massNet: integrated processing and classification of spatially resolved mass spectrometry data using deep learning for rapid tumor delineation.

MOTIVATION: Mass spectrometry imaging (MSI) provides rich biochemical information in a label-free manner and therefore holds promise to substantially impact current practice in disease diagnosis. However, the complex nature of MSI data poses computational challenges in its analysis. The complexity of the data arises from its large size, high-dimensionality and spectral nonlinearity. Preprocessing, including peak picking, has been used to reduce raw data complexity; however, peak picking is sensitive to parameter selection that, perhaps prematurely, shapes the downstream analysis for tissue classification and ensuing biological interpretation. RESULTS: We propose a deep learning model, massNet, that provides the desired qualities of scalability, nonlinearity and speed in MSI data analysis. This deep learning model was used, without prior preprocessing and peak picking, to classify MSI data from a mouse brain harboring a patient-derived tumor. The massNet architecture established automatically learning of predictive features, and automated methods were incorporated to identify peaks with potential for tumor delineation. The model's performance was assessed using cross-validation, and the results demonstrate higher accuracy and a substantial gain in speed compared to the established classical machine learning method, support vector machine. AVAILABILITY AND IMPLEMENTATION: https://github.com/wabdelmoula/massNet. The data underlying this article are available in the NIH Common Fund's National Metabolomics Data Repository (NMDR) Metabolomics Workbench under project id (PR001292) with http://dx.doi.org/10.21228/M8Q70T. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Neuroimage signature from salient keypoints is highly specific to individuals and shared by close relatives.

Neuroimaging studies typically adopt a common feature space for all data, which may obscure aspects of neuroanatomy only observable in subsets of a population, e.g. cortical folding patterns unique to individuals or shared by close relatives. Here, we propose to model individual variability using a distinctive keypoint signature: a set of unique, localized patterns, detected automatically in each image by a generic saliency operator. The similarity of an image pair is then quantified by the proportion of keypoints they share using a novel Jaccard-like measure of set overlap. Experiments demonstrate the keypoint method to be highly efficient and accurate, using a set of 7536 T1-weighted MRIs pooled from four public neuroimaging repositories, including twins, non-twin siblings, and 3334 unique subjects. All same-subject image pairs are identified by a similarity threshold despite confounds including aging and neurodegenerative disease progression. Outliers reveal previously unknown data labeling inconsistencies, demonstrating the usefulness of the keypoint signature as a computational tool for curating large neuroimage datasets.

Guidance for the Evaluation of Tuberculosis Diagnostics That Meet the World Health Organization (WHO) Target Product Profiles: An Introduction to WHO Process and Study Design Principles.

Existing high-priority target product profiles (TPPs) of the World Health Organization (WHO) establish important needs for tuberculosis (TB) diagnostic development. Building on this earlier work, this guidance series aims to provide study guidance for performing accuracy studies of novel diagnostic products that may meet the 4 high-priority WHO TPPs and thus enable adequate evidence generation to inform a WHO evidence review process. Diagnostic accuracy studies represent a fundamental step in the validation of all tests. Unfortunately, such studies often have limitations in design, execution, and reporting, leading to low certainty of the evidence about true test performance, which can delay or impede policy and scale-up decisions. This introductory paper outlines the following: (1) the purpose of this series of papers on study guidance; (2) WHO evidence needs and process for the development of policy guidelines for new TB diagnostic tests; and (3) study design considerations, ie, general diagnostic study considerations, intended use of test and role in the clinical pathway, choice of population and setting, index-test specific issues, suitable reference standard and comparators, study flow and specimen issues, and finally key issues beyond accuracy that should be considered. The other 4 papers in this series will provide more detailed guidance for each of the 4 WHO high-priority TPPs. By increasing the clarity around the clinical evaluation needs for tests that have the potential to meet the TPP specifications, we hope to support harmonized evidence generation and enable the WHO review process towards meeting the WHO End TB Strategy targets for reducing the incidence and mortality associated with TB.

