Computed tomography-based radiomics machine learning models for prediction of histological invasiveness with sub-centimeter subsolid pulmonary nodules: a retrospective study
To improve the accuracy of preoperative diagnoses and avoid over- or undertreatment, we aimed to develop and compare computed tomography-based radiomics machine learning models for the prediction of histological invasiveness using sub-centimeter subsolid pulmonary nodules. Three predictive models ba...
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| Format: | Article |
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PeerJ Inc.
2023-01-01
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| Online Access: | https://peerj.com/articles/14559.pdf |
| _version_ | 1827602512873521152 |
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| author | Haochuan Zhang Shixiong Wang Zhenkai Deng Yangli Li Yingying Yang He Huang |
| author_facet | Haochuan Zhang Shixiong Wang Zhenkai Deng Yangli Li Yingying Yang He Huang |
| author_sort | Haochuan Zhang |
| collection | DOAJ |
| description | To improve the accuracy of preoperative diagnoses and avoid over- or undertreatment, we aimed to develop and compare computed tomography-based radiomics machine learning models for the prediction of histological invasiveness using sub-centimeter subsolid pulmonary nodules. Three predictive models based on radiomics were built using three machine learning classifiers to discriminate the invasiveness of the sub-centimeter subsolid pulmonary nodules. A total of 203 sub-centimeter nodules from 177 patients were collected and assigned randomly to the training set (n = 143) or test set (n = 60). The areas under the curve of the predictive models were 0.743 (95% confidence interval CI [0.661–0.824]) for the logistic regression, 0.828 (95% CI [0.76–0.896]) for the support vector machine, and 0.917 (95% CI [0.869–0.965]) for the XGBoost classifier models in the training set, and 0.803 (95% CI [0.694–0.913]), 0.726 (95% CI [0.598–0.854]), and 0.874 (95% CI [0.776–0.972]) in the test set, respectively. In addition, the decision curve showed that the XGBoost model added more net benefit within the range of 0.06 to 0.93. |
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| institution | Directory Open Access Journal |
| issn | 2167-8359 |
| language | English |
| last_indexed | 2024-03-09T05:20:28Z |
| publishDate | 2023-01-01 |
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| spelling | doaj.art-bd39b3135c984c6996d9238d46a66ac12023-12-03T12:41:47ZengPeerJ Inc.PeerJ2167-83592023-01-0111e1455910.7717/peerj.14559Computed tomography-based radiomics machine learning models for prediction of histological invasiveness with sub-centimeter subsolid pulmonary nodules: a retrospective studyHaochuan ZhangShixiong WangZhenkai DengYangli LiYingying YangHe HuangTo improve the accuracy of preoperative diagnoses and avoid over- or undertreatment, we aimed to develop and compare computed tomography-based radiomics machine learning models for the prediction of histological invasiveness using sub-centimeter subsolid pulmonary nodules. Three predictive models based on radiomics were built using three machine learning classifiers to discriminate the invasiveness of the sub-centimeter subsolid pulmonary nodules. A total of 203 sub-centimeter nodules from 177 patients were collected and assigned randomly to the training set (n = 143) or test set (n = 60). The areas under the curve of the predictive models were 0.743 (95% confidence interval CI [0.661–0.824]) for the logistic regression, 0.828 (95% CI [0.76–0.896]) for the support vector machine, and 0.917 (95% CI [0.869–0.965]) for the XGBoost classifier models in the training set, and 0.803 (95% CI [0.694–0.913]), 0.726 (95% CI [0.598–0.854]), and 0.874 (95% CI [0.776–0.972]) in the test set, respectively. In addition, the decision curve showed that the XGBoost model added more net benefit within the range of 0.06 to 0.93.https://peerj.com/articles/14559.pdfRadiomicsMachine learningCT imagesSub-centimeter subsolid pulmonary nodulesInvasiveness |
| spellingShingle | Haochuan Zhang Shixiong Wang Zhenkai Deng Yangli Li Yingying Yang He Huang Computed tomography-based radiomics machine learning models for prediction of histological invasiveness with sub-centimeter subsolid pulmonary nodules: a retrospective study PeerJ Radiomics Machine learning CT images Sub-centimeter subsolid pulmonary nodules Invasiveness |
| title | Computed tomography-based radiomics machine learning models for prediction of histological invasiveness with sub-centimeter subsolid pulmonary nodules: a retrospective study |
| title_full | Computed tomography-based radiomics machine learning models for prediction of histological invasiveness with sub-centimeter subsolid pulmonary nodules: a retrospective study |
| title_fullStr | Computed tomography-based radiomics machine learning models for prediction of histological invasiveness with sub-centimeter subsolid pulmonary nodules: a retrospective study |
| title_full_unstemmed | Computed tomography-based radiomics machine learning models for prediction of histological invasiveness with sub-centimeter subsolid pulmonary nodules: a retrospective study |
| title_short | Computed tomography-based radiomics machine learning models for prediction of histological invasiveness with sub-centimeter subsolid pulmonary nodules: a retrospective study |
| title_sort | computed tomography based radiomics machine learning models for prediction of histological invasiveness with sub centimeter subsolid pulmonary nodules a retrospective study |
| topic | Radiomics Machine learning CT images Sub-centimeter subsolid pulmonary nodules Invasiveness |
| url | https://peerj.com/articles/14559.pdf |
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