Thoracic Radiology
Predictive value of whole nodule size and solid component size of pulmonary subsolid nodule with different window setting for the pathologic grade
Gu Yafeng, Li Qiong, Fan Li, Li Qingchu, Xiao Yi, Liu Shiyuan
Published 2017-07-10
Cite as Chin J Radiol, 2017,51(07): 484-488. DOI: 10.3760/cma.j.issn.1005-1201.2017.07.002
Abstract
ObjectiveTo investigate the predictive value of whole nodule size and solid component size of pulmonary subsolid nodules (SSNs)with different window setting on preoperative HRCT for pathologic grade in lung adenocarcinoma.
MethodsWe retrospectively evaluated preoperative chest HRCT and pathological data of 125 patients with 127 surgically resected lung adenocarcinoma manifesting as SSNs. All specimens were divided into two groups: a total of 69 SSNs in group A, including 22 adenocarcinomas in situ (AIS) and 47 minimally invasive adenocarcinoma (MIA); a total of 58 SSNs in group B, including invasive adenocarcinoma (IAC). Observer 1 used computer aided diagnosis software to measure the volume of whole nodule with lung window setting (WNLW), volume of solid component with lung window setting (SCLW), volume of solid component with mediastinal window setting (SCMW) and volume of solid component with threshold of-300 HU(SCT) of all SSNs. Observer 2 randomly selected 50 SSNs and repeated all the measurements. The interobserver agreement regarding quantitative measurements were evaluated by using intraclass correlation coefficient(ICC). The differences of all quantitative features between two groups were evaluated by Mann-Whitney U test. All the quantitative features were evaluated by using univariate logistic regression analysis, significant quantitative features identified by univariate logistic regression analysis were included in the multivariate logistic regression and independent predictors of pathological grade were obtained. Receiver operating characteristic analysis was conducted for the independent predictive factors that exhibited statistically significant differences in the multivariate logistic regression.
ResultsThe interobserver agreement regarding quantitative features were excellent (ICC>0.75) . The WNLW, SCLW, SCMW and SCT of group B were significantly larger than those of group A (P<0.001). The univariate logistic regression analysis indicated that WNLW, SCLW, SCMW and SCT were significant (P<0.001) , the multivariate logistic regression analysis indicated that SCT was the independent predictive factor (OR=1.013, 95%CI: 1.006—1.020, P<0.001) . When SCT larger than 139.00 mm3, SSN was significantly associated with IACs (AUC=0.887, sensitivity=81%, specificity=93%).
ConclusionSCT of SSNs on preoperative HRCT can be used to distinguish between AIS-MIA and IAC, which may provide information for choice of operation.
Key words:
Lung neoplasms; Pulmonary subsolid nodule; Tomography, X-ray computed; Pathology
Contributor Information
Gu Yafeng
Department of Radiology and Nuclear Medicine, Changzheng Hospital, Second Military Medical University, Shanghai 200003, China
Li Qiong
Fan Li
Li Qingchu
Xiao Yi
Liu Shiyuan
