A study on the differential of solid lung adenocarcinoma and tuberculous granuloma nodules in CT images by Radiomics machine learning
Abstract To study the classification efficiency of using texture feature machine learning method in distinguishing solid lung adenocarcinoma (SADC) and tuberculous granulomatous nodules (TGN) that appear as solid nodules (SN) in non-enhanced CT images. 200 patients with SADC and TGN who underwent th...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-04-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-32979-6 |
| _version_ | 1827966325502246912 |
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| author | Huibin Tan Ye Wang Yuanliang Jiang Hanhan Li Tao You Tingting Fu Jiaheng Peng Yuxi Tan Ran Lu Biwen Peng Wencai Huang Fei Xiong |
| author_facet | Huibin Tan Ye Wang Yuanliang Jiang Hanhan Li Tao You Tingting Fu Jiaheng Peng Yuxi Tan Ran Lu Biwen Peng Wencai Huang Fei Xiong |
| author_sort | Huibin Tan |
| collection | DOAJ |
| description | Abstract To study the classification efficiency of using texture feature machine learning method in distinguishing solid lung adenocarcinoma (SADC) and tuberculous granulomatous nodules (TGN) that appear as solid nodules (SN) in non-enhanced CT images. 200 patients with SADC and TGN who underwent thoracic non-enhanced CT examination from January 2012 to October 2019 were included in the study, 490 texture eigenvalues of 6 categories were extracted from the lesions in the non-enhanced CT images of these patients for machine learning, the classification prediction model is established by using relatively the best classifier selected according to the fitting degree of learning curve in the process of machine learning, and the effectiveness of the model was tested and verified. The logistic regression model of clinical data (including demographic data and CT parameters and CT signs of solitary nodules) was used for comparison. The prediction model of clinical data was established by logistic regression, and the classifier was established by machine learning of radiologic texture features. The area under the curve was 0.82 and 0.65 for the prediction model based on clinical CT and only CT parameters and CT signs, and 0.870 based on Radiomics characteristics. The machine learning prediction model developed by us can improve the differentiation efficiency of SADC and TGN with SN, and provide appropriate support for treatment decisions. |
| first_indexed | 2024-04-09T17:48:09Z |
| format | Article |
| id | doaj.art-5cc646e440da4ac9af37baea29722cba |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-09T17:48:09Z |
| publishDate | 2023-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-5cc646e440da4ac9af37baea29722cba2023-04-16T11:12:35ZengNature PortfolioScientific Reports2045-23222023-04-0113111210.1038/s41598-023-32979-6A study on the differential of solid lung adenocarcinoma and tuberculous granuloma nodules in CT images by Radiomics machine learningHuibin Tan0Ye Wang1Yuanliang Jiang2Hanhan Li3Tao You4Tingting Fu5Jiaheng Peng6Yuxi Tan7Ran Lu8Biwen Peng9Wencai Huang10Fei Xiong11Department of Radiology, The General Hospital of Central Theater Command, PLADepartment of Radiology, The General Hospital of Central Theater Command, PLADepartment of Radiology, The General Hospital of Central Theater Command, PLADepartment of Radiology, The General Hospital of Central Theater Command, PLADepartment of Radiology, The General Hospital of Central Theater Command, PLADepartment of Radiology, The General Hospital of Central Theater Command, PLASchool of Computer Science and Information Engineering, Hubei UniversityMedical School, Hubei Minzu UniversityDepartment of Radiology, The General Hospital of Central Theater Command, PLASchool of Basic Medical Sciences, Wuhan UniversityDepartment of Radiology, The General Hospital of Central Theater Command, PLADepartment of Radiology, The General Hospital of Central Theater Command, PLAAbstract To study the classification efficiency of using texture feature machine learning method in distinguishing solid lung adenocarcinoma (SADC) and tuberculous granulomatous nodules (TGN) that appear as solid nodules (SN) in non-enhanced CT images. 200 patients with SADC and TGN who underwent thoracic non-enhanced CT examination from January 2012 to October 2019 were included in the study, 490 texture eigenvalues of 6 categories were extracted from the lesions in the non-enhanced CT images of these patients for machine learning, the classification prediction model is established by using relatively the best classifier selected according to the fitting degree of learning curve in the process of machine learning, and the effectiveness of the model was tested and verified. The logistic regression model of clinical data (including demographic data and CT parameters and CT signs of solitary nodules) was used for comparison. The prediction model of clinical data was established by logistic regression, and the classifier was established by machine learning of radiologic texture features. The area under the curve was 0.82 and 0.65 for the prediction model based on clinical CT and only CT parameters and CT signs, and 0.870 based on Radiomics characteristics. The machine learning prediction model developed by us can improve the differentiation efficiency of SADC and TGN with SN, and provide appropriate support for treatment decisions.https://doi.org/10.1038/s41598-023-32979-6 |
| spellingShingle | Huibin Tan Ye Wang Yuanliang Jiang Hanhan Li Tao You Tingting Fu Jiaheng Peng Yuxi Tan Ran Lu Biwen Peng Wencai Huang Fei Xiong A study on the differential of solid lung adenocarcinoma and tuberculous granuloma nodules in CT images by Radiomics machine learning Scientific Reports |
| title | A study on the differential of solid lung adenocarcinoma and tuberculous granuloma nodules in CT images by Radiomics machine learning |
| title_full | A study on the differential of solid lung adenocarcinoma and tuberculous granuloma nodules in CT images by Radiomics machine learning |
| title_fullStr | A study on the differential of solid lung adenocarcinoma and tuberculous granuloma nodules in CT images by Radiomics machine learning |
| title_full_unstemmed | A study on the differential of solid lung adenocarcinoma and tuberculous granuloma nodules in CT images by Radiomics machine learning |
| title_short | A study on the differential of solid lung adenocarcinoma and tuberculous granuloma nodules in CT images by Radiomics machine learning |
| title_sort | study on the differential of solid lung adenocarcinoma and tuberculous granuloma nodules in ct images by radiomics machine learning |
| url | https://doi.org/10.1038/s41598-023-32979-6 |
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