Preoperative prediction of microvascular invasion and relapse-free survival in hepatocellular Carcinoma ≥3 cm using CT radiomics: Development and external validation
Abstract Objective To preoperatively predict microvascular invasion (MVI) and relapse-free survival (RFS) in hepatocellular carcinoma (HCC) ≥3 cm by constructing and externally validating a combined radiomics model using preoperative enhanced CT images. Methods This retrospective study recruited adu...
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BMC
2025-05-01
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| Online Access: | https://doi.org/10.1186/s12880-025-01677-2 |
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| author | Hua Zhong Yan Zhang Guanbin Zhu Xiaoli Zheng Jinan Wang Jianghe Kang Ziying Lin Xin Yue |
| author_facet | Hua Zhong Yan Zhang Guanbin Zhu Xiaoli Zheng Jinan Wang Jianghe Kang Ziying Lin Xin Yue |
| author_sort | Hua Zhong |
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| description | Abstract Objective To preoperatively predict microvascular invasion (MVI) and relapse-free survival (RFS) in hepatocellular carcinoma (HCC) ≥3 cm by constructing and externally validating a combined radiomics model using preoperative enhanced CT images. Methods This retrospective study recruited adults who underwent surgical resection between September 2016 and August 2020 in our hospital with pathologic confirmation of HCC ≥3 cm and MVI status. For external validation, adults who underwent surgical resection between September 2020 and August 2021 in our hospital were included. Histopathology was the reference standard. The HCC area was segmented on the arterial and portal venous phase CT images to develop a CT radiomics model. A combined model was developed using selected radiomics features, demographic information, laboratory index and radiological features. Analysis of variance and support vector machine were used as features selector and classifier. Receiver operating characteristic (ROC) curves, calibration curves and decision curve analysis (DCA) were used to evaluate models’ performance. The Kaplan-Meier method and log-rank test were used to evaluate the predictive value for RFS. Results A total of 202 patients were finally enrolled (median age, 59 years, 173 male). Thirteen and 24 features were selected for the CT radiomics model and the combined model, and the area under the ROC curves (AUC) were 0.752 (95 %CI 0.615, 0.889) and 0.890 (95 %CI 0.794, 0.985) in the external validation set, respectively. Calibration curves and DCA showed a higher net clinical benefit of the combined model. The high-risk group (P < 0.001) was an independent predictor for RFS. Conclusions The combined model showed high accuracy for preoperatively predicting MVI and RFS in HCC ≥3 cm. |
| format | Article |
| id | doaj-art-cecdcc2b9d4d4f4c8160002ccb1978be |
| institution | OA Journals |
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| publishDate | 2025-05-01 |
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| series | BMC Medical Imaging |
| spelling | doaj-art-cecdcc2b9d4d4f4c8160002ccb1978be2025-08-20T01:47:32ZengBMCBMC Medical Imaging1471-23422025-05-0125111010.1186/s12880-025-01677-2Preoperative prediction of microvascular invasion and relapse-free survival in hepatocellular Carcinoma ≥3 cm using CT radiomics: Development and external validationHua Zhong0Yan Zhang1Guanbin Zhu2Xiaoli Zheng3Jinan Wang4Jianghe Kang5Ziying Lin6Xin Yue7Department of Radiology, Zhongshan Hospital of Xiamen University, School of Medicine, Xiamen UniversityThe Second Department of Radiology, The Second Affiliated Hospital of Xiamen Medical CollegeDepartment of Radiology, Zhongshan Hospital of Xiamen University, School of Medicine, Xiamen UniversityDepartment of Radiology, Zhongshan Hospital of Xiamen University, School of Medicine, Xiamen UniversityDepartment of Radiology, Zhongshan Hospital of Xiamen University, School of Medicine, Xiamen UniversityDepartment of Radiology, Zhongshan Hospital of Xiamen University, School of Medicine, Xiamen UniversityDepartment of Radiology, Zhongshan Hospital of Xiamen University, School of Medicine, Xiamen UniversityDepartment of Radiology, Zhongshan Hospital of Xiamen University, School of Medicine, Xiamen UniversityAbstract Objective To preoperatively predict microvascular invasion (MVI) and relapse-free survival (RFS) in hepatocellular carcinoma (HCC) ≥3 cm by constructing and externally validating a combined radiomics model using preoperative enhanced CT images. Methods This retrospective study recruited adults who underwent surgical resection between September 2016 and August 2020 in our hospital with pathologic confirmation of HCC ≥3 cm and MVI status. For external validation, adults who underwent surgical resection between September 2020 and August 2021 in our hospital were included. Histopathology was the reference standard. The HCC area was segmented on the arterial and portal venous phase CT images to develop a CT radiomics model. A combined model was developed using selected radiomics features, demographic information, laboratory index and radiological features. Analysis of variance and support vector machine were used as features selector and classifier. Receiver operating characteristic (ROC) curves, calibration curves and decision curve analysis (DCA) were used to evaluate models’ performance. The Kaplan-Meier method and log-rank test were used to evaluate the predictive value for RFS. Results A total of 202 patients were finally enrolled (median age, 59 years, 173 male). Thirteen and 24 features were selected for the CT radiomics model and the combined model, and the area under the ROC curves (AUC) were 0.752 (95 %CI 0.615, 0.889) and 0.890 (95 %CI 0.794, 0.985) in the external validation set, respectively. Calibration curves and DCA showed a higher net clinical benefit of the combined model. The high-risk group (P < 0.001) was an independent predictor for RFS. Conclusions The combined model showed high accuracy for preoperatively predicting MVI and RFS in HCC ≥3 cm.https://doi.org/10.1186/s12880-025-01677-2Microvascular invasionHepatocellular carcinomaRadiomicsCTRelapse-free survival |
| spellingShingle | Hua Zhong Yan Zhang Guanbin Zhu Xiaoli Zheng Jinan Wang Jianghe Kang Ziying Lin Xin Yue Preoperative prediction of microvascular invasion and relapse-free survival in hepatocellular Carcinoma ≥3 cm using CT radiomics: Development and external validation BMC Medical Imaging Microvascular invasion Hepatocellular carcinoma Radiomics CT Relapse-free survival |
| title | Preoperative prediction of microvascular invasion and relapse-free survival in hepatocellular Carcinoma ≥3 cm using CT radiomics: Development and external validation |
| title_full | Preoperative prediction of microvascular invasion and relapse-free survival in hepatocellular Carcinoma ≥3 cm using CT radiomics: Development and external validation |
| title_fullStr | Preoperative prediction of microvascular invasion and relapse-free survival in hepatocellular Carcinoma ≥3 cm using CT radiomics: Development and external validation |
| title_full_unstemmed | Preoperative prediction of microvascular invasion and relapse-free survival in hepatocellular Carcinoma ≥3 cm using CT radiomics: Development and external validation |
| title_short | Preoperative prediction of microvascular invasion and relapse-free survival in hepatocellular Carcinoma ≥3 cm using CT radiomics: Development and external validation |
| title_sort | preoperative prediction of microvascular invasion and relapse free survival in hepatocellular carcinoma ≥3 cm using ct radiomics development and external validation |
| topic | Microvascular invasion Hepatocellular carcinoma Radiomics CT Relapse-free survival |
| url | https://doi.org/10.1186/s12880-025-01677-2 |
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