Dataset Information

Pulmonary tularaemia: a differential diagnosis to lung cancer.

ABSTRACT: Background:Pulmonary manifestations of tularaemia are reported to be infrequent in previous publications. During 2016, we noticed an increase in the number of hospitalised patients with pulmonary tularaemia in Eastern Norway. We aimed to investigate primary pulmonary tularaemia in Eastern Norway in terms of symptoms, radiological and microbiological findings, incidence and risk exposure. Methods:A retrospective analysis of consecutive primary pulmonary tularaemia cases from 2016 until 2018 was conducted. Results:From 1 September, 2016 until 31 December, 2018, 22 patients were diagnosed with primary pulmonary tularaemia at Innlandet Hospital Trust, representing 48% of all reported tularaemia cases in the region. A peak annual incidence of 3.4 in 100?000 was found in 2017.All 22 patients lived in, or had recently visited, rural areas. Eighteen patients reported risk exposure for tularaemia, such as wood chopping, outdoor activities and farming prior to symptom onset. All patients experienced fever, and 19 patients had respiratory symptoms. Ten patients were in spontaneous recovery at diagnosis.All patients had a chest computed tomography scan. Overall, 19 patients had pulmonary lesions and 18 had enlarged intrathoracic lymph nodes. The computed tomography images were described as suspicious of malignancy in 17 patients. Conclusion:Pulmonary manifestations in tularaemia occurred more frequently in our region than expected from previous reports. Although all patients had symptoms consistent with infection, the majority were primarily investigated considering lung cancer due to the radiology report. In endemic areas, pulmonary tularaemia may be an important differential diagnosis to lung cancer.

SUBMITTER: Kravdal A

PROVIDER: S-EPMC7322898 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Pulmonary tularaemia: a differential diagnosis to lung cancer.

Kravdal Astrid A Stubhaug Øystein Olav ØO Wågø Anne Grete AG Steien Sætereng Magnus M Amundsen Dag D Piekuviene Ruta R Kristiansen Annette A

ERJ open research 20200401 2

<h4>Background</h4>Pulmonary manifestations of tularaemia are reported to be infrequent in previous publications. During 2016, we noticed an increase in the number of hospitalised patients with pulmonary tularaemia in Eastern Norway. We aimed to investigate primary pulmonary tularaemia in Eastern Norway in terms of symptoms, radiological and microbiological findings, incidence and risk exposure.<h4>Methods</h4>A retrospective analysis of consecutive primary pulmonary tularaemia cases from 2016 u ...[more]

PMID: 32613015

Similar Datasets

Project description:Diffuse cystic lung diseases are characterized by cysts in more than one lung lobe, the cysts originating from various mechanisms, including the expansion of the distal airspaces due to airway obstruction, necrosis of the airway walls, and parenchymal destruction. The progression of these diseases is variable. One essential tool in the evaluation of these diseases is HRCT, because it improves the characterization of pulmonary cysts (including their distribution, size, and length) and the evaluation of the regularity of the cyst wall, as well as the identification of associated pulmonary and extrapulmonary lesions. When combined with clinical and laboratory findings, HRCT is often sufficient for the etiological definition of diffuse lung cysts, avoiding the need for lung biopsy. The differential diagnoses of diffuse cystic lung diseases are myriad, including neoplastic, inflammatory, and infectious etiologies. Pulmonary Langerhans cell histiocytosis, lymphangioleiomyomatosis, lymphocytic interstitial pneumonia, and follicular bronchiolitis are the most common diseases that produce this CT pattern. However, new diseases have been included as potential determinants of this pattern. RESUMO As doenças pulmonares císticas difusas se caracterizam pela presença de cistos envolvendo mais de um lobo pulmonar, que se originam por diversos mecanismos, incluindo dilatação dos espaços aéreos distais por obstrução, necrose das paredes das vias aéreas e destruição do parênquima. Essas doenças apresentam evolução variável. A TCAR é fundamental na avaliação dessas doenças uma vez que permite uma melhor caracterização dos cistos pulmonares, incluindo sua distribuição, tamanho, extensão e regularidade das paredes, assim como a determinação de outras lesões pulmonares e extrapulmonares associadas. Frequentemente a TCAR é suficiente para a definição etiológica dos cistos pulmonares difusos, associada a achados clínicos e laboratoriais, sem a necessidade de realização de biópsia pulmonar. O diagnóstico diferencial das doenças pulmonares císticas difusas é extenso, incluindo etiologias neoplásicas, inflamatórias e infecciosas, sendo as mais frequentes determinantes desse padrão tomográfico a histiocitose pulmonar de células de Langerhans, a linfangioleiomiomatose, a pneumonia intersticial linfocitária e a bronquiolite folicular. Novas etiologias foram incluídas como potenciais determinantes desse padrão.

Project description:BackgroundLung cancer is the most commonly diagnosed cancer worldwide. Its survival rate can be significantly improved by early screening. Biomarkers based on radiomics features have been found to provide important physiological information on tumors and considered as having the potential to be used in the early screening of lung cancer. In this study, we aim to establish a radiomics model and develop a tool to improve the discrimination between benign and malignant pulmonary nodules.MethodsA retrospective study was conducted on 875 patients with benign or malignant pulmonary nodules who underwent computed tomography (CT) examinations between June 2013 and June 2018. We assigned 612 patients to a training cohort and 263 patients to a validation cohort. Radiomics features were extracted from the CT images of each patient. Least absolute shrinkage and selection operator (LASSO) was used for radiomics feature selection and radiomics score calculation. Multivariate logistic regression analysis was used to develop a classification model and radiomics nomogram. Radiomics score and clinical variables were used to distinguish benign and malignant pulmonary nodules in logistic model. The performance of the radiomics nomogram was evaluated by the area under the curve (AUC), calibration curve and Hosmer-Lemeshow test in both the training and validation cohorts.ResultsA radiomics score was built and consisted of 20 features selected by LASSO from 1288 radiomics features in the training cohort. The multivariate logistic model and radiomics nomogram were constructed using the radiomics score and patients' age. Good discrimination of benign and malignant pulmonary nodules was obtained from the training cohort (AUC, 0.836; 95% confidence interval [CI]: 0.793-0.879) and validation cohort (AUC, 0.809; 95% CI: 0.745-0.872). The Hosmer-Lemeshow test also showed good performance for the logistic regression model in the training cohort (P = 0.765) and validation cohort (P = 0.064). Good alignment with the calibration curve indicated the good performance of the nomogram.ConclusionsThe established radiomics nomogram is a noninvasive preoperative prediction tool for malignant pulmonary nodule diagnosis. Validation revealed that this nomogram exhibited excellent discrimination and calibration capacities, suggesting its clinical utility in the early screening of lung cancer.

Dataset Information

Pulmonary tularaemia: a differential diagnosis to lung cancer.

Publications

Pulmonary tularaemia: a differential diagnosis to lung cancer.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets