Xianghua Hu1,2, Weichuan Ye1,2, Zhongxue Li3, Chunmiao Chen1,2, Shimiao Cheng1, Xiuling Lv1,2, Wei Weng1,2, Jie Li1,2, Qiaoyou Weng1, Peipei Pang4, Min Xu1,2, Minjiang Chen1,2, Jiansong Ji1,2. 1. Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, the Fifth Affiliated Hospital of Wenzhou Medical University /Affiliated Lishui Hospital of Zhejiang University/ The Central Hospital of Zhejiang Lishui, Lishui 323000, China. 2. Department of Radiology, the Fifth Affiliated Hospital of Wenzhou Medical University /Affiliated Lishui Hospital of Zhejiang University/ The Central Hospital of Zhejiang Lishui, Lishui 323000, China. 3. Department of Radiology, Fuyuan Hospital of Yiwu, Jinhua 321000, China. 4. GE Healthcare, Hangzhou 310000, China.
Abstract
OBJECTIVES: To investigate potential diagnostic model for predicting benign or malignant status of subcentimeter pulmonary ground-glass nodules (SPGGNs) (≤1 cm) based on CT texture analysis. METHODS: A total of 89 SPGGNs from 89 patients were included; 51 patients were diagnosed with adenocarcinoma, and 38 were diagnosed with inflamed or infected benign SPGGNs. Analysis Kit software was used to manually delineate the volume of interest of lesions and extract a total of 396 quantitative texture parameters. The statistical analysis was performed using R software. The SPGGNs were randomly divided into a training set (n = 59) and a validation set (n = 30). All pre-normalized (Z-score) feature values were subjected to dimension reduction using the LASSO algorithm,and the most useful features in the training set were selected. The selected imaging features were then combined into a Rad-score, which was further assessed by ROC curve analysis in the training and validation sets. RESULTS: Four characteristic parameters (ClusterShade_AllDirection_offset4_SD, ShortRunEmphasis_angle45_offset1, Maximum3DDiameter, SurfaceVolumeRatio) were further selected by LASSO (p < 0.05). As a cluster of imaging biomarkers, the above four parameters were used to form the Rad-score. The AUC for differentiating between benign and malignant SPGGNs in the training set was 0.792 (95% CI: 0.671, 0.913), and the sensitivity and specificity were 86.10 and 65.20%, respectively. The AUC in the validation set was 72.9% (95% CI: 0.545, 0.913), and the sensitivity and specificity were 86.70 and 60%, respectively. CONCLUSION: The present diagnostic model based on the cluster of imaging biomarkers can preferably distinguish benign and malignant SPGGNs (≤1 cm). ADVANCES IN KNOWLEDGE: Texture analysis based on CT images provide a new and credible technique for accurate identification of subcentimeter pulmonary ground-glass nodules.
OBJECTIVES: To investigate potential diagnostic model for predicting benign or malignant status of subcentimeter pulmonary ground-glass nodules (SPGGNs) (≤1 cm) based on CT texture analysis. METHODS: A total of 89 SPGGNs from 89 patients were included; 51 patients were diagnosed with adenocarcinoma, and 38 were diagnosed with inflamed or infected benign SPGGNs. Analysis Kit software was used to manually delineate the volume of interest of lesions and extract a total of 396 quantitative texture parameters. The statistical analysis was performed using R software. The SPGGNs were randomly divided into a training set (n = 59) and a validation set (n = 30). All pre-normalized (Z-score) feature values were subjected to dimension reduction using the LASSO algorithm,and the most useful features in the training set were selected. The selected imaging features were then combined into a Rad-score, which was further assessed by ROC curve analysis in the training and validation sets. RESULTS: Four characteristic parameters (ClusterShade_AllDirection_offset4_SD, ShortRunEmphasis_angle45_offset1, Maximum3DDiameter, SurfaceVolumeRatio) were further selected by LASSO (p < 0.05). As a cluster of imaging biomarkers, the above four parameters were used to form the Rad-score. The AUC for differentiating between benign and malignant SPGGNs in the training set was 0.792 (95% CI: 0.671, 0.913), and the sensitivity and specificity were 86.10 and 65.20%, respectively. The AUC in the validation set was 72.9% (95% CI: 0.545, 0.913), and the sensitivity and specificity were 86.70 and 60%, respectively. CONCLUSION: The present diagnostic model based on the cluster of imaging biomarkers can preferably distinguish benign and malignant SPGGNs (≤1 cm). ADVANCES IN KNOWLEDGE: Texture analysis based on CT images provide a new and credible technique for accurate identification of subcentimeter pulmonary ground-glass nodules.
Authors: Hiren J Mehta; James G Ravenel; Stephanie R Shaftman; Nichole T Tanner; Luca Paoletti; Katherine K Taylor; Martin C Tammemagi; Mario Gomez; Paul J Nietert; Michael K Gould; Gerard A Silvestri Journal: Chest Date: 2014-03-01 Impact factor: 9.410
Authors: Denise R Aberle; Amanda M Adams; Christine D Berg; William C Black; Jonathan D Clapp; Richard M Fagerstrom; Ilana F Gareen; Constantine Gatsonis; Pamela M Marcus; JoRean D Sicks Journal: N Engl J Med Date: 2011-06-29 Impact factor: 91.245
Authors: Sanjay Gupta; Savitri Krishnamurthy; Lyle D Broemeling; Frank A Morello; Michael J Wallace; Kamran Ahrar; David C Madoff; Ravi Murthy; Marshall E Hicks Journal: Radiology Date: 2005-02 Impact factor: 11.105
Authors: Hee-Dong Chae; Chang Min Park; Sang Joon Park; Sang Min Lee; Kwang Gi Kim; Jin Mo Goo Journal: Radiology Date: 2014-08-01 Impact factor: 11.105
Authors: Dekel Shlomi; Nir Peled; Yehuda A Schwarz; Guy W Soo Hoo; Raj K Batra; Gershon Fink; Tal Kaplan; Lahav Cohen; Scott Mollan; William R Burfeind Journal: Cancer Genet Date: 2018-05-10
Authors: Jun Shen; Ziling Liu; Nevins W Todd; Howard Zhang; Jipei Liao; Lei Yu; Maria A Guarnera; Ruiyun Li; Ling Cai; Min Zhan; Feng Jiang Journal: BMC Cancer Date: 2011-08-24 Impact factor: 4.430