scholarly journals Radiomic prediction models for the level of Ki-67 and p53 in glioma

2020 ◽  
Vol 48 (5) ◽  
pp. 030006052091446
Author(s):  
Xiaojun Sun ◽  
Peipei Pang ◽  
Lin Lou ◽  
Qi Feng ◽  
Zhongxiang Ding ◽  
...  
Keyword(s):  
Prediction Models ◽  
Area Under The Curve ◽  
Brain Magnetic Resonance Imaging ◽  
Malignant Tumours ◽  
Ki 67 ◽  
Logistic Regression Models ◽  
Immunohistochemical Markers ◽  
Conventional Mri ◽  
Mri Features ◽  
Magnetic Resonance Imaging Mri

Objective To identify glioma radiomic features associated with proliferation-related Ki-67 antigen and cellular tumour antigen p53 levels, common immunohistochemical markers for differentiating benign from malignant tumours, and to generate radiomic prediction models. Methods Patients with glioma, who were scanned before therapy using standard brain magnetic resonance imaging (MRI) protocols on T1 and T2 weighted imaging, were included. For each patient, regions-of-interest (ROI) were drawn based on tumour and peritumoral areas (5/10/15/20 mm), and features were identified using feature calculations, and used to create and assess logistic regression models for Ki-67 and p53 levels. Results A total of 92 patients were included. The best area under the curve (AUC) for the Ki-67 model was 0.773 for T2 weighted imaging in solid glioma (sensitivity, 0.818; specificity, 0.833), followed by a less reliable AUC of 0.773 (sensitivity, 0.727; specificity 0.667) in 20-mm peritumoral areas. The highest AUC for the p53 model was 0.709 (sensitivity, 1; specificity, 0.4) for T2 weighted imaging in 10-mm peritumoral areas. Conclusion Using T2-weighted imaging, the prediction model for Ki-67 level in solid glioma tissue was better than the p53 model. The 20-mm and 10-mm peritumoral areas in the Ki-67 and p53 model, respectively, showed predictive effects, suggesting value in further research into areas without conventional MRI features.

MRI features in a malignant glomus jugulare tumour

1993 ◽  
Vol 107 (11) ◽  
pp. 1066-1069 ◽  
Author(s):  
Alexandra C. Athanassopoulou ◽  
Labros L. Vlahos ◽  
Athanassios D. Gouliamos ◽  
Eliana D. Kailidou ◽  
John G. Papailiou ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Pulmonary Metastases ◽  
Distant Metastases ◽  
Resonance Imaging ◽  
Malignant Tumours ◽  
Regional Lymph Nodes ◽  
Glomus Jugulare ◽  
Mri Features ◽  
Magnetic Resonance Imaging Mri ◽  
Glomus Jugulare Tumour

AbstractMagnetic resonance imaging (MRI) features in a case of malignant glomus jugulare tumour are reported. Chemodectomas are benign in 95 per cent of cases and malignant in five per cent. Only one case report of CT findings in this unusual CP angle tumour with pulmonary metastases has been cited in the literature.It is concluded that MRI can provide useful information about the nature of chemodectomas although it cannot dislinguish between benign and malignant tumours, except when regional lymph nodes are involved or when distant metastases exist.

The influence of slice orientation on brain MRI lesion load measurement in multiple sclerosis

1997 ◽  
Vol 3 (6) ◽  
pp. 382-384
Author(s):  
M. Rovaris ◽  
MP Sormanis ◽  
MA Rocca ◽  
G. Comi ◽  
M. Filippi
Keyword(s):  
Multiple Sclerosis ◽  
Spin Echo ◽  
Brain Mri ◽  
Brain Magnetic Resonance Imaging ◽  
Lesion Load ◽  
Conventional Mri ◽  
Slice Orientation ◽  
Mri Scans ◽  
The Mean ◽  
Magnetic Resonance Imaging Mri

