Research data sets are growing to unprecedented sizes and network modeling is commonly used to extract complex relationships in diverse domains, such as genetic interactions involved in disease, logistics, and social communities. As the number of nodes increases in a network, an increasing sparsity of edges is a practical limitation due to memory restrictions. Moreover, many of these sparse networks exhibit very large numbers of nodes with no adjacent edges, as well as disjoint components of nodes with no edges connecting them. A prevalent aim in network modeling is the identification of clusters, or communities, of nodes that are highly interrelated. Several definitions of strong community structure have been introduced to facilitate this task, each with inherent assumptions and biases. We introduce an intuitive objective function for quantifying the quality of clustering results in large sparse networks. We utilize a two-step method for identifying communities which is especially well-suited for this domain as the first step efficiently divides the network into the disjoint components, while the second step optimizes clustering of the produced components based on the new objective. Using simulated networks, optimization based on the new objective function consistently yields significantly higher accuracy than those based on the modularity function, with the widest gaps appearing for the noisiest networks. Additionally, applications to benchmark problems illustrate the intuitive correctness of our approach. Finally, the practicality of our approach is demonstrated in real-world data in which we identify complex genetic interactions in large-scale networks comprised of tens of thousands of nodes. Based on these three different types of trials, our results clearly demonstrate the usefulness of our two-step procedure and the accuracy of our simple objective.

Efforts to utilize growing volumes of clinical imaging data to generate tumor evaluations continue to require significant manual data wrangling owing to the data heterogeneity. Here, we propose an artificial intelligence-based solution for the aggregation and processing of multisequence neuro-oncology MRI data to extract quantitative tumor measurements. Our end-to-end framework i) classifies MRI sequences using an ensemble classifier, ii) preprocesses the data in a reproducible manner, iii) delineates tumor tissue subtypes using convolutional neural networks, and iv) extracts diverse radiomic features. Moreover, it is robust to missing sequences and adopts an expert-in-the-loop approach, where the segmentation results may be manually refined by radiologists. Following the implementation of the framework in Docker containers, it was applied to two retrospective glioma datasets collected from the Washington University School of Medicine (WUSM; n = 384) and the M.D. Anderson Cancer Center (MDA; n = 30) comprising preoperative MRI scans from patients with pathologically confirmed gliomas. The scan-type classifier yielded an accuracy of over 99%, correctly identifying sequences from 380/384 and 30/30 sessions from the WUSM and MDA datasets, respectively. Segmentation performance was quantified using the Dice Similarity Coefficient between the predicted and expert-refined tumor masks. Mean Dice scores were 0.882 ($\pm$0.244) and 0.977 ($\pm$0.04) for whole tumor segmentation for WUSM and MDA, respectively. This streamlined framework automatically curated, processed, and segmented raw MRI data of patients with varying grades of gliomas, enabling the curation of large-scale neuro-oncology datasets and demonstrating a high potential for integration as an assistive tool in clinical practice.

Researchers at the Washington University School of Medicine use artificial intelligence (AI) deep learning to classify common brain tumors with a high degree of accuracy using a single magnetic resonance imaging (MRI) scan. The new peer-reviewed study has been accepted for publication in Radiology: Artificial Intelligence. "To the best of our knowledge, this is the first study to address the most common intracranial tumor-types and directly determine the tumor class as well as detect the absence of tumor from a 3D MR volume," wrote the researchers. Last year there were over 308,000 new cases of brain and nervous systems cancer, and more than 250,000 deaths worldwide according to the Global Cancer Statistics (GLOBOCAN) 2020 report. In the United Kingdom, over 11,000 people are diagnosed with a primary brain tumor annually, according to the National Health Service (NHS).

Figure shows coarse attention maps generated using GradCAM for correctly classified high-grade glioma (HGG), low-grade glioma (LGG), brain metastases (METS), meningioma (MEN), acoustic neuroma (AN), and pituitary adenoma (PA). For each pair, the postcontrast T1-weighted scan, and the GradCAM attention map (overlaid on scan) have been shown. In GradCAM maps, warmer and colder colors represent high and low contribution of pixels toward a correct prediction, respectively. A team of researchers at Washington University School of Medicine have developed a deep learning model that is capable of classifying a brain tumor as one of six common types using a single 3D MRI scan, according to a study published in Radiology: Artificial Intelligence. "This is the first study to address the most common intracranial tumors and to directly determine the tumor class or the absence of tumor from a 3D MRI volume," said Satrajit Chakrabarty, M.S., a doctoral student under the direction of Aristeidis Sotiras, Ph.D., and Daniel Marcus, Ph.D., in Mallinckrodt Institute of Radiology's Computational Imaging Lab at Washington University School of Medicine in St. Louis, Missouri.

