Adenocarcinoma in situ and minimally invasive adenocarcinoma in lungs of smokers: image feature differences from those in lungs of non-smokers

Purpose We aimed to examine the characteristics of imaging findings of adenocarcinoma in situ (AIS) and minimally invasive adenocarcinoma (MIA) in the lungs of smokers compared with those of non-smokers. Materials and methods We included seven cases of AIS and 20 cases of MIA in lungs of smokers (pack-years ≥ 20) and the same number of cases of AIS and MIA in lungs of non-smokers (pack-years = 0). We compared the diameter of the entire lesion and solid component measured on computed tomography (CT) images, pathological size and invasive component diameter measured from pathological specimens, and CT values of the entire lesion and ground-glass opacity (GGO) portions between the smoker and non-smoker groups. Results The diameters of AIS and MIA on CT images and pathological specimens of the smoker group were significantly larger than those of the non-smoker group (p = 0.036 and 0.008, respectively), whereas there was no significant difference in the diameter of the solid component on CT images or invasive component of pathological specimens between the two groups. Additionally, mean CT values of the entire lesion and GGO component of the lesions in the smoker group were significantly lower than those in the non-smoker group (p = 0.036 and 0.040, respectively). Conclusion AIS and MIA in smoker’s lung tended to have larger lesion diameter and lower internal CT values compared with lesions in non-smoker’s lung. This study calls an attention on smoking status in CT-based diagnosis for early stage adenocarcinoma.

Case Report: Primary Bilateral Minimally Invasive Adenocarcinoma of the Lungs in an 11-Year-Old Child: A Rare Case

There are many types of benign and malignant tissue, but primary lung tumor is very rare in children and often remains undiagnosed until after distant metastasis has occurred. Few cases of early lung adenocarcinoma in children have been reported. However, this case concerns an 11-year-old child with primary bilateral minimally invasive adenocarcinoma. As far as we know, this is the youngest reported case of its type.

Pulmonary Functional Imaging for Lung Adenocarcinoma: Combined MRI Assessment Based on IVIM-DWI and OE-UTE-MRI

PurposeThe goal of current study was to introduce noninvasive and reproducible MRI methods for in vivo functional assessment of lung adenocarcinoma (LUAD).MethodsForty-four patients with pathologically confirmed LUAD were included in this study. All the lesions were classified as adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), or invasive adenocarcinoma (IA). The IA lesions were further divided into five subtype patterns, including acinar, lepidic, papillary, micropapillary and solid. Tumors were grouped depending on predominant subtype: low grade (AIS, MIA or lepidic predominant), intermediate grade (papillary or acinar predominant) and high grade (micropapillary, or solid predominant). Spirometry was performed according to American Thoracic Society guidelines. For each patient, Intravoxel incoherent motion diffusion weighted imaging (IVIM-DWI) analysis and oxygen-enhanced MRI (OE-MRI) analysis were performed. Spearman’s test was used to assess the relationship between a) whole lung mean percent signal enhancement (PSE) and pulmonary function tests (PFTs) parameters; b) IVIM-derived parameters and PFTs parameters; c) tumor mean PSE and IVIM-derived parameters. Kruskal -Wallis tests were applied to test the difference of tumor mean PSE and IVIM-derived parameters between different histological tumor grades. Receiver operating characteristics (ROC) analysis was used to evaluate the diagnostic performance.ResultsWhole lung mean PSE was significantly positively correlated with PFTs parameters (r = 0.40 ~ 0.44, P < 0.05). f value derived from IVIM-DWI was significantly negatively correlated with PFTs parameters (r = -0.38 ~ -0.47, P < 0.05). Both tumor mean PSE (P = 0.030 < 0.05) and f (P = 0.022 < 0.05) could differentiate different histological grades. f was negatively correlated with tumor mean PSE (r = -0.61, P < 0.001). For the diagnostic performance, the combination of tumor mean PSE and f outperformed than using tumor mean PSE or f alone in both sensitivity and area under the ROC curve.ConclusionsThe combined measurement of OE-MRI and IVIM-DWI may serve as a promising method for the noninvasive and non-radiation evaluation of pulmonary function. Quantitative analyses achieved by OE-MRI and IVIM-DWI offer an approach of the classification of LUAD subtypes.

