Survival outcome prediction of esophageal squamous cell carcinoma patients based on radiomics and mutation signature
Abstract Background The present study aimed to develop a nomogram model for predicting overall survival (OS) in esophageal squamous cell carcinoma (ESCC) patients. Methods A total of 205 patients with ESCC were enrolled and randomly divided into a training cohort (n = 153) and a test cohort (n = 52)...
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| Format: | Article |
| Language: | English |
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BMC
2025-01-01
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| Series: | Cancer Imaging |
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| Online Access: | https://doi.org/10.1186/s40644-024-00821-5 |
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| author | Ting Yan Zhenpeng Yan Guohui Chen Songrui Xu Chenxuan Wu Qichao Zhou Guolan Wang Ying Li Mengjiu Jia Xiaofei Zhuang Jie Yang Lili Liu Lu Wang Qinglu Wu Bin Wang Tianyi Yan |
| author_facet | Ting Yan Zhenpeng Yan Guohui Chen Songrui Xu Chenxuan Wu Qichao Zhou Guolan Wang Ying Li Mengjiu Jia Xiaofei Zhuang Jie Yang Lili Liu Lu Wang Qinglu Wu Bin Wang Tianyi Yan |
| author_sort | Ting Yan |
| collection | DOAJ |
| description | Abstract Background The present study aimed to develop a nomogram model for predicting overall survival (OS) in esophageal squamous cell carcinoma (ESCC) patients. Methods A total of 205 patients with ESCC were enrolled and randomly divided into a training cohort (n = 153) and a test cohort (n = 52) at a ratio of 7:3. Multivariate Cox regression was used to construct the radiomics model based on CT data. The mutation signature was constructed based on whole genome sequencing data and found to be significantly associated with the prognosis of patients with ESCC. A nomogram model combining the Rad-score and mutation signature was constructed. An integrated nomogram model combining the Rad-score, mutation signature, and clinical factors was constructed. Results A total of 8 CT features were selected for multivariate Cox regression analysis to determine whether the Rad-score was significantly correlated with OS. The area under the curve (AUC) of the radiomics model was 0.834 (95% CI, 0.767–0.900) for the training cohort and 0.733 (95% CI, 0.574–0.892) for the test cohort. The Rad-score, S3, and S6 were used to construct an integrated RM nomogram. The predictive performance of the RM nomogram model was better than that of the radiomics model, with an AUC of 0. 830 (95% CI, 0.761–0.899) in the training cohort and 0.793 (95% CI, 0.653–0.934) in the test cohort. The Rad-score, TNM stage, lymph node metastasis status, S3, and S6 were used to construct an integrated RMC nomogram. The predictive performance of the RMC nomogram model was better than that of the radiomics model and RM nomogram model, with an AUC of 0. 862 (95% CI, 0.795–0.928) in the training cohort and 0. 837 (95% CI, 0.705–0.969) in the test cohort. Conclusion An integrated nomogram model combining the Rad-score, mutation signature, and clinical factors can better predict the prognosis of patients with ESCC. |
| format | Article |
| id | doaj-art-82307d2b239a43028c3b83c77801d912 |
| institution | Kabale University |
| issn | 1470-7330 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
| record_format | Article |
| series | Cancer Imaging |
| spelling | doaj-art-82307d2b239a43028c3b83c77801d9122025-02-02T12:40:46ZengBMCCancer Imaging1470-73302025-01-0125111310.1186/s40644-024-00821-5Survival outcome prediction of esophageal squamous cell carcinoma patients based on radiomics and mutation signatureTing Yan0Zhenpeng Yan1Guohui Chen2Songrui Xu3Chenxuan Wu4Qichao Zhou5Guolan Wang6Ying Li7Mengjiu Jia8Xiaofei Zhuang9Jie Yang10Lili Liu11Lu Wang12Qinglu Wu13Bin Wang14Tianyi Yan15Second Clinical Medical College, Shanxi Medical UniversityTranslational Medicine Research Center, Shanxi Medical UniversityTranslational Medicine Research Center, Shanxi Medical UniversityTranslational Medicine Research Center, Shanxi Medical UniversitySchool of Life Science, Beijing Institute of TechnologyTranslational