Gamma distribution model of diffusion MRI for the differentiation of primary central nerve system lymphomas and glioblastomas

The preoperative imaging-based differentiation of primary central nervous system lymphomas (PCNSLs) and glioblastomas (GBs) is of high importance since the therapeutic strategies differ substantially between these tumors. In this study, we investigate whether the gamma distribution (GD) model is useful in this differentiation of PNCSLs and GBs. Twenty-seven patients with PCNSLs and 57 patients with GBs were imaged with diffusion-weighted imaging using 13 b-values ranging from 0 to 1000 sec/mm2. The shape parameter (κ) and scale parameter (θ) were obtained with the GD model. Fractions of three different areas under the probability density function curve (f1, f2, f3) were defined as follows: f1, diffusion coefficient (D) <1.0×10−3 mm2/sec; f2, D >1.0×10−3 and <3.0×10−3 mm2/sec; f3, D >3.0 × 10−3 mm2/sec. The GD model-derived parameters were compared between PCNSLs and GBs. Receiver operating characteristic (ROC) curve analyses were performed to assess diagnostic performance. The correlations with intravoxel incoherent motion (IVIM)-derived parameters were evaluated. The PCNSL group's κ (2.26 ± 1.00) was significantly smaller than the GB group's (3.62 ± 2.01, p = 0.0004). The PCNSL group's f1 (0.542 ± 0.107) was significantly larger than the GB group's (0.348 ± 0.132, p<0.0001). The PCNSL group's f2 (0.372 ± 0.098) was significantly smaller than the GB group's (0.508 ± 0.127, p<0.0001). The PCNSL group's f3 (0.086 ± 0.043) was significantly smaller than the GB group's (0.144 ± 0.062, p<0.0001). The combination of κ, f1, and f3 showed excellent diagnostic performance (area under the curve, 0.909). The f1 had an almost perfect inverse correlation with D. The f2 and f3 had very strong positive correlations with D and f, respectively. The GD model is useful for the differentiation of GBs and PCNSLs.

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Diagnostic Performance of MRI Diffusion Weighted Heterogeneity Index to Discriminate Brain Metastases from Normal Brain Tissue in Hybrid PET/MR

10.21203/rs.3.rs-595957/v1 ◽

2021 ◽

Author(s):

Bahattin Özkul ◽

Bedriye Koyuncu Sokmen ◽

Ibrahim Halil Sever ◽

Nagihan Inan Gurcan

Keyword(s):

Diagnostic Performance ◽

Area Under The Curve ◽

Standardized Uptake Value ◽

Inverse Correlation ◽

Whole Body ◽

Normal Brain ◽

Heterogeneity Index ◽

Diffusion Weighted ◽

Potential Biomarker ◽

Significant Difference

Abstract IntroductionTo evaluate diagnostic performance of apparent diffusion coefficient (ADC) heterogeneity index to differentiate brain metastasis (BM) from normal appearing brain parenchyma (NABP) and to find out the correlation between 2-[18F]-fluoro-2-deoxy-D-glucose (18F-FDG) standardized uptake value (SUV) and ADC heterogeneity index derived from hybrid PET/MRI.MethodsWhole-body PET/MRI was performed to evaluate proven 40 BM of 18 oncology patients (9 females, 9 males; mean age 61±16 years), sourced from different primary cancer. Brain sequences, which were dixon and diffusion weighted imaging (DWI) protocols with simultaneous PET were used to calculate coefficient of variance of the ADC (ADCCV) and SUVmax. All images were assessed by three radiologists and the same size of VOI was placed on BM and NABP. Inter-rater reliability was tested by inter-class correlation (ICC). The correlation of ADCCV and SUVmax and the differences in ADC values and SUVmax between BM and NABP were investigated.ResultsThe excellent consistency was found between raters at ADCmean (0.972) and ADCCV (0.995). There was a strong correlation between ADCCV and SUVmax (r=0.763) and a slight inverse correlation between ADCmean and SUVmax (r=-0.122). A statistically significant difference between BM and NABP was determined for ADCCV (p<0.001) and SUVmax (p<0.001). An area under the curve (AUC) of 0.960, 0.998 and 0.574 were obtained with ROC analysis of SUVmax, ADCCV and ADCmean, respectively.ConclusionADCCV may be considered as a potential biomarker that quantitatively discriminates BM from NABP with excellent interrater reliability.

