Prediction of prognosis of immune checkpoint inhibitors combined with anti-angiogenic agents for unresectable hepatocellular carcinoma by machine learning-based radiomics
Abstract Objectives This study aims to develop and validate a novel radiomics model utilizing magnetic resonance imaging (MRI) to predict progression-free survival (PFS) in patients with unresectable hepatocellular carcinoma (uHCC) who are receiving a combination of immune checkpoint inhibitors (ICI...
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BMC
2025-05-01
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| Series: | BMC Cancer |
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| Online Access: | https://doi.org/10.1186/s12885-025-14247-0 |
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| author | Xuni Xu Xue Jiang Haoran Jiang Xiaoye Yuan Mengjing Zhao Yuqi Wang Gang Chen Gang Li Yuxia Duan |
| author_facet | Xuni Xu Xue Jiang Haoran Jiang Xiaoye Yuan Mengjing Zhao Yuqi Wang Gang Chen Gang Li Yuxia Duan |
| author_sort | Xuni Xu |
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| description | Abstract Objectives This study aims to develop and validate a novel radiomics model utilizing magnetic resonance imaging (MRI) to predict progression-free survival (PFS) in patients with unresectable hepatocellular carcinoma (uHCC) who are receiving a combination of immune checkpoint inhibitors (ICIs) and antiangiogenic agents. This is an area that has not been previously explored using MRI-based radiomics. Methods 111 patients with uHCC were enrolled in this study. After performing univariate cox regression and the least absolute shrinkage and selection operator (LASSO) algorithms to extract radiological features, the Rad-score was calculated through a Cox proportional hazards regression model and a random survival forest (RSF) model. The optimal calculation method was selected by comparing the Harrell’s concordance index (C-index) values. The Rad-score was then combined with independent clinical risk factors to create a nomogram. C-index, time-dependent receiver operating characteristics (ROC) curves, calibration curves, and decision curve analysis were employed to assess the forecast ability of the risk models. Results The combined nomogram incorporated independent clinical factors and Rad-score calculated by RSF demonstrated better prognosis prediction for PFS, with C-index of 0.846, 0.845, separately in the training and the validation cohorts. This indicates that our model performs well and has the potential to enable more precise patient stratification and personalized treatment strategies. Based on the risk level, the participants were classified into two distinct groups: the high-risk signature (HRS) group and the low-risk signature (LRS) group, with a significant difference between the groups (P < 0.01). Conclusion The effective clinical-radiomics nomogram based on MRI imaging is a promising tool in predicting the prognosis in uHCC patients receiving ICIs combined with anti-angiogenic agents, potentially leading to more effective clinical outcomes. |
| format | Article |
| id | doaj-art-502cdbd18d5f42dc9fd0e87b0d7c29b3 |
| institution | Kabale University |
| issn | 1471-2407 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Cancer |
| spelling | doaj-art-502cdbd18d5f42dc9fd0e87b0d7c29b32025-08-20T03:48:18ZengBMCBMC Cancer1471-24072025-05-0125111810.1186/s12885-025-14247-0Prediction of prognosis of immune checkpoint inhibitors combined with anti-angiogenic agents for unresectable hepatocellular carcinoma by machine learning-based radiomicsXuni Xu0Xue Jiang1Haoran Jiang2Xiaoye Yuan3Mengjing Zhao4Yuqi Wang5Gang Chen6Gang Li7Yuxia Duan8Department of Radiology, Shaoxing Central Hospital, The Central Affiliated Hospital, Shaoxing UniversityDepartment of Pathology, Jinhua Municipal Central Hospital, Affiliated Jinhua Hospital, Zhejiang University School of MedicineDepartment of Radiation and Chemotherapy Oncology, The First Affiliated Hospital of Wenzhou Medical UniversityDepartment of Radiation and Chemotherapy Oncology, The First Affiliated Hospital of Wenzhou Medical UniversityDepartment of Radiology, The First Affiliated Hospital of Wenzhou Medical UniversityDepartment of Radiation and Chemotherapy