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We present a precision medicine application developed for multiple sclerosis (MS): the MS BioScreen. This new tool addresses the challenges of dynamic management of a complex chronic disease; the interaction of clinicians and patients with such a tool illustrates the extent to which translational digital medicine - that is, the application of information technology to medicine - has the potential to radically transform medical practice. We introduce 3 key evolutionary phases in displaying data to health care providers, patients, and researchers: visualization (accessing data), contextualization (understanding the data), and actionable interpretation (real-time use of the data to assist decision making). Together, these form the stepping stones that are expected to accelerate standardization of data across platforms, promote evidence-based medicine, support shared decision making, and ultimately lead to improved outcomes.

Hyperpolarized (HP) 13 C MRI has shown promise as a valuable modality for in vivo measurements of metabolism and is currently in human trials at 15 research sites worldwide. With this growth, it is important to adopt standardized data storage practices as it will allow sites to meaningfully compare data. In this paper, we (1) describe data that we believe should be stored and (2) demonstrate pipelines and methods that utilize the Digital Imaging and Communications in Medicine (DICOM) standard. This includes proposing a set of minimum set of information that is specific to HP 13 C MRI studies. We then show where the majority of these can be fit into existing DICOM attributes, primarily via the "Contrast/Bolus" module. We also demonstrate pipelines for utilizing DICOM for HP 13 C MRI. DICOM is the most common standard for clinical medical image storage and provides the flexibility to accommodate the unique aspects of HP 13 C MRI, including the HP agent information but also spectroscopic and metabolite dimensions. The pipelines shown include creating DICOM objects for studies on human and animal imaging systems with various pulse sequences. We also show a python-based method to efficiently modify DICOM objects to incorporate the unique HP 13 C MRI information that is not captured by existing pipelines. Moreover, we propose best practices for HP 13 C MRI data storage that will support future multi-site trials, research studies, and technical developments of this imaging technique., (© 2024. The Author(s) under exclusive licence to Society for Imaging Informatics in Medicine.)

The medical imaging community has embraced Machine Learning (ML) as evidenced by the rapid increase in the number of ML models being developed, but validating and deploying these models in the clinic remains a challenge. The engineering involved in integrating and assessing the efficacy of ML models within the clinical workflow is complex. This paper presents a general-purpose, end-to-end, clinically integrated ML model deployment and validation system implemented at UCSF. Engineering and usability challenges and results from 3 use cases are presented. A generalized validation system based on free, open-source software (OSS) was implemented, connecting clinical imaging modalities, the Picture Archiving and Communication System (PACS), and an ML inference server. ML pipelines were implemented in NVIDIA's Clara Deploy framework with results and clinician feedback stored in a customized XNAT instance, separate from the clinical record but linked from within PACS. Prospective clinical validation studies of 3 ML models were conducted, with data routed from multiple clinical imaging modalities and PACS. Completed validation studies provided expert clinical feedback on model performance and usability, plus system reliability and performance metrics. Clinical validation of ML models entails assessing model performance, impact on clinical infrastructure, robustness, and usability. Study results must be easily accessible to participating clinicians but remain outside the clinical record. Building a system that generalizes and scales across multiple ML models takes the concerted effort of software engineers, clinicians, data scientists, and system administrators, and benefits from the use of modular OSS. The present work provides a template for institutions looking to translate and clinically validate ML models in the clinic, together with required resources and expected challenges., Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: NVIDIA provided 4 T4 cards as a grant to UCSF., (Copyright: This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.)