This study aimed at evaluating the influence of a different slice orientation on brain magnetic resonance imaging (MRI) lesion load in multiple sclerosis (MS). Fifteen MS patients were scanned obtaining both axial and sagittal conventional spin echo (24 slices; TR 2400, TE 30/80) brain MRI. The total lesion load (TLL) was assessed twice for each scan, using a semi-automated local thresholding technique and the same marked hardcopies. The mean TLL was 22734 mm3 for axial and 22003 mm3 for sagittal scans. The mean intra-observer coefficient of variation (COV) was 4.65% for the axial acquisitions and 4.52% for the sagittal acquisitions. This difference was not statistically significant (one-way ANOVA, P> 0.1). The lesion load was significantly higher from axial MRI as compared to the intra-observer variability (two-way ANOVA, P =0.01), but the fluctuations around this average difference between axial and sagittal scan TLL were significantly large (test for interaction, P < 0.00I). Our data indicate that the use of sagittal conventional MRI scans does not seem to be worthwhile for the quantitative assessment of lesion load in MS patients.

scholarly journals Radiomics Assessment of SPAIR and STIR MRI Sequences to Predict Axial and Peripheral Spondyloarthritis

2020 ◽  
Author(s):  
Ariane Tenorio ◽  
José Ferreira Junior ◽  
Vitor Dalto ◽  
Matheus Faleiros ◽  
Rodrigo Assad ◽  
...  
Keyword(s):  
Statistical Difference ◽  
Area Under The Curve ◽  
Neural Nets ◽  
Neural Net ◽  
Resonance Imaging ◽  
Sacroiliac Joints ◽  
Mri Features ◽  
Mri Sequences ◽  
Magnetic Resonance Imaging Mri ◽  
Peripheral Spondyloarthritis

In an attempt to aid the subtyping of spondyloarthritis (SpA), this work assessed neural nets and magnetic resonance imaging (MRI) features to predict SpA. Patients underwent SPAIR and STIR MRI sequences. Radiologists manually segmented sacroiliac joints images for extracting MRI features. A neural net used these features to predict SpA. The STIR-based model yielded higher performance than SPAIR to diagnose SpA, although no statistical difference was found between them. The SPAIR model yielded an area under the curve of 0.83 to differentiate axial and peripheral subtypes, while STIR yielded 0.57 (p < 0.05 on curves difference). Therefore, neural nets modeled with SPAIR-extracted features distinguished SpA using a single MRI exam of the sacroiliac joints.

The diagnostic role for susceptibility-weighted MRI during sporadic hemiplegic migraine

Cephalalgia ◽  
2013 ◽  
Vol 33 (15) ◽  
pp. 1258-1263 ◽  
Author(s):  
Erin M Fedak ◽  
Nicholas A Zumberge ◽  
Geoffrey L Heyer
Keyword(s):  
Diagnostic Value ◽  
Brain Regions ◽  
First Episode ◽  
Hemiplegic Migraine ◽  
Motor Weakness ◽  
Conventional Mri ◽  
Mri Features ◽  
Diagnostic Role ◽  
Mri Sequences ◽  
Magnetic Resonance Imaging Mri

Background Hemiplegic migraine is a rare form of migraine with aura that includes motor weakness. Diagnosis during the first episode can be difficult to make and costly, especially with the sporadic form. Cases Our study evaluates the ictal magnetic resonance imaging (MRI) features of four sequential pediatric patients during a first-time, sporadic hemiplegic migraine. Susceptibility-weighted imaging (SWI) revealed cerebral venous prominence and increased magnetic susceptibility affecting brain regions that corresponded with each patient’s neurologic deficits. Repeat MRI (performed in three patients) following migraine recovery demonstrated resolution of all susceptibility abnormalities. Conclusion When combined with conventional MRI sequences, SWI has diagnostic value in the acute setting of motor weakness and with clinical features consistent with hemiplegic migraine. The sequence may help to further characterize ictal cerebral blood flow changes during the hemiplegic migraine aura.

scholarly journals Rich-Club organization: an important determinant of functional outcome after acute ischemic stroke

