Your browser doesn't support javascript.
loading
Show: 20 | 50 | 100
Results 1 - 1 de 1
Filter
Add filters








Language
Year range
1.
J. bras. pneumol ; 36(1): 29-36, jan.-fev. 2010. tab, ilus
Article in English | LILACS | ID: lil-539432

ABSTRACT

OBJECTIVE: To determine the interobserver and intraobserver agreement in the diagnosis of interstitial lung diseases (ILDs) based on HRCT scans and the impact of observer expertise, clinical data and confidence level on such agreement. METHODS: Two thoracic radiologists and two general radiologists independently reviewed the HRCT images of 58 patients with ILDs on two distinct occasions: prior to and after the clinical anamnesis. The radiologists selected up to three diagnostic hypotheses for each patient and defined the confidence level for these hypotheses. One of the thoracic and one of the general radiologists re-evaluated the same images up to three months after the first readings. In the coefficient analyses, the kappa statistic was used. RESULTS: The thoracic and general radiologists, respectively, agreed on at least one diagnosis for each patient in 91.4 percent and 82.8 percent of the patients. The thoracic radiologists agreed on the most likely diagnosis in 48.3 percent (κ = 0.42) and 62.1 percent (κ = 0.58) of the cases, respectively, prior to and after the clinical anamnesis; likewise, the general radiologists agreed on the most likely diagnosis in 37.9 percent (κ = 0.32) and 36.2 percent (κ = 0.30) of the cases. For the thoracic radiologist, the intraobserver agreement on the most likely diagnosis was 0.73 and 0.63 prior to and after the clinical anamnesis, respectively. That for the general radiologist was 0.38 and 0.42.The thoracic radiologists presented almost perfect agreement for the diagnostic hypotheses defined with the high confidence level. CONCLUSIONS: Interobserver and intraobserver agreement in the diagnosis of ILDs based on HRCT scans ranged from fair to almost perfect and was influenced by radiologist expertise, clinical history and confidence level.


OBJETIVO: Determinar a concordância interobservador e intraobservador no diagnóstico de doenças pulmonares intersticiais (DPIs) por TCAR e o impacto da experiência dos observadores, dos dados clínicos e do grau de confiança nessas concordâncias. MÉTODOS: Dois radiologistas torácicos e dois gerais independentemente avaliaram imagens de TCAR de 58 pacientes com DPIs em dois momentos: antes e após da anamnese clínica. Os observadores selecionaram até três hipóteses diagnósticas para cada paciente e definiram o grau de confiança dessas hipóteses. Um dos radiologistas torácicos e um dos gerais reavaliaram as mesmas imagens até três meses após a primeira leitura. As análises estatísticas foram feitas utilizando o coeficiente kappa. RESULTADOS: Os radiologistas torácicos e os gerais, respectivamente, concordaram com uma ou mais hipóteses diagnósticas em 91,4 por cento e 82,8 por cento dos pacientes. Os radiologistas torácicos concordaram com o diagnóstico mais provável em 48,3 por cento (κ = 0,42) e 62,1 por cento (κ = 0,58) dos casos, respectivamente, antes e após a anamnese clínica; de forma semelhante; os radiologistas gerais concordaram com o diagnóstico mais provável em 37,9 por cento (κ = 0,32) e 36,2 por cento (κ = 0,30). A concordância intraobservador do radiologista torácico no diagnóstico mais provável foi de 0,73 e 0,63, antes e após da anamnese clínica, respectivamente; para o radiologista geral, essa foi de 0,38 e 0,42. Os radiologistas torácicos apresentaram graus de concordância quase perfeitos nas hipóteses diagnósticas definidas com o grau de confiança alto. CONCLUSÕES: A concordância interobservador e intraobservador no diagnóstico das DPIs por TCAR variaram de regular a quase perfeita, tendo sido influenciadas pela experiência do radiologista, pela história clínica e pelo grau de confiança.


Subject(s)
Adult , Aged , Aged, 80 and over , Female , Humans , Male , Middle Aged , Clinical Competence , Lung Diseases, Interstitial , Radiology/education , Tomography, X-Ray Computed/statistics & numerical data , Data Interpretation, Statistical , Observer Variation
SELECTION OF CITATIONS
SEARCH DETAIL