Intratumoral and peritumoral CT radiomics in predicting anaplastic lymphoma kinase mutations and survival in patients with lung adenocarcinoma: a multicenter study
Abstract Background To explore the value of intratumoral and peritumoral radiomics in preoperative prediction of anaplastic lymphoma kinase (ALK) mutation status and survival in patients with lung adenocarcinoma. Methods We retrospectively collected data from 505 eligible patients with lung adenocar...
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2025-03-01
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| Online Access: | https://doi.org/10.1186/s40644-025-00856-2 |
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| author | Weiyue Chen Guihan Lin Ye Feng Yongjun Chen Yanjun Li Jianbin Li Weibo Mao Yang Jing Chunli Kong Yumin Hu Minjiang Chen Shuiwei Xia Chenying Lu Jianfei Tu Jiansong Ji |
| author_facet | Weiyue Chen Guihan Lin Ye Feng Yongjun Chen Yanjun Li Jianbin Li Weibo Mao Yang Jing Chunli Kong Yumin Hu Minjiang Chen Shuiwei Xia Chenying Lu Jianfei Tu Jiansong Ji |
| author_sort | Weiyue Chen |
| collection | DOAJ |
| description | Abstract Background To explore the value of intratumoral and peritumoral radiomics in preoperative prediction of anaplastic lymphoma kinase (ALK) mutation status and survival in patients with lung adenocarcinoma. Methods We retrospectively collected data from 505 eligible patients with lung adenocarcinoma from four hospitals (training and external validation sets 1–3). The CT-based radiomics features were extracted separately from the gross tumor volume (GTV) and GTV incorporating peritumoral 3-, 6-, 9-, 12-, and 15-mm regions (GPTV3, GPTV6, GPTV9, GPTV12, and GPTV15), and screened the most relevant features to construct radiomics models to predict ALK (+). The combined model incorporated radiomics scores (Rad-scores) of the best radiomics model and clinical predictors was constructed. Performance was evaluated using receiver operating characteristic (ROC) analysis. Progression-free survival (PFS) outcomes were examined using the Cox proportional hazards model. Results In the four sets, 21.19% (107/505) patients were ALK (+). The GPTV3 radiomics model using a support vector machine algorithm achieved the best predictive performance, with the highest average AUC of 0.811 in the validation sets. Clinical TNM stage and pleural indentation were independent predictors. The combined model incorporating the GPTV3-Rad-score and clinical predictors achieved higher performance than the clinical model alone in predicting ALK (+) in three validation sets [AUC: 0.855 (95% CI: 0.766–0.919) vs. 0.648 (95% CI: 0.543–0.745), P = 0.001; 0.882 (95% CI: 0.801–0.962) vs. 0.634 (95% CI: 0.548–0.714), P < 0.001; 0.810 (95% CI: 0.727–0.877) vs. 0.663 (95% CI: 0.570–0.748), P = 0.006]. The prediction score of the combined model could stratify PFS outcomes in patients receiving ALK-TKI therapy (HR: 0.37; 95% CI: 0.15–0.89; P = 0.026) and immunotherapy (HR: 2.49; 95% CI: 1.22–5.08; P = 0.012). Conclusion The presented combined model based on GPTV3 effectively mined tumor features to predict ALK mutation status and stratify PFS outcomes in patients with lung adenocarcinoma. |
| format | Article |
| id | doaj-art-c09ecff78500402a9b82a5a2a6fc190c |
| institution | DOAJ |
| issn | 1470-7330 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | BMC |
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| series | Cancer Imaging |
