Algorithmic Analysis of Clinical, Neuropsychological, and Imaging Data in Localization-Related Epilepsy

2019 ◽  
pp. 46-79
Author(s):  
Masoud Latifinavid ◽  
Kost Elisevich ◽  
Hamid Soltanian-Zadeh
Keyword(s):  
Imaging Modality ◽  
Brain Structures ◽  
Anatomical Landmarks ◽  
Imaging Features ◽  
Imaging Data ◽  
Neuropsychological Profile ◽  
Algorithmic Analysis ◽  
Algorithmic Approaches ◽  
Physical Findings ◽  
Selection Of Patients

The current study examines algorithmic approaches for analysis of multimodal attributes in localization-related epilepsy (LRE), specifically, their impact on the selection of patients for surgical consideration. Invasive electrographic data is excluded here to concentrate upon the localized anatomical landmarks and identified/initialized brain structures in volumetric MR images as well as initial clinical presentation and the varied elements of the seizure history, ictal semiology, risk and seizure-precipitating factors and physical findings in addition to several features of the neuropsychological profile including various parameters of cognition and both speech and memory lateralization. First, the imaging modality data is excluded and just clinical, electrographic and neuropsychological data are investigated. Afterward, the imaging data are investigated and a comparison between the prediction results of the two types of data is done. In the case of using non-imaging multimodal data, 56% and using imaging features, about 71% of correct outcome prediction was obtained.

Algorithmic Analysis of Clinical and Neuropsychological Data in Localization-Related Epilepsy

2014 ◽  
Vol 4 (1) ◽  
pp. 33-58
Author(s):  
Masoud Latifi-Navid ◽  
Kost V. Elisevich ◽  
Hamid Soltanian-Zadeh
Keyword(s):  
Feature Selection ◽  
Imaging Data ◽  
Neuropsychological Profile ◽  
Search Tool ◽  
Clustering And Classification ◽  
Algorithmic Analysis ◽  
Algorithmic Approaches ◽  
Mlp Classifier ◽  
Classification Tool ◽  
Evaluation Approaches

The current study examines algorithmic approaches for analysis of nonimaging (i.e., clinical, electrographic and neuropsychological) attributes in localization-related epilepsy (LRE), specifically, their impact on the selection of patients for surgical consideration. Both invasive electrographic and imaging data are excluded here to concentrate upon the initial clinical presentation and the varied elements of the seizure history, ictal semiology, risk and seizure-precipitating factors and physical findings in addition to several features of the neuropsychological profile including various parameters of cognition and both speech and memory lateralization. The data was accrued in a database of temporal lobe epilepsy patients (HBIDS). Six algorithms comprising feature selection, clustering and classification approaches were used. The Correlation-Based Feature Selection (CFS) and the Classifier Subset Evaluator (CSE) with the Genetic Algorithm (GA) search tool and ReliefF Attribute Evaluation approaches provided for feature selection. The Expectation Maximization (EM) Class Clustering and Incremental Conceptual Clustering (COBWEB) provided data clustering and the Multilayer Perceptron (MLP) Classifier was the classification tool at all stages of the study. The Engel Classification was used as an output of classifier for surgical success. Attributes demonstrating the highest correlation with the outcome class and the least intercorrelation with each other, according to CFS, were selected. These were then ranked using ReliefF and the top rankings chosen. The best attribute combination for each cluster was found by MLP. COBWEB provided the best results showing an association of 56% with Engel class. In conclusion, an algorithmic approach to the study of LRE is feasible with current findings supporting the need for correlative electrographic and imaging data and a greater archival population.

scholarly journals Combining A Second-Generation Multivariate Index Assay with Ovarian Imaging Improves the Preoperative Assessment of An Adnexal Mass

2019 ◽  
pp. 1-9
Author(s):  
Rowan G. Bullock ◽  
Alan Smith ◽  
Donald G. Munroe ◽  
Frederick R. Ueland ◽  
Scott T. Goodrich ◽  
...  
Keyword(s):  
Adnexal Mass ◽  
Clinical Performance ◽  
Imaging Modality ◽  
Preoperative Assessment ◽  
Lower Sensitivity ◽  
Imaging Features ◽  
Imaging Data ◽  
Serum Samples ◽  
Pelvic Imaging ◽  
Post Menopausal

