OLIVIA MEIRA DIAS
Projetos de Pesquisa
Unidades Organizacionais
Instituto do Coração, Hospital das Clínicas, Faculdade de Medicina - Médico
LIM/09 - Laboratório de Pneumologia, Hospital das Clínicas, Faculdade de Medicina
LIM/09 - Laboratório de Pneumologia, Hospital das Clínicas, Faculdade de Medicina
63 resultados
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bookPart Abordagem do derrame pleural e toracocentese(2015) DIAS, Olivia Meira; MARIANI, Alessandro; PêGO-FERNANDES, Paulo Manuel- Computed tomography in hypersensitivity pneumonitis: main findings, differential diagnosis and pitfalls(2018) DIAS, Olivia Meira; BALDI, Bruno Guedes; PENNATI, Francesca; ALIVERTI, Andrea; CHATE, Rodrigo Caruso; SAWAMURA, Marcio Valente Yamada; CARVALHO, Carlos Roberto Ribeiro de; ALBUQUERQUE, Andre Luis Pereira deIntroduction: Hypersensitivity pneumonitis (HP) is a disease with variable clinical presentation in which inflammation in the lung parenchyma is caused by the inhalation of specific organic antigens or low molecular weight substances in genetically susceptible individuals. Alterations of the acute, subacute and chronic forms may eventually overlap, and the diagnosis based on temporality and presence of fibrosis (acute/inflammatory HP vs. chronic HP) seems to be more feasible and useful in clinical practice. Differential diagnosis of chronic HP with other interstitial fibrotic diseases is challenging due to the overlap of the clinical history, and the functional and imaging findings of these pathologies in the terminal stages.Areas covered: This article reviews the essential features of HP with emphasis on imaging features. Moreover, the main methodological limitations of high-resolution computed tomography (HRCT) interpretation are discussed, as well as new perspectives with volumetric quantitative CT analysis as a useful tool for retrieving detailed and accurate information from the lung parenchyma.Expert commentary: Mosaic attenuation is a prominent feature of this disease, but air trapping in chronic HP seems overestimated. Quantitative analysis has the potential to estimate the involvement of the pulmonary parenchyma more accurately and could correlate better with pulmonary function results.
conferenceObject Recurrence of Sarcoid Granulomas in Lung Re-Transplant Recipient(2019) PROVENCI, B.; SERRA, J. P. C.; JESUS, R. M.; DIAS, O. M.; COSTA, A. N.- Eosinophilic pneumonia: remember topical drugs as possible etiology(2018) DIAS, Olivia Meira; NASCIMENTO, Ellen Caroline Toledo do; CHATE, Rodrigo Caruso; KAIRALLA, Ronaldo Adib; BALDI, Bruno Guedes
bookPart Ventilação não invasiva na unidade de emergência(2018) FERREIRA, Graziela dos Santos Rocha; GALAS, Filomena Regina Barbosa Gomes; DIAS, Olívia MeirabookPart Abordagem do derrame pleural e toracocentese(2015) DIAS, Olívia Meira; MARIANI, Alessandro; PêGO-FERNANDES, Paulo Manuel- Interstitial Emphysema Leading to Pneumomediastinum in a Bone Marrow Transplant Patient(2013) DIAS, Olivia Meira; COELHO, David Lopes Lima Cavalcanti; CARVALHO, Carlos Roberto Ribeiro de
- Texture-based classification of lung disease patterns in chronic hypersensitivity pneumonitis and comparison to clinical outcomes(2021) PENNATI, F.; ALIBONI, L.; ANTONIAZZA, A.; BERETTA, D.; DIAS, O.; BALDI, B. G.; SAWAMURA, M.; CHATE, R. C.; CARVALHO, C. R. R. De; ALBUQUERQUE, A.; ALIVERTI, A.Computer-aided detection algorithms applied to CT lung imaging have the potential to objectively quantify pulmonary pathology. We aim to develop an automatic classification method based on textural features able to classify healthy and pathological patterns on CT lung images and to quantify the extent of each disease pattern in a group of patients with chronic hypersensitivity pneumonitis (cHP), in comparison to pulmonary function tests (PFTs). 27 cHP patients were scanned via high resolution CT (HRCT) at full-inspiration. Regions of interest (ROIs) were extracted and labeled as normal (NOR), ground glass opacity (GGO), reticulation (RET), consolidation (C), honeycombing (HB) and air trapping (AT). For each ROI, statistical, morphological and fractal parameters were computed. For automatic classification, we compared two classification methods (Bayesian and Support Vector Machine) and three ROI sizes. The classifier was therefore applied to the overall CT images and the extent of each class was calculated and compared to PFTs. Better classification accuracy was found for the Bayesian classifier and the 16x16 ROI size: 92.1 +/- 2.7%. The extent of GGO, HB and NOR significantly correlated with forced vital capacity (FVC) and the extent of NOR with carbon monoxide diffusing capacity (DLCO).
bookPart Abordagem do derrame pleural e toracocentese(2018) DIAS, Olívia Meira; MARIANI, Alessandro; TAVARES, Bruno Garcia; PêGO-FERNANDES, Paulo ManuelbookPart Hemoptises(2015) DIAS, Olívia Meira; BALDI, Bruno Guedes; CARVALHO, Carlos Roberto Ribeiro de