Medicine Research Center, Shanxi Medical UniversitySchool of Computer Information Engineering, Shanxi Technology and Business UniversityCollege of Information and Computer, Taiyuan University of TechnologySchool of Computer Information Engineering, Shanxi Technology and Business UniversityDepartment of Thoracic Surgery, Shanxi Cancer HospitalDepartment of Gastroenterology, Second Hospital of Shanxi Medical UniversityTranslational Medicine Research Center, Shanxi Medical UniversityTranslational Medicine Research Center, Shanxi Medical UniversityTranslational Medicine Research Center, Shanxi Medical UniversityCollege of Information and Computer, Taiyuan University of TechnologySchool of Life Science, Beijing Institute of TechnologyAbstract Background The present study aimed to develop a nomogram model for predicting overall survival (OS) in esophageal squamous cell carcinoma (ESCC) patients. Methods A total of 205 patients with ESCC were enrolled and randomly divided into a training cohort (n = 153) and a test cohort (n = 52) at a ratio of 7:3. Multivariate Cox regression was used to construct the radiomics model based on CT data. The mutation signature was constructed based on whole genome sequencing data and found to be significantly associated with the prognosis of patients with ESCC. A nomogram model combining the Rad-score and mutation signature was constructed. An integrated nomogram model combining the Rad-score, mutation signature, and clinical factors was constructed. Results A total of 8 CT features were selected for multivariate Cox regression analysis to determine whether the Rad-score was significantly correlated with OS. The area under the curve (AUC) of the radiomics model was 0.834 (95% CI, 0.767–0.900) for the training cohort and 0.733 (95% CI, 0.574–0.892) for the test cohort. The Rad-score, S3, and S6 were used to construct an integrated RM nomogram. The predictive performance of the RM nomogram model was better than that of the radiomics model, with an AUC of 0. 830 (95% CI, 0.761–0.899) in the training cohort and 0.793 (95% CI, 0.653–0.934) in the test cohort. The Rad-score, TNM stage, lymph node metastasis status, S3, and S6 were used to construct an integrated RMC nomogram. The predictive performance of the RMC nomogram model was better than that of the radiomics model and RM nomogram model, with an AUC of 0. 862 (95% CI, 0.795–0.928) in the training cohort and 0. 837 (95% CI, 0.705–0.969) in the test cohort. Conclusion An integrated nomogram model combining the Rad-score, mutation signature, and clinical factors can better predict the prognosis of patients with ESCC.https://doi.org/10.1186/s40644-024-00821-5Esophageal squamous cell carcinomaMutation signatureNomogramPrognosisRadiomics |
| spellingShingle | Ting Yan Zhenpeng Yan Guohui Chen Songrui Xu Chenxuan Wu Qichao Zhou Guolan Wang Ying Li Mengjiu Jia Xiaofei Zhuang Jie Yang Lili Liu Lu Wang Qinglu Wu Bin Wang Tianyi Yan Survival outcome prediction of esophageal squamous cell carcinoma patients based on radiomics and mutation signature Cancer Imaging Esophageal squamous cell carcinoma Mutation signature Nomogram Prognosis Radiomics |
| title | Survival outcome prediction of esophageal squamous cell carcinoma patients based on radiomics and mutation signature |
| title_full | Survival outcome prediction of esophageal squamous cell carcinoma patients based on radiomics and mutation signature |
| title_fullStr | Survival outcome prediction of esophageal squamous cell carcinoma patients based on radiomics and mutation signature |
| title_full_unstemmed | Survival outcome prediction of esophageal squamous cell carcinoma patients based on radiomics and mutation signature |
| title_short | Survival outcome prediction of esophageal squamous cell carcinoma patients based on radiomics and mutation signature |
| title_sort | survival outcome prediction of esophageal squamous cell carcinoma patients based on radiomics and mutation signature |
| topic | Esophageal squamous cell carcinoma Mutation signature Nomogram Prognosis Radiomics |
| url | https://doi.org/10.1186/s40644-024-00821-5 |
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