Background: To understand the relationship between imaging and the next generation multivariate index assay (MIA2G) in the preoperative assessment of an adnexal mass. Methods: Serum samples and imaging data from two previously published studies are reanalyzed using the MIA2G test. We calculated the clinical performance of MIA2G and discrete imaging features associated with malignant risk. Results: 878 women were eligible for this analysis, 48.3% post-menopausal and 51.7% pre-menopausal. The prevalence of having a malignant pathology was 18%. Ultrasound was the most frequently used imaging modality. The combination of MIA2G “or” ultrasound resulted in higher sensitivity than either test alone, 93.5% compared to 87.6% for MIA2G and 74.2% for ultrasound. The negative predictive value was high: 94.6% for ultrasound, 98.1% for MIA2G “or” ultrasound. MIA2G “and” ultrasound had higher specificity but lower sensitivity than MIA2G or ultrasound alone. Similar results were seen for CT scan when evaluated with MIA2G. Conclusion: MIA2G and pelvic imaging are complementary tests and interpreting them together can provide important information about the malignant risk of an ovarian tumor. For physicians making decisions about a referral to a specialist, the combination of MIA2G “or” ultrasound has the highest sensitivity in predicting ovarian malignancy.

Myxofibrosarcoma: clinical and prognostic value of MRI features

2020 ◽  
Vol 16 ◽  
Author(s):  
Paolo Spinnato ◽  
Andrea Sambri ◽  
Tomohiro Fujiwara ◽  
Luca Ceccarelli ◽  
Roberta Clinca ◽  
...  
Keyword(s):  
Soft Tissue ◽  
Local Recurrence ◽  
Contrast Enhancement ◽  
Imaging Modality ◽  
Soft Tissue Sarcomas ◽  
The Elderly ◽  
High Rate ◽  
Imaging Features ◽  
High Grade ◽  
Mri Features

: Myxofibrosarcoma is one of the most common soft tissue sarcomas in the elderly. It is characterized by an extremely high rate of local recurrence, higher than other soft tissue tumors, and a relatively low risk of distant metastases.Magnetic resonance imaging (MRI) is the imaging modality of choice for the assessment of myxofibrosarcoma and plays a key role in the preoperative setting of these patients.MRI features associated with high risk of local recurrence are: high myxoid matrix content (water-like appearance of the lesions), high grade of contrast enhancement, presence of an infiltrative pattern (“tail sign”). On the other hand, MRI features associated with worse sarcoma specific survival are: large size of the lesion, deep location, high grade of contrast enhancement. Recognizing the above-mentioned imaging features of myxofibrosarcoma may be helpful to stratify the risk for local recurrence and disease-specific survival. Moreover, the surgical planning should be adjusted according to the MRI features

Subtle signs of breast cancer as an important pitfall in mammographic interpretation. Establishing an accurate diagnosis in an equivocal case

2021 ◽  
pp. 1-6
Author(s):  
Nikolaos S. Salemis ◽  
Eleni Mourtzoukou ◽  
Michail Angelopoulos
Keyword(s):  
Breast Cancer ◽  
Imaging Modality ◽  
Benign Lesion ◽  
Imaging Features ◽  
Breast Cancers ◽  
Breast Clinic ◽  
Delay In Diagnosis ◽  
Delay Of Treatment ◽  
Equivocal Case ◽  
Related Mortality

Mammogram is the standard imaging modality for the early detection of breast cancer, and it has been shown to reduce disease-related mortality by up to 30%. Mammogram, however, has its limitations. It is reported that 10–30% of breast cancers may be missed on a mammogram. Delay in the diagnosis and treatment may adversely affect the prognosis of patients with breast cancer. We present a case of multifocal invasive early breast carcinoma, which was misinterpreted twice as intramammary lymph nodes, thus resulting in a delay in diagnosis for eighteen months. The tumors were detected incidentally after the patient presented to our Breast clinic for symptoms related to a concomitant benign lesion involving the same breast. We describe the tumors’ imaging features and discuss the possible reasons that likely led to repeated misinterpretation. Awareness of possible causes for missed breast cancer is necessary to avoid delay of treatment initiation that may adversely affect prognosis.

scholarly journals Radiomics in radiation oncology—basics, methods, and limitations

2020 ◽  
Vol 196 (10) ◽  
pp. 848-855
Author(s):  
Philipp Lohmann ◽  
Khaled Bousabarah ◽  
Mauritius Hoevels ◽  
Harald Treuer
Keyword(s):  
Mathematical Models ◽  
Quantitative Imaging ◽  
Human Perception ◽  
Image Features ◽  
Radiotherapy Planning ◽  
Large Field ◽  
Imaging Features ◽  
Imaging Data ◽  
Complete Evaluation ◽  
Vast Number

