scholarly journals Computational analysis of MRIs predicts osteosarcoma chemoresponsiveness

2021 ◽  
Author(s):  
Goran J Djuričić ◽  
Nemanja Rajković ◽  
Nebojša Milošević ◽  
Jelena P Sopta ◽  
Igor Borić ◽  
...  
Keyword(s):  
Fractal Dimension ◽  
Roc Curve ◽  
Computational Analysis ◽  
Regression Line ◽  
Cytotoxic Chemotherapy ◽  
Imaging Biomarkers ◽  
Morphological Complexity ◽  
Mri Scans ◽  
Association Area

Aim: This study aimed to improve osteosarcoma chemoresponsiveness prediction by optimization of computational analysis of MRIs. Patients & methods: Our retrospective predictive model involved osteosarcoma patients with MRI scans performed before OsteoSa MAP neoadjuvant cytotoxic chemotherapy. Results: We found that several monofractal and multifractal algorithms were able to classify tumors according to their chemoresponsiveness. The predictive clues were defined as morphological complexity, homogeneity and fractality. The monofractal feature CV for Λ′(G) provided the best predictive association (area under the ROC curve = 0.88; p <0.001), followed by   Y-axis intersection of the regression line  for  box fractal dimension, r²  for  FDM and tumor circularity. Conclusion: This is the first full-scale study to indicate that computational analysis of pretreatment MRIs could provide imaging biomarkers for the classification of osteosarcoma according to their chemoresponsiveness.

scholarly journals Application of MRI for the Diagnosis of Neoplasms

2018 ◽  
Vol 2018 ◽  
pp. 1-7 ◽  
Author(s):  
Ewa Bejer-Oleńska ◽  
Michael Thoene ◽  
Andrzej Włodarczyk ◽  
Joanna Wojtkiewicz
Keyword(s):  
Benign Neoplasms ◽  
Young Males ◽  
Classification Of Diseases ◽  
Icd 10 ◽  
Mri Scans ◽  
International Statistical Classification ◽  
The Central Nervous System ◽  
Spss Software ◽  
The Brain

Aim. The aim of the study was to determine the most commonly diagnosed neoplasms in the MRI scanned patient population and indicate correlations based on the descriptive variables. Methods. The SPSS software was used to determine the incidence of neoplasms within the specific diagnoses based on the descriptive variables of the studied population. Over a five year period, 791 patients and 839 MRI scans were identified in neoplasm category (C00-D48 according to the International Statistical Classification of Diseases and Related Health Problems ICD-10). Results. More women (56%) than men (44%) represented C00-D48. Three categories of neoplasms were recorded. Furthermore, benign neoplasms were the most numerous, diagnosed mainly in patients in the fifth decade of life, and included benign neoplasms of the brain and other parts of the central nervous system. Conclusions. Males ≤ 30 years of age with neoplasms had three times higher MRI scans rate than females of the same age group; even though females had much higher scans rate in every other category. The young males are more often selected for these scans if a neoplasm is suspected. Finally, the number of MRI-diagnosed neoplasms showed a linear annual increase.

Image Processing Applications Based on Texture and Fractal Analysis

2011 ◽  
pp. 226-250 ◽  
Author(s):  
Radu Dobrescu ◽  
Dan Popescu
Keyword(s):  
Fractal Dimension ◽  
Texture Classification ◽  
Malignant Tumour ◽  
Classification Performance ◽  
Textured Surfaces ◽  
Box Counting ◽  
Image Partition ◽  
Fractal Analyses ◽  
Occurrence Matrix

Texture analysis research attempts to solve two important kinds of problems: texture segmentation and texture classification. In some applications, textured image segmentation can be solved by classification of small regions obtained from image partition. Two classes of features are proposed in the decision theoretic recognition problem for textured image classification. The first class derives from the mean co-occurrence matrices: contrast, energy, entropy, homogeneity, and variance. The second class is based on fractal dimension and is derived from a box-counting algorithm. For the purpose of increasing texture classification performance, the notions “mean co-occurrence matrix” and “effective fractal dimension” are introduced and utilized. Some applications of the texture and fractal analyses are presented: road analysis for moving objective, defect detection in textured surfaces, malignant tumour detection, remote land classification, and content based image retrieval. The results confirm the efficiency of the proposed methods and algorithms.

