Eosinophilic Disorders of the Gastrointestinal Tract and Associated Abdominal Viscera: Imaging Findings and Diagnosis.

Eosinophilic gastrointestinal disorders (EGIDs) are inflammatory conditions of the gastrointestinal tract that are characterized by tissue eosinophilia and end-organ dysfunction or damage. Primary EGIDs are associated with atopy and other allergic conditions, whereas secondary EGIDs are associated with underlying systemic diseases or hypereosinophilic syndrome. Within the spectrum of EGIDs, eosinophilic esophagitis is the most prevalent. Eosinophilic gastroenteritis and eosinophilic colitis are relatively uncommon. Eosinophilic infiltration of the liver, biliary tree, and/or pancreas also can occur and mimic other inflammatory and malignant conditions. Although endoscopic evaluation is the method of choice for eosinophilic esophagitis, radiologic evaluation of the esophagus plays an important role in the assessment of disease severity. CT and MR enterography are the modalities of choice for demonstrating specific forms of eosinophilic gastroenteritis. CT and MRI are important in the detection of abdominal visceral involvement in EGIDs. Diagnosis is often challenging and relies on symptoms, imaging findings, histologic confirmation of tissue eosinophilia, and correlation with peripheral eosinophilia. Imaging is crucial for identifying characteristic organ-specific findings, although imaging findings are not specific. When promptly treated, EGIDs usually have a benign clinical course. However, a delayed diagnosis and associated surgical interventions have been associated with morbidity. Therefore, a radiologist's knowledge of the imaging findings of EGIDs in the appropriate clinical settings may aid in early diagnosis and thereby improve patient care. An overview of the clinical features and imaging findings of EGIDs and the eosinophilic disorders of associated abdominal viscera is provided. Online supplemental material is available for this article. ©RSNA, 2022.

A Classification-Based Adaptive Segmentation Pipeline: Feasibility Study Using Polycystic Liver Disease and Metastases from Colorectal Cancer CT Images.

Automated segmentation tools often encounter accuracy and adaptability issues when applied to images of different pathology. The purpose of this study is to explore the feasibility of building a workflow to efficiently route images to specifically trained segmentation models. By implementing a deep learning classifier to automatically classify the images and route them to appropriate segmentation models, we hope that our workflow can segment the images with different pathology accurately. The data we used in this study are 350 CT images from patients affected by polycystic liver disease and 350 CT images from patients presenting with liver metastases from colorectal cancer. All images had the liver manually segmented by trained imaging analysts. Our proposed adaptive segmentation workflow achieved a statistically significant improvement for the task of total liver segmentation compared to the generic single-segmentation model (non-parametric Wilcoxon signed rank test, n = 100, p-value << 0.001). This approach is applicable in a wide range of scenarios and should prove useful in clinical implementations of segmentation pipelines.

Deep learning approach for differentiating indeterminate adrenal masses using CT imaging.

PURPOSE: Distinguishing stage 1-2 adrenocortical carcinoma (ACC) and large, lipid poor adrenal adenoma (LPAA) via imaging is challenging due to overlapping imaging characteristics. This study investigated the ability of deep learning to distinguish ACC and LPAA on single time-point CT images. METHODS: Retrospective cohort study from 1994 to 2022. Imaging studies of patients with adrenal masses who had available adequate CT studies and histology as the reference standard by method of adrenal biopsy and/or adrenalectomy were included as well as four patients with LPAA determined by stability or regression on follow-up imaging. Forty-eight (48) subjects with pathology-proven, stage 1-2 ACC and 43 subjects with adrenal adenoma >3 cm in size demonstrating a mean non-contrast CT attenuation > 20 Hounsfield Units centrally were included. We used annotated single time-point contrast-enhanced CT images of these adrenal masses as input to a 3D Densenet121 model for classifying as ACC or LPAA with five-fold cross-validation. For each fold, two checkpoints were reported, highest accuracy with highest sensitivity (accuracy focused) and highest sensitivity with the highest accuracy (sensitivity focused). RESULTS: We trained a deep learning model (3D Densenet121) to predict ACC versus LPAA. The sensitivity-focused model achieved mean accuracy: 87.2% and mean sensitivity: 100%. The accuracy-focused model achieved mean accuracy: 91% and mean sensitivity: 96%. CONCLUSION: Deep learning demonstrates promising results distinguishing between ACC and large LPAA using single time-point CT images. Before being widely adopted in clinical practice, multicentric and external validation are needed.