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Central Nerve System Impairment, AMA Guides, Sixth Edition: An Overview

Guides Newsletter ◽

10.1001/amaguidesnewsletters.2018.janfeb03 ◽

2018 ◽

Vol 23 (1) ◽

pp. 10-13

Author(s):

James B. Talmage ◽

Jay Blaisdell

Keyword(s):

Spinal Cord ◽

Central Nervous System ◽

Nervous System ◽

Neurological Impairment ◽

Sixth Edition ◽

Central Nerve System ◽

The Central Nervous System ◽

The One ◽

Nerve System ◽

The Brain

Abstract Injuries that affect the central nervous system (CNS) can be catastrophic because they involve the brain or spinal cord, and determining the underlying clinical cause of impairment is essential in using the AMA Guides to the Evaluation of Permanent Impairment (AMA Guides), in part because the AMA Guides addresses neurological impairment in several chapters. Unlike the musculoskeletal chapters, Chapter 13, The Central and Peripheral Nervous System, does not use grades, grade modifiers, and a net adjustment formula; rather the chapter uses an approach that is similar to that in prior editions of the AMA Guides. The following steps can be used to perform a CNS rating: 1) evaluate all four major categories of cerebral impairment, and choose the one that is most severe; 2) rate the single most severe cerebral impairment of the four major categories; 3) rate all other impairments that are due to neurogenic problems; and 4) combine the rating of the single most severe category of cerebral impairment with the ratings of all other impairments. Because some neurological dysfunctions are rated elsewhere in the AMA Guides, Sixth Edition, the evaluator may consult Table 13-1 to verify the appropriate chapter to use.

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Aromatherapy and the Central Nerve System (CNS): Therapeutic Mechanism and its Associated Genes

Current Drug Targets ◽

10.2174/1389450111314080007 ◽

2013 ◽

Vol 14 (8) ◽

pp. 872-879 ◽

Cited By ~ 32

Author(s):

Xiao Lv ◽

Zhu Liu ◽

Huan Zhang ◽

Chi Tzeng

Keyword(s):

Central Nerve System ◽

Therapeutic Mechanism ◽

Nerve System

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Glioblastoma and primary central nervous system lymphoma: differentiation using MRI derived first-order texture analysis – a machine learning study

The Neuroradiology Journal ◽

10.1177/1971400921998979 ◽

2021 ◽

pp. 197140092199897

Author(s):

Sarv Priya ◽

Caitlin Ward ◽

Thomas Locke ◽

Neetu Soni ◽

Ravishankar Pillenahalli Maheshwarappa ◽

...

Keyword(s):

Machine Learning ◽

Central Nervous System ◽

Nervous System ◽

Diagnostic Performance ◽

Model Performance ◽

Area Under The Curve ◽

Central Nervous System Lymphoma ◽

First Order ◽

Single Slice ◽

Contrast Enhanced

Objectives To evaluate the diagnostic performance of multiple machine learning classifier models derived from first-order histogram texture parameters extracted from T1-weighted contrast-enhanced images in differentiating glioblastoma and primary central nervous system lymphoma. Methods Retrospective study with 97 glioblastoma and 46 primary central nervous system lymphoma patients. Thirty-six different combinations of classifier models and feature selection techniques were evaluated. Five-fold nested cross-validation was performed. Model performance was assessed for whole tumour and largest single slice using receiver operating characteristic curve. Results The cross-validated model performance was relatively similar for the top performing models for both whole tumour and largest single slice (area under the curve 0.909–0.924). However, there was a considerable difference between the worst performing model (logistic regression with full feature set, area under the curve 0.737) and the highest performing model for whole tumour (least absolute shrinkage and selection operator model with correlation filter, area under the curve 0.924). For single slice, the multilayer perceptron model with correlation filter had the highest performance (area under the curve 0.914). No significant difference was seen between the diagnostic performance of the top performing model for both whole tumour and largest single slice. Conclusions T1 contrast-enhanced derived first-order texture analysis can differentiate between glioblastoma and primary central nervous system lymphoma with good diagnostic performance. The machine learning performance can vary significantly depending on the model and feature selection methods. Largest single slice and whole tumour analysis show comparable diagnostic performance.

