3D synergistic tumor-liver analysis further improves the efficacy prediction in hepatocellular carcinoma: a multi-center study
Abstract Background Besides tumorous information, synergistic liver parenchyma assessments may provide additional insights into the prognosis of hepatocellular carcinoma (HCC). This study aimed to investigate whether 3D synergistic tumor-liver analysis could improve the prediction accuracy for HCC p...
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BMC
2025-01-01
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| Series: | BMC Cancer |
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| Online Access: | https://doi.org/10.1186/s12885-025-13501-9 |
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| author | Yurong Jiang Jiawei Zhang Zhaochen Liu Jinxiong Zhang Xiangrong Yu Danyan Lin Dandan Dong Mingyue Cai Chongyang Duan Shuyi Liu Wenhui Wang Yuan Chen Qiyang Li Weiguo Xu Meiyan Huang Sirui Fu |
| author_facet | Yurong Jiang Jiawei Zhang Zhaochen Liu Jinxiong Zhang Xiangrong Yu Danyan Lin Dandan Dong Mingyue Cai Chongyang Duan Shuyi Liu Wenhui Wang Yuan Chen Qiyang Li Weiguo Xu Meiyan Huang Sirui Fu |
| author_sort | Yurong Jiang |
| collection | DOAJ |
| description | Abstract Background Besides tumorous information, synergistic liver parenchyma assessments may provide additional insights into the prognosis of hepatocellular carcinoma (HCC). This study aimed to investigate whether 3D synergistic tumor-liver analysis could improve the prediction accuracy for HCC prognosis. Methods A total of 422 HCC patients from six centers were included. Datasets were divided into training and external validation datasets. Besides tumor, we also performed automatic 3D assessment of liver parenchyma by extracting morphological and high-dimensional data, respectively. Subsequently, we constructed a tumor model, a tumor-liver model, a clinical model and an integrated model combining information from clinical factors, tumor and liver parenchyma. Their discrimination and calibration were compared to determine the optimal model. Subgroup analysis was conducted to test the robustness, and survival analysis was conducted to identify high- and low-risk populations. Results The tumor-liver model was superior to the tumor model in terms of both discrimination (training dataset: 0.747 vs. 0.722; validation dataset: 0.719 vs. 0.683) and calibration. Moreover, the integrated model was superior to the clinical model and tumor-liver model, particularly in discrimination (training dataset: 0.765 vs. 0.695 vs. 0.747; validation dataset: 0.739 vs. 0.628 vs. 0.719). The AUC of the integrated model was not influenced by AFP level, BCLC stage, Child–Pugh grade, and treatment style in training (6 months p value: 0.245–0.452; 12 months p value: 0.357–0.845) and validation (6 months p value: 0.294–0.638; 12 months p value: 0.365–0.937) datasets. With a risk score of 1.06, high- and low-risk populations demonstrated significant difference for progression-free survival (p < 0.001 in both datasets). Conclusions Combined with clinical factors, 3D synergistic tumor-liver assessment improved the efficacy prediction of HCC. |
| format | Article |
| id | doaj-art-3b7528ccdf694fa5947ada329241cb92 |
| institution | Kabale University |
| issn | 1471-2407 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Cancer |
| spelling | doaj-art-3b7528ccdf694fa5947ada329241cb922025-01-26T12:38:06ZengBMCBMC Cancer1471-24072025-01-0125111210.1186/s12885-025-13501-93D synergistic tumor-liver analysis further improves the efficacy prediction in hepatocellular carcinoma: a multi-center studyYurong Jiang0Jiawei Zhang1Zhaochen Liu2Jinxiong Zhang3Xiangrong Yu4Danyan Lin5Dandan Dong6Mingyue Cai7Chongyang Duan8Shuyi Liu9Wenhui Wang10Yuan Chen11Qiyang Li12Weiguo Xu13Meiyan Huang14Sirui Fu15Department of Radiology, Zhuhai Clinical Medical College of Jinan University (Zhuhai People’s Hospital, The Affiliated Hospital of Beijing Institute of Technology)Department of Radiology, Zhuhai Clinical Medical College of Jinan University (Zhuhai People’s Hospital, The Affiliated Hospital of Beijing Institute of Technology)Department of Hepatobiliary and Pancreatic Surgery, the First Affiliated Hospital of Zhengzhou UniversityDepartment of Radiology, Zhuhai Clinical Medical College of Jinan University (Zhuhai People’s Hospital, The Affiliated Hospital of Beijing Institute of Technology)Department of Radiology, Zhuhai Clinical Medical College of Jinan University (Zhuhai People’s Hospital, The Affiliated Hospital of Beijing Institute of Technology)School of Biomedical Engineering, Southern Medical UniversityDepartment of Radiology, Zhuhai Clinical Medical College of Jinan University (Zhuhai People’s Hospital, The Affiliated Hospital of Beijing Institute of