scholarly journals A Radiomics Model for Predicting Early Recurrence in Grade II Gliomas Based on Preoperative Multiparametric Magnetic Resonance Imaging

2021 ◽  
Vol 11 ◽  
Author(s):  
Zhen-hua Wang ◽  
Xin-Lan Xiao ◽  
Zhao-Tao Zhang ◽  
Keng He ◽  
Feng Hu
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Confidence Interval ◽  
Early Recurrence ◽  
Resonance Imaging ◽  
Validation Data ◽  
Weighted Image ◽  
Logistic Analysis ◽  
Grade Ii ◽  
Grade Ii Glioma

ObjectiveThis study aimed to develop a radiomics model to predict early recurrence (<1 year) in grade II glioma after the first resection.MethodsThe pathological, clinical, and magnetic resonance imaging (MRI) data of patients diagnosed with grade II glioma who underwent surgery and had a recurrence between 2017 and 2020 in our hospital were retrospectively analyzed. After a rigorous selection, 64 patients were eligible and enrolled in the study. Twenty-two cases had a pathologically confirmed recurrent glioma. The cases were randomly assigned using a ratio of 7:3 to either the training set or validation set. T1-weighted image (T1WI), T2-weighted image (T2WI), and contrast-enhanced T1-weighted image (T1CE) were acquired. The minimum-redundancy-maximum-relevancy (mRMR) method alone or in combination with univariate logistic analysis were used to identify the most optimal predictive feature from the three image sequences. Multivariate logistic regression analysis was then used to develop a predictive model using the screened features. The performance of each model in both training and validation datasets was assessed using a receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA).ResultsA total of 396 radiomics features were initially extracted from each image sequence. After running the mRMR and univariate logistic analysis, nine predictive features were identified and used to build the multiparametric radiomics model. The model had a higher AUC when compared with the univariate models in both training and validation data sets with an AUC of 0.966 (95% confidence interval: 0.949–0.99) and 0.930 (95% confidence interval: 0.905–0.973), respectively. The calibration curves indicated a good agreement between the predictable and the actual probability of developing recurrence. The DCA demonstrated that the predictive value of the model improved when combining the three MRI sequences.ConclusionOur multiparametric radiomics model could be used as an efficient and accurate tool for predicting the recurrence of grade II glioma.

Prognostic value of initial magnetic resonance imaging growth rates for World Health Organization grade II gliomas

2006 ◽  
Vol 60 (3) ◽  
pp. 380-383 ◽  
Author(s):  
Johan Pallud ◽  
Emmanuel Mandonnet ◽  
Hugues Duffau ◽  
Michèle Kujas ◽  
Rémy Guillevin ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
World Health Organization ◽  
Growth Rates ◽  
Prognostic Value ◽  
World Health ◽  
Resonance Imaging ◽  
Grade Ii ◽  
Health Organization

scholarly journals Diagnostic Accuracy and Validity of Magnetic Resonance Imaging in the Detection of Speci!c Type of Brain Tumour

2019 ◽  
Vol 46 (3) ◽  
pp. 85-89
Author(s):  
Swati Munshi ◽  
Farid Ahmed ◽  
Bibekananda Halder ◽  
Abdullah Yousuf ◽  
Md Mahbubur Rahman ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Diagnostic Accuracy ◽  
Brain Tumour ◽  
Brain Tumours ◽  
Histopathological Examination ◽  
Test Validity ◽  
Resonance Imaging ◽  
Grade Ii ◽  
Grade Iii