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Human iPSC-Based Modeling of Central Nerve System Disorders for Drug Discovery

International Journal of Molecular Sciences ◽

10.3390/ijms22031203 ◽

2021 ◽

Vol 22 (3) ◽

pp. 1203

Author(s):

Lu Qian ◽

Julia TCW

Keyword(s):

Drug Discovery ◽

Disease Modeling ◽

Three Dimensional ◽

Structural Complexity ◽

Induced Pluripotent Stem Cell ◽

Cell Types ◽

Central Nerve System ◽

Human Ipscs ◽

Nerve System

A high-throughput drug screen identifies potentially promising therapeutics for clinical trials. However, limitations that persist in current disease modeling with limited physiological relevancy of human patients skew drug responses, hamper translation of clinical efficacy, and contribute to high clinical attritions. The emergence of induced pluripotent stem cell (iPSC) technology revolutionizes the paradigm of drug discovery. In particular, iPSC-based three-dimensional (3D) tissue engineering that appears as a promising vehicle of in vitro disease modeling provides more sophisticated tissue architectures and micro-environmental cues than a traditional two-dimensional (2D) culture. Here we discuss 3D based organoids/spheroids that construct the advanced modeling with evolved structural complexity, which propels drug discovery by exhibiting more human specific and diverse pathologies that are not perceived in 2D or animal models. We will then focus on various central nerve system (CNS) disease modeling using human iPSCs, leading to uncovering disease pathogenesis that guides the development of therapeutic strategies. Finally, we will address new opportunities of iPSC-assisted drug discovery with multi-disciplinary approaches from bioengineering to Omics technology. Despite technological challenges, iPSC-derived cytoarchitectures through interactions of diverse cell types mimic patients’ CNS and serve as a platform for therapeutic development and personalized precision medicine.

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Diagnostic Performance and Accuracy of the MNA-SF against GLIM Criteria in Community-Dwelling Older Adults from Poland

Nutrients ◽

10.3390/nu13072183 ◽

2021 ◽

Vol 13 (7) ◽

pp. 2183

Author(s):

Aleksandra Kaluźniak-Szymanowska ◽

Roma Krzymińska-Siemaszko ◽

Marta Lewandowicz ◽

Ewa Deskur-Śmielecka ◽

Katarzyna Stachnik ◽

...

Keyword(s):

At Risk ◽

Diagnostic Performance ◽

Nutritional Assessment ◽

Short Form ◽

Area Under The Curve ◽

Mini Nutritional Assessment ◽

Community Dwelling ◽

Study Results ◽

Community Dwelling Older Adults ◽

Test Result

Up to 28% of elderly residents in Europe are at risk of malnutrition. As uniform diagnostic criteria for malnutrition have not been formulated, in autumn 2018, the Global Leadership Initiative on Malnutrition (GLIM) presented a consensus on its diagnosis. According to the consensus, the diagnosis of malnutrition requires a positive screening test result for the risk of malnutrition, and the presence of at least one etiologic and one phenotypic criterion. This study aimed to assess the diagnostic performance and accuracy of the Mini Nutritional Assessment—Short Form (MNA-SF) against GLIM criteria. The analysis involved 273 community-dwelling volunteers aged ≥ 60 years. All participants were screened for malnutrition with the MNA-SF questionnaire. Next, the GLIM phenotypic and etiologic criteria were assessed in all subjects. Based on the presence of at least one phenotypic and one etiologic criterion, malnutrition was diagnosed in more than one-third of participants (n = 103, 37.7%). According to the MNA-SF, only 7.3% of subjects had malnutrition, and 28.2% were at risk of malnutrition. The agreement between the MNA-SF score and the GLIM criteria were observed in only 22.3% of the population. The sensitivity and specificity of MNA-SF against the GLIM criteria were fair (59.2% and 78.8%, respectively). The area under the curve (AUC) was 0.77, indicating the fair ability of MNA-SF to diagnose malnutrition. Based on the present study results, the best solution may be an optional replacement of the screening tool in the first step of the GLIM algorithm with clinical suspicion of malnutrition.

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Gamma Distribution Model To Provide a Direct Assessment of the Overall Quality of Quantum Monte Carlo-Generated Electron Distributions†

The Journal of Physical Chemistry A ◽

10.1021/jp075790e ◽

2008 ◽

Vol 112 (10) ◽

pp. 2012-2017 ◽

Cited By ~ 7

Author(s):

Braden Coles ◽

Paul Vrbik ◽

Robert D. Giacometti ◽

Stuart M. Rothstein

Keyword(s):

Monte Carlo ◽

Gamma Distribution ◽

Quantum Monte Carlo ◽

Distribution Model ◽

Direct Assessment ◽

Electron Distributions

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Comparison of prostate imaging reporting and data system v2.1 and 2 in transition and peripheral zones: evaluation of interreader agreement and diagnostic performance in detecting clinically significant prostate cancer

British Journal of Radiology ◽

10.1259/bjr.20201434 ◽

2021 ◽

pp. 20201434

Author(s):

Yasuyo Urase ◽

Yoshiko Ueno ◽

Tsutomu Tamada ◽

Keitaro Sofue ◽

Satoru Takahashi ◽

...