Abstract Over the past years, the quantity and complexity of imaging data available for the clinical management of patients with solid tumors has increased substantially. Without the support of methods from the field of artificial intelligence (AI) and machine learning, a complete evaluation of the available image information is hardly feasible in clinical routine. Especially in radiotherapy planning, manual detection and segmentation of lesions is laborious, time consuming, and shows significant variability among observers. Here, AI already offers techniques to support radiation oncologists, whereby ultimately, the productivity and the quality are increased, potentially leading to an improved patient outcome. Besides detection and segmentation of lesions, AI allows the extraction of a vast number of quantitative imaging features from structural or functional imaging data that are typically not accessible by means of human perception. These features can be used alone or in combination with other clinical parameters to generate mathematical models that allow, for example, prediction of the response to radiotherapy. Within the large field of AI, radiomics is the subdiscipline that deals with the extraction of quantitative image features as well as the generation of predictive or prognostic mathematical models. This review gives an overview of the basics, methods, and limitations of radiomics, with a focus on patients with brain tumors treated by radiation therapy.

scholarly journals Multidimensional brain-age prediction reveals altered brain developmental trajectory in psychiatric disorders

2020 ◽  
Author(s):  
Xin Niu ◽  
Alexei Taylor ◽  
Russell T. Shinohara ◽  
John Kounios ◽  
Fengqing Zhang
Keyword(s):  
Psychiatric Disorders ◽  
Robust Regression ◽  
Developmental Trajectories ◽  
Brain Structures ◽  
Brain Regions ◽  
Developmental Trajectory ◽  
Imaging Features ◽  
Neuroimaging Data ◽  
Brain Age ◽  
Age Prediction

AbstractBrain regions change in different ways and at different rates. This staggered developmental unfolding is determined by genetics and postnatal experience and is implicated in the progression of psychiatric and neurological disorders. Neuroimaging-based brain-age prediction has emerged as an important new approach for studying brain development. However, the unidimensional brain-age estimates provided by previous methods do not capture the divergent developmental trajectories of various brain structures. Here we propose and illustrate an analytic pipeline to compute an index of multidimensional brain-age that provides regional age predictions. First, using a database of 556 subjects that includes psychiatric and neurological patients as well as healthy controls we conducted robust regression to characterize the developmental trajectory of each MRI-based brain-imaging feature. We then utilized cluster analysis to identify subgroups of imaging features with a similar developmental trajectory. For each identified cluster, we obtained a brain-age prediction by applying machine-learning models with imaging features belonging to each cluster. Brain-age predictions from multiple clusters form a multidimensional brain-age index (MBAI). The MBAI is more sensitive to alterations in brain structures and captured distinct regional change patterns. In particular, the MBAI provided a more flexible analysis of brain age across brain regions that revealed changes in specific structures in psychiatric disorders that would otherwise have been combined in a unidimensional brain age prediction. More generally, brain-age prediction using a subset of homogeneous features circumvents the curse of dimensionality in neuroimaging data.

scholarly journals Integrating Transcriptomics, Genomics, and Imaging in Alzheimer's Disease: A Federated Model

2021 ◽  
Author(s):  
Jianfeng Wu ◽  
Yanxi Chen ◽  
Panwen Wang ◽  
Richard J Caselli ◽  
Paul M Thompson ◽  
...  
Keyword(s):  
Gene Expression ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Imaging Modality ◽  
Association Studies ◽  
Imaging Genetics ◽  
Health Concern ◽  
Genome Wide Association Studies ◽  
Imaging Data ◽  
Stable Performance

Alzheimer's disease (AD) affects more than 1 in 9 people age 65 and older and becomes an urgent public health concern as the global population ages. In clinical practice, structural magnetic resonance imaging (sMRI) is the most accessible and widely used diagnostic imaging modality. Additionally, genome-wide association studies (GWAS) and transcriptomic, the study of gene expression, also play an important role in understanding AD etiology and progression. Sophisticated imaging genetics systems have been developed to discover genetic factors that consistently affect brain function and structure. However, most studies to date focused on the relationships between brain sMRI and GWAS or brain sMRI and transcriptomics. To our knowledge, few methods have been developed to discover and infer multimodal relationships among sMRI, GWAS, and transcriptomics. To address this, we propose a novel federated model, Genotype-Expression-Imaging Data Integration (GEIDI), to identify genetic and transcriptomic influences on brain sMRI measures. The relationships between brain imaging measures and gene expression are allowed to depend on a person's genotype at the single-nucleotide polymorphism (SNP) level, making the inferences adaptive and personalized. We performed extensive experiments on publicly available Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. Experimental results demonstrated our proposed method outperformed state-of-the-art expression quantitative trait loci (eQTL) methods for detecting genetic and transcriptomic factors related to AD and has stable performance when data are integrated from multiple sites. Our GEIDI approach may offer novel insights into the relationship among image biomarkers, genotypes, and gene expression and help discover novel genetic targets for potential AD drug treatments.

scholarly journals Inter-observer agreement on the evaluation of automated breast volume scanner for breast masses

