Predicting overall survival in hepatocellular carcinoma patients via a combined MRI radiomics and pathomics signature
Objective: This study aims to develop and validate a radiopathomics model that integrates radiomic and pathomic features to predict overall survival (OS) in hepatocellular carcinoma (HCC) patients. Materials and methods: This study involved 126 HCC patients who underwent hepatectomy and were followe...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-01-01
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| Series: | Translational Oncology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1936523324003000 |
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| author | Lijuan Feng Wanyun Huang Xiaoyu Pan Fengqiu Ruan Xuan Li Siyuan Tan Liling Long |
| author_facet | Lijuan Feng Wanyun Huang Xiaoyu Pan Fengqiu Ruan Xuan Li Siyuan Tan Liling Long |
| author_sort | Lijuan Feng |
| collection | DOAJ |
| description | Objective: This study aims to develop and validate a radiopathomics model that integrates radiomic and pathomic features to predict overall survival (OS) in hepatocellular carcinoma (HCC) patients. Materials and methods: This study involved 126 HCC patients who underwent hepatectomy and were followed for more than 5 years. Radiomic features were extracted from arterial-phase (AP) and portal venous-phase (PVP) MRI scans, whereas pathomic features were obtained from whole-slide images (WSIs) of the HCC patients. Using LASSO Cox regression, both radiomics and pathomics signatures were established. A combined radiopathomics nomogram for predicting OS was constructed and validated. The correlation between the radiopathomics nomogram and OS prediction was evaluated, demonstrating its potential clinical utility in prognosis assessment. Results: We selected four radiomic features from the AP and PVP MRI scans to construct a signature, achieving a concordance index (C-index) of 0.739 in the training cohort and 0.724 in the validation cohort; these results indicate favourable 5-year OS prediction. Similarly, from 1,141 pathomics features extracted from WSIs, 15 were chosen for a pathomics signature, which had C-indexes of 0.821 and 0.808 in the training and validation cohorts, respectively. The most robust performance was delivered by a radiopathomics nomogram, with C-index values of 0.840 in the training cohort and 0.875 in the validation cohort. Decision curve analysis (DCA) confirmed the highest net benefit achievable by the combined radiopathomics nomogram. Conclusion: Our findings indicate that the radiopathomics nomogram can serve as a predictive marker for hepatectomy prognosis in HCC patients and has the potential to enhance personalized therapeutic approaches. |
| format | Article |
| id | doaj-art-77f15db48b7840b2ace1ebd8ea894306 |
| institution | OA Journals |
| issn | 1936-5233 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Translational Oncology |
| spelling | doaj-art-77f15db48b7840b2ace1ebd8ea8943062025-08-20T02:30:39ZengElsevierTranslational Oncology1936-52332025-01-015110217410.1016/j.tranon.2024.102174Predicting overall survival in hepatocellular carcinoma patients via a combined MRI radiomics and pathomics signatureLijuan Feng0Wanyun Huang1Xiaoyu Pan2Fengqiu Ruan3Xuan Li4Siyuan Tan5Liling Long6Department of Radiology, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, PR ChinaDepartment of Radiology, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, PR ChinaDepartment of Radiology, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, PR ChinaDepartment of Radiology, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, PR ChinaDepartment of Radiology, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, PR ChinaDepartment of Radiology, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, PR ChinaDepartment of Radiology, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, PR China; Key Laboratory of Early Prevention and Treatment for Regional High Frequency Tumor, Gaungxi Medical University, Ministry of Education, Nanning, Guangxi, PR China; Guangxi Key Laboratory of Immunology and Metabolism for Liver Diseases, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, PR China; Corresponding author at: Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, No 6, Shuangyong Road, Nanning 530021, Guangxi Zhuang Autonomous Region, PR China.Objective: This study aims to develop and validate a radiopathomics model that integrates radiomic and pathomic features to predict overall survival (OS) in hepatocellular carcinoma (HCC) patients. Materials and methods: This study involved 126 HCC patients who underwent hepatectomy and were followed for more than 5 years. Radiomic features were extracted from arterial-phase (AP) and portal venous-phase (PVP) MRI scans, whereas pathomic features were obtained from whole-slide images (WSIs) of the HCC patients. Using LASSO Cox regression, both radiomics and pathomics signatures were established. A combined radiopathomics nomogram for predicting OS was constructed and validated. The correlation between the radiopathomics nomogram and OS prediction was evaluated, demonstrating its potential clinical utility in prognosis assessment. Results: We selected four radiomic features from the AP and PVP MRI scans to construct a signature, achieving a concordance index (C-index) of 0.739 in the training cohort and 0.724 in the validation cohort; these results indicate favourable 5-year OS prediction. Similarly, from 1,141 pathomics features extracted from WSIs, 15 were chosen for a pathomics signature, which had C-indexes of 0.821 and 0.808 in the training and validation cohorts, respectively. The most robust performance was delivered by a radiopathomics nomogram, with C-index values of 0.840 in the training cohort and 0.875 in the validation cohort. Decision curve analysis (DCA) confirmed the highest net benefit achievable by the combined radiopathomics nomogram. Conclusion: Our findings indicate that the radiopathomics nomogram can serve as a predictive marker for hepatectomy prognosis in HCC patients and has the potential to enhance personalized therapeutic approaches.http://www.sciencedirect.com/science/article/pii/S1936523324003000Hepatocellular carcinomaRadiomicsPathomicsOverall survivalMRI |
| spellingShingle | Lijuan Feng Wanyun Huang Xiaoyu Pan Fengqiu Ruan Xuan Li Siyuan Tan Liling Long Predicting overall survival in hepatocellular carcinoma patients via a combined MRI radiomics and pathomics signature Translational Oncology Hepatocellular carcinoma Radiomics Pathomics Overall survival MRI |
| title | Predicting overall survival in hepatocellular carcinoma patients via a combined MRI radiomics and pathomics signature |
| title_full | Predicting overall survival in hepatocellular carcinoma patients via a combined MRI radiomics and pathomics signature |
| title_fullStr | Predicting overall survival in hepatocellular carcinoma patients via a combined MRI radiomics and pathomics signature |
| title_full_unstemmed | Predicting overall survival in hepatocellular carcinoma patients via a combined MRI radiomics and pathomics signature |
| title_short | Predicting overall survival in hepatocellular carcinoma patients via a combined MRI radiomics and pathomics signature |
| title_sort | predicting overall survival in hepatocellular carcinoma patients via a combined mri radiomics and pathomics signature |
| topic | Hepatocellular carcinoma Radiomics Pathomics Overall survival MRI |
| url | http://www.sciencedirect.com/science/article/pii/S1936523324003000 |
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