A CT-based subregional radiomics nomogram for predicting local recurrence-free survival in esophageal squamous cell cancer patients treated by definitive chemoradiotherapy: a multicenter study
Abstract Background To develop and validate an online individualized model for predicting local recurrence-free survival (LRFS) in esophageal squamous cell carcinoma (ESCC) treated by definitive chemoradiotherapy (dCRT). Methods ESCC patients from three hospitals were randomly stratified into the tr...
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BMC
2024-12-01
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| Series: | Journal of Translational Medicine |
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| Online Access: | https://doi.org/10.1186/s12967-024-05897-y |
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| author | Jie Gong Jianchao Lu Wencheng Zhang Wei Huang Jie Li Zhi Yang Fan Meng Hongfei Sun Lina Zhao |
| author_facet | Jie Gong Jianchao Lu Wencheng Zhang Wei Huang Jie Li Zhi Yang Fan Meng Hongfei Sun Lina Zhao |
| author_sort | Jie Gong |
| collection | DOAJ |
| description | Abstract Background To develop and validate an online individualized model for predicting local recurrence-free survival (LRFS) in esophageal squamous cell carcinoma (ESCC) treated by definitive chemoradiotherapy (dCRT). Methods ESCC patients from three hospitals were randomly stratified into the training set (715) and the internal testing set (179), and patients from the other hospital as the external testing set (120). The important radiomic features extracted from contrast-enhanced computed tomography (CECT)-based subregions clustered from the whole volume of tumor and peritumor were selected and used to construct the subregion-based radiomic signature by using COX proportional hazards model, which was compared with the tumor-based radiomic signature. The clinical model and the radiomics model combing the clinical factors and the radiomic signature were further constructed and compared, which were validated in two testing sets. Results The subresion-based radiomic signature showed better prognostic performance than the tumor-based radiomic signature (training: 0.642 vs. 0.621, internal testing: 0.657 vs. 0.638, external testing: 0.636 vs. 0.612). Although the tumor-based radiomic signature, the subregion-based radiomic signature, the tumor-based radiomics model, and the subregion-based radiomics model had better performance compared to the clinical model, only the subregion-based radiomics model showed a significant advantage (p < 0.05; training: 0.666 vs. 0.616, internal testing: 0.689 vs. 0.649, external testing: 0.642 vs. 0.604). The clinical model and the subregion-based radiomics model were visualized as the nomograms, which are available online and could interactively calculate LRFS probability. Conclusions We established and validated a CECT-based online radiomics nomogram for predicting LRFS in ESCC received dCRT, which outperformed the clinical model and might serve as a powerful tool to facilitate individualized treatment. Trial registration This retrospective study was approved by the ethics committee (KY20222145-C-1). |
| format | Article |
| id | doaj-art-afd1871011de4bfc96a5f69d0f9efd85 |
| institution | Kabale University |
| issn | 1479-5876 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | BMC |
| record_format | Article |
| series | Journal of Translational Medicine |
| spelling | doaj-art-afd1871011de4bfc96a5f69d0f9efd852024-12-08T12:44:40ZengBMCJournal of Translational Medicine1479-58762024-12-0122111110.1186/s12967-024-05897-yA CT-based subregional radiomics nomogram for predicting local recurrence-free survival in esophageal squamous cell cancer patients treated by definitive chemoradiotherapy: a multicenter studyJie Gong0Jianchao Lu1Wencheng Zhang2Wei Huang3Jie Li4Zhi Yang5Fan Meng6Hongfei Sun7Lina Zhao8Department of Radiation Oncology, Xijing Hospital, Fourth Military Medical UniversityDepartment of Radiation Oncology, Sichuan Cancer Center, School of Medicine, Sichuan Cancer Hospital and Institution, University of Electronic Science and Technology of ChinaDepartment of Radiation Oncology, Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Tianjin’s Clinical Research Center for CancerDepartment of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University, Shandong Academy of Medical SciencesDepartment of Radiation Oncology, Xijing Hospital, Fourth Military Medical UniversityDepartment