On measuring the change in size of pulmonary nodules

• Published in: IEEE transactions on medical imaging Vol. 25; no. 4; pp. 435 - 450
• Main Authors: Reeves, A.P., Chan, A.B., Yankelevitz, D.F., Henschke, C.I., Kressler, B., Kostis, W.J.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01-04-2006; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Artificial Intelligence; Biopsy; Cancer; Computed tomography; Growth rate; growth rate estimation; Humans; Image registration; Image segmentation; Imaging, Three-Dimensional - methods; Information Storage and Retrieval - methods; Lungs; Pattern Recognition, Automated - methods; Phantoms, Imaging; pulmonary nodules; Radiographic Image Enhancement - methods; Radiographic Image Interpretation, Computer-Assisted - methods; Radiography; Radiography, Thoracic - instrumentation; Radiography, Thoracic - methods; Reproducibility of Results; rule-based segmentation; Sensitivity and Specificity; Severity of Illness Index; Shape; Size measurement; Solitary Pulmonary Nodule - diagnostic imaging; Studies; Subtraction Technique; Tomography, X-Ray Computed - instrumentation; Tomography, X-Ray Computed - methods; Volume measurement
• ISSN: 0278-0062; 1558-254X
• DOI: 10.1109/TMI.2006.871548

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.