Determining the status of tertiary lymphoid structures in invasive pulmonary adenocarcinoma based on chest CT radiomic features
Abstract Objectives The aim of this study was to determine the status of tertiary lymphoid structures (TLSs) using radiomic features in patients with invasive pulmonary adenocarcinoma (IA). Methods In this retrospective study, patients with IA from November 2015 to March 2024 were recruited from two...
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SpringerOpen
2025-01-01
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| Series: | Insights into Imaging |
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| Online Access: | https://doi.org/10.1186/s13244-025-01906-w |
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| author | Ye Yu Tianshu Yang Pengfei Ma Yan Zeng Yongming Dai Yicheng Fu Aie Liu Ying Zhang Guanglei Zhuang Yan Zhou Huawei Wu |
| author_facet | Ye Yu Tianshu Yang Pengfei Ma Yan Zeng Yongming Dai Yicheng Fu Aie Liu Ying Zhang Guanglei Zhuang Yan Zhou Huawei Wu |
| author_sort | Ye Yu |
| collection | DOAJ |
| description | Abstract Objectives The aim of this study was to determine the status of tertiary lymphoid structures (TLSs) using radiomic features in patients with invasive pulmonary adenocarcinoma (IA). Methods In this retrospective study, patients with IA from November 2015 to March 2024 were recruited from two independent centers (center 1, training and internal test data set; center 2, external test data set). TLS was divided into two groups according to hematoxylin-eosin staining. Radiomic features were extracted, and support vector machine (SVM) were implemented to predict the status of TLSs. Receiver operating characteristic (ROC) curves were used to analyze diagnostic performance. Furthermore, visual assessments of the test set were also conducted by two thoracic radiologists and compared with the radiomics results. Results A total of 456 patients were included (training data set, n = 278; internal test data set, n = 115; external test data set, n = 63). The area under the curve (AUC) of the radiomics model on the validation set, the internal test set, and the external test set were 0.781 (95% confidence interval (CI): 0.659–0.905;), 0.804 (95% CI: 0.723–0.884;) and 0.747 (95% CI: 0.621–0.874;), respectively. In the visual assessments, the mean CT value and air bronchogram were important indicators of TLS, the AUC was 0.683. In the external test set, the AUC of the clinical model was 0.632. Conclusions The radiomics model has a higher AUC than the clinical model and effectively discriminates TLSs in patients with IA. Critical relevance statement This study demonstrates that the radiomics-based model can differentiate TLSs in patients with IA. As a non-invasive biomarker, it enhances our understanding of tumor prognosis and management. Key Points TLSs are closely related to favorable clinical outcomes in non-small cell lung cancer. Radiomics from Chest CT predicted TLSs in patients with IA. This study supports individualized clinical decision-making for patients with IA. Graphical Abstract |
| format | Article |
| id | doaj-art-37c8df858c274735af1f245093e20e6d |
| institution | Kabale University |
| issn | 1869-4101 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Insights into Imaging |
| spelling | doaj-art-37c8df858c274735af1f245093e20e6d2025-02-02T12:27:56ZengSpringerOpenInsights into Imaging1869-41012025-01-0116111110.1186/s13244-025-01906-wDetermining the status of tertiary lymphoid structures in invasive pulmonary adenocarcinoma based on chest CT radiomic featuresYe Yu0Tianshu Yang1Pengfei Ma2Yan Zeng3Yongming Dai4Yicheng Fu5Aie Liu6Ying Zhang7Guanglei Zhuang8Yan Zhou9Huawei Wu10Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong UniversityDepartment of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong UniversityState Key Laboratory of Oncogenes and Related Genes, Renji-Med X Clinical Stem Cell Research Center, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong UniversityDepartment of Research Center, Shanghai United Imaging Intelligence Co., Ltd.School of Biomedical Engineering, Shanghai Tech UniversityDepartment of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong UniversityDepartment of Research Center, Shanghai United Imaging Intelligence Co., Ltd.Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong UniversityShanghai Key Laboratory of Gynecologic Oncology, Ren Ji Hospital, Shanghai Jiao Tong University School of MedicineDepartment of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong UniversityDepartment of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong UniversityAbstract Objectives The aim of this study was to determine the status of tertiary lymphoid structures (TLSs) using radiomic features in patients with invasive pulmonary adenocarcinoma (IA). Methods In this retrospective study, patients with IA from November 2015 to March 2024 were recruited from two independent centers (center 1, training and internal test data set; center 2, external test data set). TLS was divided into two groups according to hematoxylin-eosin staining. Radiomic features were extracted, and support vector machine (SVM) were implemented to predict the status of TLSs. Receiver operating characteristic (ROC) curves were used to analyze diagnostic performance. Furthermore, visual assessments of the test set were also conducted by two thoracic radiologists and compared with the radiomics results. Results A total of 456 patients were included (training data set, n = 278; internal test data set, n = 115; external test data set, n = 63). The area under the curve (AUC) of the radiomics model on the validation set, the internal test set, and the external test set were 0.781 (95% confidence interval (CI): 0.659–0.905;), 0.804 (95% CI: 0.723–0.884;) and 0.747 (95% CI: 0.621–0.874;), respectively. In the visual assessments, the mean CT value and air bronchogram were important indicators of TLS, the AUC was 0.683. In the external test set, the AUC of the clinical model was 0.632. Conclusions The radiomics model has a higher AUC than the clinical model and effectively discriminates TLSs in patients with IA. Critical relevance statement This study demonstrates that the radiomics-based model can differentiate TLSs in patients with IA. As a non-invasive biomarker, it enhances our understanding of tumor prognosis and management. Key Points TLSs are closely related to favorable clinical outcomes in non-small cell lung cancer. Radiomics from Chest CT predicted TLSs in patients with IA. This study supports individualized clinical decision-making for patients with IA. Graphical Abstracthttps://doi.org/10.1186/s13244-025-01906-wRadiomicsTertiary lymphoid structuresAdenocarcinoma of lung |
| spellingShingle | Ye Yu Tianshu Yang Pengfei Ma Yan Zeng Yongming Dai Yicheng Fu Aie Liu Ying Zhang Guanglei Zhuang Yan Zhou Huawei Wu Determining the status of tertiary lymphoid structures in invasive pulmonary adenocarcinoma based on chest CT radiomic features Insights into Imaging Radiomics Tertiary lymphoid structures Adenocarcinoma of lung |
| title | Determining the status of tertiary lymphoid structures in invasive pulmonary adenocarcinoma based on chest CT radiomic features |
| title_full | Determining the status of tertiary lymphoid structures in invasive pulmonary adenocarcinoma based on chest CT radiomic features |
| title_fullStr | Determining the status of tertiary lymphoid structures in invasive pulmonary adenocarcinoma based on chest CT radiomic features |
| title_full_unstemmed | Determining the status of tertiary lymphoid structures in invasive pulmonary adenocarcinoma based on chest CT radiomic features |
| title_short | Determining the status of tertiary lymphoid structures in invasive pulmonary adenocarcinoma based on chest CT radiomic features |
| title_sort | determining the status of tertiary lymphoid structures in invasive pulmonary adenocarcinoma based on chest ct radiomic features |
| topic | Radiomics Tertiary lymphoid structures Adenocarcinoma of lung |
| url | https://doi.org/10.1186/s13244-025-01906-w |
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