scholarly journals Comparing the Performance of Two Radiomic Models to Predict Progression and Progression Speed of White Matter Hyperintensities

2021 ◽  
Vol 15 ◽  
Author(s):  
Yuan Shao ◽  
Jingru Ruan ◽  
Yuyun Xu ◽  
Zhenyu Shu ◽  
Xiaodong He
Keyword(s):  
White Matter ◽  
Rating Scale ◽  
Medical Center ◽  
External Validation ◽  
Area Under The Curve ◽  
Magnetic Resonance Images ◽  
White Matter Hyperintensity ◽  
Diagnostic Efficiency ◽  
Combined Model ◽  
Brain White Matter

Purpose: The aim of this study was to compare two radiomic models in predicting the progression of white matter hyperintensity (WMH) and the speed of progression from conventional magnetic resonance images.Methods: In this study, 232 people were retrospectively analyzed at Medical Center A (training and testing groups) and Medical Center B (external validation group). A visual rating scale was used to divide all patients into WMH progression and non-progression groups. Two regions of interest (ROIs)—ROI whole-brain white matter (WBWM) and ROI WMH penumbra (WMHp)—were segmented from the baseline image. For predicting WMH progression, logistic regression was applied to create radiomic models in the two ROIs. Then, age, sex, clinical course, vascular risk factors, and imaging factors were incorporated into a stepwise regression analysis to construct the combined diagnosis model. Finally, the presence of a correlation between radiomic findings and the speed of progression was analyzed.Results: The area under the curve (AUC) was higher for the WMHp-based radiomic model than the WBWM-based radiomic model in training, testing, and validation groups (0.791, 0.768, and 0.767 vs. 0.725, 0.693, and 0.691, respectively). The WBWM-based combined model was established by combining age, hypertension, and rad-score of the ROI WBWM. Also, the WMHp-based combined model is built by combining the age and rad-score of the ROI WMHp. Compared with the WBWM-based model (AUC = 0.779, 0.716, 0.673 in training, testing, and validation groups, respectively), the WMHp-based combined model has higher diagnostic efficiency and better generalization ability (AUC = 0.793, 0.774, 0.777 in training, testing, and validation groups, respectively). The speed of WMH progression was related to the rad-score from ROI WMHp (r = 0.49) but not from ROI WBWM.Conclusion: The heterogeneity of the penumbra could help identify the individuals at high risk of WMH progression and the rad-score of it was correlated with the speed of progression.

scholarly journals QOL-09. WHOLE-BRAIN WHITE MATTER NETWORK CONNECTIVITY IS DISRUPTED BY PEDIATRIC BRAIN TUMOR TREATMENT

2020 ◽  
Vol 22 (Supplement_3) ◽  
pp. iii432-iii432
Author(s):  
Adeoye Oyefiade ◽  
Kiran Beera ◽  
Iska Moxon-Emre ◽  
Jovanka Skocic ◽  
Ute Bartels ◽  
...  
Keyword(s):  
White Matter ◽  
Pediatric Brain Tumor ◽  
Pediatric Brain Tumors ◽  
Magnetic Resonance Images ◽  
Long Term Effects ◽  
Brain White Matter ◽  
The Right ◽  
Pediatric Brain ◽  
Betweeness Centrality