Background: Diagnostic classification of diffuse gliomas now requires an assessment of molecular features, often including IDH-mutation and 1p19q-codeletion status. Because genetic testing requires an invasive process, an alternative noninvasive approach is attractive, particularly if resection is not recommended. The goal of this study was to evaluate the effects of training strategy and incorporation of biologically relevant images on predicting genetic subtypes with deep learning., Methods: Our dataset consisted of 384 patients with newly diagnosed gliomas who underwent preoperative MRI with standard anatomical and diffusion-weighted imaging, and 147 patients from an external cohort with anatomical imaging. Using tissue samples acquired during surgery, each glioma was classified into IDH-wildtype (IDHwt), IDH-mutant/1p19q-noncodeleted (IDHmut-intact), and IDH-mutant/1p19q-codeleted (IDHmut-codel) subgroups. After optimizing training parameters, top performing convolutional neural network (CNN) classifiers were trained, validated, and tested using combinations of anatomical and diffusion MRI with either a 3-class or tiered structure. Generalization to an external cohort was assessed using anatomical imaging models., Results: The best model used a 3-class CNN containing diffusion-weighted imaging as an input, achieving 85.7% (95% CI: [77.1, 100]) overall test accuracy and correctly classifying 95.2%, 88.9%, 60.0% of the IDHwt, IDHmut-intact, and IDHmut-codel tumors. In general, 3-class models outperformed tiered approaches by 13.5%-17.5%, and models that included diffusion-weighted imaging were 5%-8.8% more accurate than those that used only anatomical imaging., Conclusion: Training a classifier to predict both IDH-mutation and 1p19q-codeletion status outperformed a tiered structure that first predicted IDH-mutation, then 1p19q-codeletion. Including apparent diffusion coefficient (ADC), a surrogate marker of cellularity, more accurately captured differences between subgroups., (© The Author(s) 2021. Published by Oxford University Press on behalf of the Society for Neuro-Oncology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Based on the expanding set of applications for hyperpolarized carbon-13 (HP- 13 C) MRI, this work aims to communicate standardized methodology implemented at the University of California, San Francisco, as a primer for conducting reproducible metabolic imaging studies of the prostate and brain. Current state-of-the-art HP- 13 C acquisition, data processing/reconstruction and kinetic modeling approaches utilized in patient studies are presented together with the rationale underpinning their usage. Organized around spectroscopic and imaging-based methods, this guide provides an extensible framework for handling a variety of HP- 13 C applications, which derives from two examples with dynamic acquisitions: 3D echo-planar spectroscopic imaging of the human prostate and frequency-specific 2D multislice echo-planar imaging of the human brain. Details of sequence-specific parameters and processing techniques contained in these examples should enable investigators to effectively tailor studies around individual-use cases. Given the importance of clinical integration in improving the utility of HP exams, practical aspects of standardizing data formats for reconstruction, analysis and visualization are also addressed alongside open-source software packages that enhance institutional interoperability and validation of methodology. To facilitate the adoption and further development of this methodology, example datasets and analysis pipelines have been made available in the supporting information., (© 2020 John Wiley & Sons, Ltd.)

Background: Differentiating treatment-induced injury from recurrent high-grade glioma is an ongoing challenge in neuro-oncology, in part due to lesion heterogeneity. This study aimed to determine whether different MR features were relevant for distinguishing recurrent tumor from the effects of treatment in contrast-enhancing lesions (CEL) and non-enhancing lesions (NEL)., Methods: This prospective study analyzed 291 tissue samples (222 recurrent tumor, 69 treatment-effect) with known coordinates on imaging from 139 patients who underwent preoperative 3T MRI and surgery for a suspected recurrence. 8 MR parameter values were tested from perfusion-weighted, diffusion-weighted, and MR spectroscopic imaging at each tissue sample location for association with histopathological outcome using generalized estimating equation models for CEL and NEL tissue samples. Individual cutoff values were evaluated using receiver operating characteristic curve analysis with 5-fold cross-validation., Results: In tissue samples obtained from CEL, elevated relative cerebral blood volume (rCBV) was associated with the presence of recurrent tumor pathology (P < 0.03), while increases in normalized choline (nCho) and choline-to-NAA index (CNI) were associated with the presence of recurrent tumor pathology in NEL tissue samples (P < 0.008). A mean CNI cutoff value of 2.7 had the highest performance, resulting in mean sensitivity and specificity of 0.61 and 0.81 for distinguishing treatment-effect from recurrent tumor within the NEL., Conclusion: Although our results support prior work that underscores the utility of rCBV in distinguishing the effects of treatment from recurrent tumor within the contrast enhancing lesion, we found that metabolic parameters may be better at differentiating recurrent tumor from treatment-related changes in the NEL of high-grade gliomas., (© The Author(s) 2020. Published by Oxford University Press on behalf of the Society for Neuro-Oncology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

