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Cortical microstructure in primary progressive aphasia: a multicenter study.
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In: Alzheimer's research & therapy, vol 14, iss 1 (2022)
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Imaging Clinical Subtypes and Associated Brain Networks in Alzheimer’s Disease
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In: Brain Sciences; Volume 12; Issue 2; Pages: 146 (2022)
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Intraoperative Brain Mapping in Multilingual Patients: What Do We Know and Where Are We Going?
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In: Brain Sciences; Volume 12; Issue 5; Pages: 560 (2022)
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An Equivocal SCC Lesion—Antiepileptic-Induced CLOCC
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In: Brain Sciences; Volume 12; Issue 3; Pages: 384 (2022)
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Increased connectivity among sensory and motor regions during visual and audiovisual speech perception
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In: Open Access Publications (2022)
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Neural substrates of verbal repetition deficits in primary progressive aphasia.
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Multimodal dataset of real-time 2D and static 3D MRI of healthy French speakers
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In: ISSN: 2052-4463 ; EISSN: 2052-4463 ; Scientific Data ; https://hal.archives-ouvertes.fr/hal-03507532 ; Scientific Data , Nature Publishing Group, 2021, 8 (1), pp.258. ⟨10.1038/s41597-021-01041-3⟩ (2021)
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BI-RADS Reading of Non-Mass Lesions on DCE-MRI and Differential Diagnosis Performed by Radiomics and Deep Learning.
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BI-RADS Reading of Non-Mass Lesions on DCE-MRI and Differential Diagnosis Performed by Radiomics and Deep Learning.
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Zhou, Jiejie; Liu, Yan-Lin; Zhang, Yang; Chen, Jeon-Hor; Combs, Freddie J; Parajuli, Ritesh; Mehta, Rita S; Liu, Huiru; Chen, Zhongwei; Zhao, Youfan; Pan, Zhifang; Wang, Meihao; Yu, Risheng; Su, Min-Ying. - : eScholarship, University of California, 2021
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Abstract:
BackgroundA wide variety of benign and malignant processes can manifest as non-mass enhancement (NME) in breast MRI. Compared to mass lesions, there are no distinct features that can be used for differential diagnosis. The purpose is to use the BI-RADS descriptors and models developed using radiomics and deep learning to distinguish benign from malignant NME lesions.Materials and methodsA total of 150 patients with 104 malignant and 46 benign NME were analyzed. Three radiologists performed reading for morphological distribution and internal enhancement using the 5th BI-RADS lexicon. For each case, the 3D tumor mask was generated using Fuzzy-C-Means segmentation. Three DCE parametric maps related to wash-in, maximum, and wash-out were generated, and PyRadiomics was applied to extract features. The radiomics model was built using five machine learning algorithms. ResNet50 was implemented using three parametric maps as input. Approximately 70% of earlier cases were used for training, and 30% of later cases were held out for testing.ResultsThe diagnostic BI-RADS in the original MRI report showed that 104/104 malignant and 36/46 benign lesions had a BI-RADS score of 4A-5. For category reading, the kappa coefficient was 0.83 for morphological distribution (excellent) and 0.52 for internal enhancement (moderate). Segmental and Regional distribution were the most prominent for the malignant group, and focal distribution for the benign group. Eight radiomics features were selected by support vector machine (SVM). Among the five machine learning algorithms, SVM yielded the highest accuracy of 80.4% in training and 77.5% in testing datasets. ResNet50 had a better diagnostic performance, 91.5% in training and 83.3% in testing datasets.ConclusionDiagnosis of NME was challenging, and the BI-RADS scores and descriptors showed a substantial overlap. Radiomics and deep learning may provide a useful CAD tool to aid in diagnosis.
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Keyword:
Biomedical Imaging; breast neoplasms; computer-assisted diagnosis; deep learning; machine learning; magnetic resonance imaging; Oncology and Carcinogenesis
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URL: https://escholarship.org/uc/item/5mj029ws
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Resting functional connectivity in the semantic appraisal network predicts accuracy of emotion identification.
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Resting functional connectivity in the semantic appraisal network predicts accuracy of emotion identification.
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A Bayesian optimization approach for rapidly mapping residual network function in stroke. ...
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| 13 |
The Neurobiological Relationship Between Childhood Maltreatment and Major Depressive Disorder (MDD)
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In: Global Tides (2021)
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Age-related differences in the neural bases of phonological and semantic processes in the context of task-irrelevant information.
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Age-related differences in the neural bases of phonological and semantic processes.
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Age-related differences in resolving semantic and phonological competition during receptive language tasks.
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Engagement of Language and Domain General Networks during Word Monitoring in a Native and Unknown Language
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In: Brain Sciences ; Volume 11 ; Issue 8 (2021)
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Assessing PD-L1 Expression Status Using Radiomic Features from Contrast-Enhanced Breast MRI in Breast Cancer Patients: Initial Results
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In: Cancers; Volume 13; Issue 24; Pages: 6273 (2021)
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Is Instructional Scaffolding a Better Strategy for Teaching Writing to EFL Learners? A Functional MRI Study in Healthy Young Adults
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In: Brain Sciences ; Volume 11 ; Issue 11 (2021)
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Functional Hyperconnectivity during a Stories Listening Task in Magnetoencephalography Is Associated with Language Gains for Children Born Extremely Preterm
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In: Brain Sciences ; Volume 11 ; Issue 10 (2021)
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