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| Study | Purpose | Method | Database | Observations |
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| Yankelevitz et al. [141] | To assess using early CT repeat to determine nodule growth rate | 2D growth rate (GR) technique by measuring the maximal diameter of nodule | Repeat CT for 15 patients (15 nodules: 9 malignant, 6 benign (5–20 mm)) + spherical phantoms of known diameters | A single repeat after 30 days detects as small as 5 mm malignant nodule; all the 15 nodules are correctly classified |
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| Yankelevitz et al. [140] | To determine the accuracy of CT volumetric measurements of small pulmonary nodules to assess growth and malignancy status | Exponential growth model to estimate the doubling time | 13 patients (nodule <10 mm) (5 malignant and 8 benign) + synthetic phantoms of spherical, deformable, and different shape and sizes | (a) The synthetic nodule studies revealed that the volume could be measured accurately to within 3%. (b) All five malignant nodules grew, and all had doubling times less than 177 days. (c) All eight benign nodules had doubling times of 396 days or greater or showed a decrease in volume |
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| Winer-Muram et al. [200] | To determine the range of growth rates of stage I lung cancers prior to treatment. | Volumetric measurement | 50 patients, 50 tumor | Comparison of tumor volumes at serial CT examinations reveals a very wide range of growth rates. Some tumors grow so slowly that biopsy is required to prove that they are malignant |
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| Borghesi et al. [201] | To evaluate the accuracy of software-calculated growth rate of small nodules in detecting malignancy | Volume doubling time was calculated on the Aquarius Workstation (TeraRecon, Inc.) with the segmentation analysis and tracking (SAT) module | 29 patients (40 nodules (solid or noncalcified) 4–15 mm, glass opacities nodules were discarded); 4 of the nodules are given their diagnosis (3 benign and 1 malignant) | 4 nodules are correctly classified |
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| Wormanns et al. [202] | To assess the measurement precision of a software tool for volumetric analysis of nodules from two consecutive low-dose CT scans | Volumetric measurement | 10 subject, 151 nodules | Taking into account all 151 nodules, 95% limits of agreement were −20.4 to 21.9% (standard error 1.5%) |
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| Revel et al. [203] | To evaluate software designed to calculate pulmonary nodule volume in 3D | Volumetric measurement | 54 nodules, 22 diagnosed: 13 benign
and 9 malignant | Software measurement error of 6.38% of the previous volume measurement |
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| Kostis et al. [147] | To determine the reproducibility of volume measurements of small pulmonary nodules on CT scans and to estimate critical time to follow-up CT | Percentage volume change (PVC) and monthly volumetric growth index (MVGI) were computed for each nodule pair | 115 nodule | Factors that affect reproducibility of nodule volume measurements and critical time to follow-up CT include nodule size at detection, type of scan (baseline or repeat) on which the nodule is detected, and presence of patient-induced artifacts |
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| Goo et al. [204] | To evaluate the effect of CT parameters and nodule segmentation thresholds on accuracy of volumetric measurement of synthetic lung nodules | Volumetric measurement | 4 types of lung phantoms | For accurate measurement of the lung nodule volume, it is critical to select a section thickness and/or segmentation threshold appropriate for the size of a nodule |
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| Reeves et al. [185] | To develop a method for measuring the change in nodule size from 2 CT image scans recorded at different times to establish the growth rate | Registration, adaptive thresholding, and knowledge-based shape matching | 50 benign or 2YNC nodule | The variance in percent volume change was reduced from 11.54% to 9.35% through the use of registration, adaptive thresholding, and knowledge-based shape matching |
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| Jennings et al. [205] | To retrospectively determine the distribution of stage I lung cancer growth rates with serial volumetric CT | Volumetric measurement | 149 patients | At serial volumetric CT measurements, there was wide variability in growth rates. Some biopsy-proved cancers decreased in volume between examinations |
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| Zheng et al. [172] | To simultaneously segment and register lung and tumor in serial CT data | Nonrigid transformation on lung deformation and rigid structure on the tumor | 6 volumes of 3 patients, 83 nodules | The mean error of boundary distances between automatic segmented boundaries of lung tumors and manual segmentation is 3.50 pixels. The mean and variance of percentages of the nodule volume variations caused by errors in segmentation are 0.8 and 0.6 |
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| Jirapatnakul et al. [206] | To measures the nodule growth without explicit segmentation | Growth analysis from density (GAD) method to measure the growth rate | 20 cases each with single nodule with scans several minutes apart (expected zero volume change), 38 cases with a stable nodule, 19 cases with a malignant nodule, and 4 malignant nodules with a complex appearance | Accuracy achieved was 37/38 for the stable benign nodules, 18/19 for the malignant nodules, and 4/4 for the complex malignant nodules |
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| Marchianò et al. [207] | To assess in vivo volumetric repeatability of an automated algorithm for volume estimation. | Semiautomatic volumetric measurement | 101 subjects, 233 nodules | The 95% confidence interval for difference in measured volumes was in the range of ±27%. About 70% of measurements had a relative difference in nodule volume of less than 10% |
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| El-Baz et al. [208] | To monitor the development of lung nodules | Global and local registration, GR volumetric measurement | 135 LDCT from 27 subjects, 27 nodules | All the 27 nodules are correctly classified based on GR measurements over 12 months |
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