CT screening for lung cancer: Frequency and significance of part-solid and nonsolid nodules
In the Early Lung Cancer Action Project (ELCAP), we found not only solid but also part-solid and nonsolid nodules in patients at both baseline and repeat CT screening for lung cancer. We report the frequency and significance of part-solid and nonsolid nodules in comparison with solid nodules.
We reviewed all instances of a positive finding in patients at baseline (from one to six noncalcified nodules) and annual repeat screenings (from one to six newly detected noncalcified nodules with interim growth) to classify each of the nodules as solid, part-solid, or nonsolid. We defined a solid nodule as a nodule that completely obscures the entire lung parenchyma within it. Part-solid nodules are those having sections that are solid in this sense, and nonsolid nodules are those with no solid parts. Chi-square statistics were used to test for differences in the malignancy rates.
Among the 233 instances of positive results at baseline screening, 44 (19%) involved a part-solid or nonsolid largest nodule (16 part-solid and 28 nonsolid). Among these 44 cases of positive findings, malignancy was diagnosed in 15 (34%) as opposed to a 7% malignancy rate for solid nodules (p = 0.000001). The malignancy rate for part-solid nodules was 63% (10/16), and the rate for nonsolid nodules was 18% (5/28). Even after standardizing for nodule size, the malignancy rate was significantly higher for part-solid nodules than for either solid ones (p = 0.004) or nonsolid ones (p = 0.03). The malignancy type in the part-solid or nonsolid nodules was predominantly bronchioloalveolar carcinoma or adenocarcinoma with bronchioloalveolar features, contrasting with other subtypes of adenocarcinoma found in the solid nodules (p = 0.0001). At annual repeat screenings, only 30 instances of positive test results have been obtained; seven of these involved part-solid or nonsolid nodules.
In CT screening for lung cancer, the detected nodule commonly is either only part-solid or nonsolid, but such a nodule is more likely to be malignant than a solid one, even when nodule size is taken into account.
Available from: Chang Min Park
- "Pulmonary ground-glass nodules (GGNs) including both pure GGNs and part-solid (PS) GGNs have been well known to have a substantially high probability to be malignant (1), with malignancy rates of 63% for PS GGNs and 18% for pure GGNs reported by Henschke et al. (1), much higher than that for solid nodules. However, a substantial proportion of GGNs is benign (2), and thus differentiation between benign and malignant GGNs is essential. "
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ABSTRACT: To compare the segmentation capability of the 2 currently available commercial volumetry software programs with specific segmentation algorithms for pulmonary ground-glass nodules (GGNs) and to assess their measurement accuracy.
In this study, 55 patients with 66 GGNs underwent unenhanced low-dose CT. GGN segmentation was performed by using 2 volumetry software programs (LungCARE, Siemens Healthcare; LungVCAR, GE Healthcare). Successful nodule segmentation was assessed visually and morphologic features of GGNs were evaluated to determine factors affecting segmentation by both types of software. In addition, the measurement accuracy of the software programs was investigated by using an anthropomorphic chest phantom containing simulated GGNs.
The successful nodule segmentation rate was significantly higher in LungCARE (90.9%) than in LungVCAR (72.7%) (p = 0.012). Vascular attachment was a negatively influencing morphologic feature of nodule segmentation for both software programs. As for measurement accuracy, mean relative volume measurement errors in nodules ≥ 10 mm were 14.89% with LungCARE and 19.96% with LungVCAR. The mean relative attenuation measurement errors in nodules ≥ 10 mm were 3.03% with LungCARE and 5.12% with LungVCAR.
LungCARE shows significantly higher segmentation success rates than LungVCAR. Measurement accuracy of volume and attenuation of GGNs is acceptable in GGNs ≥ 10 mm by both software programs.
Available from: Virendra Kumar
- "For the stability test result shown in Fig. 6, there are a few cases that have a similarity index lower than 60%. Further investigation found that most of them have a part-solid tumor  (Fig. 10) with low density and their lesion boundary is not well defined. For those cases, SCES (and LS) was not stable, additional work needs to be done, one possible method ''Shrink & Wrap'' may be used instead of the ''Click & Grow'' method, which requires a pre-defined boundary surrounding the tumor area. "
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ABSTRACT: A single click ensemble segmentation (SCES) approach based on an existing "Click&Grow" algorithm is presented. The SCES approach requires only one operator selected seed point as compared with multiple operator inputs, which are typically needed. This facilitates processing large numbers of cases. Evaluation on a set of 129 CT lung tumor images using a similarity index (SI) was done. The average SI is above 93% using 20 different start seeds, showing stability. The average SI for 2 different readers was 79.53%. We then compared the SCES algorithm with the two readers, the level set algorithm and the skeleton graph cut algorithm obtaining an average SI of 78.29%, 77.72%, 63.77% and 63.76% respectively. We can conclude that the newly developed automatic lung lesion segmentation algorithm is stable, accurate and automated.
Available from: Daniela Origgi
- "Li et al.  compared malignant and benign nodules in an LCS study and found that the prevalence of malignancy was 59% for non-solid nodules, 48% for part-solid nodules and 11% for solid nodules. In the Early Lung Cancer Action Project  , 34% (15/44) of subsolid nodules detected at baseline were malignant, whereas malignancy rates for part-solid and non-solid nodules were 63% (10/16) and 18% (5/28), respectively, compared with only 7% for solid nodules. Even if non-solid nodules are more likely to be malignant , their growth rate tends to be considerably slower than solid lesions   with substantial implication for their follow-up interval and management (Fig. 2). "
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ABSTRACT: The National Lung Cancer Screening Trial has recently demonstrated that screening of high-risk populations with the use of low-dose computed tomography (LDCT) reduces lung cancer mortality. Based on this encouraging result, the National Comprehensive Cancer Network guidelines recommended LDCT for selected patients at high risk of lung cancer. This suggests that an increasing number of CT screening examinations will be performed. The LDCT technique is relatively simple but some CT parameters are important and should be accurately defined in order to achieve good diagnostic quality and minimize the delivered dose. In addition, LDCT examinations are not as easy to read as they may initially appear; different approaches and tools are available for nodule detection and measurement. Moreover, the management of positive results can be a complex process and can differ significantly from routine clinical practice. Therefore this paper deals with the LDCT technique, reading methods and interpretation in lung cancer screening, particularly for those radiologists who have little experience of the technique.
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