Guidance for Studies Evaluating the Accuracy of Sputum-Based Tests to Diagnose Tuberculosis.

Tests that can replace sputum smear microscopy have been identified as a top priority diagnostic need for tuberculosis by the World Health Organization. High-quality evidence on diagnostic accuracy for tests that may meet this need is an essential requirement to inform decisions about policy and scale-up. However, test accuracy studies are often of low and inconsistent quality and poorly reported, leading to uncertainty about true test performance. Here we provide guidance for the design of diagnostic test accuracy studies of sputum smear-replacement tests. Such studies should have a cross-sectional or cohort design, enrolling either a consecutive series or a random sample of patients who require evaluation for tuberculosis. Adults with respiratory symptoms are the target population. The reference standard should at a minimum be a single, automated, liquid culture, but additional cultures, follow-up, clinical case definition, and specific measures to understand discordant results should also be included. Inclusion of smear microscopy and Xpert MTB/RIF (or MTB/RIF Ultra) as comparators is critical to allow broader comparability and generalizability of results, because disease spectrum can vary between studies and affects relative test performance. Given the complex nature of sputum (the primary specimen type used for pulmonary TB), careful design and reporting of the specimen flow is essential. Test characteristics other than accuracy (such as feasibility, implementation considerations, and data on impact on patient, population and health systems outcomes) are also important aspects.

Automatic 3D Nonlinear Registration of Mass Spectrometry Imaging and Magnetic Resonance Imaging Data.

Multimodal integration between mass spectrometry imaging (MSI) and radiology-established modalities such as magnetic resonance imaging (MRI) would allow the investigations of key questions in complex biological systems such as the central nervous system. Such integration would provide complementary multiscale data to bridge the gap between molecular and anatomical phenotypes, potentially revealing new insights into molecular mechanisms underlying anatomical pathologies presented on MRI. Automatic coregistration between 3D MSI/MRI is a computationally challenging process due to dimensional complexity, MSI data sparsity, lack of direct spatial-correspondences, and nonlinear tissue deformation. Here, we present a new computational approach based on stochastic neighbor embedding to nonlinearly align 3D MSI to MRI data, identify and reconstruct biologically relevant molecular patterns in 3D, and fuse the MSI datacube to the MRI space. We demonstrate our method using multimodal high-spectral resolution matrix-assisted laser desorption ionization (MALDI) 9.4 T MSI and 7 T in vivo MRI data, acquired from a patient-derived, xenograft mouse brain model of glioblastoma following administration of the EGFR inhibitor drug of Erlotinib. Results show the distribution of some identified molecular ions of the EGFR inhibitor erlotinib, a phosphatidylcholine lipid, and cholesterol, which were reconstructed in 3D and mapped to the MRI space. The registration quality was evaluated on two normal mouse brains using the Dice coefficient for the regions of brainstem, hippocampus, and cortex. The method is generic and can therefore be applied to hyperspectral images from different mass spectrometers and integrated with other established in vivo imaging modalities such as computed tomography (CT) and positron emission tomography (PET).

Estimating the reliability of eyewitness identifications from police lineups.

Laboratory-based mock crime studies have often been interpreted to mean that (i) eyewitness confidence in an identification made from a lineup is a weak indicator of accuracy and (ii) sequential lineups are diagnostically superior to traditional simultaneous lineups. Largely as a result, juries are increasingly encouraged to disregard eyewitness confidence, and up to 30% of law enforcement agencies in the United States have adopted the sequential procedure. We conducted a field study of actual eyewitnesses who were assigned to simultaneous or sequential photo lineups in the Houston Police Department over a 1-y period. Identifications were made using a three-point confidence scale, and a signal detection model was used to analyze and interpret the results. Our findings suggest that (i) confidence in an eyewitness identification from a fair lineup is a highly reliable indicator of accuracy and (ii) if there is any difference in diagnostic accuracy between the two lineup formats, it likely favors the simultaneous procedure.

Groupwise structural parcellation of the whole cortex: A logistic random effects model based approach.

