Japanese journal of radiology · 2021
Differentiation of adenocarcinoma in situ with alveolar collapse from minimally invasive adenocarcinoma or invasive adenocarcinoma appearing as part-solid ground-glass nodules (≤ 2 cm) using computed tomography.
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Xu Liyun, Lin Shuaidong, Zhang Yongkui
Аннотация
PURPOSE: To investigate the differentiating computed tomographic (CT) features between adenocarcinoma in situ (AIS) with alveolar collapse and minimally invasive adenocarcinoma (MIA) or invasive adenocarcinoma (IA) appearing as part-solid nodules.
METHODS: A total of 147 consecutive patients with 157 pathology-confirmed part-solid ground-glass nodules (GGNs) ≤ 20 mm without other pathological condition such as inflammation and fibrosis who underwent chest CT were included.
RESULTS: The 157 part-solid GGNs included 33 (21.02%) pathologically confirmed AISs with alveolar collapse. Multivariate analysis revealed that smaller lesion size (odds ratio [OR] 0.671), and well-defined border (OR 5.544), concentrated distribution (OR 7.994), and homogeneity of the solid portion (OR 4.365) were significant independent predictors for differentiating AIS with alveolar collapse from MIA (P < 0.05) with excellent accuracy (area under receiver operating characteristic [ROC] curve, 0.902). Multivariate analysis revealed that smaller lesion size (OR 0.782), and size (OR 0.821), well-defined border (OR 5.752), and homogeneity of solid portion (OR 6.182) were significant independent predictors differentiating AIS with alveolar collapse from IA (P < 0.05) with excellent accuracy (area under ROC curve 0.910).
CONCLUSION: Among part-solid GGNs, AIS with alveolar collapse can be accurately differentiated from MIA on the basis of smaller lesion size, well-defined border, concentrated distribution, and homogeneity of solid portion, and from IA according to smaller lesion size, and smaller size, well-defined border, and homogeneity of solid portion.
METHODS: A total of 147 consecutive patients with 157 pathology-confirmed part-solid ground-glass nodules (GGNs) ≤ 20 mm without other pathological condition such as inflammation and fibrosis who underwent chest CT were included.
RESULTS: The 157 part-solid GGNs included 33 (21.02%) pathologically confirmed AISs with alveolar collapse. Multivariate analysis revealed that smaller lesion size (odds ratio [OR] 0.671), and well-defined border (OR 5.544), concentrated distribution (OR 7.994), and homogeneity of the solid portion (OR 4.365) were significant independent predictors for differentiating AIS with alveolar collapse from MIA (P < 0.05) with excellent accuracy (area under receiver operating characteristic [ROC] curve, 0.902). Multivariate analysis revealed that smaller lesion size (OR 0.782), and size (OR 0.821), well-defined border (OR 5.752), and homogeneity of solid portion (OR 6.182) were significant independent predictors differentiating AIS with alveolar collapse from IA (P < 0.05) with excellent accuracy (area under ROC curve 0.910).
CONCLUSION: Among part-solid GGNs, AIS with alveolar collapse can be accurately differentiated from MIA on the basis of smaller lesion size, well-defined border, concentrated distribution, and homogeneity of solid portion, and from IA according to smaller lesion size, and smaller size, well-defined border, and homogeneity of solid portion.