2019 ◽  
Author(s):  
Markus D. Schirmer ◽  
Sofia Ira Ktena ◽  
Marco J. Nardin ◽  
Kathleen L. Donahue ◽  
Anne-Katrin Giese ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Acute Ischemic Stroke ◽  
Functional Outcome ◽  
Prediction Models ◽  
Brain Magnetic Resonance Imaging ◽  
Lesion Volume ◽  
Stroke Outcome ◽  
Rich Club ◽  
The Rich ◽  
Magnetic Resonance Imaging Mri

AbstractObjectiveTo determine whether the rich-club organization, essential for information transport in the human connectome, is an important biomarker of functional outcome after acute ischemic stroke (AIS).MethodsConsecutive AIS patients (N=344) with acute brain magnetic resonance imaging (MRI) (<48 hours) were eligible for this study. Each patient underwent a clinical MRI protocol, which included diffusion weighted imaging (DWI). All DWIs were registered to a template on which rich-club regions have been defined. Using manual outlines of stroke lesions, we automatically counted the number of affected rich-club regions and assessed its effect on the National Institute of Health Stroke Scale (NIHSS) and modified Rankin Scale (mRS; obtained at 90 days post-stroke) scores through ordinal regression.ResultsOf 344 patients (median age 65, inter-quartile range 54-76 years) with a median DWI lesion volume (DWIv) of 3cc, 64% were male. We established that an increase in number of rich-club regions affected by a stroke increases the odds of poor stroke outcome, measured by NIHSS (OR: 1.77, 95%CI 1.41-2.21) and mRS (OR: 1.38, 95%CI 1.11-1.73). Additionally, we demonstrated that the OR exceeds traditional markers, such as DWIv (ORNIHSS 1.08, 95%CI 1.06-1.11; ORmRs 1.05, 95%CI 1.03-1.07) and age (ORNIHSS 1.03, 95%CI 1.01-1.05; ORmRs 1.05, 95%CI 1.03-1.07).ConclusionIn this proof-of-concept study, the number of rich-club nodes affected by a stroke lesion presents a translational biomarker of stroke outcome, which can be readily assessed using standard clinical AIS imaging protocols and considered in functional outcome prediction models beyond traditional factors.

scholarly journals Prediction of ambulatory outcome in patients with corona radiata infarction using deep learning

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jeoung Kun Kim ◽  
Yoo Jin Choo ◽  
Hyunkwang Shin ◽  
Gyu Sang Choi ◽  
Min Cheol Chang
Keyword(s):  
Deep Learning ◽  
Language Processing ◽  
Early Stage ◽  
Model Performance ◽  
Area Under The Curve ◽  
Brain Magnetic Resonance Imaging ◽  
Validation Dataset ◽  
Corona Radiata ◽  
Magnetic Resonance Imaging Mri ◽  
Validation Set

AbstractDeep learning (DL) is an advanced machine learning approach used in diverse areas such as bioinformatics, image analysis, and natural language processing. Here, using brain magnetic resonance imaging (MRI) data obtained at early stages of infarcts, we attempted to develop a convolutional neural network (CNN) to predict the ambulatory outcome of corona radiata infarction at six months after onset. We retrospectively recruited 221 patients with corona radiata infarcts. A favorable outcome of ambulatory function was defined as a functional ambulation category (FAC) score of ≥ 4 (able to walk without a guardian’s assistance), and a poor outcome of ambulatory function was defined as an FAC score of < 4. We used a CNN algorithm. Of the included subjects, 69.7% (n = 154) were assigned randomly to the training set and the remaining 30.3% (n = 67) were assigned to the validation set to measure the model performance. The area under the curve was 0.751 (95% CI 0.649–0.852) for the prediction of ambulatory function with the validation dataset using the CNN model. We demonstrated that a CNN model trained using brain MRIs captured at an early stage after corona radiata infarction could be helpful in predicting long-term ambulatory outcomes.

scholarly journals Differentiation Between Glioblastoma Multiforme and Metastasis From the Lungs and Other Sites Using Combined Clinical/Routine MRI Radiomics

2021 ◽  
Vol 9 ◽  
Author(s):  
Yuqi Han ◽  
Lingling Zhang ◽  
Shuzi Niu ◽  
Shuguang Chen ◽  
Bo Yang ◽  
...  
Keyword(s):  
Glioblastoma Multiforme ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Curve Analysis ◽  
Chi Square ◽  
Internal Validation ◽  
Routine Magnetic Resonance Imaging ◽  
Mri Features ◽  
Chi Square Test ◽  
Magnetic Resonance Imaging Mri