| spelling | doaj-art-c09ecff78500402a9b82a5a2a6fc190c2025-08-20T03:02:22ZengBMCCancer Imaging1470-73302025-03-0125111610.1186/s40644-025-00856-2Intratumoral and peritumoral CT radiomics in predicting anaplastic lymphoma kinase mutations and survival in patients with lung adenocarcinoma: a multicenter studyWeiyue Chen0Guihan Lin1Ye Feng2Yongjun Chen3Yanjun Li4Jianbin Li5Weibo Mao6Yang Jing7Chunli Kong8Yumin Hu9Minjiang Chen10Shuiwei Xia11Chenying Lu12Jianfei Tu13Jiansong Ji14Zhejiang Key Laboratory of Imaging and Interventional Medicine, Zhejiang Engineering Research Csaenter of Interventional Medicine Engineering and Biotechnology, Key Laboratory of Precision Medicine of Lishui City, The Fifth Affiliated Hospital of Wenzhou Medical UniversityZhejiang Key Laboratory of Imaging and Interventional Medicine, Zhejiang Engineering Research Csaenter of Interventional Medicine Engineering and Biotechnology, Key Laboratory of Precision Medicine of Lishui City, The Fifth Affiliated Hospital of Wenzhou Medical UniversityZhejiang Key Laboratory of Imaging and Interventional Medicine, Zhejiang Engineering Research Csaenter of Interventional Medicine Engineering and Biotechnology, Key Laboratory of Precision Medicine of Lishui City, The Fifth Affiliated Hospital of Wenzhou Medical UniversityDepartment of Radiology, The Sixth Affiliated Hospital of Wenzhou Medical UniversityDepartment of Radiology, The First Hospital of Jiaxing, Affiliated Hospital of Jiaxing UniversityDepartment of Radiology, The Affiliated People’s Hospital of Ningbo UniversityDepartment of Pathology, The Fifth Affiliated Hospital of Wenzhou Medical UniversityHuiying Medical Technology Co., LtdZhejiang Key Laboratory of Imaging and Interventional Medicine, Zhejiang Engineering Research Csaenter of Interventional Medicine Engineering and Biotechnology, Key Laboratory of Precision Medicine of Lishui City, The Fifth Affiliated Hospital of Wenzhou Medical UniversityZhejiang Key Laboratory of Imaging and Interventional Medicine, Zhejiang Engineering Research Csaenter of Interventional Medicine Engineering and Biotechnology, Key Laboratory of Precision Medicine of Lishui City, The Fifth Affiliated Hospital of Wenzhou Medical UniversityZhejiang Key Laboratory of Imaging and Interventional Medicine, Zhejiang Engineering Research Csaenter of Interventional Medicine Engineering and Biotechnology, Key Laboratory of Precision Medicine of Lishui City, The Fifth Affiliated Hospital of Wenzhou Medical UniversityZhejiang Key Laboratory of Imaging and Interventional Medicine, Zhejiang Engineering Research Csaenter of Interventional Medicine Engineering and Biotechnology, Key Laboratory of Precision Medicine of Lishui City, The Fifth Affiliated Hospital of Wenzhou Medical UniversityZhejiang Key Laboratory of Imaging and Interventional Medicine, Zhejiang Engineering Research Csaenter of Interventional Medicine Engineering and Biotechnology, Key Laboratory of Precision Medicine of Lishui City, The Fifth Affiliated Hospital of Wenzhou Medical UniversityZhejiang Key Laboratory of Imaging and Interventional Medicine, Zhejiang Engineering Research Csaenter of Interventional Medicine Engineering and Biotechnology, Key Laboratory of Precision Medicine of Lishui City, The Fifth Affiliated Hospital of Wenzhou Medical UniversityZhejiang Key Laboratory of Imaging and Interventional Medicine, Zhejiang Engineering Research Csaenter of Interventional Medicine Engineering and Biotechnology, Key Laboratory of Precision Medicine of Lishui City, The Fifth Affiliated Hospital of Wenzhou Medical UniversityAbstract Background To explore the value of intratumoral and peritumoral radiomics in preoperative prediction of anaplastic lymphoma kinase (ALK) mutation status and survival in patients with lung adenocarcinoma. Methods We retrospectively collected data from 505 eligible patients with