Abstract INTRODUCTION Treatments for pediatric brain tumors (PBT) are neurotoxic and lead to long-term deficits that are driven by the perturbation of underlying white matter (WM). It is unclear if and how treatment may impair WM connectivity across the entire brain. METHODS Magnetic resonance images from 41 PBT survivors (mean age: 13.19 years, 53% M) and 41 typically developing (TD) children (mean age: 13.32 years, 51% M) were analyzed. Image reconstruction, segmentation, and node parcellation were completed in FreeSurfer. DTI maps and probabilistic streamline generation were completed in MRtrix3. Connectivity matrices were based on the number of streamlines connecting two nodes and the mean DTI (FA) index across streamlines. We used graph theoretical analyses to define structural differences between groups, and random forest (RF) analyses to identify hubs that reliably classify PBT and TD children. RESULTS For survivors treated with radiation, betweeness centrality was greater in the left insular (p < 0.000) but smaller in the right pallidum (p < 0.05). For survivors treated without radiation (surgery-only), betweeness centrality was smaller in the right interparietal sulcus (p < 0.05). RF analyses showed that differences in WM connectivity from the right pallidum to other parts of the brain reliably classified PBT survivors from TD children (classification accuracy = 77%). CONCLUSIONS The left insular, right pallidum, and right inter-parietal sulcus are structurally perturbed hubs in PBT survivors. WM connectivity from the right pallidum is vulnerable to the long-term effects of treatment for PBT.

[P2-420]: A COMPARISON OF BRAIN WHITE MATTER HYPERINTENSITY BURDEN ASSESSED IN VIVO AND EX VIVO

2017 ◽  
Vol 13 (7S_Part_16) ◽  
pp. P794-P795
Author(s):  
Arman P. Kulkarni ◽  
Arnold M. Evia ◽  
Julie A. Schneider ◽  
David A. Bennett ◽  
Konstantinos Arfanakis
Keyword(s):  
White Matter ◽  
Ex Vivo ◽  
White Matter Hyperintensity ◽  
Brain White Matter

scholarly journals The Effect of Training Sample Size on the Prediction of White Matter Hyperintensity Volume in a Healthy Population Using BIANCA

2022 ◽  
Vol 13 ◽  
Author(s):  
Niklas Wulms ◽  
Lea Redmann ◽  
Christine Herpertz ◽  
Nadine Bonberg ◽  
Klaus Berger ◽  
...  
Keyword(s):  
White Matter ◽  
Sample Size ◽  
Mean Absolute Error ◽  
External Validation ◽  
Similarity Index ◽  
Absolute Error ◽  
Training Sample ◽  
White Matter Hyperintensity ◽  
Training Sample Size ◽  
The Difference

Introduction: White matter hyperintensities of presumed vascular origin (WMH) are an important magnetic resonance imaging marker of cerebral small vessel disease and are associated with cognitive decline, stroke, and mortality. Their relevance in healthy individuals, however, is less clear. This is partly due to the methodological challenge of accurately measuring rare and small WMH with automated segmentation programs. In this study, we tested whether WMH volumetry with FMRIB software library v6.0 (FSL; https://fsl.fmrib.ox.ac.uk/fsl/fslwiki) Brain Intensity AbNormality Classification Algorithm (BIANCA), a customizable and trainable algorithm that quantifies WMH volume based on individual data training sets, can be optimized for a normal aging population.Methods: We evaluated the effect of varying training sample sizes on the accuracy and the robustness of the predicted white matter hyperintensity volume in a population (n = 201) with a low prevalence of confluent WMH and a substantial proportion of participants without WMH. BIANCA was trained with seven different sample sizes between 10 and 40 with increments of 5. For each sample size, 100 random samples of T1w and FLAIR images were drawn and trained with manually delineated masks. For validation, we defined an internal and external validation set and compared the mean absolute error, resulting from the difference between manually delineated and predicted WMH volumes for each set. For spatial overlap, we calculated the Dice similarity index (SI) for the external validation cohort.Results: The study population had a median WMH volume of 0.34 ml (IQR of 1.6 ml) and included n = 28 (18%) participants without any WMH. The mean absolute error of the difference between BIANCA prediction and manually delineated masks was minimized and became more robust with an increasing number of training participants. The lowest mean absolute error of 0.05 ml (SD of 0.24 ml) was identified in the external validation set with a training sample size of 35. Compared to the volumetric overlap, the spatial overlap was poor with an average Dice similarity index of 0.14 (SD 0.16) in the external cohort, driven by subjects with very low lesion volumes.Discussion: We found that the performance of BIANCA, particularly the robustness of predictions, could be optimized for use in populations with a low WMH load by enlargement of the training sample size. Further work is needed to evaluate and potentially improve the prediction accuracy for low lesion volumes. These findings are important for current and future population-based studies with the majority of participants being normal aging people.