The intrinsically disordered protein β-synuclein is known to inhibit the aggregation of its intrinsically disordered homolog, α-synuclein, which is implicated in Parkinson's disease. While β-synuclein itself does not form fibrils at the cytoplasmic pH 7.4, alteration of pH and other environmental perturbations are known to induce its fibrilization. However, the sequence and structural determinants of β-synuclein inhibition and self-aggregation are not well understood. We have utilized a series of domain-swapped chimeras of α-synuclein and β-synuclein to probe the relative contributions of the N-terminal, C-terminal, and the central non-amyloid-β component domains to the inhibition of α-synuclein aggregation. Changes in the rates of α-synuclein fibril formation in the presence of the chimeras indicate that the non-amyloid-β component domain is the primary determinant of self-association leading to fibril formation, while the N- and C-terminal domains play critical roles in the fibril inhibition process. Our data provide evidence that all three domains of β-synuclein together contribute to providing effective inhibition, and support a model of transient, multi-pronged interactions between IDP chains in both processes. Inclusion of such multi-site inhibitory interactions spread over the length of synuclein chains may be critical for the development of therapeutics that are designed to mimic the inhibitory effects of β-synuclein., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

α-Synuclein (αS) is the primary protein associated with Parkinson's disease, and it undergoes aggregation from its intrinsically disordered monomeric form to a cross-β fibrillar form. The closely related homolog β-synuclein (βS) is essentially fibril-resistant under cytoplasmic physiological conditions. Toxic gain-of-function by βS has been linked to dysfunction, but the aggregation behavior of βS under altered pH is not well-understood. In this work, we compare fibril formation of αS and βS at pH 7.3 and mildly acidic pH 5.8, and we demonstrate that pH serves as an on/off switch for βS fibrillation. Using αS/βS domain-swapped chimera constructs and single residue substitutions in βS, we localized the switch to acidic residues in the N-terminal and non-amyloid component domains of βS. Computational models of βS fibril structures indicate that key glutamate residues (Glu-31 and Glu-61) in these domains may be sites of pH-sensitive interactions, and variants E31A and E61A show dramatically altered pH sensitivity for fibril formation supporting the importance of these charged side chains in fibril formation of βS. Our results demonstrate that relatively small changes in pH, which occur frequently in the cytoplasm and in secretory pathways, may induce the formation of βS fibrils and suggest a complex role for βS in synuclein cellular homeostasis and Parkinson's disease., (© 2017 by The American Society for Biochemistry and Molecular Biology, Inc.)

Purpose: The purpose of this study was to characterize tissue-specific alterations in metabolism of hyperpolarized (HP) gluconeogenic precursors 13 C-lactate and 13 C-pyruvate by rat liver and kidneys under conditions of fasting or insulin-deprived diabetes., Methods: Seven normal rats were studied by MR spectroscopic imaging of both HP 13 C-lactate and 13 C-pyruvate in both normal fed and 24 h fasting states, and seven additional rats were scanned after induction of diabetes by streptozotocin (STZ) with insulin withdrawal. Phosphoenolpyruvate carboxykinase (PEPCK) expression levels were also measured in liver and kidney tissues of the STZ-treated rats., Results: Multiple sets of significant signal modulations were detected, with graded intensity in general between fasting and diabetic states. An approximate two-fold reduction in the ratio of 13 C-bicarbonate to total 13 C signal was observed in both organs in fasting. The ratio of HP lactate-to-alanine was markedly altered, ranging from a liver-specific 54% increase in fasting, to increases of 69% and 92% in liver and kidney, respectively, in diabetes. Diabetes resulted in a 40% increase in renal lactate signal. STZ resulted in 5.86-fold and 2.73-fold increases in PEPCK expression in liver and kidney, respectively., Conclusion: MRI of HP 13 C gluconeogenic precursors may advance diabetes research by clarifying organ-specific roles in abnormal diabetic metabolism. Magn Reson Med 77:1429-1437, 2017. © 2016 International Society for Magnetic Resonance in Medicine., (© 2016 International Society for Magnetic Resonance in Medicine.)