Current theories hold that brain function is highly related to long-range physical connections through axonal bundles, namely extrinsic connectivity. However, obtaining a groupwise cortical parcellation based on extrinsic connectivity remains challenging. Current parcellation methods are computationally expensive; need tuning of several parameters or rely on ad-hoc constraints. Furthermore, none of these methods present a model for the cortical extrinsic connectivity of the cortex. To tackle these problems, we propose a parsimonious model for the extrinsic connectivity and an efficient parceling technique based on clustering of tractograms. Our technique allows the creation of single subject and groupwise parcellations of the whole cortex. The parcellations obtained with our technique are in agreement with structural and functional parcellations in the literature. In particular, the motor and sensory cortex are subdivided in agreement with the human homunculus of Penfield. We illustrate this by comparing our resulting parcels with the motor strip mapping included in the Human Connectome Project data.

Group-wise parcellation of the cortex through multi-scale spectral clustering.

The delineation of functionally and structurally distinct regions as well as their connectivity can provide key knowledge towards understanding the brain's behaviour and function. Cytoarchitecture has long been the gold standard for such parcellation tasks, but has poor scalability and cannot be mapped in vivo. Functional and diffusion magnetic resonance imaging allow in vivo mapping of brain's connectivity and the parcellation of the brain based on local connectivity information. Several methods have been developed for single subject connectivity driven parcellation, but very few have tackled the task of group-wise parcellation, which is essential for uncovering group specific behaviours. In this paper, we propose a group-wise connectivity-driven parcellation method based on spectral clustering that captures local connectivity information at multiple scales and directly enforces correspondences between subjects. The method is applied to diffusion Magnetic Resonance Imaging driven parcellation on two independent groups of 50 subjects from the Human Connectome Project. Promising quantitative and qualitative results in terms of information loss, modality comparisons, group consistency and inter-group similarities demonstrate the potential of the method.

Direct neural current imaging in an intact cerebellum with magnetic resonance imaging.

The ability to detect neuronal currents with high spatiotemporal resolution using magnetic resonance imaging (MRI) is important for studying human brain function in both health and disease. While significant progress has been made, we still lack evidence showing that it is possible to measure an MR signal time-locked to neuronal currents with a temporal waveform matching concurrently recorded local field potentials (LFPs). Also lacking is evidence that such MR data can be used to image current distribution in active tissue. Since these two results are lacking even in vitro, we obtained these data in an intact isolated whole cerebellum of turtle during slow neuronal activity mediated by metabotropic glutamate receptors using a gradient-echo EPI sequence (TR=100ms) at 4.7T. Our results show that it is possible (1) to reliably detect an MR phase shift time course matching that of the concurrently measured LFP evoked by stimulation of a cerebellar peduncle, (2) to detect the signal in single voxels of 0.1mm(3), (3) to determine the spatial phase map matching the magnetic field distribution predicted by the LFP map, (4) to estimate the distribution of neuronal current in the active tissue from a group-average phase map, and (5) to provide a quantitatively accurate theoretical account of the measured phase shifts. The peak values of the detected MR phase shifts were 0.27-0.37°, corresponding to local magnetic field changes of 0.67-0.93nT (for TE=26ms). Our work provides an empirical basis for future extensions to in vivo imaging of neuronal currents.

Mandatory tuberculosis case notification in high tuberculosis-incidence countries: policy and practice.

Mandatory tuberculosis (TB) notification is an important policy under the End TB Strategy, but little is known about its enforcement especially in high TB incidence countries. We undertook a literature search for selected high-incidence countries, followed by a questionnaire-based survey among key informants in countries with high-, intermediate- and low-TB incidence. Published literature on TB notification in high-incidence countries was limited, but it did illustrate some of the current barriers to notification and the importance of electronic systems to facilitate reporting by private providers. Required survey data were successfully gathered from 40 out of 54 countries contacted. TB is notifiable in 11 out of 15 high-incidence countries, all 16 intermediate-incidence countries, and all nine low-incidence countries contacted. TB case notification by public sector facilities is generally systematised, but few high-incidence countries had systems and tools to facilitate notification from private care providers. In the context of the new End TB Strategy aimed at eventual TB elimination, all countries should have TB on their national list of notifiable diseases. Enhancing the ease of notification by private providers is essential for effective implementation. To that effect, investing in strengthening disease surveillance systems and introducing digital tools to simplify notification are logical ways forward.

