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

2021 ◽  
Vol 11 ◽  
Author(s):  
Jinju Sun ◽  
Kaijun Liu ◽  
Haipeng Tong ◽  
Huan Liu ◽  
Xiaoguang Li ◽  
...  

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.

2017 ◽  
Vol 26 (1) ◽  
pp. 4-11 ◽  
Author(s):  
Wei Zhao ◽  
Hui Wang ◽  
Jun Xie ◽  
Bo Tian

Background. The aim of this study was to assess the prognostic significance of the newly proposed 2015 World Health Organization (WHO) lung adenocarcinoma classification for patients undergoing resection for small (≤1 cm) lung adenocarcinoma. We also investigated whether lobectomy offers prognostic advantage over limited resection for this category of tumors. Methods. A retrospective study of resected pulmonary adenocarcinomas (n = 83) in sizes 1 cm or less was carried out in which comprehensive histologic subtyping was assessed according to the 2015 WHO classification on all consecutive patients who underwent lobectomy or limited resection between 1998 and 2012. Correlation between clinicopathologic parameters and the difference in recurrence between lobectomy and limited resection group was evaluated. Results. Our data show that the proposed 2015 WHO classification identifies histological subsets of small lung adenocarcinomas with significant differences in prognosis. No recurrence was noted for patients with adenocarcinoma in situ and minimally invasive adenocarcinoma. Invasive adenocarcinomas displayed high heterogeneity and the presence of micropapillary component of 5% or greater in adenocarcinomas was significantly related to lymph node involvement and recurrence ( P < .001). Stage IA patients who underwent limited resection had a higher risk of recurrence than did those treated by lobectomy ( P < .05). Conclusions. Application of the 2015 WHO classification identifies patients with adenocarcinoma in situ and minimally invasive adenocarcinoma had excellent prognosis. Micropapillary pattern was associated with high risk of lymph node metastasis and recurrence.


2017 ◽  
Vol 43 (7) ◽  
pp. 1756-1763 ◽  
Author(s):  
Stacy D. O’Connor ◽  
Stuart G. Silverman ◽  
Laila R. Cochon ◽  
Ramin K. Khorasani

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