Infiltrating low grade gliomas (LGGs) are heterogeneous in their behavior and the strategies used for clinical management are highly variable. A key factor in clinical decision-making is that patients with mutations in the isocitrate dehydrogenase 1 and 2 (IDH1/2) oncogenes are more likely to have a favorable outcome and be sensitive to treatment. Because of their relatively long overall median survival, more aggressive treatments are typically reserved for patients that have undergone malignant progression (MP) to an anaplastic glioma or secondary glioblastoma (GBM). In the current study, ex vivo metabolic profiles of image-guided tissue samples obtained from patients with newly diagnosed and recurrent LGG were investigated using proton high-resolution magic angle spinning spectroscopy ( 1 H HR-MAS). Distinct spectral profiles were observed for lesions with IDH-mutated genotypes, between astrocytoma and oligodendroglioma histologies, as well as for tumors that had undergone MP. Levels of 2-hydroxyglutarate (2HG) were correlated with increased mitotic activity, axonal disruption, vascular neoplasia, and with several brain metabolites including the choline species, glutamate, glutathione, and GABA. The information obtained in this study may be used to develop strategies for in vivo characterization of infiltrative glioma, in order to improve disease stratification and to assist in monitoring response to therapy.

Background: Patients with low-grade glioma (LGG) have a relatively long survival, and a balance is often struck between treating the tumor and impacting quality of life. While lesions may remain stable for many years, they may also undergo malignant transformation (MT) at the time of recurrence and require more aggressive intervention. Here we report on a state-of-the-art multiparametric MRI study of patients with recurrent LGG., Methods: One hundred and eleven patients previously diagnosed with LGG were scanned at either 1.5 T or 3 T MR at the time of recurrence. Volumetric and intensity parameters were estimated from anatomic, diffusion, perfusion, and metabolic MR data. Direct comparisons of histopathological markers from image-guided tissue samples with metrics derived from the corresponding locations on the in vivo images were made. A bioinformatics approach was applied to visualize and interpret these results, which included imaging heatmaps and network analysis. Multivariate linear-regression modeling was utilized for predicting transformation., Results: Many advanced imaging parameters were found to be significantly different for patients with tumors that had undergone MT versus those that had not. Imaging metrics calculated at the tissue sample locations highlighted the distinct biological significance of the imaging and the heterogeneity present in recurrent LGG, while multivariate modeling yielded a 76.04% accuracy in predicting MT., Conclusions: The acquisition and quantitative analysis of such multiparametric MR data may ultimately allow for improved clinical assessment and treatment stratification for patients with recurrent LGG., (© The Author(s) 2016. Published by Oxford University Press on behalf of the Society for Neuro-Oncology.)

Quantitative analysis of magnetic resonance spectroscopic imaging (MRSI) data provides maps of metabolic parameters that show promise for improving medical diagnosis and therapeutic monitoring. While anatomical images are routinely reconstructed on the scanner, formatted using the DICOM standard, and interpreted using PACS workstations, this is not the case for MRSI data. The evaluation of MRSI data is made more complex because files are typically encoded with vendor-specific file formats and there is a lack of standardized tools for reconstruction, processing, and visualization. SIVIC is a flexible open-source software framework and application suite that enables a complete scanner-to-PACS workflow for evaluation and interpretation of MRSI data. It supports conversion of vendor-specific formats into the DICOM MR spectroscopy (MRS) standard, provides modular and extensible reconstruction and analysis pipelines, and provides tools to support the unique visualization requirements associated with such data. Workflows are presented which demonstrate the routine use of SIVIC to support the acquisition, analysis, and delivery to PACS of clinical (1)H MRSI datasets at UCSF.

