Preoperative CT-based radiomics combined with tumour spread through air spaces can accurately predict early recurrence of stage I lung adenocarcinoma: a multicentre retrospective cohort study
Abstract Objective To develop and validate a prediction model for early recurrence of stage I lung adenocarcinoma (LUAD) that combines radiomics features based on preoperative CT with tumour spread through air spaces (STAS). Materials and methods The most recent preoperative thin-section chest CT sc...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2023-09-01
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| Series: | Cancer Imaging |
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| Online Access: | https://doi.org/10.1186/s40644-023-00605-3 |
| _version_ | 1797451728335929344 |
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| author | Yuhang Wang Yun Ding Xin Liu Xin Li Xiaoteng Jia Jiuzhen Li Han Zhang Zhenchun Song Meilin Xu Jie Ren Daqiang Sun |
| author_facet | Yuhang Wang Yun Ding Xin Liu Xin Li Xiaoteng Jia Jiuzhen Li Han Zhang Zhenchun Song Meilin Xu Jie Ren Daqiang Sun |
| author_sort | Yuhang Wang |
| collection | DOAJ |
| description | Abstract Objective To develop and validate a prediction model for early recurrence of stage I lung adenocarcinoma (LUAD) that combines radiomics features based on preoperative CT with tumour spread through air spaces (STAS). Materials and methods The most recent preoperative thin-section chest CT scans and postoperative pathological haematoxylin and eosin-stained sections were retrospectively collected from patients with a postoperative pathological diagnosis of stage I LUAD. Regions of interest were manually segmented, and radiomics features were extracted from the tumour and peritumoral regions extended by 3 voxel units, 6 voxel units, and 12 voxel units, and 2D and 3D deep learning image features were extracted by convolutional neural networks. Then, the RAdiomics Integrated with STAS model (RAISm) was constructed. The performance of RAISm was then evaluated in a development cohort and validation cohort. Results A total of 226 patients from two medical centres from January 2015 to December 2018 were retrospectively included as the development cohort for the model and were randomly split into a training set (72.6%, n = 164) and a test set (27.4%, n = 62). From June 2019 to December 2019, 51 patients were included in the validation cohort. RAISm had excellent discrimination in predicting the early recurrence of stage I LUAD in the training cohort (AUC = 0.847, 95% CI 0.762–0.932) and validation cohort (AUC = 0.817, 95% CI 0.625–1.000). RAISm outperformed single modality signatures and other combinations of signatures in terms of discrimination and clinical net benefits. Conclusion We pioneered combining preoperative CT-based radiomics with STAS to predict stage I LUAD recurrence postoperatively and confirmed the superior effect of the model in validation cohorts, showing its potential to assist in postoperative treatment strategies. |
| first_indexed | 2024-03-09T14:57:40Z |
| format | Article |
| id | doaj.art-fb26b3bd683c432682dc28de6b720d57 |
| institution | Directory Open Access Journal |
| issn | 1470-7330 |
| language | English |
| last_indexed | 2024-03-09T14:57:40Z |
| publishDate | 2023-09-01 |
| publisher | BMC |
| record_format | Article |
| series | Cancer Imaging |
| spelling | doaj.art-fb26b3bd683c432682dc28de6b720d572023-11-26T14:06:14ZengBMCCancer Imaging1470-73302023-09-0123111310.1186/s40644-023-00605-3Preoperative CT-based radiomics combined with tumour spread through air spaces can accurately predict early recurrence of stage I lung adenocarcinoma: a multicentre retrospective cohort studyYuhang Wang0Yun Ding1Xin Liu2Xin Li3Xiaoteng Jia4Jiuzhen Li5Han Zhang6Zhenchun Song7Meilin Xu8Jie Ren9Daqiang Sun10Graduate School, Tianjin Medical UniversityGraduate School, Tianjin Medical UniversityGraduate School, Tianjin Medical UniversityDepartment of Thoracic Surgery, Tianjin Chest Hospital of Tianjin UniversityGraduate School, Tianjin Medical UniversityGraduate School, Tianjin