Journal
CLINICAL LUNG CANCER
Volume 19, Issue 1, Pages E75-E83Publisher
CIG MEDIA GROUP, LP
DOI: 10.1016/j.cllc.2017.05.023
Keywords
Ground-glass opacity; HRCT; Lung cancer; Screening; SSN
Categories
Funding
- Department of Pulmonary Medicine, Radiology and Pathology of Shanghai Chest Hospital
- Science and Technology Committee of Shanghai Municipality [14411950800]
- Collaborative Innovation Center of Systems Biomedicine [15ZH4009]
- Shanghai Jiao Tong University Medical-Engineering Interdisciplinary Research Foundation [YG2015MS71]
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A pulmonary subsolid nodule (SSN) is a nonspecific finding on computed tomography and often presents a diagnostic challenge to clinicians. The present study found some valuable morphologic discriminators to distinguish malignant SSNs from benign SSNs after investigating the high-resolution computed tomography features of 1014 SSNs. The use of these morphologic discriminators might help reduce the overdiagnosis and overtreatment of pulmonary SSNs. Introduction: We retrospectively investigated the high-resolution computed tomography features that distinguish benign lesions (BLs) from malignant lesions (MLs) appearing as persistent solitary subsolid nodules (SSNs). Materials and Methods: In 2015, the data from patients treated in our department with persistent solitary SSNs 5 to 30 mm in size were analyzed retrospectively. The demographic data and HRCT findings were analyzed and compared between those with BLs and MLs. Results: Of the 1934 SSNs, 94 were BLs and 1840 were MLs. One half of the MLs (920 SSNs) were randomly selected and analyzed. The BLs were classified into 2 subgroups: 28 pure ground-glass nodules (pGGNs) and 66 part-solid nodules (PSNs). After matching in each group, 56 pGGNs and 132 PSNs in the ML group were selected. In the pGGN subgroup, multivariate analysis found that a well-defined border (odds ratio [OR], 4.320; 95% confidence interval [CI], 1.534-12.168; P = .006; area under the curve, 0.705; 95% CI, 0.583-0.828; P = .002) and a higher average CT value (OR, 1.007; 95% CI, 1.001-1.013; P = .026; area under the curve, 0.715; 95% CI, 0.599-0.831; P = .001) favored the diagnosis of malignancy. In the PSN subgroup, multivariate analysis revealed that a larger size (OR, 1.084; 95% CI, 1.015-1.158; P = .016), a well-defined border (OR, 3.447; 95% CI, 1.675-7.094; P = .001), and a spiculated margin (OR, 2.735; 95% CI, 1.359-5.504; P = .005) favored the diagnosis of malignancy. Conclusion: In pGGNs, a well-defined lesion border and a larger average CT value can be valuable discriminators to distinguish between MLs and BLs. In PSNs, a larger size, well-defined border, and spiculated margin had greater predictive value for malignancy.
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