Shortened first-line TB treatment in Brazil: potential cost savings for patients and health services.

BACKGROUND: Shortened treatment regimens for tuberculosis are under development to improve treatment outcomes and reduce costs. We estimated potential savings from a societal perspective in Brazil following the introduction of a hypothetical four-month regimen for tuberculosis treatment. METHODS: Data were gathered in ten randomly selected health facilities in Rio de Janeiro. Health service costs were estimated using an ingredient approach. Patient costs were estimated from a questionnaire administered to 126 patients. Costs per visits and per case treated were analysed according to the type of therapy: self-administered treatment (SAT), community- and facility-directly observed treatment (community-DOT, facility-DOT). RESULTS: During the last 2 months of treatment, the largest savings could be expected for community-DOT; on average USD 17,351-18,203 and USD 43,660-45,856 (bottom-up and top-down estimates) per clinic. Savings to patients could also be expected as the median (interquartile range) patient-related costs during the two last months were USD 108 (13-291), USD 93 (36-239) and USD 11 (7-126), respectively for SAT, facility-DOT and community-DOT. CONCLUSION: Introducing a four-month regimen may result in significant cost savings for both the health service and patients, especially the poorest. In particular, a community-DOT strategy, including treatment at home, could maximise health services savings while limiting patient costs. Our cost estimates are likely to be conservative because a 4-month regimen could hypothetically increase the proportion of patients cured by reducing the number of patients defaulting and we did not include the possible cost benefits from the subsequent prevention of costs due to downstream transmission averted and rapid clinical improvement with less side effects in the last two months.

Target product profile of a molecular drug-susceptibility test for use in microscopy centers.

BACKGROUND: Current phenotypic testing for drug resistance in patients with tuberculosis is inadequate primarily with respect to turnaround time. Molecular tests hold the promise of an improved time to diagnosis. METHODS: A target product profile for a molecular drug-susceptibility test (DST) was developed on the basis of a collaborative effort that included opinions gathered from researchers, clinicians, policy makers, and test developers on optimal clinical and operational characteristics in settings of intended use. In addition, the current diagnostic ecosystem and the diagnostic development landscape were mapped. RESULTS: Molecular DSTs for detecting tuberculosis in microscopy centers should ideally evaluate for resistance to rifampin, fluoroquinolones, isoniazid, and pyrazinamide and enable the selection of the most appropriate treatment regimen. Performance characteristics of DSTs need to be optimized, but compromises can be made that depend on the trade-off between a false-positive result and a false-negative result. The operational requirements of a test will vary depending on the site of implementation. However, the most-important considerations pertain to quality control, maintenance and calibration, and the ability to export data. CONCLUSION: This target product profile defines the needs as perceived by the tuberculosis stakeholder community and attempts to provide a means of communication with test developers to ensure that fit-for-purpose DSTs are being developed.

Transperineal in-bore 3-T MR imaging-guided prostate biopsy: a prospective clinical observational study.

