Radiomics integration based on intratumoral and peritumoral computed tomography improves the diagnostic efficiency of invasiveness in patients with pure ground-glass nodules: a machine learning, cross-sectional, bicentric study
Abstract Background Radiomics has shown promise in the diagnosis and prognosis of lung cancer. Here, we investigated the performance of computed tomography-based radiomic features, extracted from gross tumor volume (GTV), peritumoral volume (PTV), and GTV + PTV (GPTV), for predicting the pathologica...
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BMC
2025-02-01
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| Series: | Journal of Cardiothoracic Surgery |
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| Online Access: | https://doi.org/10.1186/s13019-024-03289-3 |
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| author | Ying Zeng Jing Chen Shanyue Lin Haibo Liu Yingjun Zhou Xiao Zhou |
| author_facet | Ying Zeng Jing Chen Shanyue Lin Haibo Liu Yingjun Zhou Xiao Zhou |
| author_sort | Ying Zeng |
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| description | Abstract Background Radiomics has shown promise in the diagnosis and prognosis of lung cancer. Here, we investigated the performance of computed tomography-based radiomic features, extracted from gross tumor volume (GTV), peritumoral volume (PTV), and GTV + PTV (GPTV), for predicting the pathological invasiveness of pure ground-glass nodules present in lung adenocarcinoma. Methods This was a retrospective, cross-sectional, bicentric study with data collected from January 1, 2018, to June 1, 2022. We divided the dataset into a training cohort (n = 88) from one center and an external validation cohort (n = 59) from another center. Radiomic signatures (rad-scores) were obtained after features were selected through correlation and least absolute shrinkage and selection operator analysis. Three machine learning models, a support vector machine model, a random forest model, and a generalized linear model, were then applied to build radiomic models. Results Invasive adenocarcinoma had a higher rad-score (P<0.001) in the GTV and GPTV. The area under the curves (AUC) of GTV, PTV, and GPTV were 0.839, 0.809, and 0.855 in the training cohort and 0.755, 0.777, and 0.801 in the external validation cohort, respectively. The GPTV model had higher AUCs for predicting pathological invasiveness. The random forest model had the best validity and fit for the proposed machine learning approach, suggesting that it may be the most appropriate model. Conclusions GPTV had the highest diagnostic efficiency for predicting pathological invasiveness in patients with pure ground-grass nodules, and the random forest model outperformed other predictive models. |
| format | Article |
| id | doaj-art-7040e7d34ab6414580f2e1c338caee4b |
| institution | OA Journals |
| issn | 1749-8090 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | BMC |
| record_format | Article |
| series | Journal of Cardiothoracic Surgery |
| spelling | doaj-art-7040e7d34ab6414580f2e1c338caee4b2025-08-20T02:13:02ZengBMCJournal of Cardiothoracic Surgery1749-80902025-02-012011910.1186/s13019-024-03289-3Radiomics integration based on intratumoral and peritumoral computed tomography improves the diagnostic efficiency of invasiveness in patients with pure ground-glass nodules: a machine learning, cross-sectional, bicentric studyYing Zeng0Jing Chen1Shanyue Lin2Haibo Liu3Yingjun Zhou4Xiao Zhou5Department of Radiology, Xiangtan Central HospitalDepartment of Radiology, The Affiliated Hospital of Southwest Medical UniversityDepartment of Radiology, Affiliated Hospital of Guilin Medical UniversityDepartment of Radiology, Xiangtan Central HospitalDepartment of Radiology, Xiangtan Central HospitalDepartment of Radiology, Xiangtan Central HospitalAbstract Background Radiomics has shown promise in the diagnosis and prognosis of lung cancer. Here, we investigated the performance of computed tomography-based radiomic features, extracted from gross tumor volume (GTV), peritumoral volume (PTV), and GTV + PTV (GPTV), for predicting the pathological invasiveness of pure ground-glass nodules present in lung adenocarcinoma. Methods This was a retrospective, cross-sectional, bicentric study with data collected from January 1, 2018, to June 1, 2022. We divided the dataset into a training cohort (n = 88) from one center and an external validation cohort (n = 59) from another center. Radiomic signatures (rad-scores) were obtained after features were selected through correlation and least absolute shrinkage and selection operator analysis. Three machine learning models, a support vector machine model, a random forest model, and a generalized linear model, were then applied to build radiomic models. Results Invasive adenocarcinoma had a higher rad-score (P<0.001) in the GTV and GPTV. The area under the curves (AUC) of GTV, PTV, and GPTV were 0.839, 0.809, and 0.855 in the training cohort and 0.755, 0.777, and 0.801 in the external validation cohort, respectively. The GPTV model had higher AUCs for predicting pathological invasiveness. The random forest model had the best validity and fit for the proposed machine learning approach, suggesting that it may be the most appropriate model. Conclusions GPTV had the highest diagnostic efficiency for predicting pathological invasiveness in patients with pure ground-grass nodules, and the random forest model outperformed other predictive models.https://doi.org/10.1186/s13019-024-03289-3Computed tomographyPure ground-glass nodulesMachine learningRadiomicsInvasiveness |
| spellingShingle | Ying Zeng Jing Chen Shanyue Lin Haibo Liu Yingjun Zhou Xiao Zhou Radiomics integration based on intratumoral and peritumoral computed tomography improves the diagnostic efficiency of invasiveness in patients with pure ground-glass nodules: a machine learning, cross-sectional, bicentric study Journal of Cardiothoracic Surgery Computed tomography Pure ground-glass nodules Machine learning Radiomics Invasiveness |
| title | Radiomics integration based on intratumoral and peritumoral computed tomography improves the diagnostic efficiency of invasiveness in patients with pure ground-glass nodules: a machine learning, cross-sectional, bicentric study |
| title_full | Radiomics integration based on intratumoral and peritumoral computed tomography improves the diagnostic efficiency of invasiveness in patients with pure ground-glass nodules: a machine learning, cross-sectional, bicentric study |
| title_fullStr | Radiomics integration based on intratumoral and peritumoral computed tomography improves the diagnostic efficiency of invasiveness in patients with pure ground-glass nodules: a machine learning, cross-sectional, bicentric study |
| title_full_unstemmed | Radiomics integration based on intratumoral and peritumoral computed tomography improves the diagnostic efficiency of invasiveness in patients with pure ground-glass nodules: a machine learning, cross-sectional, bicentric study |
| title_short | Radiomics integration based on intratumoral and peritumoral computed tomography improves the diagnostic efficiency of invasiveness in patients with pure ground-glass nodules: a machine learning, cross-sectional, bicentric study |
| title_sort | radiomics integration based on intratumoral and peritumoral computed tomography improves the diagnostic efficiency of invasiveness in patients with pure ground glass nodules a machine learning cross sectional bicentric study |
| topic | Computed tomography Pure ground-glass nodules Machine learning Radiomics Invasiveness |
| url | https://doi.org/10.1186/s13019-024-03289-3 |
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