CT Texture Analysis for Differentiating Bronchiolar Adenoma, Adenocarcinoma In Situ, and Minimally Invasive Adenocarcinoma of the Lung

Purpose: This study aimed to investigate the potential of computed tomography (CT) imaging features and texture analysis to distinguish bronchiolar adenoma (BA) from adenocarcinoma in situ (AIS)/minimally invasive adenocarcinoma (MIA).Materials and Methods: Fifteen patients with BA, 38 patients with AIS, and 36 patients with MIA were included in this study. Clinical data and CT imaging features of the three lesions were evaluated. Texture features were extracted from the thin-section unenhanced CT images using Artificial Intelligence Kit software. Then, multivariate logistic regression analysis based on selected texture features was employed to distinguish BA from AIS/MIA. Receiver operating characteristics curves were performed to determine the diagnostic performance of the features.Results: By comparison with AIS/MIA, significantly different CT imaging features of BA included nodule type, tumor size, and pseudo-cavitation sign. Among them, pseudo-cavitation sign had a moderate diagnostic value for distinguishing BA and AIS/MIA (AUC: 0.741 and 0.708, respectively). Further, a total of 396 quantitative texture features were extracted. After comparation, the top six texture features showing the most significant difference between BA and AIS or MIA were chosen. The ROC results showed that these key texture features had a high diagnostic value for differentiating BA from AIS or MIA, among which the value of a comprehensive model with six selected texture features was the highest (AUC: 0.977 or 0.976, respectively) for BA and AIS or MIA. These results indicated that texture analyses can effectively improve the efficacy of thin-section unenhanced CT for discriminating BA from AIS/MIA.Conclusion: CT texture analysis can effectively improve the efficacy of thin-section unenhanced CT for discriminating BA from AIS/MIA, which has a potential clinical value and helps pathologist and clinicians to make diagnostic and therapeutic strategies.

Development and Validation a Nomogram Incorporating CT Radiomics Signatures and Radiological Features for Differentiating Invasive Adenocarcinoma From Adenocarcinoma In Situ and Minimally Invasive Adenocarcinoma Presenting as Ground-Glass Nodules Measuring 5-10mm in Diameter

PurposeTo develop and validate a nomogram for differentiating invasive adenocarcinoma (IAC) from adenocarcinoma in situ (AIS) and minimally invasive adenocarcinoma (MIA) presenting as ground-glass nodules (GGNs) measuring 5-10mm in diameter.Materials and MethodsThis retrospective study included 446 patients with 478 GGNs histopathologically confirmed AIS, MIA or IAC. These patients were assigned to a primary cohort, an internal validation cohort and an external validation cohort. The segmentation of these GGNs on thin-slice computed tomography (CT) were performed semi-automatically with in-house software. Radiomics features were then extracted from unenhanced CT images with PyRadiomics. Radiological features of these GGNs were also collected. Radiomics features were investigated for usefulness in building radiomics signatures by spearman correlation analysis, minimum redundancy maximum relevance (mRMR) feature ranking method and least absolute shrinkage and selection operator (LASSO) classifier. Multivariable logistic regression analysis was used to develop a nomogram incorporating the radiomics signature and radiological features. The performance of the nomogram was assessed with discrimination, calibration, clinical usefulness and evaluated on the validation cohorts.ResultsFive radiomics features remained after features selection. The model incorporating radiomics signatures and four radiological features (bubble-like appearance, tumor-lung interface, mean CT value, average diameter) showed good calibration and good discrimination with AUC of 0.831(95%CI, 0.772~0.890). Application of the nomogram in the internal validation cohort with AUC of 0.792 (95%CI, 0.712~0.871) and in the external validation cohort with AUC of 0.833 (95%CI, 0.729-0.938) also indicated good calibration and good discrimination. The decision curve analysis demonstrated that the nomogram was clinically useful.ConclusionThis study presents a nomogram incorporating the radiomics signatures and radiological features, which can be used to predict the risk of IAC in patients with GGNs measuring 5-10mm in diameter individually.