Ultrasound of the abdominal wall: What lies beneath?

The Benign Side of the Abdominal Wall: A Pictorial Review of Non-Neoplastic Diseases

The abdominal wall is the location of a wide spectrum of pathological conditions, from benign to malignant ones. Imaging is often recommended for the evaluation of known palpable abdominal masses. However, abdominal wall pathologies are often incidentally discovered and represent a clinical and diagnostic challenge. Knowledge of the possible etiologies and complications, combined with clinical history and laboratory findings, is crucial for the correct management of these conditions. Specific imaging clues can help the radiologist narrow the differential diagnosis and distinguish between malignant and benign processes. In this pictorial review, we will focus on the non-neoplastic benign masses and processes that can be encountered on the abdominal wall on cross-sectional imaging, with a particular focus on their management. Distinctive sonographic imaging clues, compared with computed tomography (CT) and magnetic resonance (MR) findings will be highlighted, together with clinical and practical tips for reaching the diagnosis and guiding patient management, to provide a complete diagnostic guide for the radiologist.

The “Black Pattern”, a Simplified Ultrasound Approach to Non-Traumatic Abdominal Emergencies

Background: A key issue in abdominal US is the assessment of fluid, which is usually anechoic, thus appearing “black”. Our approach focuses on searching for fluid in non-traumatic patients, providing a new, simplified method for point-of-care US (POCUS). Objective: Fluid assessment is based on a three-step analysis that we can thus summarize. 1. Look for black where it should not be. This means searching for effusions or collections. 2. Check if black is too much. This means evaluating anatomical landmarks where fluid should normally be present but may be abnormally abundant. 3. Look for black that is not clearly black. This means evaluating fluid aspects, whether wholly anechoic or not (suggesting heterogeneous or corpusculated fluid). Discussion: Using this simple method focused on US fluid presence and appearance should help clinicians to make a timely diagnosis. Although our simplified, systematic algorithm of POCUS may identify abnormalities; this usually entails a second-level imaging. An accurate knowledge of the physio–pathological and anatomical ultrasound bases remains essential in applying this algorithm. Conclusion: The black pattern approach in non -traumatic emergencies may be applied to a broad spectrum of abnormalities. It may represent a valuable aid for emergency physicians, especially if inexperienced, involved in a variety of non-traumatic scenarios. It may also be a simple and effective teaching aid for US beginners.

Pore texture analysis in automated 3D breast ultrasound images for implanted lightweight hernia mesh identification: a preliminary study

Background

Precise visualization of meshes and their position would greatly aid in mesh shrinkage evaluation, hernia recurrence risk assessment, and the preoperative planning of salvage repair. Lightweight (LW) meshes are able to preserve abdominal wall compliance by generating less post-implantation fibrosis and rigidity. However, conventional 3D imaging techniques such as computed tomography (CT) and magnetic resonance imaging (MRI) cannot visualize the LW meshes. Patients sometimes have to undergo a second-look operation for visualizing the mesh implants. The goal of this work is to investigate the potential advantages of Automated 3D breast ultrasound (ABUS) pore texture analysis for implanted LW hernia mesh identification.

Methods

In vitro, the appearances of four different flat meshes in both ABUS and 2D hand-held ultrasound (HHUS) images were evaluated and compared. In vivo, pore texture patterns of 87 hernia regions were analyzed both in ABUS images and their corresponding HHUS images.

Results

In vitro studies, the imaging results of ABUS for implanted LW meshes are much more visualized and effective in comparison to HHUS. In vivo, the inter-class distance of 40 texture features was calculated. The texture features of 2D sectional plans (axial and sagittal plane) have no significant contribution to implanted LW mesh identification. Significant contribution was observed in coronal plane. However, since the mesh may have spatial variation such as shrinkage after implantation surgery, the inter-class distance of 3D coronal plane pore texture features are bigger than 2D coronal plane, so the contribution of 3D coronal plane pore texture features are more valuable than 2D coronal plane for implanted LW mesh identification. The use of 3D pore texture features significantly improved the robustness of the identification method in distinguishing between LW mesh and fascia.

Conclusions

An innovative new ABUS provides additional pore texture visualization, by separating the LW mesh from the fascia tissues. Therefore, ABUS has the potential to provides more accurate features to characterize pore texture patterns, and ultimately provide more accurate measures for implanted LW mesh identification.

Model to estimate abdominal wall thickness in children undergoing placement or replacement of gastrostomy devices

OBJECTIVES

Abdominal wall thickness (AWT) is a key measurement when placing or replacing low profile gastrostomy devices. This measurement varies, depending on nutritional status and body habitus. We developed a mathematical model to estimate AWT using a compendium of body measurements.

METHODS

Ultrasonography was used to measure AWT at the initial gastrostomy site in subjects aged 22 days to 24 years old. Other body measurements (height, weight, waist circumference and distance from xiphisternum to pubis) were also obtained. Multiple linear regression was used to develop two separate models using age of 2 years to separate the groups. For analysis, AWT is log transformed.

RESULTS

Data from 97 subjects were used for analysis. The final model for those ≤24 months old is the following: ln(Estimated AWT) = -1.255 + 0.082*(1 if age 3-24 months, 0 if <3 months) + 0.022*(waist circumference in cm). The final model for those >24 months old is the following: ln(Estimated AWT) = -1.335 + 0.271*(1 if age >84 months, 0 if 24-84 months) + 0.082*(BMI) CONCLUSION: This model to estimate AWT is useful for determining the length of a gastrostomy device at initial placement and with subsequent changes. More data are needed to refine and further validate the model.

LEVEL OF EVIDENCE

Level IV, study of prognostic test.

Ultrasound of Soft Tissue Masses and Fluid Collections

Soft tissue masses and fluid collections are frequently encountered in sonographic practice, either as principal indication for diagnostic examination or as an incidental finding during an examination performed for other indications. Sonography is a good first-line imaging modality for evaluation of superficial masses and fluid collections, but requires meticulous attention to technique to avoid diagnostic pitfalls. Although many superficial masses are diagnosed with ultrasound, there are several potential diagnostic challenges, including differentiating hematomas from sarcomas. This article provides an image-rich review of the sonographic features of common soft tissue masses, with emphasis on practical tips to accurately recognize important pathology.

High accuracy of ultrasound in diagnosing the presence and type of groin hernia

BACKGROUND

To evaluate the accuracy of ultrasound (US) in diagnosing the the presence and type of groin hernia.

METHODS

We retrospectively studied the results of 172 US examinations of the groin in 151 patients (101 men and 50 women; mean age, 59 years) who had undergone US examination for suspected groin hernia. In total, 119 of the groin hernias had been diagnosed on US, and 108 (91%) had required subsequent surgery. All patients who had had positive results for hernia on US and did not undergo surgery (n = 11) and most of the patients whose US results had been negative for hernia (n = 48) underwent limited MRI or CT scanning. We determined the sensitivity, specificity, and accuracy of US in diagnosing the presence and type of groin hernia. To identify any change in the accuracy of US over time at our institution, we compared the sensitivity, specificity, and accuracy of its use from January 2002 through December 2010 (n = 54 groins) with those from January 2011 through December 2012 (n = 118 groins).

RESULTS

The overall rates of sensitivity and specificity of US for diagnosing the presence of groin hernia were 96% and 96%. These rates reflect improvements from 92% and 88% prior to 2011 to 98% and 100% beginning in 2011. In addition, the overall accuracy of US for diagnosing the type of groin hernia was 96%. This also improved over time at our center from 91% prior to 2011 to 98% beginning in 2011.

CONCLUSIONS

US is highly accurate at diagnosing the presence and type of groin hernia.