Literature DB >> 8797751

Diagnosing left lower lobe pneumonia: usefulness of the 'spine sign' on lateral chest radiographs.

J W Ely1, K S Berbaum, G R Bergus, B H Thompson, B T Levy, M A Graber, E R Evans, D A Bedell, D S Fick.   

Abstract

BACKGROUND: Left lower lobe pneumonia may be obscured by the heart on the postero-anterior (PA) chest radiograph. In such cases, the lateral projection may be helpful, especially if it exhibits the "spine sign", which is an interruption in the progressive increase in lucency of the vertebral bodies from superior to inferior. We investigated whether the spine sign would help family physicians diagnose left lower lobe pneumonia on chest radiographs.
METHODS: We selected the chest radiographs of all patients with left lower lobe pneumonia who were seen between 1983 and 1995 at a family practice training program (N = 78) and an equal number of chest radiographs of patients without pneumonia. Six family physicians read these radiographs under two viewing conditions: PA only vs PA and lateral. We used receiver operating characteristic (ROC) curve methodology to compare the two viewing conditions.
RESULTS: There was no significant difference in performance between the two viewing conditions. The lateral view was helpful in some patients but misleading in others. Among patients with pneumonia, the lateral view was helpful when the spine sign was present, but it was misleading when the spine sign was absent.
CONCLUSIONS: In this study of family physicians, the lateral chest radiograph did not improve overall diagnostic accuracy in patients with left lower lobe pneumonia. Among pneumonia patients with the spine sign, however, the lateral view was often helpful.

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Year:  1996        PMID: 8797751

Source DB:  PubMed          Journal:  J Fam Pract        ISSN: 0094-3509            Impact factor:   0.493


  2 in total

1.  Value of the "spine sign" on lateral chest views.

Authors:  M Medjek; M Hackx; B Ghaye; V De Maertelaer; P A Gevenois
Journal:  Br J Radiol       Date:  2015-04-01       Impact factor: 3.039

2.  Deep Learning-Based Automatic Assessment of Lung Impairment in COVID-19 Pneumonia: Predicting Markers of Hypoxia With Computer Vision.

Authors:  Yauhen Statsenko; Tetiana Habuza; Tatsiana Talako; Mikalai Pazniak; Elena Likhorad; Aleh Pazniak; Pavel Beliakouski; Juri G Gelovani; Klaus Neidl-Van Gorkom; Taleb M Almansoori; Fatmah Al Zahmi; Dana Sharif Qandil; Nazar Zaki; Sanaa Elyassami; Anna Ponomareva; Tom Loney; Nerissa Naidoo; Guido Hein Huib Mannaerts; Jamal Al Koteesh; Milos R Ljubisavljevic; Karuna M Das
Journal:  Front Med (Lausanne)       Date:  2022-07-26
  2 in total

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