PURPOSE: To determine the detection rate, clinical relevance, Gleason grade, and location of prostate cancer ( PCa prostate cancer ) diagnosed with and the safety of an in-bore transperineal 3-T magnetic resonance (MR) imaging-guided prostate biopsy in a clinically heterogeneous patient population. MATERIALS AND METHODS: This prospective retrospectively analyzed study was HIPAA compliant and institutional review board approved, and informed consent was obtained. Eighty-seven men (mean age, 66.2 years ± 6.9) underwent multiparametric endorectal prostate MR imaging at 3 T and transperineal MR imaging-guided biopsy. Three subgroups of patients with at least one lesion suspicious for cancer were included: men with no prior PCa prostate cancer diagnosis, men with PCa prostate cancer who were undergoing active surveillance, and men with treated PCa prostate cancer and suspected recurrence. Exclusion criteria were prior prostatectomy and/or contraindication to 3-T MR imaging. The transperineal MR imaging-guided biopsy was performed in a 70-cm wide-bore 3-T device. Overall patient biopsy outcomes, cancer detection rates, Gleason grade, and location for each subgroup were evaluated and statistically compared by using χ(2) and one-way analysis of variance followed by Tukey honestly significant difference post hoc comparisons. RESULTS: Ninety biopsy procedures were performed with no serious adverse events, with a mean of 3.7 targets sampled per gland. Cancer was detected in 51 (56.7%) men: 48.1% (25 of 52) with no prior PCa prostate cancer , 61.5% (eight of 13) under active surveillance, and 72.0% (18 of 25) in whom recurrence was suspected. Gleason pattern 4 or higher was diagnosed in 78.1% (25 of 32) in the no prior PCa prostate cancer and active surveillance groups. Gleason scores were not assigned in the suspected recurrence group. MR targets located in the anterior prostate had the highest cancer yield (40 of 64, 62.5%) compared with those for the other parts of the prostate (P < .001). CONCLUSION: In-bore 3-T transperineal MR imaging-guided biopsy, with a mean of 3.7 targets per gland, allowed detection of many clinically relevant cancers, many of which were located anteriorly.

Magnetic resonance imaging of ionic currents in solution: the effect of magnetohydrodynamic flow.

PURPOSE: Reliably detecting MRI signals in the brain that are more tightly coupled to neural activity than blood-oxygen-level-dependent fMRI signals could not only prove valuable for basic scientific research but could also enhance clinical applications such as epilepsy presurgical mapping. This endeavor will likely benefit from an improved understanding of the behavior of ionic currents, the mediators of neural activity, in the presence of the strong magnetic fields that are typical of modern-day MRI scanners. THEORY: Of the various mechanisms that have been proposed to explain the behavior of ionic volume currents in a magnetic field, only one-magnetohydrodynamic flow-predicts a slow evolution of signals, on the order of a minute for normal saline in a typical MRI scanner. METHODS: This prediction was tested by scanning a volume-current phantom containing normal saline with gradient-echo-planar imaging at 3 T. RESULTS: Greater signal changes were observed in the phase of the images than in the magnitude, with the changes evolving on the order of a minute. CONCLUSION: These results provide experimental support for the MHD flow hypothesis. Furthermore, MHD-driven cerebrospinal fluid flow could provide a novel fMRI contrast mechanism.

Automatic iceball segmentation with adapted shape priors for MRI-guided cryoablation.

PURPOSE: To develop and evaluate an automatic segmentation method that extracts the 3D configuration of the ablation zone, the iceball, from images acquired during the freezing phase of MRI-guided cryoablation. MATERIALS AND METHODS: Intraprocedural images at 63 timepoints from 13 kidney tumor cryoablation procedures were examined retrospectively. The images were obtained using a 3 Tesla wide-bore MRI scanner and axial HASTE sequence. Initialized with semiautomatically localized cryoprobes, the iceball was segmented automatically at each timepoint using the graph cut (GC) technique with adapted shape priors. RESULTS: The average Dice Similarity Coefficients (DSC), compared with manual segmentations, were 0.88, 0.92, 0.92, 0.93, and 0.93 at 3, 6, 9, 12, and 15 min timepoints, respectively, and the average DSC of the total 63 segmentations was 0.92 ± 0.03. The proposed method improved the accuracy significantly compared with the approach without shape prior adaptation (P = 0.026). The number of probes involved in the procedure had no apparent influence on the segmentation results using our technique. The average computation time was 20 s, which was compatible with an intraprocedural setting. CONCLUSION: Our automatic iceball segmentation method demonstrated high accuracy and robustness for practical use in monitoring the progress of MRI-guided cryoablation.

ReMIND: The Brain Resection Multimodal Imaging Database.