Medical UniversityGraduate School, Tianjin Medical UniversityDepartment of Imaging, Tianjin Chest Hospital of Tianjin UniversityDepartment of Pathology, Tianjin Chest Hospital of Tianjin UniversityGraduate School, Tianjin Medical UniversityGraduate School, Tianjin Medical UniversityAbstract Objective To develop and validate a prediction model for early recurrence of stage I lung adenocarcinoma (LUAD) that combines radiomics features based on preoperative CT with tumour spread through air spaces (STAS). Materials and methods The most recent preoperative thin-section chest CT scans and postoperative pathological haematoxylin and eosin-stained sections were retrospectively collected from patients with a postoperative pathological diagnosis of stage I LUAD. Regions of interest were manually segmented, and radiomics features were extracted from the tumour and peritumoral regions extended by 3 voxel units, 6 voxel units, and 12 voxel units, and 2D and 3D deep learning image features were extracted by convolutional neural networks. Then, the RAdiomics Integrated with STAS model (RAISm) was constructed. The performance of RAISm was then evaluated in a development cohort and validation cohort. Results A total of 226 patients from two medical centres from January 2015 to December 2018 were retrospectively included as the development cohort for the model and were randomly split into a training set (72.6%, n = 164) and a test set (27.4%, n = 62). From June 2019 to December 2019, 51 patients were included in the validation cohort. RAISm had excellent discrimination in predicting the early recurrence of stage I LUAD in the training cohort (AUC = 0.847, 95% CI 0.762–0.932) and validation cohort (AUC = 0.817, 95% CI 0.625–1.000). RAISm outperformed single modality signatures and other combinations of signatures in terms of discrimination and clinical net benefits. Conclusion We pioneered combining preoperative CT-based radiomics with STAS to predict stage I LUAD recurrence postoperatively and confirmed the superior effect of the model in validation cohorts, showing its potential to assist in postoperative treatment strategies.https://doi.org/10.1186/s40644-023-00605-3RadiomicsLung adenocarcinomaPreoperative CTDeep learningSTAS |
| spellingShingle | Yuhang Wang Yun Ding Xin Liu Xin Li Xiaoteng Jia Jiuzhen Li Han Zhang Zhenchun Song Meilin Xu Jie Ren Daqiang Sun Preoperative CT-based radiomics combined with tumour spread through air spaces can accurately predict early recurrence of stage I lung adenocarcinoma: a multicentre retrospective cohort study Cancer Imaging Radiomics Lung adenocarcinoma Preoperative CT Deep learning STAS |
| title | Preoperative CT-based radiomics combined with tumour spread through air spaces can accurately predict early recurrence of stage I lung adenocarcinoma: a multicentre retrospective cohort study |
| title_full | Preoperative CT-based radiomics combined with tumour spread through air spaces can accurately predict early recurrence of stage I lung adenocarcinoma: a multicentre retrospective cohort study |
| title_fullStr | Preoperative CT-based radiomics combined with tumour spread through air spaces can accurately predict early recurrence of stage I lung adenocarcinoma: a multicentre retrospective cohort study |
| title_full_unstemmed | Preoperative CT-based radiomics combined with tumour spread through air spaces can accurately predict early recurrence of stage I lung adenocarcinoma: a multicentre retrospective cohort study |
| title_short | Preoperative CT-based radiomics combined with tumour spread through air spaces can accurately predict early recurrence of stage I lung adenocarcinoma: a multicentre retrospective cohort study |
| title_sort | preoperative ct based radiomics combined with tumour spread through air spaces can accurately predict early recurrence of stage i lung adenocarcinoma a multicentre retrospective cohort study |
| topic | Radiomics Lung adenocarcinoma Preoperative CT Deep learning STAS |
| url | https://doi.org/10.1186/s40644-023-00605-3 |
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