Extrarenal multiorgan metastases of collecting duct carcinoma of the kidney: A case series

<p>Abstract</p> <p>Introduction</p> <p>Collecting duct carcinoma is a rare type of renal cell carcinoma. The primary is difficult to diagnose on imaging, and metastases are often present on initial presentation. Extensive multiorgan metastases can result in complex presentations that can be difficult to diagnose.</p> <p>Case presentation</p> <p>We present two case reports of multiorgan metastases of collecting duct carcinoma that were autopsy confirmed. The first case was a 55-year-old man who presented with fever and abdominal pain. Abdominal computed tomography showed enlargement of the right kidney. Pyelonephritis was considered on the basis of laboratory test results and imaging findings. However, multiple cavitary lesions were found on routine chest radiography. These lesions were biopsied, resulting in a histological diagnosis of metastatic adenocarcinoma. A renal tumor was considered. Transitional cell carcinoma was suspected, which proved to be misdiagnosed and chemotherapy was given accordingly. However, this was not effective and the patient died after 2 months. Autopsy demonstrated the primary tumor to be collecting duct carcinoma, with metastases to lung, liver, spleen, bone marrow, right adrenal gland, and para-aortic lymph node. Computed tomography done while the patient was alive detected lung, liver, and para-aortic lymph node metastases. The second case was a 77-year-old man who presented with fever. Pyelonephritis was considered on the basis of the laboratory test results and imaging findings. Antibiotic therapy improved his symptoms and laboratory indicators of inflammation. One year later, he developed backache. Computed tomography revealed a progressively enlarging right renal lesion, multiple liver masses, enlargement of the para-aortic lymph nodes, and multiple osteoblastic and osteoclastic lesions. A renal tumor with multiple metastases was diagnosed. Chemotherapy was given without effect, and the patient died of cardiac failure 1 year later. Autopsy revealed a primary tumor of collecting duct carcinoma with metastases to the liver, right adrenal gland, right upper ureter, bone marrow, para-aortic and mediastinal lymph nodes, and bone.</p> <p>Conclusion</p> <p>We present the radiological findings of lung, liver, lymph node, and bone metastases in two patients with collecting duct carcinoma.</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

Inflammatory pseudo-tumor of the liver: a rare pathological entity

Inflammatory pseudo-tumor (IPT) of the liver is a rare benign neoplasm and is often mistaken as a malignant entity. Few cases have been reported in the literature and the precise etiology of inflammatory pseudotumor remains unknown. Patients usually present with fever, abdominal pain and jaundice. The proliferation of spindled myofibroblast cells mixed with variable amounts of reactive inflammatory cells is characteristics of IPT. We reviewed the literature regarding possible etiology for IPT with a possible suggested etiology

Weldability of Helium-containing Stainless Steels Using YAG Laser

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