Seminars in Ultrasound Ct and Mri最新文献

The hypoglossal nerve is the 12th cranial nerve, exiting the brainstem in the preolivary sulcus, passing through the premedullary cistern, and exiting the skull through the hypoglossal canal. This is a purely motor nerve, responsible for the innervation of all the intrinsic tongue muscles (superior longitudinal muscle, inferior longitudinal muscle, transverse muscle, and vertical muscle), 3 extrinsic tongue muscles (styloglossus, hyoglossus, and genioglossus), and the geniohyoid muscle. Magnetic resonance imaging (MRI) is the best imaging exam to evaluate patients with clinical signs of hypoglossal nerve palsy, and computed tomography may have a complementary role in the evaluation of bone lesions affecting the hypoglossal canal. A heavily T2-weighted sequence, such as fast imaging employing steady-state acquisition (FIESTA) or constructive interference steady state (CISS) is important to evaluate this nerve on MRI. There are multiple causes of hypoglossal nerve palsy, being neoplasia the most common cause, but vascular lesions, inflammatory diseases, infections, and trauma can also affect this nerve. The purpose of this article is to review the hypoglossal nerve anatomy, discuss the best imaging techniques to evaluate this nerve and demonstrate the imaging aspect of the main diseases that affect it.

The facial nerve is the seventh cranial nerve and consists of motor, parasympathetic and sensory branches, which arise from the brainstem through 3 different nuclei (1). After leaving the brainstem, the facial nerve divides into 5 intracranial segments (cisternal, canalicular, labyrinthine, tympanic, and mastoid) and continues as the intraparotid extracranial segment (2). A wide variety of pathologies, including congenital abnormalities, traumatic disorders, infectious and inflammatory disease, and neoplastic conditions, can affect the facial nerve along its pathway and lead to ​​weakness or paralysis of the facial musculature (1,2). The knowledge of its complex anatomical pathway is essential to clinical and imaging evaluation to establish if the cause of the facial dysfunction is a central nervous system process or a peripheral disease. Both computed tomography (CT) and magnetic resonance imaging (MRI) are the modalities of choice for facial nerve assessment, each of them providing complementary information in this evaluation (1).

The glossopharyngeal, vagus, and accessory nerves are discussed in this article, given their intimate anatomical and functional associations. Abnormalities of these lower cranial nerves may be intrinsic or extrinsic due to various disease processes. This article aims to review these nerves' anatomy and demonstrates the imaging aspect of the diseases which most commonly affect them.

Mammographic breast density is widely accepted as an independent risk factor for the development of breast cancer. In addition, because dense breast tissue may mask breast malignancies, breast density is inversely related to the sensitivity of screening mammography. Given the risks associated with breast density, as well as ongoing efforts to stratify individual risk and personalize breast cancer screening and prevention, numerous studies have sought to better understand the factors that impact breast density, and to develop and implement reproducible, quantitative methods to assess mammographic density. Breast density assessments have been incorporated into risk assessment models to improve risk stratification. Recently, novel techniques for analyzing mammographic parenchymal complexity, or texture, have been explored as potential means of refining mammographic tissue-based risk assessment beyond breast density.

High-risk lesions or lesions of uncertain malignant potential are frequent findings on image-guided needle biopsy of the breast and comprise a number of distinct entities. These lesions are known for having risk of underlying malignancy and are usually associated with an increased lifetime risk for breast cancer. Surgical excision was traditionally recommended for all high-risk lesions but recent studies have demonstrated that vacuum-assisted excision or surveillance may be adequate for some lesions. While management of high-risk lesion varies among institutions, this chapter describes the management recommendations based on recent literature of the most frequent types of lesions.

Transgender patients are seen in breast imaging centers for routine screening mammography and diagnostic imaging of the symptomatic breast. This comprehensive review of transgender breast imaging aims to update the radiologist on appropriate terminology, breast cancer risk in different patient populations, screening guidelines, and diagnostic scenarios. The chapter concludes with practical tips on how to optimize the patient experience.

When cancer is detected in a screening mammogram, on occasion retrospective review of prior screening (pre-index) mammograms indicates a likely presence of cancer. These missed cancers during pre-index screens constitute a delay in detection and diagnosis. This study was undertaken to quantify the missed cancer rate by auditing pre-index screens to improve the quality of mammography screening practice. From a cohort of 135 screen-detected cancers, 120 pre-index screening mammograms could be retrieved and served as the study sample. A consensus read by 2 radiologists who interpreted the pre-index screens in an unblinded manner with full knowledge of cancer location, cancer type, lesion type, and pathology served as the truth or reference standard. Five radiologists interpreted the pre-index screens in a blinded manner. Established performance metrics such as sensitivity and specificity were quantified for each reader in interpreting these pre-index screens in a blinded manner. All five radiologists detected lesions in 8/120 (6.7%) screens. Excluding the 2 readers whose performance was close to random, all the 3 remaining readers detected lesions in 13 pre-index screens. This indicates that there is a delay in diagnosis by at least one cycle from 8/120 (6.7%) to 13/120 (10.8%). There were no observable trends in terms of either the cancer type or the lesion type. Auditing prior screening mammograms in screen-detected cancers can help in identifying the proportion of cases that were missed during interpretation and help in quantifying the delay in breast cancer detection.

This topical review is focused on the clinical breast x-ray imaging applications of the rapidly evolving field of artificial intelligence (AI). The range of AI applications is broad. AI can be used for breast cancer risk estimation that could allow for tailoring the screening interval and the protocol that are woman-specific and for triaging the screening exams. It also can serve as a tool to aid in the detection and diagnosis for improved sensitivity and specificity and as a tool to reduce radiologists’ reading time. AI can also serve as a potential second ‘reader’ during screening interpretation. During the last decade, numerous studies have shown the potential of AI-assisted interpretation of mammography and to a lesser extent digital breast tomosynthesis; however, most of these studies are retrospective in nature. There is a need for prospective clinical studies to evaluate these technologies to better understand their real-world efficacy. Further, there are ethical, medicolegal, and liability concerns that need to be considered prior to the routine use of AI in the breast imaging clinic.

Cybersecurity in healthcare is a very real threat with the potential to severely disrupt patient care, place extra burden on an already strained system, and result in significant financial losses for a hospital or healthcare network. In October 2020, on the backdrop of the ongoing COVID-19 pandemic, our institution experienced one of the most significant cyberattacks on a healthcare system to date, lasting for nearly 40 days. By sharing our experience in radiology, and specifically in breast imaging, including the downtime procedures we relied upon and the lessons that we learned emerging from this cyberattack, we hope to help future victims of a healthcare cyberattack successfully weather such an experience.