A multimodal fusion system predicting survival benefits of immune checkpoint inhibitors in unresectable hepatocellular carcinoma
Abstract Early identification of unresectable hepatocellular carcinoma (HCC) patients who may benefit from immune checkpoint inhibitors (ICIs) is crucial for optimizing outcomes. Here, we developed a multimodal fusion (MMF) system integrating CT-derived deep learning features and clinical data to pr...
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Nature Portfolio
2025-06-01
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| Series: | npj Precision Oncology |
| Online Access: | https://doi.org/10.1038/s41698-025-00979-6 |
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| author | Jun Xu Tengfei Wang Junjun Li Yong Wang Zhangxiang Zhu Xiao Fu Junjie Wang Zhenglin Zhang Wei Cai Ruipeng Song Changlong Hou Li-Zhuang Yang Hongzhi Wang Stephen T. C. Wong Hai Li |
| author_facet | Jun Xu Tengfei Wang Junjun Li Yong Wang Zhangxiang Zhu Xiao Fu Junjie Wang Zhenglin Zhang Wei Cai Ruipeng Song Changlong Hou Li-Zhuang Yang Hongzhi Wang Stephen T. C. Wong Hai Li |
| author_sort | Jun Xu |
| collection | DOAJ |
| description | Abstract Early identification of unresectable hepatocellular carcinoma (HCC) patients who may benefit from immune checkpoint inhibitors (ICIs) is crucial for optimizing outcomes. Here, we developed a multimodal fusion (MMF) system integrating CT-derived deep learning features and clinical data to predict overall survival (OS) and progression-free survival (PFS). Using retrospective multicenter data (n = 859), the MMF combining an ensemble deep learning (Ensemble-DL) model with clinical variables achieved strong external validation performance (C-index: OS = 0.74, PFS = 0.69), outperforming radiomics (29.8% OS improvement), mRECIST (27.6% OS improvement), clinical benchmarks (C-index: OS = 0.67, p = 0.0011; PFS = 0.65, p = 0.033), and Ensemble-DL (C-index: OS = 0.69, p = 0.0028; PFS = 0.66, p = 0.044). The MMF system effectively stratified patients across clinical subgroups and demonstrated interpretability through activation maps and radiomic correlations. Differential gene expression analysis revealed enrichment of the PI3K/Akt pathway in patients identified by the MMF system. The MMF system provides an interpretable, clinically applicable approach to guide personalized ICI treatment in unresectable HCC. |
| format | Article |
| id | doaj-art-3ebd9e43fddb41eab60b110fcf252ee3 |
| institution | OA Journals |
| issn | 2397-768X |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Precision Oncology |
| spelling | doaj-art-3ebd9e43fddb41eab60b110fcf252ee32025-08-20T02:06:23ZengNature Portfolionpj Precision Oncology2397-768X2025-06-019111610.1038/s41698-025-00979-6A multimodal fusion system predicting survival benefits of immune checkpoint inhibitors in unresectable hepatocellular carcinomaJun Xu0Tengfei Wang1Junjun Li2Yong Wang3Zhangxiang Zhu4Xiao Fu5Junjie Wang6Zhenglin Zhang7Wei Cai8Ruipeng Song9Changlong Hou10Li-Zhuang Yang11Hongzhi Wang12Stephen T. C. Wong13Hai Li14Hefei Cancer Hospital of CAS, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of SciencesHefei Cancer Hospital of CAS, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of SciencesDepartment of Radiology, The First Affiliated Hospital of University of Science and Technology of ChinaDepartment of Radiology, The First Affiliated Hospital of Wannan Medical College (Yijishan Hospital of Wannan Medical College)Department of Radiology, The First Affiliated Hospital of Anhui Medical UniversityHefei Cancer Hospital of CAS, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of SciencesHefei Cancer Hospital of CAS, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of SciencesHefei Cancer Hospital of CAS, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of SciencesDepartment of Hepatobiliary Surgery, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, the University of Science and Technology of China, Anhui Province Key Laboratory of Hepatopancreatobiliary Surgery, Anhui Provincial Clinical Research Center for Hepatobiliary DiseasesDepartment of Hepatobiliary Surgery, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, the University of Science and Technology of China, Anhui Province Key Laboratory of Hepatopancreatobiliary Surgery, Anhui Provincial Clinical Research Center for Hepatobiliary DiseasesDepartment of Interventional Radiology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of ChinaHefei Cancer Hospital of CAS, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of SciencesHefei Cancer Hospital of CAS, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of SciencesDepartment of Systems Medicine and Bioengineering, Houston Methodist Neal Cancer Center, Houston Methodist HospitalHefei Cancer Hospital of CAS, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of SciencesAbstract Early identification of unresectable hepatocellular carcinoma (HCC) patients who may benefit from immune checkpoint inhibitors (ICIs) is crucial for optimizing outcomes. Here, we developed a multimodal fusion (MMF) system integrating CT-derived deep learning features and clinical data to predict overall survival (OS) and progression-free survival (PFS). Using retrospective multicenter data (n = 859), the MMF combining an ensemble deep learning (Ensemble-DL) model with clinical variables achieved strong external validation performance (C-index: OS = 0.74, PFS = 0.69), outperforming radiomics (29.8% OS improvement), mRECIST (27.6% OS improvement), clinical benchmarks (C-index: OS = 0.67, p = 0.0011; PFS = 0.65, p = 0.033), and Ensemble-DL (C-index: OS = 0.69, p = 0.0028; PFS = 0.66, p = 0.044). The MMF system effectively stratified patients across clinical subgroups and demonstrated interpretability through activation maps and radiomic correlations. Differential gene expression analysis revealed enrichment of the PI3K/Akt pathway in patients identified by the MMF system. The MMF system provides an interpretable, clinically applicable approach to guide personalized ICI treatment in unresectable HCC.https://doi.org/10.1038/s41698-025-00979-6 |
| spellingShingle | Jun Xu Tengfei Wang Junjun Li Yong Wang Zhangxiang Zhu Xiao Fu Junjie Wang Zhenglin Zhang Wei Cai Ruipeng Song Changlong Hou Li-Zhuang Yang Hongzhi Wang Stephen T. C. Wong Hai Li A multimodal fusion system predicting survival benefits of immune checkpoint inhibitors in unresectable hepatocellular carcinoma npj Precision Oncology |
| title | A multimodal fusion system predicting survival benefits of immune checkpoint inhibitors in unresectable hepatocellular carcinoma |
| title_full | A multimodal fusion system predicting survival benefits of immune checkpoint inhibitors in unresectable hepatocellular carcinoma |
| title_fullStr | A multimodal fusion system predicting survival benefits of immune checkpoint inhibitors in unresectable hepatocellular carcinoma |
| title_full_unstemmed | A multimodal fusion system predicting survival benefits of immune checkpoint inhibitors in unresectable hepatocellular carcinoma |
| title_short | A multimodal fusion system predicting survival benefits of immune checkpoint inhibitors in unresectable hepatocellular carcinoma |
| title_sort | multimodal fusion system predicting survival benefits of immune checkpoint inhibitors in unresectable hepatocellular carcinoma |
| url | https://doi.org/10.1038/s41698-025-00979-6 |
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