Chen Gao1, Ping Xiang1, Jianfeng Ye1, Peipei Pang2, Shiwei Wang1, Maosheng Xu3. 1. The First Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, China; Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China. 2. GE Healthcare Life Sciences, Hangzhou, China. 3. The First Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, China; Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China. Electronic address: xums166@zcmu.edu.cn.
Abstract
OBJECTIVES: To investigate the validity and efficacy of comparing texture features from contrast-enhanced images with non-enhanced images in identifying infiltrative lung adenocarcinoma represented as ground glass nodules (GGN). MATERIALS AND METHODS: A retrospective cohort study was conducted with patients presenting with lung adenocarcinoma and treated at a single centre between January 2015 to December 2017. All patients underwent standard and contrast-enhanced thoracic CT scans with 0.5 mm collimation and 1 mm slice reconstruction thickness before surgery. A total of 34 lung adenocarcinoma patients (representing 34 lesions) were analysed; including 21 instances of invasive adenocarcinoma (IAC) lesions, 4 instances of adenocarcinoma in situ (AIS) lesions, and 9 minimally invasive adenocarcinoma (MIA) lesions. After radiologists manually segmented the lesions, texture features were quantitatively extracted using Artificial Intelligence Kit (AK) software. Then, multivariate logistic regression analysis based on standard and contrast-enhanced CT texture features was employed to analyse the invasiveness of lung adenocarcinoma lesions appearing as GGNs. A receiver operating characteristic (ROC) curve analysis was used to evaluate the performance of those models. RESULTS: A total of 21 quantitative texture features were extracted using the AK software. After dimensionality reduction, 5 and 3 features extracted from thin-section unenhanced and contrast-enhanced CT, respectively, were used to establish the model. The area under the ROC curve (AUC) values for unenhanced CT and enhanced CT features were 0.890 and 0.868, respectively. There was no significant difference (P = 0.190) in the AUC between models based on non-enhanced and contrast-enhanced CT texture features. CONCLUSION: Compared with unenhanced CT, texture features extracted from contrast-enhanced CT provided no benefit in improving the differential diagnosis of infiltrative lung adenocarcinoma from non-infiltrative malignancies appearing as GGNs.
OBJECTIVES: To investigate the validity and efficacy of comparing texture features from contrast-enhanced images with non-enhanced images in identifying infiltrative lung adenocarcinoma represented as ground glass nodules (GGN). MATERIALS AND METHODS: A retrospective cohort study was conducted with patients presenting with lung adenocarcinoma and treated at a single centre between January 2015 to December 2017. All patients underwent standard and contrast-enhanced thoracic CT scans with 0.5 mm collimation and 1 mm slice reconstruction thickness before surgery. A total of 34 lung adenocarcinomapatients (representing 34 lesions) were analysed; including 21 instances of invasive adenocarcinoma (IAC) lesions, 4 instances of adenocarcinoma in situ (AIS) lesions, and 9 minimally invasive adenocarcinoma (MIA) lesions. After radiologists manually segmented the lesions, texture features were quantitatively extracted using Artificial Intelligence Kit (AK) software. Then, multivariate logistic regression analysis based on standard and contrast-enhanced CT texture features was employed to analyse the invasiveness of lung adenocarcinoma lesions appearing as GGNs. A receiver operating characteristic (ROC) curve analysis was used to evaluate the performance of those models. RESULTS: A total of 21 quantitative texture features were extracted using the AK software. After dimensionality reduction, 5 and 3 features extracted from thin-section unenhanced and contrast-enhanced CT, respectively, were used to establish the model. The area under the ROC curve (AUC) values for unenhanced CT and enhanced CT features were 0.890 and 0.868, respectively. There was no significant difference (P = 0.190) in the AUC between models based on non-enhanced and contrast-enhanced CT texture features. CONCLUSION: Compared with unenhanced CT, texture features extracted from contrast-enhanced CT provided no benefit in improving the differential diagnosis of infiltrative lung adenocarcinoma from non-infiltrative malignancies appearing as GGNs.
Authors: Liliana Caldeira; Thorsten Persigehl; Simon Lennartz; Alina Mager; Nils Große Hokamp; Sebastian Schäfer; David Zopfs; David Maintz; Hans Christian Reinhardt; Roman K Thomas Journal: Cancer Imaging Date: 2021-01-26 Impact factor: 3.909
Authors: Ke Sun; Shouyu Chen; Jiabi Zhao; Bin Wang; Yang Yang; Yin Wang; Chunyan Wu; Xiwen Sun Journal: Front Oncol Date: 2021-12-21 Impact factor: 6.244