lung adenocarcinoma from four hospitals (training and external validation sets 1–3). The CT-based radiomics features were extracted separately from the gross tumor volume (GTV) and GTV incorporating peritumoral 3-, 6-, 9-, 12-, and 15-mm regions (GPTV3, GPTV6, GPTV9, GPTV12, and GPTV15), and screened the most relevant features to construct radiomics models to predict ALK (+). The combined model incorporated radiomics scores (Rad-scores) of the best radiomics model and clinical predictors was constructed. Performance was evaluated using receiver operating characteristic (ROC) analysis. Progression-free survival (PFS) outcomes were examined using the Cox proportional hazards model. Results In the four sets, 21.19% (107/505) patients were ALK (+). The GPTV3 radiomics model using a support vector machine algorithm achieved the best predictive performance, with the highest average AUC of 0.811 in the validation sets. Clinical TNM stage and pleural indentation were independent predictors. The combined model incorporating the GPTV3-Rad-score and clinical predictors achieved higher performance than the clinical model alone in predicting ALK (+) in three validation sets [AUC: 0.855 (95% CI: 0.766–0.919) vs. 0.648 (95% CI: 0.543–0.745), P = 0.001; 0.882 (95% CI: 0.801–0.962) vs. 0.634 (95% CI: 0.548–0.714), P < 0.001; 0.810 (95% CI: 0.727–0.877) vs. 0.663 (95% CI: 0.570–0.748), P = 0.006]. The prediction score of the combined model could stratify PFS outcomes in patients receiving ALK-TKI therapy (HR: 0.37; 95% CI: 0.15–0.89; P = 0.026) and immunotherapy (HR: 2.49; 95% CI: 1.22–5.08; P = 0.012). Conclusion The presented combined model based on GPTV3 effectively mined tumor features to predict ALK mutation status and stratify PFS outcomes in patients with lung adenocarcinoma.https://doi.org/10.1186/s40644-025-00856-2Lung adenocarcinomaAnaplastic lymphoma kinasePeritumoralMachine learningPrognosis |
| spellingShingle | Weiyue Chen Guihan Lin Ye Feng Yongjun Chen Yanjun Li Jianbin Li Weibo Mao Yang Jing Chunli Kong Yumin Hu Minjiang Chen Shuiwei Xia Chenying Lu Jianfei Tu Jiansong Ji Intratumoral and peritumoral CT radiomics in predicting anaplastic lymphoma kinase mutations and survival in patients with lung adenocarcinoma: a multicenter study Cancer Imaging Lung adenocarcinoma Anaplastic lymphoma kinase Peritumoral Machine learning Prognosis |
| title | Intratumoral and peritumoral CT radiomics in predicting anaplastic lymphoma kinase mutations and survival in patients with lung adenocarcinoma: a multicenter study |
| title_full | Intratumoral and peritumoral CT radiomics in predicting anaplastic lymphoma kinase mutations and survival in patients with lung adenocarcinoma: a multicenter study |
| title_fullStr | Intratumoral and peritumoral CT radiomics in predicting anaplastic lymphoma kinase mutations and survival in patients with lung adenocarcinoma: a multicenter study |
| title_full_unstemmed | Intratumoral and peritumoral CT radiomics in predicting anaplastic lymphoma kinase mutations and survival in patients with lung adenocarcinoma: a multicenter study |
| title_short | Intratumoral and peritumoral CT radiomics in predicting anaplastic lymphoma kinase mutations and survival in patients with lung adenocarcinoma: a multicenter study |
| title_sort | intratumoral and peritumoral ct radiomics in predicting anaplastic lymphoma kinase mutations and survival in patients with lung adenocarcinoma a multicenter study |
| topic | Lung adenocarcinoma Anaplastic lymphoma kinase Peritumoral Machine learning Prognosis |
| url | https://doi.org/10.1186/s40644-025-00856-2 |
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