MRI radiomics and nutritional-inflammatory biomarkers: a powerful combination for predicting progression-free survival in cervical cancer patients undergoing concurrent chemoradiotherapy
Abstract Objective This study aims to develop and validate a predictive model that integrates clinical features, MRI radiomics, and nutritional-inflammatory biomarkers to forecast progression-free survival (PFS) in cervical cancer (CC) patients undergoing concurrent chemoradiotherapy (CCRT). The goa...
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| Format: | Article |
| Language: | English |
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BMC
2024-10-01
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| Series: | Cancer Imaging |
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| Online Access: | https://doi.org/10.1186/s40644-024-00789-2 |
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| author | Qi Yan Menghan- Wu Jing Zhang Jiayang- Yang Guannan- Lv Baojun- Qu Yanping- Zhang Xia Yan Jianbo- Song |
| author_facet | Qi Yan Menghan- Wu Jing Zhang Jiayang- Yang Guannan- Lv Baojun- Qu Yanping- Zhang Xia Yan Jianbo- Song |
| author_sort | Qi Yan |
| collection | DOAJ |
| description | Abstract Objective This study aims to develop and validate a predictive model that integrates clinical features, MRI radiomics, and nutritional-inflammatory biomarkers to forecast progression-free survival (PFS) in cervical cancer (CC) patients undergoing concurrent chemoradiotherapy (CCRT). The goal is to identify high-risk patients and guide personalized treatment. Methods We performed a retrospective analysis of 188 patients from two centers, divided into training (132) and validation (56) sets. Clinical data, systemic inflammatory markers, and immune-nutritional indices were collected. Radiomic features from three MRI sequences were extracted and selected for predictive value. We developed and evaluated five models incorporating clinical features, nutritional-inflammatory indicators, and radiomics using C-index. The best-performing model was used to create a nomogram, which was validated through ROC curves, calibration plots, and decision curve analysis (DCA). Results Model 5, which integrates clinical features, Systemic Immune-Inflammation Index (SII), Prognostic Nutritional Index (PNI), and MRI radiomics, showed the highest performance. It achieved a C-index of 0.833 (95% CI: 0.792–0.874) in the training set and 0.789 (95% CI: 0.679–0.899) in the validation set. The nomogram derived from Model 5 effectively stratified patients into risk groups, with AUCs of 0.833, 0.941, and 0.973 for 1-year, 3-year, and 5-year PFS in the training set, and 0.812, 0.940, and 0.944 in the validation set. Conclusions The integrated model combining clinical features, nutritional-inflammatory biomarkers, and radiomics offers a robust tool for predicting PFS in CC patients undergoing CCRT. The nomogram provides precise predictions, supporting its application in personalized patient management. |
| format | Article |
| id | doaj-art-e46fc784b27e4d9a8257d038544d512b |
| institution | OA Journals |
| issn | 1470-7330 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | BMC |
| record_format | Article |
| series | Cancer Imaging |
| spelling | doaj-art-e46fc784b27e4d9a8257d038544d512b2025-08-20T02:12:03ZengBMCCancer Imaging1470-73302024-10-0124111210.1186/s40644-024-00789-2MRI radiomics and nutritional-inflammatory biomarkers: a powerful combination for predicting progression-free survival in cervical cancer patients undergoing concurrent chemoradiotherapyQi Yan0Menghan- Wu1Jing Zhang2Jiayang- Yang3Guannan- Lv4Baojun- Qu5Yanping- Zhang6Xia Yan7Jianbo- Song8Cancer Center, Shanxi Bethune Hospital, Third Hospital of Shanxi Medical University, Shanxi Academy of Medical Sciences Tongji Shanxi HospitalCancer Center, Tongji Shanxi Hospital, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Third Hospital of Shanxi Medical UniversityChina institute for radiation protectionCancer Center, Tongji Shanxi Hospital, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Third Hospital of Shanxi Medical UniversityGynecological Tumor Treatment Center, the Second People’s Hospital of Datong, Cancer HospitalGynecological Tumor