This review demonstrates the unique advantages of sonography in the oncologic setting. Although computed tomography, magnetic resonance imaging, and positron emission tomography–computed tomography are primary imaging modalities for evaluation of the oncologic patient, sonography is useful for evaluation of various conditions and clinical scenarios associated with cancer. The following article will illustrate the utility of sonography at a tertiary cancer center for diagnosis and problem solving.
This article reviews the current technology, literature, teaching models, and methods associated with simulation-based point-of-care ultrasound training. Patient simulation appears particularly well suited for learning point-of-care ultrasound, which is a required core competency for emergency medicine and other specialties. Work hour limitations have reduced the opportunities for clinical practice, and simulation enables practicing a skill multiple times before it may be used on patients. Ultrasound simulators can be categorized into 2 groups: low and high fidelity. Low-fidelity simulators are usually static simulators, meaning that they have nonchanging anatomic examples for sonographic practice. Advantages are that the model may be reused over time, and some simulators can be homemade. High-fidelity simulators are usually high-tech and frequently consist of many computer-generated cases of virtual sonographic anatomy that can be scanned with a mock probe. This type of equipment is produced commercially and is more expensive. High-fidelity simulators provide students with an active and safe learning environment and make a reproducible standardized assessment of many different ultrasound cases possible. The advantages and disadvantages of using low- versus high-fidelity simulators are reviewed. An additional concept used in simulation-based ultrasound training is blended learning. Blended learning may include face-to-face or online learning often in combination with a learning management system. Increasingly, with simulation and Web-based learning technologies, tools are now available to medical educators for the standardization of both ultrasound skills training and competency assessment.
Objectives—The purpose of this study was to investigate the value of liver stiffness in patients without liver disease using shear wave elastography and to determine the liver stiffness threshold value for identifying patients with chronic liver diseases.
Methods—A total of 150 patients who underwent liver sonography coupled with shear wave elastography were enrolled. On the basis of clinical and pathologic criteria, they were assigned to 1 of 2 groups: nondiseased liver (n = 97) and noncirrhotic chronic liver disease (n = 53). Liver stiffness was measured in the right liver, and the median value of 10 measurements was calculated. Both mean and median values in the nondiseased liver group were compared with those in the noncirrhotic chronic liver disease group. To validate this comparison, liver stiffness of the patients who underwent liver biopsy revealing either no fibrosis (fibrosis score F0; n = 5) or substantial fibrosis (F2; n = 14) was also investigated and compared. To determine the optimal threshold value for determining chronic liver disease, a receiver operating characteristic curve analysis was performed.
Results—The mean liver stiffness value in the nondiseased liver group was 5.4 kPa. In the noncirrhotic chronic liver disease group, the mean value was 8.1 kPa. Differences between the nondiseased liver and both noncirrhotic chronic liver disease groups were statistically significant (P < .001). The optimal liver stiffness threshold value for discriminating nondiseased liver from noncirrhotic chronic liver disease was 6.9 kPa. The sensitivity using this threshold was 94%. In the biopsy-proven patients, the mean liver stiffness values were 6.0 kPa in the F0 group and 9.9 kPa in the F2 group.
Conclusions—The range of liver stiffness in patients with nondiseased liver and the optimal threshold value for discriminating these patients from those with chronic liver disease were identified.
Objectives—Shear wave elastography is a novel noninvasive method for assessing liver fibrosis by measuring liver stiffness. This study was conducted to evaluate how pathologic changes could have an impact on measured elasticity values in both resected hepatocellular carcinomas and adjacent liver tissue.
Methods—Intraoperative shear wave elastography was performed in 7 patients who underwent liver resection at our institution; 7 hepatocellular carcinomas and adjacent liver tissue were subjected to elastographic measurements. A total of 48 circular regions of interest (ROIs; 3–8 mm in diameter) were located in the hepatocellular carcinomas (n = 37) and adjacent liver tissue (n = 11), and mean stiffness values were obtained from each ROI. All of the histologic images corresponding to the 48 ROIs after surgery were transformed into digital microscopic images by a scanning system, and histologic parameters, such as the proportions of nuclear areas, fatty areas, fibrous areas, and vessel areas, were quantitatively assessed. Relationships between the mean stiffness and the histologic parameters were investigated by the mixed effects model.
Results—By univariate analysis, the proportions of collagen fiber areas (P = .039), fibrous areas (P = .045), hepatocellular nuclear areas (P = .045), and nuclear areas other than hepatocellular and lymphoplasmacytic areas (P = .039) showed statistically positive associations with mean stiffness values. Multivariate analysis indicated that the proportion of collagen fiber areas was the strongest pathologic determinant of mean stiffness (P = .008), with hepatocellular nuclear areas also having a significant effect (P = .010).
Conclusions—Fibrosis predictably affects elastographic estimation, but hepatocellular density (ie, hepatocellular nuclear areas) also alters elastographic assessment.
Objectives—The purpose of this study was to assess the sonographic morphology of the clinical and subclinical pathology of facial acne vulgaris.
Methods—We studied patients with facial acne vulgaris diagnosed by certified dermatologists, and using a standardized protocol for sonographic examinations, we sequentially described the sonographic pathomorphologic characteristics. Lesions of particular interest to the referring clinician were also analyzed separately. Additionally, acne involvement was staged clinically and sonographically (SOS-Acne) using morphologic definitions of the relevant lesions and predefined scoring systems for gradation of the severity of acne lesions.
Results—A total of 245 acne lesions in 20 consecutive patients were studied. Sonographic abnormalities consisted of pseudocysts, folliculitis, fistulas, and calcinosis. Most conditions were subclinical and mostly due to lesion extensions deep into the dermis and hypodermis (52% of pseudocysts and 68% of fistulas). The statistical concordance between acne severity scores assigned by two separate clinicians was strong (κ = 0.8020), but the corresponding sonographic scores generally showed more severe and clinically occult involvement.
Conclusions—Facial acne vulgaris often involves deeper tissues, beyond the reach of the spatially restricted clinical examination; these subclinical conditions can be detected and defined with sonography. Additionally, acne vulgaris is amenable to sonographic scoring.
Objectives—The purpose of this study was to determine whether the elasticity imaging/B-mode ratio on strain elastography can predict breast cancer tumor grades.
Methods—A retrospective review of patients with breast lesions who underwent strain elastography and had a diagnosis of breast cancer by image-guided or surgical biopsy was performed. The axis of the maximum elastographic dimension was compared to the B-mode dimension to form an elasticity imaging/B-mode ratio. Lesions were categorized according to their pathologic type, including atypical ductal hyperplasia (ADH), mucinous or colloid cancer, ductal carcinoma in situ (DCIS), grade I invasive ductal carcinoma (IDC), grade II IDC, grade III IDC, invasive lobular carcinoma (ILC), and lymphoma. The mean elasticity imaging/B-mode ratio of each tumor type was calculated. The elasticity imaging/B-mode ratio of the tumor was compared to the tumor type by Kruskal-Wallis and Tukey-Kramer tests (lymphoma and ADH excluded because of small numbers).
Results—Tumor grades included lymphoma (n = 3), ADH (n = 2), mucinous cancer (n = 11), DCIS (n = 19), IDC (grades I–III; n = 200), and ILC (n = 31). The mean elasticity imaging/B-mode ratio varied with increasing tumor grade. Tumor grades could not have been selected at random from one population (P < .0001, χ2 test). Invasive lobular carcinoma and grade III IDC were statistically different from mucinous or colloid cancer, DCIS, and grade I and II IDC.
Conclusions—The elasticity imaging/B-mode ratio on strain elastography is related to the tumor grade.