Classification of Left Atrial Appendage Morphology Using Deep Learning

2020 ◽  
Author(s):  
Mikayle A. Holm ◽  
Alex Deakyne ◽  
Erik Gaasedelen ◽  
Weston Upchurch ◽  
Paul A. Iaizzo
Keyword(s):  
Left Atrial ◽  
Left Atrial Appendage ◽  
Blood Clot ◽  
3D Models ◽  
Atrial Appendage ◽  
Blood Clots ◽  
Mri Scans ◽  
2D Images ◽  
Trained Network

Abstract Atrial fibrillation, a common cardiac arrhythmia, can lead to blood clots in the left atrial appendage (LAA) of the heart, increasing the risk of stroke. Understanding the LAA morphology can indicate the likelihood of a blood clot. Therefore, a classification convolutional neural network was implemented to predict the LAA morphology. Using 2D images of 3D models created from MRI scans of fixed human hearts and a pre-trained network, an 8.7% error rate was achieved. The network can be improved with more data or expanded to classify the LAA from the automatically segmented DICOM datasets and measure the LAA ostia.

scholarly journals Immunomodulation with Azathioprine therapy in Rasmussen syndrome: A multimodal evaluation

Neurology ◽  
2020 ◽  
pp. 10.1212/WNL.0000000000011004
Author(s):  
Serena Pellegrin ◽  
Torsten Baldeweg ◽  
Suresh Pujar ◽  
Felice D’Arco ◽  
Gaetano Cantalupo ◽  
...  
Keyword(s):  
Cox Regression ◽  
Group Differences ◽  
Class Iii ◽  
Epilepsia Partialis Continua ◽  
Azathioprine Therapy ◽  
Surgical Decision ◽  
Rasmussen Syndrome ◽  
Mri Scans ◽  
One Year

Objective:To verify safety and efficacy of the corticosteroid-sparing drug Azathioprine (AZA) in Rasmussen syndrome (RS), we retrospectively analyzed a cohort of RS patients recruited in a single pediatric neuroscience center.Methods:We compared outcomes in 30 RS patients who received AZA with 23 patients who were not treated with this drug. We used a multimodal approach to correlate therapy with clinical features (seizures, epilepsia partialis continua [EPC], hemiparesis) and neuroimaging markers of progressive brain atrophy.Results:AZA was well tolerated; only one patient discontinued treatment due to pancytopenia. In 27/30 AZA patients, all of whom were corticosteroid responders, corticosteroid therapy could be weaned or reduced without worsening of seizures in 89%. AZA patients had a lower prevalence of EPC (42% vs. 67% in controls) and hemiparesis (64% vs. 92%, respectively). Cox regression showed for the AZA group compared to controls a delayed time to: 1) EPC (of about 2 years, Exp(B)=0.295, 95%CI[0.108, 0.807];p=0.017), 2) hemiparesis (about one year, Exp(B)=0.315, 95%CI[0.137, 0.724];p=0.007), and 3) surgery (about 2 years, Exp(B)=2.068, 95%CI[1.012, 4.227];p=0.046). However, there were no group differences in cognitive decline over time (IQ change per year) or in hemispheric grey matter atrophy on serial MRI scans.Conclusion:AZA treatment appears to slow clinical progression of Rasmussen syndrome in steroid responders; this will give most advantage in patients in the early stages of the disease in whom surgical decision-making may require further time.Classification of Evidence:This study provides Class III evidence that for pediatric RS patients AZA is well tolerated and slows hemiparesis and appearance of EPC.

