Due to the exponential growth of computational algorithms, artificial intelligence (AI) methods are poised to improve the precision of diagnostic and therapeutic methods in medicine. The field of ...radiomics in neuro-oncology has been and will likely continue to be at the forefront of this revolution. A variety of AI methods applied to conventional and advanced neuro-oncology MRI data can already delineate infiltrating margins of diffuse gliomas, differentiate pseudoprogression from true progression, and predict recurrence and survival better than methods used in daily clinical practice. Radiogenomics will also advance our understanding of cancer biology, allowing noninvasive sampling of the molecular environment with high spatial resolution and providing a systems-level understanding of underlying heterogeneous cellular and molecular processes. By providing in vivo markers of spatial and molecular heterogeneity, these AI-based radiomic and radiogenomic tools have the potential to stratify patients into more precise initial diagnostic and therapeutic pathways and enable better dynamic treatment monitoring in this era of personalized medicine. Although substantial challenges remain, radiologic practice is set to change considerably as AI technology is further developed and validated for clinical use.
In the era of personalized medicine, the emphasis of health care is shifting from populations to individuals. Artificial intelligence (AI) is capable of learning without explicit instruction and has ...emerging applications in medicine, particularly radiology. Whereas much attention has focused on teaching radiology trainees about AI, here our goal is to instead focus on how AI might be developed to better teach radiology trainees. While the idea of using AI to improve education is not new, the application of AI to medical and radiological education remains very limited. Based on the current educational foundation, we highlight an AI-integrated framework to augment radiology education and provide use case examples informed by our own institution's practice. The coming age of "AI-augmented radiology" may enable not only "precision medicine" but also what we describe as "precision medical education," where instruction is tailored to individual trainees based on their learning styles and needs.
Multi-omic data, i.e., clinical measures, radiomic, and genetic data, capture multi-faceted tumor characteristics, contributing to a comprehensive patient risk assessment. Here, we investigate the ...additive value and independent reproducibility of integrated diagnostics in prediction of overall survival (OS) in isocitrate dehydrogenase (IDH)-wildtype GBM patients, by combining conventional and deep learning methods. Conventional radiomics and deep learning features were extracted from pre-operative multi-parametric MRI of 516 GBM patients. Support vector machine (SVM) classifiers were trained on the radiomic features in the discovery cohort (n = 404) to categorize patient groups of high-risk (OS < 6 months) vs all, and low-risk (OS ≥ 18 months) vs all. The trained radiomic model was independently tested in the replication cohort (n = 112) and a patient-wise survival prediction index was produced. Multivariate Cox-PH models were generated for the replication cohort, first based on clinical measures solely, and then by layering on radiomics and molecular information. Evaluation of the high-risk and low-risk classifiers in the discovery/replication cohorts revealed area under the ROC curves (AUCs) of 0.78 (95% CI 0.70-0.85)/0.75 (95% CI 0.64-0.79) and 0.75 (95% CI 0.65-0.84)/0.63 (95% CI 0.52-0.71), respectively. Cox-PH modeling showed a concordance index of 0.65 (95% CI 0.6-0.7) for clinical data improving to 0.75 (95% CI 0.72-0.79) for the combination of all omics. This study signifies the value of integrated diagnostics for improved prediction of OS in GBM.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
The basal ganglia and thalamus are paired deep gray matter structures that may be involved by a wide variety of disease entities. The basal ganglia are highly metabolically active and are ...symmetrically affected in toxic poisoning, metabolic abnormalities, and neurodegeneration with brain iron accumulation. Both the basal ganglia and thalamus may be affected by other systemic or metabolic disease, degenerative disease, and vascular conditions. Focal flavivirus infections, toxoplasmosis, and primary central nervous system lymphoma may also involve both deep gray matter structures. The thalamus is more typically affected alone by focal conditions than by systemic disease. Radiologists may detect bilateral abnormalities of the basal ganglia and thalamus in different acute and chronic clinical situations, and although magnetic resonance (MR) imaging is the modality of choice for evaluation, the correct diagnosis can be made only by taking all relevant clinical and laboratory information into account. The neuroimaging diagnosis is influenced not only by detection of specific MR imaging features such as restricted diffusion and the presence of hemorrhage, but also by detection of abnormalities involving other parts of the brain, especially the cerebral cortex, brainstem, and white matter. Judicious use of confirmatory neuroimaging investigations, especially diffusion-weighted imaging, MR angiography, MR venography, and MR spectroscopy during the same examination, may help improve characterization of these abnormalities and help narrow the differential diagnosis.
Complex anatomy and a wide spectrum of diseases in the head and neck predispose interpretation of neck imaging to cognitive pitfalls and perceptual errors. Extra attention to common blind spots in ...the neck and familiarity with common interpretive challenges could aid radiologists in preventing these diagnostic errors.