Abstract TMP56: Differential Effect of Left versus Right White Matter Hyperintensity Burden on Functional Decline: The Northern Manhattan Study

Stroke ◽  
2017 ◽  
Vol 48 (suppl_1) ◽  
Author(s):  
Mandip S Dhamoon ◽  
Ying-Kuen Cheung ◽  
Ahmet M Bagci ◽  
Dalila Varela ◽  
Noam Alperin ◽  
...  
Keyword(s):  
White Matter ◽  
Large Population ◽  
Functional Decline ◽  
Frontal Lobes ◽  
White Matter Hyperintensity ◽  
Periventricular White Matter ◽  
Parietal Lobes ◽  
Brain White Matter ◽  
The Right

Background: We previously showed that overall brain white matter hyperintensity volume (WMHV) was associated with accelerated long-term functional decline. Asymmetry of brain dysfunction may disrupt brain network efficiency. We hypothesized that greater left-right WMHV asymmetry was associated with functional trajectories. Methods: In the Northern Manhattan MRI study, participants had brain MRI with axial T1, T2, and fluid attenuated inversion recovery sequences, with baseline interview and examination. Volumetric WMHV distribution across 14 brain regions (brainstem, cerebellum, and bilateral frontal, occipital, temporal, and parietal lobes, and bilateral anterior and posterior periventricular white matter) was determined separately by combining bimodal image intensity distribution and atlas based methods.. Participants had annual functional assessments with the Barthel index (BI, range 0-100) over a mean of 7.3 years. Generalized estimating equations models estimated associations of regional WMHV and regional left-right asymmetry with baseline BI and change over time, adjusted for baseline medical risk factors, sociodemographics, and cognition, and stroke and myocardial infarction during follow-up. Results: Among 1195 participants, mean age was 71 (SD 9) years, 39% were male, 67% had hypertension and 19% diabetes. Greater WMHV asymmetry in the frontal lobes (-3.53 BI points per unit greater WMHV on the right compared to left, 95% CI -0.18, -6.88) and whole brain (-7.23 BI points, 95% CI 0.07, -14.54) was associated with lower overall function. Greater WMHV asymmetry in the frontal lobes (-0.74 additional BI points per year per unit greater WMHV on the right compared to left, 95% CI 0.05, -1.54) and parietal lobes (1.11 additional BI points per year, 95% CI 0.30, 1.93) was independently associated with accelerated functional decline. Periventricular WMHV asymmetry was not associated with function. Conclusions: In this large population-based study with long-term repeated measures of function, greater regional WMHV asymmetry was associated with lower function and functional decline, especially with greater WMHV on the right. In addition to global WMHV, WHMV asymmetry may be an important predictor of long-term functional decline.

P4-184: Elevated arterial stiffness and deep-brain white matter hyperintensity in patients with mild cognitive impairment

2012 ◽  
Vol 8 (4S_Part_19) ◽  
pp. P700-P701
Author(s):  
Benjamin Tseng ◽  
Muhammad Ayaz ◽  
Estee Brunk ◽  
Kyle Armstrong ◽  
Kristin Martin-Cook ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
White Matter ◽  
Arterial Stiffness ◽  
White Matter Hyperintensity ◽  
Brain White Matter ◽  
Deep Brain

scholarly journals Severity of white matter hyperintensities: Lesion patterns, cognition, and microstructural changes

