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Previous studies on primary pulmonary epithelioid angiosarcoma (PEA) have been mostly clinical or pathological case reports. We here summarize findings from computed tomography (CT) and positron emission tomography/computed tomography (PET/CT) analyses of PEA to improve the diagnosis and differentiation of this rare tumor.We conducted a retrospective analysis of the clinical findings, radiological imaging, and pathological findings of 6 cases of primary PEA confirmed by surgery, biopsy, and pathology. All cases were evaluated by CT and x-ray prior to surgery, and 2 cases were further examined by PET/CT.CT images indicated maximum tumor diameters of 2.4 to 9.8 cm and inhomogeneous density, with 1 case exhibiting nodular calcification. Contrast-enhanced CT revealed inhomogeneous enhancement with visible necrosis in all 6 cases, while 3 cases had hilar and mediastinal lymph node metastasis. Five cases displayed extensive tumor involvement with extension into the chest wall, mild-to-moderate levels of pleural effusion, and varying degrees of volume loss in the corresponding hemithorax. One case had limited pleural thickening and invasion. Preoperative PET/CT of 1 case revealed abnormal fluorine-18 fluorodeoxyglucose (F-FDG) uptake by the tumor and multiple enlarged right hilar and mediastinal lymph nodes, right diffuse pleural thickening, and systemic multiple bone metastasis. In the other case, PET/CT scan at 7 months after surgery revealed pleural thickening and mediastinal lymph nodes with increased F-FDG uptake on the surgical side. Immunohistochemistry analyses determined that all 6 tumors were positive for CD34, CD31, ERG, and vimentin.CT and PET/CT findings reveal that malignant characteristics, including extensive pleural thickening, invasion and metastasis, and pleural effusion, are common in PEA. Imaging data are only supportive; therefore, the final diagnosis should be based on pathology and immunohistochemistry analyses.

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BackgroundPituitary adenoma (PA) is a benign neuroendocrine tumor caused by adenohypophysial cells, and accounts for 10%-20% of all primary intracranial tumors. The surgical outcomes and prognosis of giant pituitary adenomas measuring ≥3 cm in diameter differ significantly due to the influence of multiple factors such as tumor morphology, invasion site, pathological characteristics and so on. The aim of this study was to explore the risk factors related to the recurrence or progression of giant and large PAs after transnasal sphenoidal surgery, and develop a predictive model for tumor prognosis.MethodsThe clinical and follow-up data of 172 patients with large or giant PA who underwent sphenoidal surgery at the Second Affiliated Hospital of Zhejiang University School of Medicine from January 2011 to December 2017 were retrospectively analyzed. The basic clinical information (age, gender, past medical history etc.), imaging features (tumor size, invasion characteristics, extent of resection etc.), and histopathological characteristics (pathological results, Ki-67, P53 etc.) were retrieved. SPSS 21.0 software was used for statistical analysis, and the R software was used to establish the predictive nomogram.ResultsSeventy out of the 172 examined cases (40.7%) had tumor recurrence or progression. The overall progress free survival (PFS) rates of the patients at 1, 3 and 5 years after surgery were 90.70%, 79.65% and 59.30% respectively. Log-rank test indicated that BMI (P < 0.001), Knosp classification (P < 0.001), extent of resection (P < 0.001), Ki-67 (P < 0.001), sphenoidal sinus invasion (P = 0.001), Hardy classification (P = 0.003) and smoking history (P = 0.018) were significantly associated with post-surgery recurrence or progression. Cox regression analysis further indicated that smoking history, BMI ≥25 kg/m2, Knosp classification grade 4, partial resection and ≥3% Ki-67 positive rate were independent risk factors of tumor recurrence or progression (P < 0.05). In addition, the nomogram and ROC curve based on the above results indicated significant clinical value.ConclusionThe postoperative recurrence or progression of large and giant PAs is related to multiple factors and a prognostic nomogram based on BMI (≥25 kg/m2), Knosp classification (grade 4), extent of resection (partial resection) and Ki-67 (≥3%) can predict the recurrence or progression of large and giant PAs after transnasal sphenoidal surgery.

Superpixel segmentation is a popular image pre‐processing technique in many computer vision applications. In this paper, we present a novel superpixel generation algorithm by agglomerative clustering with quadratic error minimization. We use a quadratic error metric (QEM) to measure the difference of spatial compactness and colour homogeneity between superpixels. Based on the quadratic function, we propose a bottom‐up greedy clustering algorithm to obtain higher quality superpixel segmentation. There are two steps in our algorithm: merging and swapping. First, we calculate the merging cost of two superpixels and iteratively merge the pair with the minimum cost until the termination condition is satisfied. Then, we optimize the boundary of superpixels by swapping pixels according to their swapping cost to improve the compactness. Due to the quadratic nature of the energy function, each of these atomic operations has only O(1) time complexity. We compare the new method with other state‐of‐the‐art superpixel generation algorithms on two datasets, and our algorithm demonstrates superior performance.