Effect of Varying CT Section Width on Volumetric Measurement of Lung Tumors and Application of Compensatory Equations

PURPOSE: To determine how volume measurements of simulated and clinical lung tumors at standard computed tomographic (CT) lung window and level settings vary with section width and to derive and apply compensatory equations.

MATERIALS AND METHODS: Spherical simulated tumors of varying diameters were imaged with varying CT section widths, the images were displayed on a workstation, the cross-sectional area of the tumor on each section was measured by using elliptical and perimeter methods, and the areas were integrated to compute tumor volume. The actual and measured tumor volumes for differing section widths and tumor diameters were compared, and compensatory equations were derived. The equations were applied to contemporaneous chest CT images obtained in patients with stage I lung cancer, and the difference between thick- and thin-section–derived volumes before and after application of the equations was determined.

RESULTS: All simulated tumor volumes were overestimated 11%–278%; overestimation varied directly with section width and inversely with tumor diameter. With both measurement methods, mean thin-section volumes of clinical tumors in 55 patients were significantly smaller (P < .01) than mean thick-section volumes: Mean elliptical measurements were 15,025 mm3 (thin) and 18,037 mm3 (thick), with a 20.0% difference; mean perimeter measurements were 16,164 mm3 (thin) and 20,718 mm3 (thick), with a 22.2% difference. The thin-section–to–thick-section volume difference was larger for the smallest tumors. Thin-section volumes were smaller than thick-section volumes in 53 patients with the elliptical method and in 51 patients with the perimeter method. Applying the equations decreased the difference between thick- and thin-section volumes in 37 (67%) of the 55 patients with the elliptical method and in 41 (74%) patients with the perimeter method. The mean thin-section–to–thick-section volume difference became nonsignificant with the perimeter method but remained significant with the elliptical method.

CONCLUSION: Measured lung tumor volumes vary significantly with varying CT section width; overestimation varies directly with section width and inversely with tumor size. Compensatory equations that are somewhat effective in reducing these effects can be derived.

© RSNA, 2003


  • 1 Yankelevitz DF, Reeves AP, Kostis WJ, Zhao B, Henschke CI. Small pulmonary nodules: volumetrically determined growth rates based on CT evaluation. Radiology 2000; 217:251-256. LinkGoogle Scholar
  • 2 Baxter BS, Sorenson JA. Factors affecting the measurement of size and CT number in computed tomography. Invest Radiol 1981; 16:337-341. Crossref, MedlineGoogle Scholar
  • 3 Diederich S, Lenzen H, Windmann R, et al. Pulmonary nodules: experimental and clinical studies at low-dose CT. Radiology 1999; 213:289-298. LinkGoogle Scholar
  • 4 Wormanns D, Diederich S, Lentschig MG, Winter F, Heindel W. Spiral CT of pulmonary nodules: interobserver variation in assessment of lesion size. Eur Radiol 2000; 10:710-713. Crossref, MedlineGoogle Scholar
  • 5 Van Hoe LV, Haven F, Bellon E, et al. Factors influencing the accuracy of volume measurements in spiral CT: a phantom study. J Comput Assist Tomogr 1997; 21:332-338. Crossref, MedlineGoogle Scholar
  • 6 Harris KM, Adams H, Lloyd DCF, Harvey DJ. The effect on apparent size of simulated pulmonary nodules of using three standard CT window settings. Clin Radiol 1993; 47:241-244. Crossref, MedlineGoogle Scholar
  • 7 Koehler PR, Anderson RE, Baxter B. The effect of computed tomography viewer controls on anatomical measurements. Radiology 1979; 130:189-194. LinkGoogle Scholar
  • 8 Disler DG, Marr DS, Rosenthal DI. Accuracy of volume measurements of computed tomography and magnetic resonance imaging phantoms by three-dimensional reconstruction and preliminary clinical application. Invest Radiol 1994; 29:739-745. Crossref, MedlineGoogle Scholar
  • 9 Tiitola M, Kivisaari L, Tervartiala P, et al. Estimation or quantification of tumor volume? CT study on irregular phantoms. Acta Radiol 2001; 42:101-105. Crossref, MedlineGoogle Scholar
  • 10 Winer-Muram HT, Jennings SG, Tarver RD, et al. Volumetric growth rate of stage I lung cancer prior to treatment using serial CT imaging. Radiology 2002; 223:798-805. LinkGoogle Scholar
  • 11 Geddes DM. The natural history of lung cancer: a review based on rates of tumor growth. Br J Dis Chest 1979; 73:1-17. Crossref, MedlineGoogle Scholar
  • 12 Yankelevitz DF, Gupta R, Zhao B, Henschke C. Small pulmonary nodules: evaluation with repeat CT—preliminary experience. Radiology 1999; 212:561-566. LinkGoogle Scholar
  • 13 Zhao B, Yankelevitz D. Two-dimensional multi-criterion segmentation of pulmonary nodules on helical CT images. Med Phys 1999; 26:889-895. Crossref, MedlineGoogle Scholar
  • 14 Maertens R, Rousseau R. A new approximate formula for the perimeter of an ellipse. Wiskunde en Onderwijs 2000; 26:249-258. Google Scholar
  • 15 Naidich DP, Webb WR, Muller NL, et al. Computed tomography and magnetic resonance of the thorax 3rd ed. Philadelphia, Pa: Lippincott-Raven, 1999. Google Scholar
  • 16 Plewes DB, Dean PB. The influence of partial volume averaging on sphere detectability in computed tomography. Phys Med Biol 1981; 26:913-919. Crossref, MedlineGoogle Scholar
  • 17 Staron RB, Ford E. Computed tomography volumetric calculation reproducibility. Invest Radiol 1986; 21:272-274. Crossref, MedlineGoogle Scholar
  • 18 Hopper KD, Kasales CJ, Van Slyke MA, Schwartz TA, TenHave TR, Jozefiak JA. Analysis of interobserver and intraobserver variability in CT tumor measurements. AJR Am J Roentgenol 1996; 167:851-854. Crossref, MedlineGoogle Scholar

Article History

Published in print: Oct 2003