2019 ◽  
Vol 40 (12) ◽  
pp. 2454-2463 ◽  
Author(s):  
Weiyi Zeng ◽  
Yaojing Chen ◽  
Zhibao Zhu ◽  
Shudan Gao ◽  
Jianan Xia ◽  
...  
Keyword(s):  
White Matter ◽  
Episodic Memory ◽  
Specific Pattern ◽  
White Matter Hyperintensity ◽  
Common Finding ◽  
Superior Cerebellar Peduncle ◽  
Brain White Matter ◽  
Brain Microstructure ◽  
Cerebellar Peduncle ◽  
Fazekas Scale

White matter hyperintensity (WMH) is a common finding in aging population and considered to be a contributor to cognitive decline. Our study aimed to characterize the spatial patterns of WMH in different severities and explore its impact on cognition and brain microstructure in non-demented elderly. Lesions were both qualitatively (Fazekas scale) and quantitatively assessed among 321 community-dwelled individuals with MRI scanning. Voxel- and atlas-based analyses of the whole-brain white matter microstructure were performed. The WMH of the same severities was found to occur uniformly with a specific pattern of lesions. The severity of WMH had a significant negative association with the performance of working and episodic memory, beginning to appear in Fazekas 3 and 4. The white matter tracts presented significant impairments in Fazekas 3, which showed brain-wide changes above Fazekas 4. Lower FA in the superior cerebellar peduncle and left posterior thalamic radiation was mainly associated with episodic memory, and the middle cerebellar peduncle was significantly associated with working memory. These results support that memory is the primary domain to be affected by WMH, and the effect may potentially be influenced by tract-specific WM abnormalities. Fazekas scale 3 might be the critical stage predicting a future decline in cognition.

scholarly journals SAA4 is a Diagnostic Marker to Enhance Detection Forrheumatoid Arthritis Combined with Anti-CCP

2022 ◽  
Author(s):  
Jiahui Li ◽  
Haina Liu ◽  
Bingbing Dai ◽  
Zhijun Fan ◽  
Qiao Wang ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Area Under The Curve ◽  
Density Lipoprotein ◽  
Serum Levels ◽  
Diagnostic Model ◽  
Diagnostic Efficiency ◽  
Combined Model ◽  
Specificity And Sensitivity ◽  
Diagnostic Models ◽  
In Series

Abstract Objective Serum amyloid A4 (SAA4) is an apolipoprotein that is associated with high-density lipoprotein (HDL) in plasma. In this present investigation, we appraised the potential of SAA4 as a novel diagnostic biomarker for rheumatoid arthritis (RA) combined with other established RA biomarkers, including anticitrullinated protein antibody (anti-CCP), rheumatoid factor (RF)，and C-reactive protein (CRP). Based on the correlative measures of the biomarkers, we developed a diagnostic model of RA by integrating serum levels of SAA4 with these clinical parameters. Methods A number of 316 patients were recruited in the current research. The serum levels of SAA4 were assessed by quantitative ELISA. The specificity and sensitivity of biomarkers were evaluated by using a receiver-operator curve (ROC) analysis to determine their diagnostic efficiency. Univariate and multivariate logistic regression analyses were used to screen and construct the diagnostic models for RA , consisting of diagnostic biomarkers and clinical data. A diagnostic nomogram was then generated based on logistic regression analysis results. Results The serum levels of SAA4 were considerably greatest in RA patients in comparison to other control subjects (P<0.001). Compared with anti-CCP, RF and CRP respectively, SAA4 had the highest specificity (88.60%) for diagnosing RA. The combination of SAA4 with anti-CCP could have the highest diagnostic accuracy when paired together, with highest sensitivity (91.14%) in parallel and highest specificity(98.10) in series. We successfully developed two diagnostic models: the combined model of SAA4 and anti-CCP (model A), and the combined model of SAA4, CRP, anti-CCP, RF and history of diabetes (model B). Both models showed a great area under the curve of ROC for either the training cohort or the validation cohort. The data indicated that the novel RA diagnostic models possessed an advantageous discrimination capacity and application potential. Conclusion Serum SAA4 has utility as a biomarker for RA’s diagnosis and can enhance the detection of RA when combined with anti-CCP.

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