The properties of damaged and undamaged suture used in metal and bioabsorbable anchors: an in vitro study.

PURPOSE: The purpose of this study was to determine the mechanical properties of undamaged and damaged sutures in metal and bioabsorbable suture anchors. METHODS: Undamaged and damaged FiberWire (Arthrex, Naples, FL), Tevdek (Deknatel, Mansfield, MA), and PDS (Ethicon, Somerville, NJ) sutures were tested by a single pull to failure while being pulled parallel to the axis of either a metal or bioabsorbable suture anchor. Sutures were damaged by use of a razor blade incorporated into a custom-designed jig. The friction of the sutures through the anchor eyelets was also tested. RESULTS: For both anchor types, FiberWire was the strongest suture studied. Undamaged PDS had a significantly greater load to failure than Tevdek. Although all sutures lost significant strength when damaged, PDS lost the most, with damaged PDS becoming significantly weaker than damaged Tevdek. Damaged FiberWire was significantly stronger in metal anchors compared with bioabsorbable anchors, with failure of the bioabsorbable suture eyelet preventing testing of undamaged FiberWire. Neither undamaged nor damaged PDS or Tevdek had a significant difference in strength between metal and bioabsorbable anchors. However, in metal anchors the mechanical properties of undamaged Tevdek were inferior to those of the other undamaged sutures tested. For undamaged or damaged sutures through either anchor type, PDS suture had the highest coefficient of friction, significantly higher than FiberWire and Tevdek. All sutures, undamaged or damaged, had significantly less friction in bioabsorbable anchors compared with metal anchors. CONCLUSIONS: The FiberWire-anchor construct is significantly weaker when bioabsorbable anchors are used instead of metal anchors. For Tevdek and PDS sutures, the anchor type does not affect the strength of the construct, as the suture is the limiting factor. When used with suture anchors, PDS has the most friction of the sutures tested, potentially leading to suture damage, which disproportionately weakens PDS compared with the other sutures tested. For both undamaged and damaged sutures, bioabsorbable anchors lead to less friction than do metal anchors, which may lessen suture damage in vivo. CLINICAL RELEVANCE: The mechanical properties of damaged suture are important to all surgeons who use suture arthroscopically.

Strength of damaged suture: an in vitro study.

PURPOSE: To determine the mechanical properties of damaged suture. METHODS: Undamaged and damaged sutures were tested by a single pull to failure. Sutures were damaged with a razor blade incorporated into a custom-designed jig. Sutures were tested to failure by straight pull and by pulling at 180 degrees through a suture anchor eyelet. The friction of sutures through anchors was also tested. RESULTS: For the straight line pull test, undamaged FiberWire (Arthrex, Naples, FL) had the highest load to failure (LTF) and ultimate tensile strength (UTS) of all sutures tested. Undamaged Orthocord (Mitek, Somerville, NJ) ranked second in both properties. Uncut polydioxanone (PDS) suture (Ethicon, Somerville, NJ) had a higher LTF and a comparable UTS with respect to Tevdek (Deknatel, Mansfield, MA) and Ethibond (Ethicon). For cut sutures, FiberWire and Orthocord had a significantly higher LTF and UTS than the other sutures tested. Suture stiffness was not significantly affected when the suture was cut. For the suture anchor test, FiberWire and Orthocord had the highest LTF, whether undamaged or damaged. When cut, PDS had the greatest loss of LTF and UTS during both tests. CONCLUSIONS: The newer polyethylene core sutures (FiberWire and Orthocord) have superior mechanical properties compared with other sutures. Their superior properties are maintained even when cut. Although uncut PDS had equivalent or superior strength compared with Ethibond and Tevdek, once cut, PDS suture was weakened significantly more compared with all other sutures tested. CLINICAL RELEVANCE: The mechanical properties of damaged suture are important to all surgeons who use suture arthroscopically.

