Radiologic clinics of North America
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Radiol. Clin. North Am. · Jan 2021
ReviewApplications of Artificial Intelligence in Breast Imaging.
Artificial intelligence (AI) technology shows promise in breast imaging to improve both interpretive and noninterpretive tasks. AI-based screening triage may help identify normal examinations and AI-based computer-aided detection (AI-CAD) may increase cancer detection and reduce false positives. ⋯ AI adoption will depend on robust evidence of improved quality, increased efficiency, and cost-effectiveness. Reliance on AI will likely proceed through stages and will involve careful attention to its limitations to prevent overconfidence in its application.
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Radiol. Clin. North Am. · Sep 2020
ReviewRadiomics and Artificial Intelligence for Renal Mass Characterization.
Radiomics allows for high throughput extraction of quantitative data from images. This is an area of active research as groups try to capture and quantify imaging parameters and convert these into descriptive phenotypes of organs or tumors. ⋯ This is used with or without associated machine learning classifiers or a deep learning approach is applied to similar types of data. These tools have shown utility in characterizing renal masses, renal cell carcinoma, and assessing response to targeted therapeutic agents in metastatic renal cell carcinoma.
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Radiol. Clin. North Am. · Nov 2019
ReviewAdult Primary Brain Neoplasm, Including 2016 World Health Organization Classification.
In 2016, the World Health Organization (WHO) central nervous system (CNS) classification scheme incorporated molecular parameters in addition to traditional microscopic features for the first time. Molecular markers add a level of objectivity that was previously missing for tumor categories heavily dependent on microscopic observation for pathologic diagnosis. This article provides a brief discussion of the major 2016 updates to the WHO CNS classification scheme and reviews typical MR imaging findings of adult primary CNS neoplasms, including diffuse infiltrating gliomas, ependymal tumors, neuronal/glioneuronal tumors, pineal gland tumors, meningiomas, nerve sheath tumors, solitary fibrous tumors, and lymphoma.
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Radiol. Clin. North Am. · Nov 2019
ReviewImaging Glioblastoma Posttreatment: Progression, Pseudoprogression, Pseudoresponse, Radiation Necrosis.
Radiographic monitoring of posttreatment glioblastoma is important for clinical trials and determining next steps in management. Evaluation for tumor progression is confounded by the presence of treatment-related radiographic changes, making a definitive determination less straight-forward. The purpose of this article was to describe imaging tools available for assessing treatment response in glioblastoma, as well as to highlight the definitions, pathophysiology, and imaging features typical of true progression, pseudoprogression, pseudoresponse, and radiation necrosis.
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Stroke is the clinical syndrome of acute onset of neurologic deficit caused by ischemia or hemorrhage. Neuroimaging has a crucial role in differentiating ischemic from hemorrhagic stroke. Advanced neuroimaging has become essential in the management of patients with acute ischemic stroke mainly because of improved awareness of the imaging findings and their role in patient selection for novel treatment options as highlighted in recent clinical trials, including "late window" (8-24 hours post ictus!) intra-arterial thrombectomy. This article focuses on the role of neuroimaging in the management of patients with acute ischemic stroke.