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Discrimination between adrenocortical carcinoma and lipid-poor cortical adenoma preoperatively is frequently difficult as these two entities have overlapping imaging characteristics. Differentiation will allow for the selection of the most appropriate operative approach and may help prevent over-treatment. We aimed to identify imaging features that could preoperatively differentiate adrenocortical carcinoma from lipid-poor cortical adenoma and use them in a novel imaging-based score.We conducted a retrospective analysis of patients with pathologically proven adrenocortical carcinoma and lipid-poor cortical adenoma who underwent resection in a single tertiary referral center between March 1998 and August 2020. The inclusion criteria were diameter1 cm, attenuation10 Hounsfield units on nonenhanced computed tomography, and histopathologic diagnosis. Patients with metastatic or locally advanced adrenocortical carcinoma adenoma (stages 3-4) were excluded. We developed a score using binary logistic multivariate regression model in 5-fold derivation (∼70%) cohorts with stepwise backward conditional regression as feature selection. Standardized mean regression weight was used as variable score points.We identified 232 adrenals resected across 211 patients. By comparing the imaging characteristics of adrenocortical carcinoma (n = 56) and lipid-poor cortical adenoma (n = 156), we revealed statistically significant differences between the groups in 9 parameters: size, attenuation, thin and thick rim enhancement patterns, heterogeneity, calcification, necrosis, fat infiltration, and lymph node prominence. The score mean performance was 100% sensitivity for the exclusion of adrenocortical carcinoma, 80% specificity (95% confidence interval, 68.3-91.5), 66% positive predictive value (95% confidence interval, 52.3-78.7), and 100% negative predictive value with area under the curve of 0.974.We defined and evaluated a novel 9-variable, imaging-based score. This score outperformed any single variable and could facilitate safe preoperative discrimination of adrenocortical carcinoma and lipid-poor cortical adenoma.

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.

To compare the utility of a novel metal artifact reduction algorithm to standard imaging in improving visualization of key structures, diagnostic confidence, and patient-level confidence in malignancy in patients with suspected bladder cancer.Patients with hip implants undergoing CT urography for suspected bladder malignancy were enrolled. Images were reconstructed using 3 methods: (1) Filtered Back Projection (FBP), (2) Iterative Metal Artifact Reduction (iMAR), and (3) Adaptive Iterative Metal Artifact Reduction (AiMAR) strength 4. In multiple reading sessions, three radiologists graded visualization of critical anatomic structures and artifact severity (6-point scales, lower scores desirable), and diagnostic confidence in blinded fashion. They also graded patient-level confidence in malignancy based on imaging findings in each patient.Thirty-two patients (8 females) with a mean age of 74.5 ± 8.5 years were included. The median (range) visualization scores for FBP, iMAR, and AiMAR were 3.6 (1.1-4.9), 1.6 (0.3-2.8), and 1.6 (0.3-2.6), respectively. Both iMAR and AiMAR had anatomic visualization and artifact scores better than FBP (P 0.001 for both) and similar to each other (P 0.05). Structures with the most improvement in visualization score with the use of metal artifact reduction algorithms included the obturator internus muscle, internal and external iliac nodal chains, and vagina. iMAR and AiMAR improved diagnostic confidence (P 0.001) and patient-level confidence in malignancy (P ≤ 0.24).For patients with hip prostheses and suspected bladder malignancy, the use of iMAR or AiMAR was shown to significantly reduce metal artifacts, thus improving diagnostic confidence and patient-level confidence in malignancy.