Dynamic Susceptibility Contrast-MRI Quantification Software Tool: Development and Evaluation.

Relative cerebral blood volume (rCBV) is a magnetic resonance imaging biomarker that is used to differentiate progression from pseudoprogression in patients with glioblastoma multiforme, the most common primary brain tumor. However, calculated rCBV depends considerably on the software used. Automating all steps required for rCBV calculation is important, as user interaction can lead to increased variability and possible inaccuracies in clinical decision-making. Here, we present an automated tool for computing rCBV from dynamic susceptibility contrast-magnetic resonance imaging that includes leakage correction. The entrance and exit bolus time points are automatically calculated using wavelet-based detection. The proposed tool is compared with 3 Food and Drug Administration-approved software packages, 1 automatic and 2 requiring user interaction, on a data set of 43 patients. We also evaluate manual and automated white matter (WM) selection for normalization of the cerebral blood volume maps. Our system showed good agreement with 2 of the 3 software packages. The intraclass correlation coefficient for all comparisons between the same software operated by different people was >0.880, except for FuncTool when operated by user 1 versus user 2. Little variability in agreement between software tools was observed when using different WM selection techniques. Our algorithm for automatic rCBV calculation with leakage correction and automated WM selection agrees well with 2 out of the 3 FDA-approved software packages.

Variability and accuracy of different software packages for dynamic susceptibility contrast magnetic resonance imaging for distinguishing glioblastoma progression from pseudoprogression.

Determining whether glioblastoma multiforme (GBM) is progressing despite treatment is challenging due to the pseudoprogression phenomenon seen on conventional MRIs, but relative cerebral blood volume (CBV) has been shown to be helpful. As CBV's calculation from perfusion-weighted images is not standardized, we investigated whether there were differences between three FDA-cleared software packages in their CBV output values and subsequent performance regarding predicting survival/progression. Forty-five postradiation therapy GBM cases were retrospectively identified as having indeterminate MRI findings of progression versus pseudoprogression. The dynamic susceptibility contrast MR images were processed with different software and three different relative CBV metrics based on the abnormally enhancing regions were computed. The intersoftware intraclass correlation coefficients were 0.8 and below, depending on the metric used. No statistically significant difference in progression determination performance was found between the software packages, but performance was better for the cohort imaged at 3.0 T versus those imaged at 1.5 T for many relative CBV metric and classification criteria combinations. The results revealed clinically significant variation in relative CBV measures based on the software used, but minimal interoperator variation. We recommend against using specific relative CBV measurement thresholds for GBM progression determination unless the same software or processing algorithm is used.

Comparative study of two image space noise reduction methods for computed tomography: bilateral filter and nonlocal means.

Optimal noise control is important for improving image quality and reducing radiation dose in computed tomography. Here we investigated two image space based nonlinear filters for noise reduction: the bilateral filter (BF) and the nonlocal means (NLM) algorithm. Images from both methods were compared against those from a commercially available weighted filtered backprojection (WFBP) method. A standard phantom for quality assurance testing was used to quantitatively compare noise and spatial resolution, as well as low contrast detectability (LCD). Additionally, an image dataset from a patient's abdominal CT exam was used to assess the effectiveness of the filters on full dose and simulated half dose acquisitions. We found that both the BF and NLM methods improve the tradeoff between noise and high contrast spatial resolution with no significant difference in LCD. Results from the patient dataset demonstrated the potential of dose reduction with the denoising methods. Care must be taken when choosing the NLM parameters in order to minimize the generation of artifacts that could possibly compromise diagnostic value.