Alveolar haemorrhage revealing epitheloid haemangioendothelioma

Briens, E.; Caulet‐Maugendre, Sylvie; Desrues, B.; Ml, Quinquenel; Léna, H.; Turlin, Bruno; Delaval, P

doi:10.1016/s0954-6111(97)90077-9

Cited by 12 publications

(5 citation statements)

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“…Moreover, solitary pulmonary nodules and endobronchial lesions were found in 4 cases and 1 case, respectively, which are rarely reported. Unfortunately, the pattern of bilateral multifocal areas of reticulonodular was initially misdiagnosed as diffuse alveolar hemorrhage in 2 cases of our study, as reported in previous studies ( 29 , 30 ). Similarly, the pattern of bilateral multiple pulmonary nodules was misdiagnosed with lung metastases or lung cancer, which is consistent with previous studies ( 31 - 33 ).…”

Section: Discussionsupporting

confidence: 48%

Imaging features and deep learning for prediction of pulmonary epithelioid hemangioendothelioma in CT images

Huang,

Xie,

Huang

et al. 2024

J Thorac Dis

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Background Pulmonary epithelioid hemangioendothelioma (PEH) is a rare vascular tumour, and its early diagnosis remains challenging. This study aims to comprehensively analyse the imaging features of PEH and develop a model for predicting PEH. Methods Retrospective and pooled analyses of imaging findings were performed in PEH patients at our center (n=25) and in published cases (n=71), respectively. Relevant computed tomography (CT) images were extracted and used to build a deep learning model for PEH identification and differentiation from other diseases. Results In this study, bilateral multiple nodules/masses (n=19) appeared to be more common with most nodules less than 2 cm. In addition to the common types and features, the pattern of mixed type (n=4) and isolated nodules (n=4), punctate calcifications (5/25) and lymph node enlargement were also observed (10/25). The presence of pleural effusion is associated with a poor prognosis in PEH. The deep learning model, with an area under the receiver operating characteristic curve (AUC) of 0.71 [95% confidence interval (CI): 0.69–0.72], has a differentiation accuracy of 100% and 74% for the training and test sets respectively. Conclusions This study confirmed the heterogeneity of the imaging findings in PEH and showed several previously undescribed types and features. The current deep learning model based on CT has potential for clinical application and needs to be further explored in the future.

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Section: Discussionsupporting

confidence: 48%

Imaging features and deep learning for prediction of pulmonary epithelioid hemangioendothelioma in CT images

Huang,

Xie,

Huang

et al. 2024

J Thorac Dis

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“…Chest pain, cough and sputum are common as are nonspecific symptoms. Two patients presented with alveolar haemorrhage [28,29] and one with rapidly deteriorating pulmonary hypertension [30].…”

Section: Discussionmentioning

confidence: 99%

Pulmonary epithelioid haemangioendothelioma in 21 patients, including three with partial spontaneous regression

