title: Computerized scheme for determination of the likelihood measure of malignancy for pulmonary nodules on low-dose CT images.
abstract: An automated computerized scheme has been developed for determination of the likelihood measure of malignancy of pulmonary nodules on low-dose helical CT (LDCT) images. Our database consisted of 76 primary lung cancers (147 slices) and 413 benign nodules (576 slices). With this automated computerized scheme, the location of a nodule was first indicated by a radiologist. The outline of the nodule was segmented automatically by use of a dynamic programming technique. Various objective features on the nodules were determined by use of outline analysis and image analysis, and the likelihood measure of malignancy was determined by use of linear discriminant analysis (LDA). The effect of many different combinations of features and the performance of LDA in distinguishing benign nodules from malignant ones were evaluated by means of receiver operating characteristic (ROC) analysis. The Az value (area under the ROC curve) obtained by the computerized scheme in distinguishing benign nodules from malignant ones was 0.828 when a single slice was employed for each of the nodules. However, the Az value was improved to 0.846 when multiple slices were used for determination of the likelihood measure of malignancy. The Az values obtained by the computerized scheme on LDCT images were significantly greater than the Az value of 0.70, which was obtained from our previous observer studies by radiologists in distinguishing benign nodules from malignant ones on LDCT images. The automated computerized scheme for determination of the likelihood measure of malignancy would be useful in assisting radiologists to distinguish between benign and malignant pulmonary nodules on LDCT images.
authors.affiliation: Kurt Rossmann Laboratory for Radiologic Image Research, Department of Radiology, The University of Chicago, 5841 S. Maryland Avenue, Chicago, Illinois 60637, USA.
authors.forename: Masahito
authors.initials: M
authors.lastname: Aoyama
authors.forename: Qiang
authors.initials: Q
authors.lastname: Li
authors.forename: Shigehiko
authors.initials: S
authors.lastname: Katsuragawa
authors.forename: Feng
authors.initials: F
authors.lastname: Li
authors.forename: Shusuke
authors.initials: S
authors.lastname: Sone
authors.forename: Kunio
authors.initials: K
authors.lastname: Doi
mesh_terms: D000465:Algorithms; D016208:Databases, Factual; D016002:Discriminant Analysis; D005189:False Positive Reactions; D005260:Female; D006801:Humans; D016013:Likelihood Functions; D008175:Lung Neoplasms / Q000145:classification / Q000000981:diagnostic imaging; D008297:Male; D010363:Pattern Recognition, Automated; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D018570:Risk Assessment / Q000379:methods; D012680:Sensitivity and Specificity; D012720:Severity of Illness Index; D003074:Solitary Pulmonary Nodule / Q000145:classification* / Q000000981:diagnostic imaging*; D014057:Tomography, X-Ray Computed / Q000379:methods*