Frequent visitors account for a high proportion of Emergency Department (ED) visits and costs. Some of these visits could be handled effectively in less expensive primary care settings. Effective interventions to redirect these patients to primary care depend on an in-depth understanding of frequent visitors and the reasons they seek care in the ED. The objective of this study was to explore the differences between frequent visitors and infrequent visitors who seek medical care in one urban ED, as a first step toward developing effective interventions to direct patients to effective sources of care. In structured interviews, we asked 69 frequent visitors and 99 infrequent visitors to an inner-city, adult ED about medical diagnoses, general health, depression, alcohol abuse, physical functioning, self-perceived social support, primary care and ED service use, payment method, satisfaction with their primary care physician, and demographic characteristics. Differences in responses between groups were compared using t-tests for continuous variables and chi-square for categorical variables. Frequent visitors were more likely than infrequent visitors to be insured by Medicaid (53% vs. 39%, respectively) and less likely to be uninsured (13% vs. 24%, respectively) or have private insurance (6% vs. 15%, respectively). They reported higher levels of stress, lower levels of social support, and worse general health status. They were much more likely to screen positive for depression (47% vs. 27%, respectively, p = 0.017). Frequent visitors were more likely to have a primary care physician (75% vs. 66%, respectively), and 45% of the frequent visitors had a primary care physician at the ED hospital compared to 23% of the infrequent visitors. These findings suggest the need to improve access to frequent visitors' primary care physicians, screen them for depression, and offer psychological and social services more aggressively. These findings may apply to other inner city EDs., (Copyright (c) 2010 Elsevier Inc. All rights reserved.)

Pulsed electrochemical detection (PED) following liquid chromatographic separation has been applied to the indirect determination of amino acids and proteins. Here, the adsorption of these analytes at noble metal electrodes is exploited to suppress the oxidation of polyols and carbohydrates under alkaline conditions to elicit an indirect response. Of the reagents tested, gluconic acid gave the best overall signal-to-noise values for the indirect detection of amino acids following high performance anion-exchange chromatography (HPAEC). Limits of detection of amino acids were found to be 2-30pmol using optimized potential-time waveforms at an Au electrode. Indirect PED provided much greater detection sensitivity toward amino acids than direct PED. Analytical sensitivity of indirect PED is a function of both the analyte's ability to adsorb to the electrode surface and its molecular size, which was demonstrated by the separation and detection of bovine serum albumin, ovalbumin, and myoglobin following gel-filtration chromatography (GFC).

Objective: We describe q-sequence deconvolution (QSD), a new data acquisition/analysis method for evoked-responses that solves the problem of waveform distortion at high stimulus repetition-rates, due to response overlap. QSD can increase the sensitivity of clinically useful evoked-responses because it is well known that high stimulus repetition-rates are better for detecting pathophysiology., Methods: QSD is applicable to a variety of experimental conditions. Because some QSD-parameters must be chosen by the experimenter, the underlying principles and assumptions of the method are described in detail. The theoretical and mathematical bases of the QSD method are also described, including some equivalent computational formulations., Results: QSD was applied to recordings of the human auditory brainstem response (ABR) at stimulus repetition-rates that overlapped the responses. The transient ABR was recovered at all rates tested (highest 160/s), and showed systematic changes with stimulus repetition-rate within a single subject., Conclusions: QSD offers a new method of recovering brain evoked-response activity having a duration longer than the time between stimuli., Significance: The use of this new technique for analysis of evoked responses will permit examination of brain activation patterns across a broad range of stimulus repetition-rates, some never before studied. Such studies will improve the sensitivity of evoked-responses for the detection of brain pathophysiology. New measures of brain activity may be discovered using QSD. The method also permits the recovery of the transient brain waveforms that overlap to form 'steady-state' waveforms. An additional benefit of the QSD method is that repetition-rate can be isolated as a variable, independent of other stimulus characteristics, even if the response is a nonlinear function of rate.