Polyp volume versus linear size measurements at CT colonography: implications for noninvasive surveillance of unresected colorectal lesions

OBJECTIVE

Proposed surveillance of unresected medium-sized (6.0-9.9 mm) polyps will require reliable detection of small incremental changes. The purpose of this study was to assess the reproducibility of linear versus volume measurements of polyp size at CT colonography (CTC) and to correlate results with a hemispheric model.

MATERIALS AND METHODS

The study group consisted of 30 polyps on supine and prone CTC data sets. Measurements were performed separately by two experienced radiologists using the same CTC system (Vitrea 2), resulting in 120 linear and volume measurements. Linear size was defined as the longest dimension among the 2D multiplanar reconstruction views. Semiautomated volume determination required manual tracing of polyp boundaries on 2D multiplanar reconstruction views. The relative error between reviewers 1 and 2 was defined as 100 x (/D1 - D2/)/D(ave) for linear size (D) and as 100 x (/V1 - V2)/V(ave) for volume (V), where ave is average.

RESULTS

The mean relative error for linear size and volume measurement was 10.4% +/- 10.7% and 16.9% +/- 13.2%, respectively. Median linear size and volume of polyps were 9.4 mm and 270 mm3, respectively. CTC-derived volumes for medium-sized polyps closely approximated hemispheric volume (V = (pi/12) x D(b), where b =3.13, r2 = 0.90). Small incremental changes in hemispheric size result in a 3:1 relative change in volume versus diameter, such that a 1-mm diameter change in a medium-sized hemispheric polyp results in a relative change in linear size and volume ranging from approximately 11-18% and 31-53%, respectively.

CONCLUSION

Because changes in polyp volume are amplified compared with linear dimension, volume measurement rather than diameter measurement will better allow detection of small incremental changes in polyp size using CTC.

Radiologic assessment of rectosigmoid cancer before and after neoadjuvant radiation therapy: comparison between quantitation techniques

OBJECTIVE

Volumetric analysis was compared with conventional unidimensional measurements for follow-up of rectosigmoid cancer before and after radiation therapy.

SUBJECTS AND METHODS

Fifteen patients with rectosigmoid cancer underwent helical CT before and after neoadjuvant radiation therapy. The helical CT examination was performed after colon distention with air and IV administration of an antiperistaltic drug. Two scans were obtained: one with the patient in the supine position and the other with the patient in the prone position after contrast medium injection. The maximal wall thickness and the volumetric analysis of the tumor were obtained through manual segmentation.

RESULTS

The mean of the differences between the volumetric analysis of the scans obtained before and after radiation therapy was 8.3 +/- 10.3 (SD) mL (-22.7%) (p <0.05). The mean of the differences between the maximal wall thickness of the pre- and post-radiation therapy scans was 3.4 +/- 2.6 mm (-19.1%) (p <0.05). A significant difference was observed between the variation of the maximal wall thickness and the variation of volumetric analysis in pre- and post-radiation therapy scans (p <0.05). The patients could be classified in different response categories depending on the measurement method and on the response criteria.

CONCLUSION

Volumetric analysis of rectosigmoid cancer is feasible. A long-term study is needed to correlate volumetric assessment with patient outcome.

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.

Measurement of observer agreement

Statistical measures are described that are used in diagnostic imaging for expressing observer agreement in regard to categorical data. The measures are used to characterize the reliability of imaging methods and the reproducibility of disease classifications and, occasionally with great care, as the surrogate for accuracy. The review concentrates on the chance-corrected indices, kappa and weighted kappa. Examples from the imaging literature illustrate the method of calculation and the effects of both disease prevalence and the number of rating categories. Other measures of agreement that are used less frequently, including multiple-rater kappa, are referenced and described briefly.