Magnetic Resonance Imaging (MRI) is a widely accessible imaging technique for the detection of brain tumours and cancer, which are further confirmed by histopathological examination. Accurate detection of the tumours and its extent is very difficult. The present study attempted to evaluate the convenience of MRI in detection of different grades of astrocytomas, which are the most commonly occurring brain tumours. This cross-sectional study was conducted at the Department of Radiology and Imaging with the collaboration of Department of Neurosurgery and Department of Pathology at Sir Salimullah Medical College (SSMC & MH), Dhaka from January 2013 to December 2013 for a period of one year. The study population was all the diagnosed cases of intracranial astrocytoma patients regardless of their age and sex. The studied included 48 brain tumour (astrocytoma) patients, ages between 13 and 69 years old. All cases having no contraindication for MRI underwent MR examination followed by histopathological examination of the postoperative resected tissues. The findings of the MRI and histopathological examination were compared to find out the test validity of the MRI findings of the different grades of astrocytoma’s. The highest sensitivity was found in grade III astrocytoma (90.5%) followed by grade II (85.7%) grade IV (75.0%) and grade I (60.0%). The highest specificity was found in grade I astrocytoma (97.7%) followed by Grade III (96.3%), grade IV (92.5%) and grade II (91.5%). The highest accuracy was found in both grade I astrocytoma (93.7%) and grade III (93.7%) followed by grade II (92.5%) and grade IV (89.6%). As per the study findings it can be concluded that,MRI has a high diagnostic accuracy and validity for the detection of different grades of astrocytoma. Bangladesh Med J. 2017 Sep; 46 (3): 85-89

Magnetic Resonance Imaging, Chest Radiography, Computed Tomography and Ultrasonography in Malignant Lymphoma

1987 ◽  
Vol 28 (3) ◽  
pp. 253-262 ◽  
Author(s):  
R. Nyman ◽  
S. Rehn ◽  
B. Glimelius ◽  
H. Hagberg ◽  
A. Hemmingsson ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Computed Tomography ◽  
Magnetic Resonance ◽  
Lymph Nodes ◽  
Malignant Lymphoma ◽  
Chest Radiography ◽  
Resonance Imaging ◽  
Weighted Image ◽  
Non Hodgkin Lymphoma ◽  
Magnetic Resonance Imaging Mri

Magnetic resonance imaging (MRI) was compared with chest radiography, computed tomography (CT) and ultrasonography (US) for demonstration of spleen and liver engagement and enlarged lymph nodes in patients with malignant lymphoma. The investigation comprised 24 patients with Hodgkin's disease (HD) and 39 with non-Hodgkin lymphoma (NHL). MRI demonstrated enlarged lymph nodes, distinctly separated from vessels, fat, muscle, liver and occasionally also pancreas without any contrast medium. The distinction between lymph nodes and spleen was, however, poor in the images. In the mediastinum, MRI was superior to chest radiography and had an accuracy similar to that of CT. In the abdomen and the pelvis MRI had slight advantages over CT in detection of enlarged lymph nodes. Compared with US the MRI results were similar in the abdomen and somewhat better in the pelvis. MRI and US were better than CT in revealing HD infiltrates in the spleen. Infiltration of NHL in the spleen was slightly better disclosed at US than at CT and MRI; most of the NHL infiltration, confirmed at histopathology, could, however, not be revealed with any of the modalities, except when the size of the spleen was considered. Regions in the spleen, displayed with low image intensity in the T2 weighted image, were most likely due to increased amount of fibrotic tissue in the lymphomatous lesions. Good demonstration of lymph nodes and lymphomatous lesions in the spleen with MRI required two sequences; one with short TR and TE (T1 weighted image) and one with long TR and TE (T2 weighted image).

scholarly journals Susceptibility Weighted Imaging as a Useful Imaging Adjunct in Hemichorea Hyperglycaemia

2013 ◽  
Vol 2013 ◽  
pp. 1-3 ◽  
Author(s):  
Ferry Dharsono ◽  
Andrew Thompson ◽  
Jolandi van Heerden ◽  
Andrew Cheung
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Corpus Striatum ◽  
Clinical Entity ◽  
Susceptibility Weighted Imaging ◽  
Resonance Imaging ◽  
Weighted Image ◽  
Susceptibility Weighted Image ◽  
Mri Sequences ◽  
Magnetic Resonance Imaging Mri

Hyperglycaemia with hemichorea (HGHC) is an unusual clinical entity that can be associated with corpus striatum hyperintensity on T1-weighted (T1W) magnetic resonance imaging (MRI) sequences. We report the utility of the susceptibility weighted image (SWI) sequence and the filtered phase SWI sequence in the imaging assessment of HGHC.

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