Improving Multiple Sclerosis Plaque Detection Using a Semiautomated Assistive Approach

BACKGROUND AND PURPOSE:Treating MS with disease-modifying drugs relies on accurate MR imaging follow-up to determine the treatment effect. We aimed to develop and validate a semiautomated software platform to facilitate detection of new lesions and improved lesions.MATERIALS AND METHODS:We developed VisTarsier to assist manual comparison of volumetric FLAIR sequences by using interstudy registration, resectioning, and color-map overlays that highlight new lesions and improved lesions. Using the software, 2 neuroradiologists retrospectively assessed MR imaging MS comparison study pairs acquired between 2009 and 2011 (161 comparison study pairs met the study inclusion criteria). Lesion detection and reading times were recorded. We tested inter- and intraobserver agreement and comparison with original clinical reports. Feedback was obtained from referring neurologists to assess the potential clinical impact.RESULTS:More comparison study pairs with new lesions (reader 1, n = 60; reader 2, n = 62) and improved lesions (reader 1, n = 28; reader 2, n = 39) were recorded by using the software compared with original radiology reports (new lesions, n = 20; improved lesions, n = 5); the difference reached statistical significance (P < .001). Interobserver lesion number agreement was substantial (≥1 new lesion: κ = 0.87; 95% CI, 0.79–0.95; ≥1 improved lesion: κ = 0.72; 95% CI, 0.59–0.85), and overall interobserver lesion number correlation was good (Spearman ρ: new lesion = 0.910, improved lesion = 0.774). Intraobserver agreement was very good (new lesion: κ = 1.0, improved lesion: κ = 0.94; 95% CI, 0.82–1.00). Mean reporting times were <3 minutes. Neurologists indicated retrospective management alterations in 79% of comparative study pairs with newly detected lesion changes.CONCLUSIONS:Using software that highlights changes between study pairs can improve lesion detection. Neurologist feedback indicated a likely impact on management.

Multiple sclerosis affects approximately 2 million people worldwide, predominantly young adults.¹ During the past decade, a number of novel disease-modifying drugs have emerged that are effective during the early phases of the disease; reducing the frequency of relapses, potentially halting disease progression, and even reversing early neurologic deficits.² This choice in therapeutic options allows treating neurologists to alter management strategies when progression is detected.²Because most demyelinating events are asymptomatic, MR imaging has been the primary biomarker for disease progression, and both physical disability and cognitive function have been shown to have a nonplateauing association with white matter demyelinating lesion burden, as seen on FLAIR and T2-weighted sequences.^2–6Recent advances in imaging, including 3T 3D volumetric T2 FLAIR sequences, allow better resolution of small demyelinating lesions, resulting in better clinicoradiologic correlation.^7,8 Despite advances in imaging techniques, conventional side-by-side comparison (CSSC) is often subject to a reader''s expertise.⁹ The sensitivity of detecting new lesions is also likely to be reduced when the section number is increased and scan planes are unmatched; however, to our knowledge, this reduction has not yet been investigated. In an attempt to facilitate accurate lesion-load and lesion-volume detection, much research has been devoted to fully automated computational approaches with unsatisfactory results. Robust lesion segmentation has been identified as a critical obstacle to widespread clinical adoption for several reasons: difficulties specific to MS, problems inherent to segmentation, and data variability.¹⁰A review of fully automated MS segmentation techniques concluded that basic data-driven methods are inherently inaccurate; supervised learning methods (such as artificial neural networks) require costly and extensive training on representative data; deformable models are better; and statistical models are most promising, though these also require training on representative data.³An alternative to total automation is to assist manual reporting with partial automation. A few semiautomated lesion-subtraction strategies have been used in the research setting on small patient populations with good lesion detection and interreader correlation.^11,12Semiautomation without segmentation is inherently easier, more robust, and less affected by data variability because the lesion count is judged manually. The software can present a number of false-positives without a negative impact on accuracy.Our aim was to design a nonsegmentation semiautomated assistive software platform that can be integrated into vendor-agnostic PACS and validated by application to a large number of existing routine clinical scans in patients with an established diagnosis of multiples sclerosis. The approach is to merely draw the attention of the radiologist to potentially new or improved lesions rather than automate the entire process, thus preserving the expertise of neuroradiologists in determining whether a finding is real.Our hypothesis was that CSSCs of volumetric FLAIR studies in patients with MS were prone to false-negative errors in the perception of both new and improved lesions and that more lesions would be identified by using the assistive software with improved inter- and intrareader reliability. Secondly, we hypothesized that presenting this information to clinicians would likely have changed patient management.     

