Application of CT Radiomics in Predicting Differentiation Level of Lung Adenocarcinoma
ObjectiveTo investigate the value of prediction of the differentiation level in lung adenocarcinoma based on CT radiomics model. MethodsData from 507 patients with postoperative pathological confirmed lung adenocarcinoma and clearly defined differentiation level of lung adenocarcinoma were retrospec...
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| Format: | Article |
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Editorial Office of Chinese Journal of Medical Instrumentation
2024-11-01
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| Series: | Zhongguo yiliao qixie zazhi |
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| Online Access: | https://zgylqxzz.xml-journal.net/article/doi/10.12455/j.issn.1671-7104.240152 |
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| _version_ | 1850175017441034240 |
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| author | Shuai ZHANG Peng HAN Suya ZHANG Dingli YE Zhicheng HUANG |
| author_facet | Shuai ZHANG Peng HAN Suya ZHANG Dingli YE Zhicheng HUANG |
| author_sort | Shuai ZHANG |
| collection | DOAJ |
| description | ObjectiveTo investigate the value of prediction of the differentiation level in lung adenocarcinoma based on CT radiomics model. MethodsData from 507 patients with postoperative pathological confirmed lung adenocarcinoma and clearly defined differentiation level of lung adenocarcinoma were retrospective analyzed. The enrolled cases were divided into poorly differentiation group and moderate-to-high differentiation group based on the grading criteria. CT image features were extracted, and seven machine learning algorithms were used to construct prediction models to obtain the AUC, accuracy, specificity, and sensitivity. ResultsThe poorly differentiation group consisted of 175 cases, while the moderate-to-high differentiation group had 332 cases. The XGBoost model demonstrated the best performance, with the AUC, accuracy, specificity, and sensitivity of this model on the validation set being 0.878, 0.829, 0.667, and 0.727, respectively. ConclusionCT radiomics model can effectively predict the differentiation level of poorly differentiation and moderate-to-high differentiation in lung adenocarcinoma. |
| format | Article |
| id | doaj-art-fb40fc8444bd43aca608f81fb4d09633 |
| institution | OA Journals |
| issn | 1671-7104 |
| language | zho |
| publishDate | 2024-11-01 |
| publisher | Editorial Office of Chinese Journal of Medical Instrumentation |
| record_format | Article |
| series | Zhongguo yiliao qixie zazhi |
| spelling | doaj-art-fb40fc8444bd43aca608f81fb4d096332025-08-20T02:19:33ZzhoEditorial Office of Chinese Journal of Medical InstrumentationZhongguo yiliao qixie zazhi1671-71042024-11-0148659159410.12455/j.issn.1671-7104.2401522024-0152Application of CT Radiomics in Predicting Differentiation Level of Lung AdenocarcinomaShuai ZHANG0Peng HAN1Suya ZHANG2Dingli YE3Zhicheng HUANG4Department of Radiology, Jilin Cancer Hospital, Changchun, 130021Department of Radiology, Jilin Cancer Hospital, Changchun, 130021Department of Radiology, Jilin Cancer Hospital, Changchun, 130021Department of Radiology, Jilin Cancer Hospital, Changchun, 130021Department of Radiology, Jilin Cancer Hospital, Changchun, 130021ObjectiveTo investigate the value of prediction of the differentiation level in lung adenocarcinoma based on CT radiomics model. MethodsData from 507 patients with postoperative pathological confirmed lung adenocarcinoma and clearly defined differentiation level of lung adenocarcinoma were retrospective analyzed. The enrolled cases were divided into poorly differentiation group and moderate-to-high differentiation group based on the grading criteria. CT image features were extracted, and seven machine learning algorithms were used to construct prediction models to obtain the AUC, accuracy, specificity, and sensitivity. ResultsThe poorly differentiation group consisted of 175 cases, while the moderate-to-high differentiation group had 332 cases. The XGBoost model demonstrated the best performance, with the AUC, accuracy, specificity, and sensitivity of this model on the validation set being 0.878, 0.829, 0.667, and 0.727, respectively. ConclusionCT radiomics model can effectively predict the differentiation level of poorly differentiation and moderate-to-high differentiation in lung adenocarcinoma.https://zgylqxzz.xml-journal.net/article/doi/10.12455/j.issn.1671-7104.240152lung adenocarcinomatumour differentiationct imagingfeature extractionmachine learning |
| spellingShingle | Shuai ZHANG Peng HAN Suya ZHANG Dingli YE Zhicheng HUANG Application of CT Radiomics in Predicting Differentiation Level of Lung Adenocarcinoma Zhongguo yiliao qixie zazhi lung adenocarcinoma tumour differentiation ct imaging feature extraction machine learning |
| title | Application of CT Radiomics in Predicting Differentiation Level of Lung Adenocarcinoma |
| title_full | Application of CT Radiomics in Predicting Differentiation Level of Lung Adenocarcinoma |
| title_fullStr | Application of CT Radiomics in Predicting Differentiation Level of Lung Adenocarcinoma |
| title_full_unstemmed | Application of CT Radiomics in Predicting Differentiation Level of Lung Adenocarcinoma |
| title_short | Application of CT Radiomics in Predicting Differentiation Level of Lung Adenocarcinoma |
| title_sort | application of ct radiomics in predicting differentiation level of lung adenocarcinoma |
| topic | lung adenocarcinoma tumour differentiation ct imaging feature extraction machine learning |
| url | https://zgylqxzz.xml-journal.net/article/doi/10.12455/j.issn.1671-7104.240152 |
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