CT Features of Stage IA Invasive Mucinous Adenocarcinoma of the Lung and Establishment of a Prediction Model

Abstract

Objective To investigate computed tomography (CT) features of stage IA invasive mucinous adenocarcinoma (IMA) of the lung and establish a predictive model. Methods Fifty-three lesions from 53 cases of stage IA IMA between January 2017 and December 2019 were examined, while 141 lesions from 141 cases of invasive non-mucinous lung adenocarcinoma (INMA) served as control cases. Univariate analysis was performed to compare differences in demographics and CT features between the two groups, and multivariate logistic regression analysis was performed to determine primary influencing factors of solitary nodular IMA. A risk score prediction model was established based on the regression coefficients of these factors, and receiver operating characteristic (ROC) curve analysis was performed to evaluate the predictive performance of the model. Results Univariate analysis showed that age, nodule type, maximum nodule diameter, tumor lung interface, lobulation, spiculation, air bronchogram or vacuolar signs, and abnormal vascular changes differed significantly between the two groups (p < 0.05). Compared to INMA, spiculation of IMA was relatively longer and softer. Multivariate logistic regression analysis showed that nodule type, indistinct tumor lung interface, air bronchogram or vacuolar signs, and abnormal vascular changes were the primary influencing factors. A prediction model based on the regression coefficients of these factors was established. ROC curve analysis indicated that the area under the curve was 0.882 (p < 0.05). Conclusion Compared to INMA, solitary peripheral stage IA nodular IMA were more common in older patients; they more frequently had indistinct tumor lung interface and air bronchogram or vacuolar signs on CT; spiculation was relatively longer and softer; our risk score prediction model based on nodule type, tumor lung interface, air bronchogram or vacuolar signs, and abnormal vascular changes was established with good predictive efficacy for solitary nodular IMA.

Radiomics Nomogram for Preoperative Differentiation Between Clinical Stage IA Solitary Pulmonary Nodule-Type Invasive Mucinous Adenocarcinoma and Invasive Non-mucinous Adenocarcinoma

“…Notably, our study revealed an independent association between higher rad scores and IMA in the radiomics model. This observation can be explained by the heightened IMA heterogeneity, attributed to the abundant presence of mucin within the cytoplasm of IMA tumor cells and the aggressive nature of tumor neovascularization, closely associated with its malignant behavior [17]. The radiomics model achieved AUC values of 0.770 and 0.753 for the training and test sets, respectively, indicating higher diagnostic e ciency than that of the clinical model.…”

“…CT images currently provide limited information for differentiating between SPN-type IMA and non-mucinous LADC. Previous studies have suggested that certain CT features, including vacuolar signs, air bronchograms, and abnormal vascular changes, may help predict SPN-type IMA [16][17][18]. However, interpreting these features can be subjective and lack consistency or typicality.…”

Invasive mucinous adenocarcinoma misdiagnosed as pneumonia: A case report

“…The diagnosis of IMA is challenging. Imaging findings can vary and be nonspecific, including consolidation of the lung parenchyma, nodules, honeycomb signs and ground-glass changes (13). However, common findings including irregular masses and absence of lung cancer signs.…”

18F-FDG PET/CT characteristics of IASLC grade 3 invasive adenocarcinoma and the value of18F-FDG PET/CT for preoperative prediction

“…The size of the lesion [expressed as the maximum diameter of the lesion (cm)], and consolidation-tumor-ratio (CTR) were measured according to the published literatures. 26,27…”