Pulmonary Nodules: growth rate assessment in patients by using serial CT and three-dimensional volumetry

Abstract

Purpose: To determine the precision of a three-dimensional (3D) method for measuring the growth rate of solid and subsolid nodules and its ability to detect abnormal growth rates.

Materials and methods: This study was approved by the Institutional Research Board and was HIPAA compliant. Informed consent was waived. The growth rates of 123 lung nodules in 59 patients who had undergone lung cancer screening computed tomography (CT) were measured by using a 3D semiautomated computer-assisted volume method. Clinical stability was established with long-term CT follow-up (mean, 6.4 years±1.9 [standard deviation]; range, 2.0-8.5 years). A mean of 4.1 CT examinations per patient±1.2 (range, two to seven CT examinations per patient) was analyzed during 2.4 years±0.5 after baseline CT. Nodule morphology, attenuation, and location were characterized. The analysis of standard deviation of growth rate in relation to time between scans yielded a normative model for detecting abnormal growth.

Results: Growth rate precision increased with greater time between scans. Overall estimate for standard deviation of growth rate, on the basis of 939 growth rate determinations in clinically stable nodules, was 36.5% per year. Peripheral location (P=.01; 37.1% per year vs 25.6% per year) and adjacency to pleural surface (P=.05; 38.9% per year vs 34.0% per year) significantly increased standard deviation of growth rate. All eight malignant nodules had an abnormally high growth rate detected. By using 3D volumetry, growth rate-based diagnosis of malignancy was made at a mean of 183 days±158, compared with radiologic or clinical diagnosis at 344 days±284.

Conclusion: A normative model derived from the variability of growth rates of nodules that were stable for an average of 6.4 years may enable identification of lung cancer.

Figure 1:

Axial chest CT images of two representative stable nodules. Top: Ovoid solid nodule abuts the costal pleural surface in the right lower lobe. Bottom: Round nodule in the right lower lobe. Both nodules were imaged at four time points (left to right).

Figure 2:

Plots show annual growth rate (GR) for clinically stable nodules by using all 939 available pairs of time points T₁ and T₂ with T₂ greater than T₁ for solid and subsolid nodules. The growth values were centered around zero growth. There was no significant difference in growth rate distribution between solid and nonsolid nodules.

Figure 3:

Plot shows standard deviation (σ) of growth rate (GR) is well approximated by a power of time interval (ΔT).

Figure 4:

List shows method for identifying nodules with abnormal growth rates. σ = standard deviation.

Figure 5:

Graphs show growth of eight malignant nodules. Volume (black lines, ●, left vertical scale in cubic millimeters) and linear (gray lines, ▲, right vertical scale in millimeters) dimensions of malignant nodules plotted in relation to time (in days, horizontal axis). Significant volume increase was observed in each case by using volume measurement.