Computer-aided diagnosis in lung nodule assessment.
Computed tomography (CT) imaging is playing an increasingly important role in cancer detection, diagnosis, and lesion characterization, and it is the most sensitive test for lung nodule detection. Interpretation of lung nodules involves characterization and integration of clinical and other imaging information. Advances in lung nodule management using CT require optimization of CT data acquisition, postprocessing tools, and computer-aided diagnosis (CAD). The goal of CAD systems being developed is to both assist radiologists in the more sensitive detection of nodules and noninvasively differentiate benign from malignant lesions; the latter is important given that malignant lesions account for between 1% and 11% of pulmonary nodules. The aim of this review is to summarize the current state of the art regarding CAD techniques for the detection and characterization of solitary pulmonary nodules and their potential applications in the clinical workup of these lesions.
Department of Radiological Sciences, Thoracic Imaging Research Group, David Geffen School of Medicine, University of California, Los Angeles, CA 90024, USA. jgoldin@mednet.ucla.edu
Jonathan G
JG
Goldin
Matthew S
MS
Brown
Iva
I
Petkovska
D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D003937:Diagnosis, Differential; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods; D008168:Lung / Q000000981:diagnostic imaging*; D008171:Lung Diseases / Q000175:diagnosis; D008175:Lung Neoplasms / Q000175:diagnosis*; D011856:Radiographic Image Enhancement / Q000379:methods; D014057:Tomography, X-Ray Computed / Q000379:methods*