Pulmonary Nodules: Growth Rate Assessment in Patients by Using Serial CT and Three-dimensional Volumetry

Ko, Jane P.; Berman, Erika J.; Kaur, Manmeen; Babb, James S.; Bomsztyk, Elan; Greenberg, Alissa K.; Naidich, David P.; Rusinek, Henry

doi:10.1148/radiol.11100878

Cited by 75 publications

(69 citation statements)

References 49 publications

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“…It is a common imaging artefact when a limited spatial resolution is used to perform CT scans and, consequently, different tissues are included in the same pixel/voxel [50, 52,[65][66][67][68][69]. When attenuation value is not sufficient to distinguish nodule borders, segmentation errors could occur, as in the case of nonspherical or irregular lesions [41,65,68,[70][71][72], as well as in juxtavascular or juxtapleural ones [72][73][74]. Reduced nodule attenuation, as in the case of SSNs, could also affect nodule segmentation when using the commonest threshold density technique, because of the low attenuation difference between nodule borders and the surrounding parenchyma [50].…”

Section: Factors Influencing Nodule Measurement Variationsmentioning

confidence: 99%

“…Intuitively, the direct assessment of nodule volume and VDT provides an accurate estimation of nodule growth [51]. Combined with lower uncertainty of measurements, the 3D method allows detection of changes even within a shorter period of time, resulting in a higher sensitivity of volume-based techniques in growth evaluation [26,73] (figure 3). Estimations of nodule growth rates obtained from automated 3D volumetric measurements showed a good correlation with 2D diameter measurements, with a greater divergence for irregular lesions [70].…”

Section: Effect Of Measurement Variations On Nodule Growthmentioning

confidence: 99%

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Lung nodules: size still matters

et al. 2017

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The incidence of indeterminate pulmonary nodules has risen constantly over the past few years. Determination of lung nodule malignancy is pivotal, because the early diagnosis of lung cancer could lead to a definitive intervention. According to the current international guidelines, size and growth rate represent the main indicators to determine the nature of a pulmonary nodule. However, there are some limitations in evaluating and characterising nodules when only their dimensions are taken into account. There is no single method for measuring nodules, and intrinsic errors, which can determine variations in nodule measurement and in growth assessment, do exist when performing measurements either manually or with automated or semi-automated methods. When considering subsolid nodules the presence and size of a solid component is the major determinant of malignancy and nodule management, as reported in the latest guidelines. Nevertheless, other nodule morphological characteristics have been associated with an increased risk of malignancy. In addition, the clinical context should not be overlooked in determining the probability of malignancy. Predictive models have been proposed as a potential means to overcome the limitations of a sized-based assessment of the malignancy risk for indeterminate pulmonary nodules.

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Section: Factors Influencing Nodule Measurement Variationsmentioning

confidence: 99%

Section: Effect Of Measurement Variations On Nodule Growthmentioning

confidence: 99%

Lung nodules: size still matters

et al. 2017

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“…They are from a set of lung nodules used in a previous study in which an average CT follow-up period of 6.4 years was employed to analyze the precision of growth rate measurements [14]. The mean linear dimension of nodules in our set is 5.4 mm±3.0, with a range from 1.9 to 28.5 mm.…”

Section: Patient-based Comparisonmentioning

confidence: 99%

“…Therefore, computer-assisted methods of lung nodule characterization have been investigated. Assessment of lung nodules thus far has focused primarily on growth rate, attenuation, shape, texture, and the use of neural networks [13][14][15][16][17][18][19]. Assessing change in a nodule entails evaluating not only the lesion's growth rate but also any alterations in morphology.…”

Section: Introductionmentioning

confidence: 99%

The Self-Overlap Method for Assessment of Lung Nodule Morphology in Chest CT

Stember

Naidich

et al. 2012

J Digit Imaging

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Surface morphology is an important indicator of malignant potential for solid-type lung nodules detected at CT, but is difficult to assess subjectively. Automated methods for morphology assessment have previously been described using a common measure of nodule shape, representative of the broad class of existing methods, termed area-to-perimeter-length ratio (APR). APR is static and thus highly susceptible to alterations by random noise and artifacts in image acquisition. We introduce and analyze the self-overlap (SO) method as a dynamic automated morphology detection scheme. SO measures the degree of change of nodule masks upon Gaussian blurring. We hypothesized that this new metric would afford equally high accuracy and superior precision than APR. Application of the two methods to a set of 119 patient lung nodules and a set of simulation nodules showed our approach to be slightly more accurate and on the order of ten times as precise, respectively. The dynamic quality of this new automated metric renders it less sensitive to image noise and artifacts than APR, and as such, SO is a potentially useful measure of cancer risk for solid-type lung nodules detected on CT.

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“…It was found that the texture at the edge of the nodules is critical in distinguishing malignant from benign nodules (Wang et al, 2010). Curvelet transformation (Guo et al, 2012), which is ideally suited to the analysis of two-dimensional (2D) images, has proven to be particularly effective at detecting image activity along curves instead of radial directions when compared with other transforms (Ko et al, 2012). It has been used in the analysis of medical images (Dettori and Semler, 2007;Eltoukhy et al, 2010;Meselhy Eltoukhy et al, 2010), such as CT scans, endoscope images, X-rays, etc.…”

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confidence: 99%