Does Gender Impact a Diagnosis of Ankylosing Spondylitis?

Objective: The study objective was to explore differences in ankylosing spondylitis (AS) diagnosis experiences between men and women by examining the coding of health events over the 2 years preceding AS diagnosis.

Methods: Claims data (January 2006-April 2019) from the MarketScan databases were examined. Patients who had received two or more AS diagnoses at least 30 days apart and had at least 2 years of insurance enrollment before their first AS diagnosis were analyzed.

Longitudinal analysis of the patient pathways to diagnosis of psoriatic arthritis.

Background: The occurrence of health events preceding a psoriatic arthritis (PsA) diagnosis may serve as predictors of diagnosis. We sought to assess patients' real-world experiences in obtaining a PsA diagnosis.

Methods: This retrospective cohort study analyzed MarketScan claims data from January 2006 to April 2019.

Integrative radiomic analysis for pre-surgical prognostic stratification of glioblastoma patients: from advanced to basic MRI protocols.

Glioblastoma, the most common and aggressive adult brain tumor, is considered non-curative at diagnosis. Current literature shows promise on imaging-based overall survival prediction for patients with glioblastoma while integrating advanced (structural, perfusion, and diffusion) multipara metric magnetic resonance imaging (Adv-mpMRI). However, most patients prior to initiation of therapy typically undergo only basic structural mpMRI (Bas-mpMRI, i.

Predicting pediatric optic pathway glioma progression using advanced magnetic resonance image analysis and machine learning.

Background: Optic pathway gliomas (OPGs) are low-grade tumors of the white matter of the visual system with a highly variable clinical course. The aim of the study was to generate a magnetic resonance imaging (MRI)-based predictive model of OPG tumor progression using advanced image analysis and machine learning techniques.

Methods: We performed a retrospective case-control study of OPG patients managed between 2009 and 2015 at an academic children's hospital.

Overall survival prediction in glioblastoma patients using structural magnetic resonance imaging (MRI): advanced radiomic features may compensate for lack of advanced MRI modalities.

Glioblastoma, the most common and aggressive adult brain tumor, is considered noncurative at diagnosis, with 14 to 16 months median survival following treatment. There is increasing evidence that noninvasive integrative analysis of radiomic features can predict overall and progression-free survival, using advanced multiparametric magnetic resonance imaging (Adv-mpMRI). If successfully applicable, such noninvasive markers can considerably influence patient management.

Use of machine learning techniques in the development and refinement of a predictive model for early diagnosis of ankylosing spondylitis.

Objective: To develop a predictive mathematical model for the early identification of ankylosing spondylitis (AS) based on the medical and pharmacy claims history of patients with and without AS.

Methods: This retrospective study used claims data from Truven databases from January 2006 to September 2015 (Segment 1) and October 2015 to February 2018 (Segment 2). Machine learning identified features differentiating patients with AS from matched controls; selected features were used as inputs in developing Model A/B to identify patients likely to have AS.

Application of machine learning in the diagnosis of axial spondyloarthritis.

Purpose Of Review: In this review article, we describe the development and application of machine-learning models in the field of rheumatology to improve the detection and diagnosis rates of underdiagnosed rheumatologic conditions, such as ankylosing spondylitis and axial spondyloarthritis (axSpA).

Recent Findings: In an attempt to aid in the earlier diagnosis of axSpA, we developed machine-learning models to predict a diagnosis of ankylosing spondylitis and axSpA using administrative claims and electronic medical record data. Machine-learning algorithms based on medical claims data predicted the diagnosis of ankylosing spondylitis better than a model developed based on clinical characteristics of ankylosing spondylitis.

Brain Cancer Imaging Phenomics Toolkit (brain-CaPTk): An Interactive Platform for Quantitative Analysis of Glioblastoma.

Quantitative research, especially in the field of radio(geno)mics, has helped us understand fundamental mechanisms of neurologic diseases. Such research is integrally based on advanced algorithms to derive extensive radiomic features and integrate them into diagnostic and predictive models. To exploit the benefit of such complex algorithms, their swift translation into clinical practice is required, currently hindered by their complicated nature.

In vivo evaluation of EGFRvIII mutation in primary glioblastoma patients via complex multiparametric MRI signature.

Background: Epidermal growth factor receptor variant III (EGFRvIII) is a driver mutation and potential therapeutic target in glioblastoma. Non-invasive in vivo EGFRvIII determination, using clinically acquired multiparametric MRI sequences, could assist in assessing spatial heterogeneity related to EGFRvIII, currently not captured via single-specimen analyses. We hypothesize that integration of subtle, yet distinctive, quantitative imaging/radiomic patterns using machine learning may lead to non-invasively determining molecular characteristics, and particularly the EGFRvIII mutation.

Radiomic MRI signature reveals three distinct subtypes of glioblastoma with different clinical and molecular characteristics, offering prognostic value beyond IDH1.

The remarkable heterogeneity of glioblastoma, across patients and over time, is one of the main challenges in precision diagnostics and treatment planning. Non-invasive in vivo characterization of this heterogeneity using imaging could assist in understanding disease subtypes, as well as in risk-stratification and treatment planning of glioblastoma. The current study leveraged advanced imaging analytics and radiomic approaches applied to multi-parametric MRI of de novo glioblastoma patients (n = 208 discovery, n = 53 replication), and discovered three distinct and reproducible imaging subtypes of glioblastoma, with differential clinical outcome and underlying molecular characteristics, including isocitrate dehydrogenase-1 (IDH1), O-methylguanine-DNA methyltransferase, epidermal growth factor receptor variant III (EGFRvIII), and transcriptomic subtype composition.