Oncology, The First Affiliated Hospital of Wenzhou Medical UniversityDepartment of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical UniversityDepartment of Radiation and Chemotherapy Oncology, The First Affiliated Hospital of Wenzhou Medical UniversityDepartment of Radiology, The First Affiliated Hospital of Wenzhou Medical UniversityAbstract Objectives This study aims to develop and validate a novel radiomics model utilizing magnetic resonance imaging (MRI) to predict progression-free survival (PFS) in patients with unresectable hepatocellular carcinoma (uHCC) who are receiving a combination of immune checkpoint inhibitors (ICIs) and antiangiogenic agents. This is an area that has not been previously explored using MRI-based radiomics. Methods 111 patients with uHCC were enrolled in this study. After performing univariate cox regression and the least absolute shrinkage and selection operator (LASSO) algorithms to extract radiological features, the Rad-score was calculated through a Cox proportional hazards regression model and a random survival forest (RSF) model. The optimal calculation method was selected by comparing the Harrell’s concordance index (C-index) values. The Rad-score was then combined with independent clinical risk factors to create a nomogram. C-index, time-dependent receiver operating characteristics (ROC) curves, calibration curves, and decision curve analysis were employed to assess the forecast ability of the risk models. Results The combined nomogram incorporated independent clinical factors and Rad-score calculated by RSF demonstrated better prognosis prediction for PFS, with C-index of 0.846, 0.845, separately in the training and the validation cohorts. This indicates that our model performs well and has the potential to enable more precise patient stratification and personalized treatment strategies. Based on the risk level, the participants were classified into two distinct groups: the high-risk signature (HRS) group and the low-risk signature (LRS) group, with a significant difference between the groups (P < 0.01). Conclusion The effective clinical-radiomics nomogram based on MRI imaging is a promising tool in predicting the prognosis in uHCC patients receiving ICIs combined with anti-angiogenic agents, potentially leading to more effective clinical outcomes.https://doi.org/10.1186/s12885-025-14247-0Hepatocellular carcinomaRadiomicsProgression‑free survivalImmune checkpoint inhibitorsAnti-angiogenic agents |
| spellingShingle | Xuni Xu Xue Jiang Haoran Jiang Xiaoye Yuan Mengjing Zhao Yuqi Wang Gang Chen Gang Li Yuxia Duan Prediction of prognosis of immune checkpoint inhibitors combined with anti-angiogenic agents for unresectable hepatocellular carcinoma by machine learning-based radiomics BMC Cancer Hepatocellular carcinoma Radiomics Progression‑free survival Immune checkpoint inhibitors Anti-angiogenic agents |
| title | Prediction of prognosis of immune checkpoint inhibitors combined with anti-angiogenic agents for unresectable hepatocellular carcinoma by machine learning-based radiomics |
| title_full | Prediction of prognosis of immune checkpoint inhibitors combined with anti-angiogenic agents for unresectable hepatocellular carcinoma by machine learning-based radiomics |
| title_fullStr | Prediction of prognosis of immune checkpoint inhibitors combined with anti-angiogenic agents for unresectable hepatocellular carcinoma by machine learning-based radiomics |
| title_full_unstemmed | Prediction of prognosis of immune checkpoint inhibitors combined with anti-angiogenic agents for unresectable hepatocellular carcinoma by machine learning-based radiomics |
| title_short | Prediction of prognosis of immune checkpoint inhibitors combined with anti-angiogenic agents for unresectable hepatocellular carcinoma by machine learning-based radiomics |
| title_sort | prediction of prognosis of immune checkpoint inhibitors combined with anti angiogenic agents for unresectable hepatocellular carcinoma by machine learning based radiomics |
| topic | Hepatocellular carcinoma Radiomics Progression‑free survival Immune checkpoint inhibitors Anti-angiogenic agents |
| url | https://doi.org/10.1186/s12885-025-14247-0 |
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