2022 ◽  
pp. 1-11
Author(s):  
Yingchun Liu ◽  
Lin Chen ◽  
Jia Zhan ◽  
Xuehong Diao ◽  
Yun Pang ◽  
...  
Keyword(s):  
Imaging Modality ◽  
Observer Agreement ◽  
Breast Volume ◽  
Imaging Data ◽  
Predictive Values ◽  
Breast Masses ◽  
Cutoff Value ◽  
Automated Breast Volume Scanner ◽  
The Masses ◽  
Sensitivity Specificity

Objective: To explore inter-observer agreement on the evaluation of automated breast volume scanner (ABVS) for breast masses. Methods: A total of 846 breast masses in 630 patients underwent ABVS examinations. The imaging data were independently interpreted by senior and junior radiologists regarding the mass size ([Formula: see text][Formula: see text]cm, [Formula: see text][Formula: see text]cm and total). We assessed inter-observer agreement of BI-RADS lexicons, unique descriptors of ABVS coronal planes. Using BI-RADS 3 or 4a as a cutoff value, the diagnostic performances for 331 masses with pathological results in 253 patients were assessed. Results: The overall agreements were substantial for BI-RADS lexicons ([Formula: see text]–0.779) and the characteristics on the coronal plane of ABVS ([Formula: see text]), except for associated features ([Formula: see text]). However, the overall agreement was moderate for orientation ([Formula: see text]) for the masses [Formula: see text][Formula: see text]cm. The agreements were substantial to be perfect for categories 2, 3, 4, 5 and overall ([Formula: see text]–0.918). However, the agreements were moderate to substantial for categories 4a ([Formula: see text]), 4b ([Formula: see text]), and 4c ([Formula: see text]), except for category 4b of the masses [Formula: see text][Formula: see text]cm ([Formula: see text]). Moreover, for radiologists 1 and 2, there were no significant differences in sensitivity, specificity, accuracy, positive and negative predictive values with BI-RADS 3 or 4a as a cutoff value ([Formula: see text] for all). Conclusion: ABVS is a reliable imaging modality for the assessment of breast masses with good inter-observer agreement.

scholarly journals Correlating MRI-Based Brain Volumetry and Cognitive Assessment in Down Syndrome Patients

2021 ◽  
Author(s):  
Osama Hamadelseed ◽  
Thomas Skutella
Keyword(s):  
Down Syndrome ◽  
Cognitive Abilities ◽  
Cognitive Assessment ◽  
Brain Structures ◽  
Brain Regions ◽  
Cognitive Behavioral ◽  
Brain Volumetry ◽  
Neuropsychological Profile ◽  
Magnetic Resonance Imaging Mri ◽  
And Gender

Abstract INTRODUCTION: Down syndrome (DS) is the most common genetic cause of intellectual disability. Here, we use magnetic resonance imaging (MRI) on children and adults with DS to characterize changes in the volume of specific brain structures involved in memory and language and their relationship to features of cognitive-behavioral phenotypes.METHODS: Thirteen children and adults with the DS phenotype and 12 age- and gender-matched healthy controls were analyzed by MRI and underwent a psychological evaluation for language and cognitive abilities.RESULTS: The neuropsychological profile of DS patients showed deficits in different cognition and language domains in correlation with reduced volumes of specific regional and subregional brain structures.CONCLUSIONS: The memory functions and language skills affected in our DS patients correlate significantly with the reduced volume of specific brain regions, allowing us to understand DS's cognitive-behavioral phenotype. Our results provide an essential basis for early intervention and the design of rehabilitation management protocols.

Dumbbell Nerve Sheath Tumors

2018 ◽  
Author(s):  
Hussam Abou-Al-Shaar ◽  
Mark A. Mahan
Keyword(s):  
Imaging Modality ◽  
Spinal Nerve ◽  
Surgical Approaches ◽  
Nerve Sheath Tumor ◽  
Imaging Features ◽  
Neural Foramen ◽  
Dumbbell Tumor ◽  
Nerve Sheath Tumors ◽  
Nerve Sheath ◽  
The Individual

A dumbbell tumor is a nerve sheath tumor that arises from a spinal nerve in the neural foramen and grows as a dumbbell-shaped mass. The differential diagnosis for a dumbbell tumor includes schwannoma, neurofibroma, malignant peripheral nerve sheath tumor, and metastases, among others. MR imaging is considered the gold-standard imaging modality for diagnosis of dumbbell tumors. Surgical approaches that are tailored to the individual patient’s case can be utilized. The chapter reviews dumbbell tumors, including a case example and covers the incidence, clinical presentation, imaging features, decision-making strategy, surgical approaches, outcomes, and potential complications associated with their management.

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