scholarly journals A radiological model based on the standard MR sequences for detecting MGMT methylation in glioma using texture analysis

2021 ◽  
Author(s):  
Wei‐yuan Huang ◽  
Ling‐hua Wen ◽  
Gang Wu ◽  
Pei‐pei Pang ◽  
Richard Ogbuji ◽  
...  
Keyword(s):  
Texture Analysis ◽  
Mgmt Methylation ◽  
Model Based ◽  
Radiological Model ◽  
Mr Sequences

Supervised classification of brain tissues through local multi-scale texture analysis by coupling DIR and FLAIR MR sequences

2012 ◽  
Author(s):  
Enea Poletti ◽  
Elisa Veronese ◽  
Massimiliano Calabrese ◽  
Alessandra Bertoldo ◽  
Enrico Grisan
Keyword(s):  
Texture Analysis ◽  
Supervised Classification ◽  
Multi Scale ◽  
Mr Sequences ◽  
Brain Tissues

scholarly journals Diagnostic performance of qualitative and radiomics approach to parotid gland tumors: which is the added benefit of texture analysis?

2021 ◽  
Author(s):  
Federica Vernuccio ◽  
Federica Arnone ◽  
Roberto Cannella ◽  
Barbara Verro ◽  
Albert Comelli ◽  
...  
Keyword(s):  
Differential Diagnosis ◽  
Parotid Gland ◽  
Head And Neck ◽  
Texture Analysis ◽  
Pleomorphic Adenoma ◽  
Diagnostic Performance ◽  
Model Based ◽  
Parotid Gland Tumors ◽  
Mri Sequences ◽  
Sensitivity Specificity

Objective: To investigate whether MRI-based texture analysis improves diagnostic performance for the diagnosis of parotid gland tumors compared to conventional radiological approach. Methods: Patients with parotid gland tumors who underwent salivary glands MRI between 2008 and 2019 were retrospectively selected. MRI analysis included a qualitative assessment by two radiologists (one of which subspecialized on head and neck imaging), and texture analysis on various sequences. Diagnostic performances including sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC) of qualitative features, radiologists’ diagnosis, and radiomic models were evaluated. Results: Final study cohort included 57 patients with 74 tumors (27 pleomorphic adenomas, 40 Warthin tumors, 8 malignant tumors). Sensitivity, specificity, and AUROC for the diagnosis of malignancy were 75%, 97% and 0.860 for non-subspecialized radiologist, 100%, 94% and 0.970 for subspecialized radiologist and 57.2%, 93.4%, and 0.927 using a MRI radiomics model obtained combining texture analysis on various MRI sequences. Sensitivity, specificity, and AUROC for the differential diagnosis between pleomorphic adenoma and Warthin tumors were 81.5%, 70%, and 0.757 for non-subspecialized radiologist, 81.5%, 95% and 0.882 for subspecialized radiologist and 70.8%, 82.5%, and 0.808 using a MRI radiomics model based on texture analysis of T2 weighted sequence. A combined radiomics model obtained with all MRI sequences yielded a sensitivity of 91.5% for the diagnosis of pleomorphic adenoma. Conclusion: MRI qualitative radiologist assessment outperforms radiomic analysis for the diagnosis of malignancy. MRI predictive radiomics models improves the diagnostic performance of non-subspecialized radiologist for the differential diagnosis between pleomorphic adenoma and Warthin tumor, achieving similar performance to the subspecialized radiologist. Advances in knowledge: Radiologists outperform radiomic analysis for the diagnosis of malignant parotid gland tumors, with some MRI qualitative features such as ill-defined margins, perineural spread, invasion of adjacent structures and enlarged lymph nodes being highly specific for malignancy. A radiomic model based on texture analysis of T2 weighted images yields higher specificity for the diagnosis of pleomorphic adenoma compared to a radiologist non-subspecialized in head and neck radiology, thus minimizing false-positive pleomorphic adenoma diagnosis rate and reducing unnecessary surgical complications.

Development of criteria to compare model-based texture analysis methods

1991 ◽  
Author(s):  
Young-Sung Soh ◽  
S. N. J. Murthy ◽  
Terrance L. Huntsberger
Keyword(s):  
Texture Analysis ◽  
Model Based ◽  
Analysis Methods

scholarly journals Model-Based Generation of Large Databases of Cardiac Images: Synthesis of Pathological Cine MR Sequences From Real Healthy Cases

2018 ◽  
Vol 37 (3) ◽  
pp. 755-766 ◽  
Author(s):  
Nicolas Duchateau ◽  
Maxime Sermesant ◽  
Herve Delingette ◽  
Nicholas Ayache
Keyword(s):  
Model Based ◽  
Cine Mr ◽  
Large Databases ◽  
Cardiac Images ◽  
Mr Sequences

Texture analysis of common renal masses in multiple MR sequences for prediction of pathology

2017 ◽  
Author(s):  
Uyen N. Hoang ◽  
Ashkan A. Malayeri ◽  
Nathan S. Lay ◽  
Ronald M. Summers ◽  
Jianhua Yao
Keyword(s):  
Texture Analysis ◽  
Renal Masses ◽  
Mr Sequences
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