title: Derivation and validation of a CT scan scoring system for discriminating malignant from benign pleural effusions.
abstract: BACKGROUND
  Chest CT scanning has become an integral part of the workup for undiagnosed pleural effusions. We aimed to develop a CT scan-based scoring system for differentiating between benign and malignant pleural effusions.
  
  
  METHODS
  A number of chest CT scan abnormalities were compared between 228 patients with benign and 115 with malignant effusions (derivation cohort). A logistic regression analysis was used to identify the independent predictors of malignancy and generate CT scan scores, with more points assigned to those findings associated with higher β-coefficient values. The diagnostic accuracy of the CT scan scoring system was calculated for the derivation cohort and further evaluated in two independent populations (n = 80 and 42, respectively) by two radiologists.
  
  
  RESULTS
  CT scan scores predicting malignancy included any pleural lesion (ie, nodule, mass, or thickening) ≥ 1 cm (5 points); the presence of liver metastases, an abdominal mass, or a lung mass or nodule ≥ 1 cm (3 points each); and the absence of either pleural loculations, pericardial effusions, or cardiomegaly (2 points each). In the first validation cohort, a sum score of ≥ 7 yielded a sensitivity of 88% (95% CI, 73%-95%), specificity of 94% (95% CI, 83%-98%), likelihood ratio positive of 13.8 (95% CI, 4.6-41.5), likelihood ratio negative of 0.13 (95% CI, 0.05-0.33), and area under the receiver operating characteristics curve of 0.919 (95% CI, 0.849-0.990). Moreover, 69% of 42 patients with pathologically unconfirmed malignant effusions from a second independent cohort would have been correctly labeled by the predictive score.
  
  
  CONCLUSIONS
  A simple CT scan-based scoring system can help physicians to separate malignant from benign pleural effusions.
authors.affiliation: Department of Internal Medicine, Pleural Diseases Unit, Arnau de Vilanova University Hospital, Biomedical Research Institute of Lleida, Lleida, Spain. Electronic address: jporcelp@yahoo.es.
authors.forename: José M
authors.initials: JM
authors.lastname: Porcel
authors.affiliation: Department of Radiology, Pleural Diseases Unit, Arnau de Vilanova University Hospital, Biomedical Research Institute of Lleida, Lleida, Spain.
authors.forename: Marina
authors.initials: M
authors.lastname: Pardina
authors.affiliation: Department of Internal Medicine, Pleural Diseases Unit, Arnau de Vilanova University Hospital, Biomedical Research Institute of Lleida, Lleida, Spain.
authors.forename: Silvia
authors.initials: S
authors.lastname: Bielsa
authors.affiliation: Department of Radiology, Pleural Diseases Unit, Arnau de Vilanova University Hospital, Biomedical Research Institute of Lleida, Lleida, Spain.
authors.forename: Antonio
authors.initials: A
authors.lastname: González
authors.affiliation: Division of Allergy, Pulmonary, and Critical Care, Vanderbilt University Medical Center, Nashville, TN. Electronic address: jporcelp@yahoo.es.
authors.forename: Richard W
authors.initials: RW
authors.lastname: Light
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D003937:Diagnosis, Differential; D005260:Female; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D010996:Pleural Effusion / Q000000981:diagnostic imaging*; D016066:Pleural Effusion, Malignant / Q000000981:diagnostic imaging*; D011237:Predictive Value of Tests; D014057:Tomography, X-Ray Computed