Peritumoral features for assessing invasiveness of lung adenocarcinoma manifesting as ground-glass nodules
Abstract We evaluated the predictive value of radiomics features from different peritumoral ranges for the invasiveness of ground-glass nodular lung adenocarcinoma using various machine learning models. This retrospective study included 317 patients with 323 ground-glass nodules diagnosed as minimal...
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Nature Portfolio
2025-04-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-99180-9 |
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| author | Xiao Wang Hui Xue Wei Ding Fei Huang Yu Zhang Xin Pang |
| author_facet | Xiao Wang Hui Xue Wei Ding Fei Huang Yu Zhang Xin Pang |
| author_sort | Xiao Wang |
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| description | Abstract We evaluated the predictive value of radiomics features from different peritumoral ranges for the invasiveness of ground-glass nodular lung adenocarcinoma using various machine learning models. This retrospective study included 317 patients with 323 ground-glass nodules diagnosed as minimally invasive adenocarcinoma (MIA) or invasive adenocarcinoma (IAC) at Benxi Central Hospital (January 2019–December 2023). Radiomic features from tumor margins of 1, 2, 3, 4, and 5 mm were extracted. Eight machine learning models were constructed following dimensionality reduction. The models were evaluated using receiver operating characteristic curves. All models had area under the curve values > 0.75, effectively distinguishing between MIA and IAC. Only the decision tree model showed statistically significant differences (P < 0.05); no differences were found between the other models (P > 0.05). In the training set, the 1-mm margin model achieved the highest ranking, followed by the 2-, 4-, 5-, and 3-mm models. In the validation group, the 3-mm margin model ranked the highest, followed by the 2-, 1-, 4-, and 5-mm models, with no statistically significant differences (P > 0.05). All machine learning models demonstrated good predictive performance for both MIA and IAC. Radiomic features from 1 to 5-mm margins showed strong predictive value, though no optimal margin range was identified. |
| format | Article |
| id | doaj-art-e848928e7b824e648a30ff8d18e8bb04 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
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| spelling | doaj-art-e848928e7b824e648a30ff8d18e8bb042025-08-20T03:13:54ZengNature PortfolioScientific Reports2045-23222025-04-0115111210.1038/s41598-025-99180-9Peritumoral features for assessing invasiveness of lung adenocarcinoma manifesting as ground-glass nodulesXiao Wang0Hui Xue1Wei Ding2Fei Huang3Yu Zhang4Xin Pang5Department of Radiology, Benxi Central HospitalDepartment of Ultrasonography, Benxi Central HospitalDepartment of Radiology, Benxi Central HospitalDepartment of Radiology, Liaoyang Third People’s HospitalDepartment of Radiology, Benxi Central HospitalDepartment of Radiology, Benxi Central HospitalAbstract We evaluated the predictive value of radiomics features from different peritumoral ranges for the invasiveness of ground-glass nodular lung adenocarcinoma using various machine learning models. This retrospective study included 317 patients with 323 ground-glass nodules diagnosed as minimally invasive adenocarcinoma (MIA) or invasive adenocarcinoma (IAC) at Benxi Central Hospital (January 2019–December 2023). Radiomic features from tumor margins of 1, 2, 3, 4, and 5 mm were extracted. Eight machine learning models were constructed following dimensionality reduction. The models were evaluated using receiver operating characteristic curves. All models had area under the curve values > 0.75, effectively distinguishing between MIA and IAC. Only the decision tree model showed statistically significant differences (P < 0.05); no differences were found between the other models (P > 0.05). In the training set, the 1-mm margin model achieved the highest ranking, followed by the 2-, 4-, 5-, and 3-mm models. In the validation group, the 3-mm margin model ranked the highest, followed by the 2-, 1-, 4-, and 5-mm models, with no statistically significant differences (P > 0.05). All machine learning models demonstrated good predictive performance for both MIA and IAC. Radiomic features from 1 to 5-mm margins showed strong predictive value, though no optimal margin range was identified.https://doi.org/10.1038/s41598-025-99180-9Computed tomographyGround-glass noduleLung adenocarcinomaRadiomics |
| spellingShingle | Xiao Wang Hui Xue Wei Ding Fei Huang Yu Zhang Xin Pang Peritumoral features for assessing invasiveness of lung adenocarcinoma manifesting as ground-glass nodules Scientific Reports Computed tomography Ground-glass nodule Lung adenocarcinoma Radiomics |
| title | Peritumoral features for assessing invasiveness of lung adenocarcinoma manifesting as ground-glass nodules |
| title_full | Peritumoral features for assessing invasiveness of lung adenocarcinoma manifesting as ground-glass nodules |
| title_fullStr | Peritumoral features for assessing invasiveness of lung adenocarcinoma manifesting as ground-glass nodules |
| title_full_unstemmed | Peritumoral features for assessing invasiveness of lung adenocarcinoma manifesting as ground-glass nodules |
| title_short | Peritumoral features for assessing invasiveness of lung adenocarcinoma manifesting as ground-glass nodules |
| title_sort | peritumoral features for assessing invasiveness of lung adenocarcinoma manifesting as ground glass nodules |
| topic | Computed tomography Ground-glass nodule Lung adenocarcinoma Radiomics |
| url | https://doi.org/10.1038/s41598-025-99180-9 |
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