area. This can lead to unintentional damage to the eloquent cortex. We present a soft real-time computer based visualization system that uses recorded SSEPs with a subdural grid to aid in cortical mapping. The neural data during electrical stimulation of the median nerve at 0.6Hz are picked up with a bio-amplifier at 2.4kHz. The stimulation artifact recorded from the bipolar electromyogram (EMG) is used as the stimulation onset. The ECoG data are assessed online with MATLAB Simulink to process and visualize the SSEPs waveform. The visualization system is programmed to display the SSEPs peak activation as a heat map on a 2D grid and projected onto a screen, showcasing the nature of the cortical activities over the contact surface area. Since the grid occupies a large cortical surface, the heatmap is able to delineate the central sulcus. The map can be viewed at any time point along the SSEP trace without the need for peak interpretation. With the goal to provide additional information during cortical mapping and facilitate interpretation of ECoG grid data, we believe that this visualization system will aid in rapid definition of the sensorimotor area during surgical planning. Clinical Relevance- This real-time visualization system can be used to delineate the central sulcus in a short time during awake craniotomies.

Transcranial magnetic stimulation (TMS) seeded tractography provides superior prediction of eloquence compared to anatomic seeded tractography.

Background: For patients with brain tumors, maximizing the extent of resection while minimizing postoperative neurological morbidity requires accurate preoperative identification of eloquent structures. Recent studies have provided evidence that anatomy may not always predict eloquence. In this study, we directly compare transcranial magnetic stimulation (TMS) data combined with tractography to traditional anatomic grading criteria for predicting permanent deficits in patients with motor eloquent gliomas. Methods: We selected a cohort of 42 glioma patients with perirolandic tumors who underwent preoperative TMS mapping with subsequent resection and intraoperative mapping. We collected clinical outcome data from their chart with the primary outcome being new or worsened motor deficit present at 3 month follow up, termed "permanent deficit". We overlayed the postoperative resection cavity onto the preoperative MRI containing preoperative imaging features. Results: Almost half of the patients showed TMS positive points significantly displaced from the precentral gyrus, indicating tumor induced neuroplasticity. In multivariate regression, resection of TMS points was significantly predictive of permanent deficits while the resection of the precentral gyrus was not. TMS tractography showed significantly greater predictive value for permanent deficits compared to anatomic tractography, regardless of the fractional anisotropic (FA) threshold. For the best performing FA threshold of each modality, TMS tractography provided both higher positive and negative predictive value for identifying true nonresectable, eloquent cortical and subcortical structures. Conclusion: TMS has emerged as a preoperative mapping modality capable of capturing tumor induced plastic reorganization, challenging traditional presurgical imaging modalities.

Validation of Non-invasive Language Mapping Modalities for Eloquent Tumor Resection: A Pilot Study.

Many studies have established a link between extent of resection and survival in patients with gliomas. Surgeons must optimize the oncofunctional balance by maximizing the extent of resection and minimizing postoperative neurological morbidity. Preoperative functional imaging modalities are important tools for optimizing the oncofunctional balance. Transcranial magnetic stimulation (TMS) and functional magnetic resonance imaging (fMRI) are non-invasive imaging modalities that can be used for preoperative functional language mapping. Scarce data exist evaluating the accuracy of these preoperative modalities for language mapping compared with gold standard intraoperative data in the same cohort. This study compares the accuracy of fMRI and TMS for language mapping compared with intraoperative direct cortical stimulation (DCS). We also identified significant predictors of preoperative functional imaging accuracy, as well as significant predictors of functional outcomes. Evidence from this study could inform clinical judgment as well as provide neuroscientific insight. We used geometric distances to determine copositivity between preoperative data and intraoperative data. Twenty-eight patients were included who underwent both preoperative fMRI and TMS procedures, as well as an awake craniotomy and intraoperative language mapping. We found that TMS shows significantly superior correlation to intraoperative DCS compared with fMRI. TMS also showed significantly higher sensitivity and negative predictive value than specificity and positive predictive value. Poor cognitive baseline was associated with decreased TMS accuracy as well as increased risk for worsened aphasia postoperatively. TMS has emerged as a promising preoperative language mapping tool. Future work should be done to identify the proper role of each imaging modality in a comprehensive, multimodal approach to optimize the oncofunctional balance.