Kitaichi¹,

Nagai²,

Nishimura³

et al. 1998

Eur Respir J

220

272

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aaPulmonary epithelioid haemangioendothelioma (PEH) is a rare tumour of the lung. The disease was originally reported by DAIL and LIEBOW [1] in 1975 as "intravascular bronchoalveolar tumour" (IVBAT), and the majority (65%) patients of the reported 20 patients showed multiple bilateral nodular lesions. In addition, one patient had bronchoalveolar cell carcinoma. The recognition of Weibel-Palade bodies in the cytoplasm of tumour cells of IVBAT by electron microscopy, along with the immunoreactivity of the cytoplasm of tumour cells for factor VIII-related antigen, prompted the realization that IVBAT probably derived from vascular endothelial cells [2][3][4][5][6][7][8][9]. Forty-one patients with soft-tissue neoplasms exhibiting histopathological features similar to IVBAT were reported by WEISS and ENZINGER [10] in 1982. The tumour was termed epithelioid haemangioendothelioma [2,[9][10][11][12], and by 1986 had been reported to occur also in the liver, head and neck area, oral mucosa, bone, mediastinum, diaphragm and brain [13].We reported on two asymptomatic patients with IVBAT (PEH) in the mid-1980s [14,15], and observed partial spontaneous regression of the disease during follow-up in both. In order to evaluate more thoroughly the clinicopathological spectrum of PEH, 21 patients with PEH recruited in Asia (Japan, Korea, Taiwan and Singapore) were studied, who included an additional patient with partial spontaneous regression [16]. The description of all of these patients and their prognostic features form the basis of the present report. Methods Study populationTo obtain data on the clinical and pathological features of PEH in as many patients as possible, a survey was carried out of the Japanese literature of the past 15 yrs and letters were sent to Departments of Medicine, Respiratory Medicine, Radiology and Pathology of 230 major hospitals in Japan. In addition, letters were sent to five major hospitals in Korea, Taiwan, Hong Kong and Singapore. In order to confirm the diagnosis of PEH, the histopathological specimens from 23 patients were reviewed at an International Meeting on PEH held in 1994 in Kyoto, Japan. Six out of the 23 patients have been reviewed previously by one of the present authors (M. Kitaichi) [14][15][16][17].The clinical summary of patients with PEH focused on the features described in the Results section at the time of the first examination by their primary physicians (see Acknowledgements) and at the time of the last clinical contact.The patients were also questioned about the type and duration of therapy, if any. The clinical onset of PEH was defined as the time of onset of pulmonary symptoms attributable to the disease. Clinical improvement or deterioration was assessed by chest radiography. Histopathological and immunohistochemical studiesHistopathological features were analysed according to 18 items [2,9]. The cellularity of tumour cells was graded from Pulmonary epithelioid haemangioendothelioma in 21 patients, including three with partial spontaneous regression. M. Kitaichi, S. Nag...

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“…Ebenso wurde kasuistisch über eine DAH in Assoziati− on mit einer Colitis ulcerosa und einer primär sklerosierenden Cholangitis [107] oder beim M. Crohn berichtet [108]. Eine Rari− tät ist ein Hämangioendotheliom [109,110]. Selten ist eine pul− monale Sarkoidose von einer DAH begleitet [111].…”

Section: äTiologieunclassified

“…Nur selten ist bei Patienten mit einer DAH histologisch der Nachweis spezi− fischer Diagnosen möglich, z. B. bei der Lymphangioleiomyoma− tose, der pulmonalen kapillären Hämangiomatose und bei malignen Erkrankungen [109,124]. Obwohl DAH relativ selten sind, haben sie aufgrund ihres oft schweren, potenziell letalen Verlaufs eine erhebliche klinische Bedeutung.…”

Section: äTiologieunclassified

Differenzialdiagnostik der diffusen alveolären Hämorrhagie

Schreiber¹,

Knolle²,

Kachel³

et al. 2006

Pneumologie

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Diffuse alveolar haemorrhage (DAH) denotes a diffuse intra-alveolar bleeding from small vessels as a result of severe damage of the alveolocapillary membrane of the lungs. These are comparatively rare, but than often life threatening events. The differential diagnosis is broad and comprises immunologic as well as toxic, infectious, hemodynamic, neoplastic and physical causes. DAH may be a manifestation of systemic diseases, as well as a result of an injury restricted to the lungs. The most frequent systemic diseases, that cause DAH are the so-called small vessel vasculititis-microscopic polyangiitis and Wegener's granulomatosis. More rarely Goodpasture's syndrome and systemic lupus erythematosus are causal. Alongside the immunologic processes mainly diseases with increased left-ventricular preload, infections and side effects of drugs have to be kept in mind. This review summarizes the differential diagnostic spectrum and the diagnostic workup of patients with DAH. Because early aggressive treatment can be life-saving, early and aimed diagnostics is essential.

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Alveolar haemorrhage revealing epitheloid haemangioendothelioma

Cited by 12 publications

References 11 publications

Imaging features and deep learning for prediction of pulmonary epithelioid hemangioendothelioma in CT images

Imaging features and deep learning for prediction of pulmonary epithelioid hemangioendothelioma in CT images

Pulmonary epithelioid haemangioendothelioma in 21 patients, including three with partial spontaneous regression

Differenzialdiagnostik der diffusen alveolären Hämorrhagie

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