Keyword(s):

Prostate Cancer ◽

Diagnostic Performance ◽

Area Under The Curve ◽

Data System ◽

Transition Zones ◽

Prostate Imaging ◽

Institutional Review ◽

3.0 T ◽

Clinically Significant ◽

Interreader Agreement

Objectives: To evaluate the interreader agreement and diagnostic performance of the Prostate Imaging Reporting and Data System (PI-RADS) v2.1, in comparison with v2. Methods: Institutional review board approval was obtained for this retrospective study. Seventy-seven consecutive patients who underwent a prostate multiparametric magnetic resonance imaging at 3.0 T before radical prostatectomy were included. Four radiologists (two experienced uroradiologists and two inexperienced radiologists) independently scored eight regions [six peripheral zones (PZ) and two transition zones (TZ)] using v2.1 and v2. Interreader agreement was assessed using κ statistics. To evaluate diagnostic performance for clinically significant prostate cancer (csPC), area under the curve (AUC) was estimated. Results 228 regions were pathologically diagnosed as positive for csPC. With a cutoff ≥3, the agreement among all readers was better with v2.1 than v2 in TZ, PZ, or both zones combined (κ-value: TZ, 0.509 vs 0.414; PZ, 0.686 vs 0.568; both zones combined, 0.644 vs 0.531). With a cutoff ≥4, the agreement among all readers was also better with v2.1 than v2 in the PZ or both zones combined (κ-value: PZ, 0.761 vs 0.701; both zones combined, 0.756 vs 0.709). For all readers, AUC with v2.1 was higher than with v2 (TZ, 0.826–0.907 vs 0.788–0.856; PZ, 0.857–0.919 vs 0.853–0.902). Conclusions: Our study suggests that the PI-RADS v2.1 could improve the interreader agreement and might contribute to improved diagnostic performance compared with v2. Advances in knowledge: PI-RADS v2.1 has a potential to improve interreader variability and diagnostic performance among radiologists with different levels of expertise.

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Development of the first georeferenced map of Rhipicephalus (Boophilus) spp. in Mexico from 1970 to date and prediction of its spatial distribution

Geospatial health ◽

10.4081/gh.2018.624 ◽

2018 ◽

Vol 13 (1) ◽

Cited By ~ 2

Author(s):

Yazmin Alcala-Canto ◽

Juan Antonio Figueroa-Castillo ◽

Froylán Ibarra-Velarde ◽

Yolanda Vera-Montenegro ◽

María Eugenia Cervantes-Valencia ◽

...

Keyword(s):

Spatial Distribution ◽

Environmental Variables ◽

Control Strategies ◽

Integrated Control ◽

Area Under The Curve ◽

Entropy Method ◽

Distribution Model ◽

Economic Losses ◽

Precipitation Seasonality ◽

Epidemiological Risk

The tick genus Ripicephalus (Boophilus), particularly R. microplus, is one of the most important ectoparasites that affects livestock health and considered an epidemiological risk because it causes significant economic losses due, mainly, to restrictions in the export of infested animals to several countries. Its spatial distribution has been tied to environmental factors, mainly warm temperatures and high relative humidity. In this work, we integrated a dataset consisting of 5843 records of Rhipicephalus spp., in Mexico covering close to 50 years to know which environmental variables mostly influence this ticks’ distribution. Occurrences were georeferenced using the software DIVA-GIS and the potential current distribution was modelled using the maximum entropy method (Maxent). The algorithm generated a map of high predictive capability (Area under the curve = 0.942), providing the various contribution and permutation importance of the tested variables. Precipitation seasonality, particularly in March, and isothermality were found to be the most significant climate variables in determining the probability of spatial distribution of Rhipicephalus spp. in Mexico (15.7%, 36.0% and 11.1%, respectively). Our findings demonstrate that Rhipicephalus has colonized Mexico widely, including areas characterized by different types of climate. We conclude that the Maxent distribution model using Rhipicephalus records and a set of environmental variables can predict the extent of the tick range in this country, information that should support the development of integrated control strategies.

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