title: Lung nodules: improved detection with software that suppresses the rib and clavicle on chest radiographs.
abstract: OBJECTIVE
  To demonstrate possible superiority in the performance of a radiologist who is tasked with detecting actionable nodules and aided by the bone suppression and soft-tissue visualization algorithm of a new software program that produces a modified image by suppressing the ribs and clavicles, filtering noise, and equalizing the contrast in the area of the lungs.
  
  
  METHODS
  The study and use of anonymized and deidentified data received approval from the MedStar-Georgetown University Oncology Institutional Review Board. Informed consent was obtained from 15 study radiologists. The study radiologists participated as observers in a reader study of 368 patients in an approximately 2:1 cancer-free-to-cancer ratio. The localized receiver operating characteristic (LROC) method was used for analyses. Images were rerandomized for each radiologist. Each patient image was sequentially read, first with the standard radiograph and then with the software-aided image. Normal studies were confirmed with computed tomography (CT), follow-up, and/or panel consensus.
  
  
  RESULTS
  Each reader and the combined scores of the 15 readers showed improvement. The area under the combined LROC curve increased significantly from 0.460 unaided to 0.558 aided by visualization software (P = .0001). When measured according to the reader's indication that a case should be sent or not sent for CT or biopsy, sensitivity for cancer detection increased from 49.5% unaided to 66.3% aided by software (P < .0001); specificity decreased from 96.1% to 91.8% (P = .004). Seventy-four percent of the aided detections occurred in cancers with 70% or greater overlap of the bone and the nodule.
  
  
  CONCLUSIONS
  The radiologists using visualization software significantly increased their detection of lung cancers and benign nodules.
authors.affiliation: Lombardi Comprehensive Cancer Center and Department of Oncology, Georgetown University Medical Center, 3800 Reservoir Rd NW, Room S150, Washington, DC 20057-1465, USA. Freedmmt@georgetown .edu
authors.forename: Matthew Thomas
authors.initials: MT
authors.lastname: Freedman
authors.forename: Shih-Chung Benedict
authors.initials: SC
authors.lastname: Lo
authors.forename: John C
authors.initials: JC
authors.lastname: Seibel
authors.forename: Christina M
authors.initials: CM
authors.lastname: Bromley
mesh_terms: D000368:Aged; D000465:Algorithms; D002968:Clavicle / Q000000981:diagnostic imaging*; D005260:Female; D006801:Humans; D008175:Lung Neoplasms / Q000000981:diagnostic imaging*; D008297:Male; D008875:Middle Aged; D011856:Radiographic Image Enhancement / Q000379:methods*; D013902:Radiography, Thoracic / Q000379:methods*; D015203:Reproducibility of Results; D012272:Ribs / Q000000981:diagnostic imaging*; D012680:Sensitivity and Specificity; D012984:Software; D003074:Solitary Pulmonary Nodule / Q000000981:diagnostic imaging*