title: On measuring the change in size of pulmonary nodules.
abstract: The pulmonary nodule is the most common manifestation of lung cancer, the most deadly of all cancers. Most small pulmonary nodules are benign, however, and currently the growth rate of the nodule provides for one of the most accurate noninvasive methods of determining malignancy. In this paper, we present methods for measuring the change in nodule size from two computed tomography image scans recorded at different times; from this size change the growth rate may be established. The impact of partial voxels for small nodules is evaluated and isotropic resampling is shown to improve measurement accuracy. Methods for nodule location and sizing, pleural segmentation, adaptive thresholding, image registration, and knowledge-based shape matching are presented. The latter three techniques provide for a significant improvement in volume change measurement accuracy by considering both image scans simultaneously. Improvements in segmentation are evaluated by measuring volume changes in benign or slow growing nodules. In the analysis of 50 nodules, the variance in percent volume change was reduced from 11.54% to 9.35% (p = 0.03) through the use of registration, adaptive thresholding, and knowledge-based shape matching.
authors.affiliation: *A. P. Reeves is with the School of Electrical and Computer Engineering, Cornell University, Ithaca, NY 14853, USA. reeves@ece.cornell.edu
authors.forename: Anthony P
authors.initials: AP
authors.lastname: Reeves
authors.forename: Antoni B
authors.initials: AB
authors.lastname: Chan
authors.forename: David F
authors.initials: DF
authors.lastname: Yankelevitz
authors.forename: Claudia I
authors.initials: CI
authors.lastname: Henschke
authors.forename: Bryan
authors.initials: B
authors.lastname: Kressler
authors.forename: William J
authors.initials: WJ
authors.lastname: Kostis
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods; D010363:Pattern Recognition, Automated / Q000379:methods*; D019047:Phantoms, Imaging; D011856:Radiographic Image Enhancement / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D013902:Radiography, Thoracic / Q000295:instrumentation / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012720:Severity of Illness Index; D003074:Solitary Pulmonary Nodule / Q000000981:diagnostic imaging*; D013382:Subtraction Technique; D014057:Tomography, X-Ray Computed / Q000295:instrumentation / Q000379:methods*