Washington [US], August 14 (ANI): Researchers have developed a deep learning model that is capable of classifying a brain tumour as one of six common types, using a single 3D MRI scan, during a new study. The study by researchers from the Washington University School of Medicine has been published in Radiology: Artificial Intelligence. "This is the first study to address the most common intracranial tumours and to directly determine the tumour class or the absence of tumour from a 3D MRI volume," said Satrajit Chakrabarty, M.S., a doctoral student under the direction of Aristeidis Sotiras, PhD, and Daniel Marcus, PhD, in Mallinckrodt Institute of Radiology's Computational Imaging Lab at Washington University School of Medicine in St. Louis, Missouri. The six most common intracranial tumour types are high-grade glioma, low-grade glioma, brain metastases, meningioma, pituitary adenoma and acoustic neuroma. Each was documented through histopathology, which requires surgically removing tissue from the site of suspected cancer and examining it under a microscope.

A novel device designed to help stroke patients recover wrist and hand function has been approved by the US Food and Drug Administration (FDA). Called IpsiHand, the system is the first brain-computer interface (BCI) device to ever receive FDA market approval. The IpsiHand device consists of two separate parts – a wireless exoskeleton that is positioned over the wrist, and a small headpiece that records brain activity using non-invasive electroencephalography (EEG) electrodes. The system is based on a discovery made by Eric Leuthardt and colleagues at the Washington University School of Medicine over a decade ago. It is well known that each side of the brain controls movement on the opposite side of the body, so if a stroke damages motor function on the right side of the brain movement on a person's left side will be affected.

Washington University researchers are working to develop artificial intelligence (AI) systems for health care, which have the potential to transform the diagnosis and treatment of diseases, helping to ensure that patients get the right treatment at the right time. In a new Viewpoint article published Dec. 10 in the Journal of the American Medical Association (JAMA), two AI experts at Washington University School of Medicine in St. Louis--Philip Payne, the Robert J. Terry Professor and director of the Institute for Informatics; and Thomas M. Maddox, MD, a professor of medicine and director of the Health Systems Innovation Lab--discuss the best uses for AI in health care and outline some of the challenges for implementing the technology in hospitals and clinics. In health care, artificial intelligence relies on the power of computers to sift through and make sense of reams of electronic data about patients--such as their ages, medical histories, health status, test results, medical images, DNA sequences, and many other sources of health information. AI excels at the complex identification of patterns in these reams of data, and it can do this at a scale and speed beyond human capacity. The hope is that this technology can be harnessed to help doctors and patients make better health-care decisions.

We also acknowledge L. Trani for performing manual review and for valuable discussion on the project. The authors also thank the patients and their families for their selfless contribution to the advancement of science. Part of this work was performed as part of the Washington University School of Medicine Genomics Tumor Board, which was funded with private research support from the Division of Oncology and the McDonnell Genome Institute. E.K.B. was supported by the National Cancer Institute (T32GM007200 and U01CA209936). T.E.R. received support from the National Institutes of Health/National Cancer Institute (NIH/NCI) (R01CA142942) and the Breast Cancer Research Foundation.

Every field of scientific study has its own holy grail. For particle physicists, it's the quest for physics beyond the Standard Model; for astrophysicists, it is the hunt for the elusive dark matter and dark energy; and for neuroscientists, it is explaining how inanimate matter becomes conscious. Obviously, the first step in understanding the mysteries of consciousness would be to map the tool through which it manifests itself -- the brain. On Wednesday, a team of neuroscientists released an extremely detailed map of terra incognita of the human brain -- the cerebral cortex. The cerebral cortex is the brain's outermost layer and is responsible for sensory perception, language, attention, tool use and abstract thinking.