title: Assessment methodologies and statistical issues for computer-aided diagnosis of lung nodules in computed tomography: contemporary research topics relevant to the lung image database consortium.
abstract: Cancer of the lung and bronchus is the leading fatal malignancy in the United States. Five-year survival is low, but treatment of early stage disease considerably improves chances of survival. Advances in multidetector-row computed tomography technology provide detection of smaller lung nodules and offer a potentially effective screening tool. The large number of images per exam, however, requires considerable radiologist time for interpretation and is an impediment to clinical throughput. Thus, computer-aided diagnosis (CAD) methods are needed to assist radiologists with their decision making. To promote the development of CAD methods, the National Cancer Institute formed the Lung Image Database Consortium (LIDC). The LIDC is charged with developing the consensus and standards necessary to create an image database of multidetector-row computed tomography lung images as a resource for CAD researchers. To develop such a prospective database, its potential uses must be anticipated. The ultimate applications will influence the information that must be included along with the images, the relevant measures of algorithm performance, and the number of required images. In this article we outline assessment methodologies and statistical issues as they relate to several potential uses of the LIDC database. We review methods for performance assessment and discuss issues of defining "truth" as well as the complications that arise when truth information is not available. We also discuss issues about sizing and populating a database.
authors.affiliation: Biometrics Research Branch, Division of Cancer Treatment and Diagnosis, National Cancer Institute, 6130 Executive Blvd, MSC 7434, Bethesda, MD 20892, USA. doddl@mail.nih.gov
authors.forename: Lori E
authors.initials: LE
authors.lastname: Dodd
authors.forename: Robert F
authors.initials: RF
authors.lastname: Wagner
authors.forename: Samuel G
authors.initials: SG
authors.lastname: Armato
authors.forename: Michael F
authors.initials: MF
authors.lastname: McNitt-Gray
authors.forename: Sergey
authors.initials: S
authors.lastname: Beiden
authors.forename: Heang-Ping
authors.initials: HP
authors.lastname: Chan
authors.forename: David
authors.initials: D
authors.lastname: Gur
authors.forename: Geoffrey
authors.initials: G
authors.lastname: McLennan
authors.forename: Charles E
authors.initials: CE
authors.lastname: Metz
authors.forename: Nicholas
authors.initials: N
authors.lastname: Petrick
authors.forename: Berkman
authors.initials: B
authors.lastname: Sahiner
authors.forename: Jim
authors.initials: J
authors.lastname: Sayre
mesh_terms: D000465:Algorithms; D003936:Diagnosis, Computer-Assisted; D006801:Humans; D008175:Lung Neoplasms / Q000000981:diagnostic imaging*; D011873:Radiology Information Systems; D013223:Statistics as Topic; D014057:Tomography, X-Ray Computed; D014481:United States