title: Pulmonary nodule detection on MDCT images: evaluation of diagnostic performance using thin axial images, maximum intensity projections, and computer-assisted detection.
abstract: This study aimed at evaluating the diagnostic benefits of maximum intensity projections (MIP) and a commercially available computed-assisted detection system (CAD) for the detection of pulmonary nodules on MDCT as compared with standard 1-mm images on lung cancer screening material. Thirty subjects were randomly selected from our database. Three radiologists independently reviewed three types of images: axial 1-mm images, axial MIP slabs, and CAD system detections. Two independent experienced chest radiologists decided which were true-positive nodules. Two hundred eighty-five nodules > or =1 mm were identified as true-positive by consensus of two independent chest radiologists. The detection rates of the three independent observers with 1-mm axial images were 22 +/- 4.8%, 30 +/- 5.3%, and 47 +/- 2.8%; with MIP: 33 +/- 5.4%, 39 +/- 5.7%, and 45 +/- 5.8%; and with CAD: 35 +/- 5.6%, 36 +/- 5.6%, and 36 +/- 5.6%. There was a reading technique effect on the observers' sensitivity for nodule detection: sensitivities with MIP were higher than with 1-mm images or CAD for all nodules (F-values = 0.046). For nodules > or =3 mm, readers' sensitivities were higher with 1-mm images or MIP than with CAD (p < 0.0001). CAD was the most and MIP the less time-consuming technique (p < 0.0001). MIP and CAD reduced the number of overlooked small nodules. As MIP is more sensitive and less time consuming than the CAD we used, we recommend viewing MIP and 1-mm images for the detection of pulmonary nodules.
authors.affiliation: Service Central de Radiologie et d'Imagerie Médicale, CHU Grenoble, BP 217, 38043, Grenoble Cedex 09, France. AJankowski@chu-grenoble.fr
authors.forename: A
authors.initials: A
authors.lastname: Jankowski
authors.forename: T
authors.initials: T
authors.lastname: Martinelli
authors.forename: J F
authors.initials: JF
authors.lastname: Timsit
authors.forename: C
authors.initials: C
authors.lastname: Brambilla
authors.forename: F
authors.initials: F
authors.lastname: Thony
authors.forename: M
authors.initials: M
authors.lastname: Coulomb
authors.forename: G
authors.initials: G
authors.lastname: Ferretti
mesh_terms: D000368:Aged; D000704:Analysis of Variance; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D003937:Diagnosis, Differential; D005260:Female; D006801:Humans; D008175:Lung Neoplasms / Q000000981:diagnostic imaging*; D008297:Male; D008875:Middle Aged; D011857:Radiographic Image Interpretation, Computer-Assisted; D012680:Sensitivity and Specificity; D003074:Solitary Pulmonary Nodule / Q000000981:diagnostic imaging*; D014057:Tomography, X-Ray Computed / Q000379:methods*