Treatment Center, the Second People’s Hospital of Datong, Cancer HospitalImaging Department, the Second People’s Hospital of Datong, Cancer HospitalCancer Center, Tongji Shanxi Hospital, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Third Hospital of Shanxi Medical UniversityCancer Center, Shanxi Bethune Hospital, Third Hospital of Shanxi Medical University, Shanxi Academy of Medical Sciences Tongji Shanxi HospitalAbstract Objective This study aims to develop and validate a predictive model that integrates clinical features, MRI radiomics, and nutritional-inflammatory biomarkers to forecast progression-free survival (PFS) in cervical cancer (CC) patients undergoing concurrent chemoradiotherapy (CCRT). The goal is to identify high-risk patients and guide personalized treatment. Methods We performed a retrospective analysis of 188 patients from two centers, divided into training (132) and validation (56) sets. Clinical data, systemic inflammatory markers, and immune-nutritional indices were collected. Radiomic features from three MRI sequences were extracted and selected for predictive value. We developed and evaluated five models incorporating clinical features, nutritional-inflammatory indicators, and radiomics using C-index. The best-performing model was used to create a nomogram, which was validated through ROC curves, calibration plots, and decision curve analysis (DCA). Results Model 5, which integrates clinical features, Systemic Immune-Inflammation Index (SII), Prognostic Nutritional Index (PNI), and MRI radiomics, showed the highest performance. It achieved a C-index of 0.833 (95% CI: 0.792–0.874) in the training set and 0.789 (95% CI: 0.679–0.899) in the validation set. The nomogram derived from Model 5 effectively stratified patients into risk groups, with AUCs of 0.833, 0.941, and 0.973 for 1-year, 3-year, and 5-year PFS in the training set, and 0.812, 0.940, and 0.944 in the validation set. Conclusions The integrated model combining clinical features, nutritional-inflammatory biomarkers, and radiomics offers a robust tool for predicting PFS in CC patients undergoing CCRT. The nomogram provides precise predictions, supporting its application in personalized patient management.https://doi.org/10.1186/s40644-024-00789-2Cervical cancerConcurrent chemoradiotherapyMRI radiomicsNutritional-inflammatory biomarkersPrognosis |
| spellingShingle | Qi Yan Menghan- Wu Jing Zhang Jiayang- Yang Guannan- Lv Baojun- Qu Yanping- Zhang Xia Yan Jianbo- Song MRI radiomics and nutritional-inflammatory biomarkers: a powerful combination for predicting progression-free survival in cervical cancer patients undergoing concurrent chemoradiotherapy Cancer Imaging Cervical cancer Concurrent chemoradiotherapy MRI radiomics Nutritional-inflammatory biomarkers Prognosis |
| title | MRI radiomics and nutritional-inflammatory biomarkers: a powerful combination for predicting progression-free survival in cervical cancer patients undergoing concurrent chemoradiotherapy |
| title_full | MRI radiomics and nutritional-inflammatory biomarkers: a powerful combination for predicting progression-free survival in cervical cancer patients undergoing concurrent chemoradiotherapy |
| title_fullStr | MRI radiomics and nutritional-inflammatory biomarkers: a powerful combination for predicting progression-free survival in cervical cancer patients undergoing concurrent chemoradiotherapy |
| title_full_unstemmed | MRI radiomics and nutritional-inflammatory biomarkers: a powerful combination for predicting progression-free survival in cervical cancer patients undergoing concurrent chemoradiotherapy |
| title_short | MRI radiomics and nutritional-inflammatory biomarkers: a powerful combination for predicting progression-free survival in cervical cancer patients undergoing concurrent chemoradiotherapy |
| title_sort | mri radiomics and nutritional inflammatory biomarkers a powerful combination for predicting progression free survival in cervical cancer patients undergoing concurrent chemoradiotherapy |
| topic | Cervical cancer Concurrent chemoradiotherapy MRI radiomics Nutritional-inflammatory biomarkers Prognosis |
| url | https://doi.org/10.1186/s40644-024-00789-2 |
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