of Radiation Oncology, Xijing Hospital, Fourth Military Medical UniversityDepartment of Radiation Oncology, Xijing Hospital, Fourth Military Medical UniversityDepartment of Radiation Oncology, Xijing Hospital, Fourth Military Medical UniversityDepartment of Radiation Oncology, Xijing Hospital, Fourth Military Medical UniversityAbstract Background To develop and validate an online individualized model for predicting local recurrence-free survival (LRFS) in esophageal squamous cell carcinoma (ESCC) treated by definitive chemoradiotherapy (dCRT). Methods ESCC patients from three hospitals were randomly stratified into the training set (715) and the internal testing set (179), and patients from the other hospital as the external testing set (120). The important radiomic features extracted from contrast-enhanced computed tomography (CECT)-based subregions clustered from the whole volume of tumor and peritumor were selected and used to construct the subregion-based radiomic signature by using COX proportional hazards model, which was compared with the tumor-based radiomic signature. The clinical model and the radiomics model combing the clinical factors and the radiomic signature were further constructed and compared, which were validated in two testing sets. Results The subresion-based radiomic signature showed better prognostic performance than the tumor-based radiomic signature (training: 0.642 vs. 0.621, internal testing: 0.657 vs. 0.638, external testing: 0.636 vs. 0.612). Although the tumor-based radiomic signature, the subregion-based radiomic signature, the tumor-based radiomics model, and the subregion-based radiomics model had better performance compared to the clinical model, only the subregion-based radiomics model showed a significant advantage (p < 0.05; training: 0.666 vs. 0.616, internal testing: 0.689 vs. 0.649, external testing: 0.642 vs. 0.604). The clinical model and the subregion-based radiomics model were visualized as the nomograms, which are available online and could interactively calculate LRFS probability. Conclusions We established and validated a CECT-based online radiomics nomogram for predicting LRFS in ESCC received dCRT, which outperformed the clinical model and might serve as a powerful tool to facilitate individualized treatment. Trial registration This retrospective study was approved by the ethics committee (KY20222145-C-1).https://doi.org/10.1186/s12967-024-05897-yEsophageal squamous cell cancerDefinitive chemoradiotherapyLocal recurrence-free survivalRadiomicsSubregion |
| spellingShingle | Jie Gong Jianchao Lu Wencheng Zhang Wei Huang Jie Li Zhi Yang Fan Meng Hongfei Sun Lina Zhao A CT-based subregional radiomics nomogram for predicting local recurrence-free survival in esophageal squamous cell cancer patients treated by definitive chemoradiotherapy: a multicenter study Journal of Translational Medicine Esophageal squamous cell cancer Definitive chemoradiotherapy Local recurrence-free survival Radiomics Subregion |
| title | A CT-based subregional radiomics nomogram for predicting local recurrence-free survival in esophageal squamous cell cancer patients treated by definitive chemoradiotherapy: a multicenter study |
| title_full | A CT-based subregional radiomics nomogram for predicting local recurrence-free survival in esophageal squamous cell cancer patients treated by definitive chemoradiotherapy: a multicenter study |
| title_fullStr | A CT-based subregional radiomics nomogram for predicting local recurrence-free survival in esophageal squamous cell cancer patients treated by definitive chemoradiotherapy: a multicenter study |
| title_full_unstemmed | A CT-based subregional radiomics nomogram for predicting local recurrence-free survival in esophageal squamous cell cancer patients treated by definitive chemoradiotherapy: a multicenter study |
| title_short | A CT-based subregional radiomics nomogram for predicting local recurrence-free survival in esophageal squamous cell cancer patients treated by definitive chemoradiotherapy: a multicenter study |
| title_sort | ct based subregional radiomics nomogram for predicting local recurrence free survival in esophageal squamous cell cancer patients treated by definitive chemoradiotherapy a multicenter study |
| topic | Esophageal squamous cell cancer Definitive chemoradiotherapy Local recurrence-free survival Radiomics Subregion |
| url | https://doi.org/10.1186/s12967-024-05897-y |
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