•Review clinical data, post-treatment history, and prior imaging•Systematic review of the images with a comprehensive checklist and knowledge of blind spots•Familiarity with common interpretive errors
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
ABSTRACT
Perivascular spaces (PVSs), also known as Virchow‐Robin spaces, are pial‐lined, fluid‐filled structures found in characteristic locations throughout the brain. They can become abnormally ...enlarged or dilated and in rare cases can cause hydrocephalus. Dilated PVSs can pose a diagnostic dilemma for radiologists because of their varied appearance, sometimes mimicking more serious entities such as cystic neoplasms, including dysembryoplastic neuroepithelial tumor and multinodular and vacuolating neuronal tumor, or cystic infections including toxoplasmosis and neurocysticercosis. In addition, various pathologic processes, including cryptococcosis and chronic lymphocytic inflammation with pontine perivascular enhancement responsive to steroids, can spread into the brain via PVSs, resulting in characteristic magnetic resonance imaging appearances. This review aims to describe the key imaging characteristics of normal and dilated PVSs, as well as cystic mimics and pathologic processes that directly involve PVSs.
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
Masses near the angle of the mandible containing extracellular matrix or mucin on cytology raise concern for various benign and malignant parotid gland neoplasms. Here a 76‐year‐old female with a ...history of cosmetic hyaluronic acid (HA) filler injections presented with a painless 6 mm left sided facial mass. Injection of hyaluronidase into the mass had failed to cause regression, raising concern for a neoplastic process. Fine‐needle aspiration (FNA) showed amorphous, mucinous/extracellular matrix‐like material in a background of numerous histiocytes and occasional multinucleated giant cells, consistent with a foreign body giant cell reaction to HA. This uncommon reaction to HA filler creates previously unrecognized diagnostic pitfalls because of its resemblance on FNA to the extracellular matrix or mucin found in many salivary neoplasms.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
Machine learning (ML) integrated with medical imaging has introduced new perspectives in precision diagnostics of high-grade gliomas, through radiomics and radiogenomics. This has raised hopes for ...characterizing noninvasive and in vivo biomarkers for prediction of patient survival, tumor recurrence, and genomics and therefore encouraging treatments tailored to individualized needs. Characterization of tumor infiltration based on pre-operative multi-parametric magnetic resonance imaging (MP-MRI) scans may allow prediction of the loci of future tumor recurrence and thereby aid in planning the course of treatment for the patients, such as optimizing the extent of resection and the dose and target area of radiation. Imaging signatures of tumor genomics can help in identifying the patients who benefit from certain targeted therapies. Specifying molecular properties of gliomas and prediction of their changes over time and with treatment would allow optimization of treatment. In this article, we provide neuro-oncology, neuropathology, and computational perspectives on the promise of radiomics and radiogenomics for allowing personalized treatments of patients with gliomas and discuss the challenges and limitations of these methods in multi-institutional clinical trials and suggestions to mitigate the issues and the future directions.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
ABSTRACT
Wallerian degeneration (WD) is defined as progressive anterograde disintegration of axons and accompanying demyelination after an injury to the proximal axon or cell body. Since the 1980s ...and 1990s, conventional magnetic resonance imaging (MRI) sequences have been shown to be sensitive to changes of WD in the subacute to chronic phases. More recently, advanced MRI techniques, such as diffusion‐weighted imaging (DWI) and diffusion tensor imaging (DTI), have demonstrated some of earliest changes attributed to acute WD, typically on the order of days. In addition, there is increasing evidence on the value of advanced MRI techniques in providing important prognostic information related to WD. This article reviews the utility of conventional and advanced MRI techniques for assessing WD, by focusing not only on the corticospinal tract but also other neural tracts less commonly thought of, including corticopontocerebellar tract, dentate‐rubro‐olivary pathway, posterior column of the spinal cord, corpus callosum, limbic circuit, and optic pathway. The basic anatomy of these neural pathways will be discussed, followed by a comprehensive review of existing literature supported by instructive clinical examples. The goal of this review is for readers to become more familiar with both conventional and advanced MRI findings of WD involving important neural pathways, as well as to illustrate increasing utility of advanced MRI techniques in providing important prognostic information for various pathologies.
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
10.
Neuroimaging in Central Nervous System Lymphoma Nabavizadeh, Seyed Ali; Vossough, Arastoo; Hajmomenian, Mehrdad ...
Hematology/oncology clinics of North America,
08/2016, Volume:
30, Issue:
4
Journal Article
Peer reviewed
Primary central nervous system lymphoma (PCNSL) is a rare aggressive high-grade type of extranodal lymphoma. PCNSL can have a variable imaging appearance and can mimic other brain disorders such as ...encephalitis, demyelination, and stroke. In addition to PCNSL, the CNS can be secondarily involved by systemic lymphoma. Computed tomography and conventional MRI are the initial imaging modalities to evaluate these lesions. Recently, however, advanced MRI techniques are more often used in an effort to narrow the differential diagnosis and potentially inform diagnostic and therapeutic decisions.
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