The standard of care for brain tumors is maximal safe surgical resection. Neuronavigation augments the surgeon's ability to achieve this but loses validity as surgery progresses due to brain shift. Moreover, gliomas are often indistinguishable from surrounding healthy brain tissue. Intraoperative magnetic resonance imaging (iMRI) and ultrasound (iUS) help visualize the tumor and brain shift. iUS is faster and easier to incorporate into surgical workflows but offers a lower contrast between tumorous and healthy tissues than iMRI. With the success of data-hungry Artificial Intelligence algorithms in medical image analysis, the benefits of sharing well-curated data cannot be overstated. To this end, we provide the largest publicly available MRI and iUS database of surgically treated brain tumors, including gliomas (n=92), metastases (n=11), and others (n=11). This collection contains 369 preoperative MRI series, 320 3D iUS series, 301 iMRI series, and 356 segmentations collected from 114 consecutive patients at a single institution. This database is expected to help brain shift and image analysis research and neurosurgical training in interpreting iUS and iMRI.

TractoSCR: a novel supervised contrastive regression framework for prediction of neurocognitive measures using multi-site harmonized diffusion MRI tractography.

Neuroimaging-based prediction of neurocognitive measures is valuable for studying how the brain's structure relates to cognitive function. However, the accuracy of prediction using popular linear regression models is relatively low. We propose a novel deep regression method, namely TractoSCR, that allows full supervision for contrastive learning in regression tasks using diffusion MRI tractography. TractoSCR performs supervised contrastive learning by using the absolute difference between continuous regression labels (i.e., neurocognitive scores) to determine positive and negative pairs. We apply TractoSCR to analyze a large-scale dataset including multi-site harmonized diffusion MRI and neurocognitive data from 8,735 participants in the Adolescent Brain Cognitive Development (ABCD) Study. We extract white matter microstructural measures using a fine parcellation of white matter tractography into fiber clusters. Using these measures, we predict three scores related to domains of higher-order cognition (general cognitive ability, executive function, and learning/memory). To identify important fiber clusters for prediction of these neurocognitive scores, we propose a permutation feature importance method for high-dimensional data. We find that TractoSCR obtains significantly higher accuracy of neurocognitive score prediction compared to other state-of-the-art methods. We find that the most predictive fiber clusters are predominantly located within the superficial white matter and projection tracts, particularly the superficial frontal white matter and striato-frontal connections. Overall, our results demonstrate the utility of contrastive representation learning methods for regression, and in particular for improving neuroimaging-based prediction of higher-order cognitive abilities. Our code will be available at: https://github.com/SlicerDMRI/TractoSCR.

ReMIND: The Brain Resection Multimodal Imaging Database.

The standard of care for brain tumors is maximal safe surgical resection. Neuronavigation augments the surgeon's ability to achieve this but loses validity as surgery progresses due to brain shift. Moreover, gliomas are often indistinguishable from surrounding healthy brain tissue. Intraoperative magnetic resonance imaging (iMRI) and ultrasound (iUS) help visualize the tumor and brain shift. iUS is faster and easier to incorporate into surgical workflows but offers a lower contrast between tumorous and healthy tissues than iMRI. With the success of data-hungry Artificial Intelligence algorithms in medical image analysis, the benefits of sharing well-curated data cannot be overstated. To this end, we provide the largest publicly available MRI and iUS database of surgically treated brain tumors, including gliomas (n = 92), metastases (n = 11), and others (n = 11). This collection contains 369 preoperative MRI series, 320 3D iUS series, 301 iMRI series, and 356 segmentations collected from 114 consecutive patients at a single institution. This database is expected to help brain shift and image analysis research and neurosurgical training in interpreting iUS and iMRI.

The returning tide: how China, the world's most populous country, is building a competitive research base.

When China turned its back on the Cultural Revolution, it aimed to build a thriving capitalist sector. It got one. Now, it wants a world-class research enterprise. How far has it progressed in the biosciences, how did it get there, and how far does it have to go?