Massive hemoptysis and deep venous thrombosis presenting in a woman with Hughes-Stovin syndrome: a case report

<p>Abstract</p> <p>Introduction</p> <p>Hughes-Stovin syndrome is a very rare disease with fewer than 30 cases reported in the literature. The disease is thought to be a variant of Behcet's disease and is defined by the presence of pulmonary artery aneurysm in association with peripheral venous thrombosis.</p> <p>Case presentation</p> <p>A previously healthy 23-year-old Saudi woman presented with massive hemoptysis a day prior to her admission to our hospital. She had a six-month history of recurrent fever, cough, dyspnea, and recurrent oral ulceration. Contrast-enhanced computed tomography scan of her chest and pulmonary angiogram demonstrated a single right-lower lobe pulmonary artery aneurysm. She underwent thoracotomy and right lower lobe resection. Her postoperative course was complicated by deep vein thrombosis. She also developed headache and papilledema, while a magnetic resonance imaging of her brain suggested vasculitis. Based on these clinical presentations, she was diagnosed and treated with Hughes-Stovin syndrome.</p> <p>Conclusion</p> <p>The majority of cases of Hughes-Stovin syndrome are reported among men, with only two cases occurring in women. A case of Hughes-Stovin syndrome occurring in a woman is presented in this report. She was treated successfully with multimodality treatment that includes surgery, steroids and cytotoxic agents.</p

Support vector machine model for diagnosis of lymph node metastasis in gastric cancer with multidetector computed tomography: a preliminary study

<p>Abstract</p> <p>Background</p> <p>Lymph node metastasis (LNM) of gastric cancer is an important prognostic factor regarding long-term survival. But several imaging techniques which are commonly used in stomach cannot satisfactorily assess the gastric cancer lymph node status. They can not achieve both high sensitivity and specificity. As a kind of machine-learning methods, Support Vector Machine has the potential to solve this complex issue.</p> <p>Methods</p> <p>The institutional review board approved this retrospective study. 175 consecutive patients with gastric cancer who underwent MDCT before surgery were included. We evaluated the tumor and lymph node indicators on CT images including serosal invasion, tumor classification, tumor maximum diameter, number of lymph nodes, maximum lymph node size and lymph nodes station, which reflected the biological behavior of gastric cancer. Univariate analysis was used to analyze the relationship between the six image indicators with LNM. A SVM model was built with these indicators above as input index. The output index was that lymph node metastasis of the patient was positive or negative. It was confirmed by the surgery and histopathology. A standard machine-learning technique called k-fold cross-validation (5-fold in our study) was used to train and test SVM models. We evaluated the diagnostic capability of the SVM models in lymph node metastasis with the receiver operating characteristic (ROC) curves. And the radiologist classified the lymph node metastasis of patients by using maximum lymph node size on CT images as criterion. We compared the areas under ROC curves (AUC) of the radiologist and SVM models.</p> <p>Results</p> <p>In 175 cases, the cases of lymph node metastasis were 134 and 41 cases were not. The six image indicators all had statistically significant differences between the LNM negative and positive groups. The means of the sensitivity, specificity and AUC of SVM models with 5-fold cross-validation were 88.5%, 78.5% and 0.876, respectively. While the diagnostic power of the radiologist classifying lymph node metastasis by maximum lymph node size were only 63.4%, 75.6% and 0.757. Each SVM model of the 5-fold cross-validation performed significantly better than the radiologist.</p> <p>Conclusions</p> <p>Based on biological behavior information of gastric cancer on MDCT images, SVM model can help diagnose the lymph node metastasis preoperatively.</p

Multidetector row CT of the pancreas

Multidetector CT (MDCT) has gained a great role in clinical imaging practice in a short time. Its major advantages are faster image acquisition and improved z-axis resolution. In spite of the advent of other imaging modalities CT is still the gold standard for the evaluation of pancreatic pathology. In this article, a review of the utility of MDCT in pancreatic applications is given with emphasis on acquisition techniques tailored to contrast material pharmacokinetics to improve lesion detection and characterization. (C) 2004 Elsevier Ireland Ltd. All rights reserved

Carcinoma of stomach and duodenum: radiologic diagnosis and staging

Gastric carcinoma is one of the leading causes of cancer mortality worldwide. Accurate preoperative staging of gastric cancer is important in planning the most effective therapy toward cure or palliation. This article reviews the radiologic diagnosis and staging of gastric cancer. (C) 2002 Elsevier Science Ireland Ltd. All rights reserved

Frequency of sternal variations and anomalies evaluated by MDCT

OBJECTIVE. Our objective was to reveal the frequency and MDCT appearances of sternal variations and anomalies in subjects without sternal deformities

MRI of cerebral alveolar echinococcosis

Cerebral alveolar echinococcosis is rare. We report a case with multiple intracranial masses which show cauliflower-like contrast enhancement pattern on MRI. The lesions originated from hepatic involvement with invasion of the inferior vena cava

Palliation of malignant esophageal strictures with self-expanding nitinol stents: Drawbacks and complications

PURPOSE: To evaluate the usefulness of self-expanding nitinol stents in the palliative treatment of malignant dysphagia

Cyclic sciatica caused by infiltrative endometriosis: MRI findings

Endometriosis, an important gynecological disorder of reproductive women, affects most commonly the ovaries and less frequently the gastrointestinal tract, chest, urinary tract, and soft tissues. Endometriosis classically appears on MRI as a mass with a large cystic component and variable signal intensities on T1- and T2-weighted images due to the presence of variable degradation of hemorrhagic products. Endometriosis in an atypical location, an infiltrative appearance and without cystic-hemorrhagic components has rarely been described. We report on a 33-year-old woman with cyclic sciatica due to histologically documented infiltrative endometriosis involving the area of the left sciatic notch

Hepatic alveolar echinococcosis: MRI findings

The purpose of this study was to describe the magnetic resonance imaging (MRI) appearance of hepatic alveolar echinococcosis (HAE) on T-1-weighted, T-2-weighted and postgadolinium images. A total of 13 lesions were demonstrated in 13 patients. All patients underwent MR examination at 1 T imager. MR examinations included precontrast T-1-weighted breathing averaged spin echo (SE), breath-hold spoiled gradient echo, T-2-weighted TSE sequences with and without fat suppression, and T-1-weighted breath-hold spoiled gradient echo (SGE) sequence following i.v. after gadolinium administration. All lesions were confirmed with histopathology. HAE hepatic lesions revealed geographic patterns of variable signal intensities on noncontrast T-1- and T-2-weighted images. Slightly hyperintense, iso- and hypointense signal on T-1-weighted images corresponded to calcified regions, which appeared hypo-isointense signal on T-2-weighted images. Necrotic areas were hypointense signal on T-1-weighted and hyperintense signal on T-2-weighted images. On postgadolinium images, lesions did not reveal enhancement. Dilatation of intrahepatic bile ducts distal to HAE abscesses were observed in five patients and portal vein invasion or compression was observed in four patients, lobar atrophy of the liver was coexistent finding in cases with portal vein compression. The MRI appearance of HAE abscesses included large irregularly marginated masses with heterogenous signal on T-1- and T-2-weighted images and lack of enhancement with gadolinium. (C) 2000 Elsevier Science Inc. All rights reserved