Evidence for novel susceptibility genes for late-onset Alzheimer's disease from a genome-wide association study of putative functional variants

This study sets out to identify novel susceptibility genes for late-onset Alzheimer's disease (LOAD) in a powerful set of samples from the UK and USA (1808 LOAD cases and 2062 controls). Allele frequencies of 17 343 gene-based putative functional single nucleotide polymorphisms (SNPs) were tested for association with LOAD in a discovery case-control sample from the UK. A tiered strategy was used to follow-up significant variants from the discovery sample in four independent sample sets. Here, we report the identification of several candidate SNPs that show significant association with LOAD. Three of the identified markers are located on chromosome 19 (meta-analysis: full sample P = 6.94E - 81 to 0.0001), close to the APOE gene and exhibit linkage disequilibrium (LD) with the APOEepsilon4 and epsilon2/3 variants (0.09 < D'<1). Two of the three SNPs can be regarded as study-wide significant (expected number of false positives reaching the observed significance level less than 0.05 per study). Sixteen additional SNPs show evidence for association with LOAD [P = 0.0010-0.00006; odds ratio (OR) = 1.07-1.45], several of which map to known linkage regions, biological candidate genes and novel genes. Four SNPs not in LD with APOE show a false positive rate of less than 2 per study, one of which shows study-wide suggestive evidence taking account of 17 343 tests. This is a missense mutation in the galanin-like peptide precursor gene (P = 0.00005, OR = 1.2, false positive rate = 0.87).     

Three-dimensional modeling of T-24 human bladder carcinoma cell line: A new simulated microgravity culture vessel

Summary We present a new device and method for culture of cell lines and primary tissues requiring high oxygen tensions. The High Aspect Rotating-Wall Vessel (HARV) described successfully propagated T-24, a human bladder transitional epithelial cell line, on Cytodex-3 microcarriers in three-dimensional cellular aggregates up to 0.5 cm in diameter. The HARV is a horizontally rotated tissue culture vessel with a large surface-area-to-volume ratio silicone membrane oxygenator. This design augments the principle of the rotating-wall vessel termed the Slow-Turning Lateral Vessel (STLV) by providing a low turbulence, low shear, cell growing environment with increased oxygen delivery capability. Comparisons of glucose metabolism, oxygen consumption, and morphology as a function of cell growth for a T-24 bladder carcinoma were performed in the HARV vs. the STLV. The HARV was superior in the culture of a variety of cell types including normal and neoplastic, anchorage-dependent and suspension cells. This work has been supported by the Microgravity Sciences and Applications Division of the National Aeronautics and Space Administration, Washington, DC, under contract NAS9-18492.     

Neuroradiologists Compared with Non-Neuroradiologists in the Detection of New Multiple Sclerosis Plaques

BACKGROUND AND PURPOSE:Multiple sclerosis monitoring is based on the detection of new lesions on brain MR imaging. Outside of study populations, MS imaging studies are reported by radiologists with varying expertise. The aim of this study was to investigate the accuracy of MS reporting performed by neuroradiologists (someone who had spent at least 1 year in neuroradiology subspecialty training) versus non-neuroradiologists.MATERIALS AND METHODS:Patients with ≥2 MS studies with 3T MR imaging that included a volumetric T2 FLAIR sequence performed between 2009 and 2011 inclusive were recruited into this study. The reports for these studies were analyzed for lesions detected, which were categorized as either progressed or stable. The results from a previous study using a semiautomated assistive software for lesion detection were used as the reference standard.RESULTS:There were 5 neuroradiologists and 5 non-neuroradiologists who reported all studies. In total, 159 comparison pairs (ie, 318 studies) met the selection criteria. Of these, 96 (60.4%) were reported by a neuroradiologist. Neuroradiologists had higher sensitivity (82% versus 42%), higher negative predictive value (89% versus 64%), and lower false-negative rate (18% versus 58%) compared with non-neuroradiologists. Both groups had a 100% positive predictive value.CONCLUSIONS:Neuroradiologists detect more new lesions than non-neuroradiologists in reading MR imaging for follow-up of MS. Assistive software that aids in the identification of new lesions has a beneficial effect for both neuroradiologists and non-neuroradiologists, though the effect is more profound in the non-neuroradiologist group.

Multiple sclerosis is the most common disease of the central nervous system in young patients, with a major impact on patients'' lives.¹ Over the past decade, a number of disease-modifying drugs that are especially effective during early disease have been developed. Neurologists increasingly are aiming for zero disease progression and, in many instances, will alter management when progression is detected.² Because most demyelinating lesions are clinically occult, MR imaging has become the primary biomarker for disease progression. Both physical and cognitive disability have been shown to have a nonplateauing association with white matter demyelinating lesion burden as seen on T2-weighted and T2-weighted FLAIR sequences.^2–6 Detecting new lesions can be an arduous task, particularly when there is a large number of pre-existing lesions.Outside of study populations, MS MR imaging studies are reported by radiologists with varying expertise, ranging from general radiologists to fellowship-trained neuroradiologists. Although the accuracy of neuroradiologists (NRs) versus non-neuroradiologists (NNRs) has been examined in a number of settings, with varying results,^7,8 to our knowledge, no studies to date have investigated the efficacy of NR versus NNR reporting for MS.We aimed to investigate the accuracy of MS reporting performed by NRs versus NNRs, with results from a previously published validated semiautomated assistive software platform (VisTarsier; VT) as a “gold standard.” In this study, we hypothesized that nonspecialty reporters would perform at a slightly lower accuracy compared with subspecialty reporters.