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. 2018 Jul;91(1088):20180251.
doi: 10.1259/bjr.20180251. Epub 2018 Jun 6.

Effect of CT window settings on size measurements of the solid component in subsolid nodules: evaluation of prediction efficacy of the degree of pathological malignancy in lung adenocarcinoma

Qiong Li  1 , Ya-Feng Gu  1 , Li Fan  1 , Qing-Chu Li  1 , Yi Xiao, Shi-Yuan Liu  1
Affiliations

Effect of CT window settings on size measurements of the solid component in subsolid nodules: evaluation of prediction efficacy of the degree of pathological malignancy in lung adenocarcinoma

Qiong Li et al. Br J Radiol. 2018 Jul.

Abstract

To investigate the predictive value of size measurements of the solid components in pulmonary subsolid nodules with different CT window settings and to evaluate the degree of pathological malignancy in lung adenocarcinoma. Methods: The preoperative chest CT images and pathological data of 125 patients were retrospectively evaluated. The analysis included 127 surgically resected lung adenocarcinomas that manifested as subsolid nodules. All subsolid nodules were divided into two groups: 69 in group A, including 22 adenocarcinomas in situ (AIS) and 47 minimally invasive adenocarcinomas (MIA); 58 in group B that included invasive pulmonary adenocarcinomas (IPA). The size of the solid component in the pulmonary subsolid nodules were calculated in one dimensional, two dimensional and three dimensional views using lung and mediastinal windows that were recorded as 1D-SCLW, 2D-SCLW, 3D-SCLW, 1D-SCMW, 2D-SCMW and 3D-SCMW, respectively. Furthermore, the volume of solid component with a threshold of -300HU was measured using lung window (3D-SCT). All the quantitative features were evaluated by the Mann-Whitney U test. Multivariate analysis was used to identify the significant predictor of the degree of pathological malignancy. Results: The 1D-SCLW, 2D-SCLW, 3D-SCLW, 1D-SCMW, 2D-SCMW, 3D-SCMW and 3D-SCT views of group B were significantly larger than those of group A (p < 0.001). The multivariate logistic regression analysis indicated that 3D-SCT (OR = 1.018, 95%CI: 1.005 ~ 1.03, p <0.05＝was the independent predictive factor. The larger SCT was significantly associated with IPAs. Conclusion: 3D-SCT of subsolid nodules during preoperative CT can be used to predict the degree of pathological malignancy in lung adenocarcinoma, which may provide a more objective and convenient selection criterion for clinical application. Advances in knowledge: Applying threshold of -300 HU with lung window setting would be better than other window setting for the evaluation of solid component in subsolid nodules. Computer-aided volumetry of the solid component in subsolid nodules can more accurately predict the degree of pathological malignancy than the other dimensional measurements.

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Figures

Figure 1.
Figure 1.
Size measurements of the solid component in a 66-year-old male diagnosed with invasive adenocarcinoma in the right lower lobe with different window settings. (a) SCLW: solid component size of lung window setting; (b) SCMW: solid component size of mediastinal window setting; (c) 3D- SCT: volume of solid component with threshold of −300HU using lung window. (d) The longitudinal axis in the histograms illustrates numbers of pixels within subsolid nodule, the horizontal axis revealed the CT number; The blue area represents the distribution of CT number below −300 HU, whereas the green one represents the distribution of CT number above −300 HU.
See this image and copyright information in PMC

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