Incidental Primary Intrathoracic Goiter: Dual-Isotope Scintigraphy and Early-MIBI SPECT/CT.

Primary intrathoracic goiter is an uncommon congenital entity resulting from over decent ectopic thyroid tissue. As compared with secondary intrathoracic goiter, primary entities are discrete from orthotopic thyroid tissue and may lead to potentially serious complications such as malignancy and shortness of breath. Intrathoracic goiters have been described as showing mild or absent uptake of 99m Tc-pertechnetate on planar scintigraphy. We present an incidental primary intrathoracic goiter found in a patient undergoing evaluation with multimodal scintigraphy and early 99m Tc-sestamibi single-photon emission computed tomography/computed tomography (SPECT/CT) for localization of parathyroid adenomas. The mass was inconspicuous on TcO 4- scintigraphy but methoxyisobutylisonitrile-avid on early planar and SPECT/CT.

Prevalence of Sigmoid Sinus Dehiscence and Diverticulum among Adults with Skull Base Cephaloceles.

BACKGROUND AND PURPOSE: Cephaloceles are relatively rare conditions caused by a congenital and/or acquired skull defect. The incidence of associated venous brain anomalies with regard to cephaloceles remains to be fully elucidated. Accordingly, we sought to assess the prevalence of sigmoid sinus dehiscence and diverticula in patients with spontaneous skull base cephaloceles. MATERIALS AND METHODS: Our institutional data base was retrospectively queried from 2005 to 2018. Patients in whom spontaneous skull base cephaloceles were identified were ultimately included in the study cohort. These patients subsequently had their sigmoid sinuses re-evaluated with focused attention on the possible presence of dehiscence and/or diverticula. RESULTS: We identified 56 patients: 12 men and 44 women. After re-evaluation of the sigmoid sinuses, evidence of dehiscence and/or diverticula was noted in 21 patients. The right sigmoid sinus was involved in 11 patients, and the left sigmoid sinus was involved in 7 patients, including 3 cases of diverticulum. In 3 patients, evidence of bilateral sigmoid sinus dehiscence and diverticula was noted. Female sex was associated with sigmoid sinus dehiscence and diverticula by univariate analysis (P = .019). By linear regression, cephalocele volume was negatively associated with sigmoid sinus dehiscence and diverticula (coefficient, -2266, P value < .007, adjusted R 2 = 0.1077). By univariate logistic regression using average cephalocele volume as a cutoff, we demonstrate a statistically significant finding of lower volumes being associated with sigmoid sinus dehiscence and diverticula with an odds ratio of 3.58 (P = .05). CONCLUSIONS: The prevalence of sigmoid sinus dehiscence and diverticula in patients with cephalocele is high. Female sex is associated with sigmoid sinus dehiscence and diverticula. The cephalocele volume appears to be inversely proportional to sigmoid sinus dehiscence and diverticula.

Differentiating autoimmune pancreatitis from pancreatic ductal adenocarcinoma with CT radiomics features.

PURPOSE: The purpose of this study was to determine whether computed tomography (CT)-based machine learning of radiomics features could help distinguish autoimmune pancreatitis (AIP) from pancreatic ductal adenocarcinoma (PDAC). MATERIALS AND METHODS: Eighty-nine patients with AIP (65 men, 24 women; mean age, 59.7±13.9 [SD] years; range: 21-83 years) and 93 patients with PDAC (68 men, 25 women; mean age, 60.1±12.3 [SD] years; range: 36-86 years) were retrospectively included. All patients had dedicated dual-phase pancreatic protocol CT between 2004 and 2018. Thin-slice images (0.75/0.5mm thickness/increment) were compared with thick-slices images (3 or 5mm thickness/increment). Pancreatic regions involved by PDAC or AIP (areas of enlargement, altered enhancement, effacement of pancreatic duct) as well as uninvolved parenchyma were segmented as three-dimensional volumes. Four hundred and thirty-one radiomics features were extracted and a random forest was used to distinguish AIP from PDAC. CT data of 60 AIP and 60 PDAC patients were used for training and those of 29 AIP and 33 PDAC independent patients were used for testing. RESULTS: The pancreas was diffusely involved in 37 (37/89; 41.6%) patients with AIP and not diffusely in 52 (52/89; 58.4%) patients. Using machine learning, 95.2% (59/62; 95% confidence interval [CI]: 89.8-100%), 83.9% (52:67; 95% CI: 74.7-93.0%) and 77.4% (48/62; 95% CI: 67.0-87.8%) of the 62 test patients were correctly classified as either having PDAC or AIP with thin-slice venous phase, thin-slice arterial phase, and thick-slice venous phase CT, respectively. Three of the 29 patients with AIP (3/29; 10.3%) were incorrectly classified as having PDAC but all 33 patients with PDAC (33/33; 100%) were correctly classified with thin-slice venous phase with 89.7% sensitivity (26/29; 95% CI: 78.6-100%) and 100% specificity (33/33; 95% CI: 93-100%) for the diagnosis of AIP, 95.2% accuracy (59/62; 95% CI: 89.8-100%) and area under the curve of 0.975 (95% CI: 0.936-1.0). CONCLUSIONS: Radiomic features help differentiate AIP from PDAC with an overall accuracy of 95.2%.