Technology)Department of Minimally Invasive Interventional Radiology, the Second Affiliated Hospital of Guangzhou Medical UniversityDepartment of Biostatistics, School of Public Health, Southern Medical UniversityDepartment of Clinical Medicine, First Clinical Medical College, Southern Medical UniversityDepartment of Medical Imaging, First Clinical Medical College, Southern Medical UniversityDepartment of Interventional Treatment, Zhongshan City People’s HospitalDepartment of Interventional Radiology, The Second Clinical Medical College, Shenzhen People’s Hospital, The Second Clinical Medical College, Jinan University; The First Affiliated Hospital, Southern University of Sciences and TechnologyZhuhai Interventional Medical Center, Zhuhai People’s Hospital, The Affiliated Hospital of Beijing Institute of Technology, Zhuhai Clinical Medical College of Jinan UniversitySchool of Biomedical Engineering, Southern Medical UniversityDepartment of Radiology, Zhuhai Clinical Medical College of Jinan University (Zhuhai People’s Hospital, The Affiliated Hospital of Beijing Institute of Technology)Abstract Background Besides tumorous information, synergistic liver parenchyma assessments may provide additional insights into the prognosis of hepatocellular carcinoma (HCC). This study aimed to investigate whether 3D synergistic tumor-liver analysis could improve the prediction accuracy for HCC prognosis. Methods A total of 422 HCC patients from six centers were included. Datasets were divided into training and external validation datasets. Besides tumor, we also performed automatic 3D assessment of liver parenchyma by extracting morphological and high-dimensional data, respectively. Subsequently, we constructed a tumor model, a tumor-liver model, a clinical model and an integrated model combining information from clinical factors, tumor and liver parenchyma. Their discrimination and calibration were compared to determine the optimal model. Subgroup analysis was conducted to test the robustness, and survival analysis was conducted to identify high- and low-risk populations. Results The tumor-liver model was superior to the tumor model in terms of both discrimination (training dataset: 0.747 vs. 0.722; validation dataset: 0.719 vs. 0.683) and calibration. Moreover, the integrated model was superior to the clinical model and tumor-liver model, particularly in discrimination (training dataset: 0.765 vs. 0.695 vs. 0.747; validation dataset: 0.739 vs. 0.628 vs. 0.719). The AUC of the integrated model was not influenced by AFP level, BCLC stage, Child–Pugh grade, and treatment style in training (6 months p value: 0.245–0.452; 12 months p value: 0.357–0.845) and validation (6 months p value: 0.294–0.638; 12 months p value: 0.365–0.937) datasets. With a risk score of 1.06, high- and low-risk populations demonstrated significant difference for progression-free survival (p < 0.001 in both datasets). Conclusions Combined with clinical factors, 3D synergistic tumor-liver assessment improved the efficacy prediction of HCC.https://doi.org/10.1186/s12885-025-13501-9Hepatocellular carcinomaTumorLiver parenchyma3D assessmentAutomatic segmentationTranscatheter arterial chemoembolization |
| spellingShingle | Yurong Jiang Jiawei Zhang Zhaochen Liu Jinxiong Zhang Xiangrong Yu Danyan Lin Dandan Dong Mingyue Cai Chongyang Duan Shuyi Liu Wenhui Wang Yuan Chen Qiyang Li Weiguo Xu Meiyan Huang Sirui Fu 3D synergistic tumor-liver analysis further improves the efficacy prediction in hepatocellular carcinoma: a multi-center study BMC Cancer Hepatocellular carcinoma Tumor Liver parenchyma 3D assessment Automatic segmentation Transcatheter arterial chemoembolization |
| title | 3D synergistic tumor-liver analysis further improves the efficacy prediction in hepatocellular carcinoma: a multi-center study |
| title_full | 3D synergistic tumor-liver analysis further improves the efficacy prediction in hepatocellular carcinoma: a multi-center study |
| title_fullStr | 3D synergistic tumor-liver analysis further improves the efficacy prediction in hepatocellular carcinoma: a multi-center study |
| title_full_unstemmed | 3D synergistic tumor-liver analysis further improves the efficacy prediction in hepatocellular carcinoma: a multi-center study |
| title_short | 3D synergistic tumor-liver analysis further improves the efficacy prediction in hepatocellular carcinoma: a multi-center study |
| title_sort | 3d synergistic tumor liver analysis further improves the efficacy prediction in hepatocellular carcinoma a multi center study |
| topic | Hepatocellular carcinoma Tumor Liver parenchyma 3D assessment Automatic segmentation Transcatheter arterial chemoembolization |
| url | https://doi.org/10.1186/s12885-025-13501-9 |
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