Literature DB >> 29329732

Changes in quantitative CT image features of ground-glass nodules in differentiating invasive pulmonary adenocarcinoma from benign and in situ lesions: histopathological comparisons.

Y P Zhang1, M A Heuvelmans2, H Zhang1, M Oudkerk2, G X Zhang1, X Q Xie3.   

Abstract

AIM: To evaluate progressive changes in quantitative CT features of the non-solid component of ground-glass nodules (GGNs) from baseline to follow-up to differentiate invasive (minimally invasive adenocarcinoma [MIA] and invasive adenocarcinoma [IA]) GGNs from benign or pre-invasive (adenocarcinoma in situ [AIS]) lesions.
MATERIALS AND METHODS: Patients with a GGN detected at baseline and follow-up computed tomography (CT), examined by tissue sampling were included in the study. The diameter and mean, maximum, minimum CT density and density deviation from the non-solid component of whole GGNs were measured. Progression of these features over time was analysed by linear regression analysis. Multivariate receiver operating characteristics analyses of combined measures created by a logistic regression model were performed to evaluate diagnostic performance for invasive GGNs.
RESULTS: Sixty-one patients (24 males) with 70 GGNs were included. Fifteen GGNs were benign, six AIS, 38 MIA, and 11 IA. The mean diameter of all histological subtypes increased from baseline to follow-up, the largest increase was found in the MIA group (p<0.001). For MIA and IA, the mean, maximum, minimum density, and density deviation had a positive correlation over time, whilst benign and pre-invasive GGNs showed a negative correlation for these features. A diagnostic model based on three GGN features (increasing in diameter, mean density, and density deviation) identified invasive GGNs with a sensitivity, specificity and area under the receiver operating characteristic (ROC) curve (AUC) of 83.7%, 61.9%, and 0.786, respectively (p<0.001).
CONCLUSION: In GGN follow-up, the diameter of benign and AIS, and invasive GGNs significantly increased. Additional analysis of mean density and density deviation in the non-solid component may help to identify invasive GGNs.
Copyright © 2017 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.

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Year:  2018        PMID: 29329732     DOI: 10.1016/j.crad.2017.12.011

Source DB:  PubMed          Journal:  Clin Radiol        ISSN: 0009-9260            Impact factor:   2.350


  8 in total

1.  A deep residual learning network for predicting lung adenocarcinoma manifesting as ground-glass nodule on CT images.

Authors:  Jing Gong; Jiyu Liu; Wen Hao; Shengdong Nie; Bin Zheng; Shengping Wang; Weijun Peng
Journal:  Eur Radiol       Date:  2019-12-06       Impact factor: 5.315

2.  The combined nomogram based on the CT features may be used as a complementary method of frozen sections to predict invasive lung adenocarcinoma manifesting as ground-glass nodules.

Authors:  Yangyang Sun; Bin Wang; Ke Bi; Xue Meng; Lei Zhang; Xiwen Sun
Journal:  J Thorac Dis       Date:  2020-05       Impact factor: 2.895

3.  Incidental extensive adenocarcinoma in lungs explanted from a transplant recipient with an idiopathic pulmonary fibrosis flare-up: A clinical dilemma.

Authors:  Pradnya D Patil; Samir Sultan; M Frances Hahn; Sreeja Biswas Roy; Mitchell D Ross; Hesham Abdelrazek; Ross M Bremner; Nitika Thawani; Rajat Walia; Tanmay S Panchabhai
Journal:  Respir Med Case Rep       Date:  2018-06-12

4.  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.

Authors:  Lili Shi; Weiya Shi; Xueqing Peng; Yi Zhan; Linxiao Zhou; Yunpeng Wang; Mingxiang Feng; Jinli Zhao; Fei Shan; Lei Liu
Journal:  Front Oncol       Date:  2021-04-21       Impact factor: 6.244

5.  CT-Assisted Improvements in the Accuracy of the Intraoperative Frozen Section Examination of Ground-Glass Density Nodules.

Authors:  Wang Xinli; Sun Xiaoshuang; Yan Chengxin; Zhang Qiang
Journal:  Comput Math Methods Med       Date:  2022-01-07       Impact factor: 2.238

6.  Classification of moving coronary calcified plaques based on motion artifacts using convolutional neural networks: a robotic simulating study on influential factors.

Authors:  Magdalena Dobrolińska; Niels van der Werf; Marcel Greuter; Beibei Jiang; Riemer Slart; Xueqian Xie
Journal:  BMC Med Imaging       Date:  2021-10-19       Impact factor: 1.930

7.  High versus low attenuation thresholds to determine the solid component of ground-glass opacity nodules.

Authors:  Jae Ho Lee; Tae Hoon Kim; Sungsoo Lee; Kyunghwa Han; Min Kwang Byun; Yoon Soo Chang; Hyung Jung Kim; Geun Dong Lee; Chul Hwan Park
Journal:  PLoS One       Date:  2018-10-18       Impact factor: 3.240

8.  Comparison of Comprehensive Morphological and Radiomics Features of Subsolid Pulmonary Nodules to Distinguish Minimally Invasive Adenocarcinomas and Invasive Adenocarcinomas in CT Scan.

Authors:  Lu Qiu; Xiuping Zhang; Haixia Mao; Xiangming Fang; Wei Ding; Lun Zhao; Hongwei Chen
Journal:  Front Oncol       Date:  2022-01-04       Impact factor: 6.244

  8 in total

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