BackgroundDifferentiation between cerebral glioblastoma multiforme (GBM) and solitary brain metastasis (MET) is important. The existing radiomic differentiation method ignores the clinical and routine magnetic resonance imaging (MRI) features.PurposeTo differentiate between GBM and MET and between METs from the lungs (MET-lung) and other sites (MET-other) through clinical and routine MRI, and radiomics analyses.Methods and MaterialsA total of 350 patients were collected from two institutions, including 182 patients with GBM and 168 patients with MET, which were all proven by pathology. The ROI of the tumor was obtained on axial postcontrast MRI which was performed before operation. Seven radiomic feature selection methods and four classification algorithms constituted 28 classifiers in two classification strategies, with the best classifier serving as the final radiomics model. The clinical and combination models were constructed using the nomograms developed. The performance of the nomograms was evaluated in terms of calibration, discrimination, and clinical usefulness. Student’s t-test or the chi-square test was used to assess the differences in the clinical and radiological characteristics between the training and internal validation cohorts. Receiver operating characteristic curve analysis was performed to assess the performance of developed models with the area under the curve (AUC).ResultsThe classifier fisher_decision tree (fisher_DT) showed the best performance (AUC: 0.696, 95% CI:0.608-0.783) for distinguishing between GBM and MET in internal validation cohorts; the classifier reliefF_random forest (reliefF_RF) showed the best performance (AUC: 0.759, 95% CI: 0.613-0.904) for distinguishing between MET-lung and MET-other in internal validation cohorts. The combination models incorporating the radiomics signature and clinical-radiological characteristics were superior to the clinical-radiological models in the two classification strategies (AUC: 0.764 for differentiation between GBM in internal validation cohorts and MET and 0.759 or differentiation between MET-lung and MET-other in internal validation cohorts). The nomograms showed satisfactory performance and calibration and were considered clinically useful, as revealed in the decision curve analysis.Data ConclusionThe combination of radiomic and non-radiomic features is helpful for the differentiation among GBM, MET-lung, and MET-other.

Systematic Analysis and Review of Magnetic Resonance Imaging (MRI) Reconstruction Techniques

2021 ◽  
Vol 16 ◽  
Author(s):  
Penta Anil Kumar ◽  
R. Gunasundari ◽  
R. Aarthi
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Optimization Technique ◽  
Motion Artifacts ◽  
Reconstruction Technique ◽  
Resonance Imaging ◽  
Research Gaps ◽  
Conventional Mri ◽  
Mri Reconstruction ◽  
Magnetic Resonance Imaging Mri

Background: Magnetic Resonance Imaging (MRI) plays an important role in the field of medical diagnostic imaging as it poses non-invasive acquisition and high soft-tissue contrast. However, the huge time is needed for the MRI scanning process that results in motion artifacts, degrades image quality, misinterpretation of data, and may cause uncomfortable to the patient. Thus, the main goal of MRI research is to accelerate data acquisition processing without affecting the quality of the image. Introduction: This paper presents a survey based on distinct conventional MRI reconstruction methodologies. In addition, a novel MRI reconstruction strategy is proposed based on weighted Compressive Sensing (CS), Penalty-aided minimization function, and Meta-heuristic optimization technique. Methods: An illustrative analysis is done concerning adapted methods, datasets used, execution tools, performance measures, and values of evaluation metrics. Moreover, the issues of existing methods and the research gaps considering conventional MRI reconstruction schemes are elaborated to obtain improved contribution for devising significant MRI reconstruction techniques. Results: The proposed method will reduce conventional aliasing artifacts problems, may attain lower Mean Square Error (MSE), higher Peak Signal-to-Noise Ratio (PSNR), and Structural SIMilarity (SSIM) index. Conclusion: The issues of existing methods and the research gaps considering conventional MRI reconstruction schemes are elaborated to devising an improved significant MRI reconstruction technique.

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