Radiomic signature of infiltration in peritumoral edema predicts subsequent recurrence in glioblastoma: implications for personalized radiotherapy planning.

Standard surgical resection of glioblastoma, mainly guided by the enhancement on postcontrast T1-weighted magnetic resonance imaging (MRI), disregards infiltrating tumor within the peritumoral edema region (ED). Subsequent radiotherapy typically delivers uniform radiation to peritumoral FLAIR-hyperintense regions, without attempting to target areas likely to be infiltrated more heavily. Noninvasive delineation of the areas of tumor infiltration and prediction of early recurrence in peritumoral ED could assist in targeted intensification of local therapies, thereby potentially delaying recurrence and prolonging survival.

Use of Fetal Magnetic Resonance Image Analysis and Machine Learning to Predict the Need for Postnatal Cerebrospinal Fluid Diversion in Fetal Ventriculomegaly.

Importance: Which children with fetal ventriculomegaly, or enlargement of the cerebral ventricles in utero, will develop hydrocephalus requiring treatment after birth is unclear.

Objective: To determine whether extraction of multiple imaging features from fetal magnetic resonance imaging (MRI) and integration using machine learning techniques can predict which patients require postnatal cerebrospinal fluid (CSF) diversion after birth.

Design, Setting, And Patients: This retrospective case-control study used an institutional database of 253 patients with fetal ventriculomegaly from January 1, 2008, through December 31, 2014, to generate a predictive model.

Multisite Machine Learning Analysis Provides a Robust Structural Imaging Signature of Schizophrenia Detectable Across Diverse Patient Populations and Within Individuals.

Past work on relatively small, single-site studies using regional volumetry, and more recently machine learning methods, has shown that widespread structural brain abnormalities are prominent in schizophrenia. However, to be clinically useful, structural imaging biomarkers must integrate high-dimensional data and provide reproducible results across clinical populations and on an individual person basis. Using advanced multi-variate analysis tools and pooled data from case-control imaging studies conducted at 5 sites (941 adult participants, including 440 patients with schizophrenia), a neuroanatomical signature of patients with schizophrenia was found, and its robustness and reproducibility across sites, populations, and scanners, was established for single-patient classification.

Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features.

Gliomas belong to a group of central nervous system tumors, and consist of various sub-regions. Gold standard labeling of these sub-regions in radiographic imaging is essential for both clinical and computational studies, including radiomic and radiogenomic analyses. Towards this end, we release segmentation labels and radiomic features for all pre-operative multimodal magnetic resonance imaging (MRI) (n=243) of the multi-institutional glioma collections of The Cancer Genome Atlas (TCGA), publicly available in The Cancer Imaging Archive (TCIA).

Segmentation of Gliomas in Pre-operative and Post-operative Multimodal Magnetic Resonance Imaging Volumes Based on a Hybrid Generative-Discriminative Framework.

We present an approach for segmenting both low- and high-grade gliomas in multimodal magnetic resonance imaging volumes. The proposed framework is an extension of our previous work [6,7], with an additional component for segmenting post-operative scans. The proposed approach is based on a hybrid generative-discriminative model.

Detection of EGFRvIII in Glioblastoma via Perfusion Magnetic Resonance Imaging Signature Consistent with Deep Peritumoral Infiltration: The -Index.

The epidermal growth factor receptor variant III () mutation has been considered a driver mutation and therapeutic target in glioblastoma, the most common and aggressive brain cancer. Currently, detecting requires postoperative tissue analyses, which are and unable to capture the tumor's spatial heterogeneity. Considering the increasing evidence of imaging signatures capturing molecular characteristics of cancer, this study aims to detect in primary glioblastoma noninvasively, using routine clinically acquired imaging.

Correlations of atrial diameter and frontooccipital horn ratio with ventricle size in fetal ventriculomegaly.

OBJECTIVE Fetal ventriculomegaly (FV), or enlarged cerebral ventricles in utero, is defined in fetal studies as an atrial diameter (AD) greater than 10 mm. In postnatal studies, the frontooccipital horn ratio (FOHR) is commonly used as a proxy for ventricle size (VS); however, its role in FV has not been assessed. Using image analysis techniques to quantify VS on fetal MR images, authors of the present study examined correlations between linear measures (AD and FOHR) and VS in patients with FV.

GLISTRboost: Combining Multimodal MRI Segmentation, Registration, and Biophysical Tumor Growth Modeling with Gradient Boosting Machines for Glioma Segmentation.

We present an approach for segmenting low- and high-grade gliomas in multimodal magnetic resonance imaging volumes. The proposed approach is based on a hybrid generative-discriminative model. Firstly, a generative approach based on an Expectation-Maximization framework that incorporates a glioma growth model is used to segment the brain scans into tumor, as well as healthy tissue labels.

Memory, executive, and multidomain subtle cognitive impairment: clinical and biomarker findings.

Objective: We studied the biomarker signatures and prognoses of 3 different subtle cognitive impairment (SCI) groups (executive, memory, and multidomain) as well as the subjective memory complaints (SMC) group.

Methods: We studied 522 healthy controls in the Alzheimer's Disease Neuroimaging Initiative (ADNI). Cutoffs for executive, memory, and multidomain SCI were defined using participants who remained cognitively normal (CN) for 7 years.