scholarly journals NIMG-07. LACUNARITY AND FRACTAL DIMENSION ACT AS PRETREATMENT IMAGING BIOMARKERS FOR SURVIVAL IN GLIOBLASTOMA PATIENTS

2019 ◽  
Vol 21 (Supplement_6) ◽  
pp. vi162-vi163
Author(s):  
Lee Curtin ◽  
Paula Whitmire ◽  
Haylye White ◽  
Maciej Mrugala ◽  
Leland Hu ◽  
...  
Keyword(s):  
Fractal Dimension ◽  
Progression Free Survival ◽  
Standard Of Care ◽  
Primary Brain Tumor ◽  
Imaging Biomarkers ◽  
Free Survival ◽  
Patient Cohort ◽  
Biological Phenomena ◽  
Microenvironmental Factors ◽  
Standard Of Care Treatment

Abstract Glioblastoma (GBM) is the most aggressive primary brain tumor with a median survival of only 15 months with standard of care treatment. Lacunarity, a quantitative morphological measure of how shapes fill space, and fractal dimension, another morphological measure of the complexity of pixel arrangement, of segmented necrotic regions on gadolinium-enhanced T1 weighted (T1gd) MRI have previously been shown to distinguish both overall survival (OS) and progression free survival (PFS) in GBM (n = 95). In our larger patient cohort (n = 389), we sought to validate or refute previously published results connecting morphological metrics and patient survival. We identified pretreatment necrotic regions of our retrospective first-diagnosis GBM patient cohort using segmented T1gd MRI enhancing regions. We calculated lacunarity and fractal dimension across all T1gd MRI slices with enhancing tumor, and used the median lacunarity and fractal dimension values for our analysis. We find that a lacunarity threshold can significantly distinguish OS (14 months vs 19 months median, log-rank p = 0.015, n = 389) and a fractal dimension threshold can significantly distinguish PFS (8 months vs 11 months median, log-rank p = 0.015, n = 123). We believe that morphological metrics such as lacunarity and fractal dimension could play a role in standard-of-care prognostic considerations at tumor presentation. This link between morphological and survival metrics could be driven by underlying biological phenomena or microenvironmental factors that should be further explored.

DEFINITION AND CLASSIFICATION OF ONE-DIMENSIONAL CONTINUOUS FUNCTIONS WITH UNBOUNDED VARIATION

Fractals ◽  
2017 ◽  
Vol 25 (05) ◽  
pp. 1750048 ◽  
Author(s):  
Y. S. LIANG
Keyword(s):  
Fractal Dimension ◽  
Bounded Variation ◽  
Continuous Functions ◽  
Functions Of Bounded Variation ◽  
One Dimensional ◽  
Nondifferentiable Functions ◽  
Differentiable Functions ◽  
Closed Intervals ◽  
Bounded Variation Functions

The present paper mainly investigates the definition and classification of one-dimensional continuous functions on closed intervals. Continuous functions can be classified as differentiable functions and nondifferentiable functions. All differentiable functions are of bounded variation. Nondifferentiable functions are composed of bounded variation functions and unbounded variation functions. Fractal dimension of all bounded variation continuous functions is 1. One-dimensional unbounded variation continuous functions may have finite unbounded variation points or infinite unbounded variation points. Number of unbounded variation points of one-dimensional unbounded variation continuous functions maybe infinite and countable or uncountable. Certain examples of different one-dimensional continuous functions have been given in this paper. Thus, one-dimensional continuous functions are composed of differentiable functions, nondifferentiable continuous functions of bounded variation, continuous functions with finite unbounded variation points, continuous functions with infinite but countable unbounded variation points and continuous functions with uncountable unbounded variation points. In the end of the paper, we give an example of one-dimensional continuous function which is of unbounded variation everywhere.

Fractal dimension and classification of music

2000 ◽  
Vol 11 (14) ◽  
pp. 2179-2192 ◽  
Author(s):  
M. Bigerelle ◽  
A. Iost
Keyword(s):  
Fractal Dimension
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