Transfer learning radiomic model predicts intratumoral tertiary lymphoid structures in hepatocellular carcinoma: a multicenter study
Background Intratumoral tertiary lymphoid structures (iTLS) in hepatocellular carcinoma (HCC) are associated with improved survival and may influence treatment decisions. However, their non-invasive detection remains challenging in HCC. We aim to develop a non-invasive model using baseline contrast-...
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BMJ Publishing Group
2025-03-01
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| Series: | Journal for ImmunoTherapy of Cancer |
| Online Access: | https://jitc.bmj.com/content/13/3/e011126.full |
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| author | Juan Chen Xiong Chen Kai Fu Feng Yi Shichao Long Mengsi Li Linhui Zhong Wenguang Liu Deng Pan Ganmian Dai Wenzheng Li Yue Ruan Bocheng Zou |
| author_facet | Juan Chen Xiong Chen Kai Fu Feng Yi Shichao Long Mengsi Li Linhui Zhong Wenguang Liu Deng Pan Ganmian Dai Wenzheng Li Yue Ruan Bocheng Zou |
| author_sort | Juan Chen |
| collection | DOAJ |
| description | Background Intratumoral tertiary lymphoid structures (iTLS) in hepatocellular carcinoma (HCC) are associated with improved survival and may influence treatment decisions. However, their non-invasive detection remains challenging in HCC. We aim to develop a non-invasive model using baseline contrast-enhanced MRI to predict the iTLS status.Methods A total of 660 patients with HCC who underwent surgery were retrospectively recruited from four centers between October 2015 and January 2023 and divided into training, internal test, and external validation sets. After features dimensionality and selection, corresponding features were used to construct transfer learning radiomic (TLR) models for diagnosing iTLS, and model interpretability was explored with pathway analysis in The Cancer Genome Atlas-Liver HCC. The performances of models were assessed using the area under the receiver operating characteristic curve (AUC). The log-rank test was used to evaluate the prognostic value of the TLR model. The combination therapy set of 101 patients with advanced HCC treated with first-line anti-programmed death 1 or ligand 1 plus antiangiogenic treatment between January 2021 and January 2024 was used to investigate the value of the TLR model for evaluating the treatment response.Results The presence of iTLS was identified in 46.0% (n=308) patients. The TLR model demonstrated excellent performance in predicting the presence of iTLS in training (AUC=0.91, 95% CI: 0.87, 0.94), internal test (AUC=0.85, 95% CI: 0.77, 0.93) and external validation set (AUC=0.85, 95% CI: 0.81, 0.90). The TLR model-predicted iTLS group has favorable overall survival (HR=0.66; 95% CI: 0.48, 0.90; p=0.007) and relapse-free survival (HR=0.64; 95% CI: 0.48, 0.85; p=0.001) in the external validation set. The model-predicted iTLS status was associated with inflammatory response and specific tumor-associated signaling activation (all p<0.001). The proportion of treatment responders was significantly higher in the model-predicted group with iTLS than in the group without iTLS (36% vs 13.73%, p=0.009).Conclusion The TLR model has indicated accurate prediction of iTLS status, which may assist in the risk stratification for patients with HCC in clinical practice. |
| format | Article |
| id | doaj-art-c22eb4f5191e4209b459b84ed99356c3 |
| institution | DOAJ |
| issn | 2051-1426 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | BMJ Publishing Group |
| record_format | Article |
| series | Journal for ImmunoTherapy of Cancer |
| spelling | doaj-art-c22eb4f5191e4209b459b84ed99356c32025-08-20T03:15:17ZengBMJ Publishing GroupJournal for ImmunoTherapy of Cancer2051-14262025-03-0113310.1136/jitc-2024-011126Transfer learning radiomic model predicts intratumoral tertiary lymphoid structures in hepatocellular carcinoma: a multicenter studyJuan Chen0Xiong Chen1Kai Fu2Feng Yi3Shichao Long4Mengsi Li5Linhui Zhong6Wenguang Liu7Deng Pan8Ganmian Dai9Wenzheng Li10Yue Ruan11Bocheng Zou12Department of Radiology, and National Clinical Research Center for Geriatric Disorders, Xiangya Hospital Central South University, Changsha, Hunan, ChinaDepartment of Oncology, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, Fujian, ChinaInstitute of Molecular Precision Medicine and Hunan Key Laboratory of Molecular Precision Medicine, Xiangya Hospital Central South University Department of General Surgery, Changsha, Hunan, ChinaInstitute of Molecular Precision Medicine and Hunan Key Laboratory of Molecular Precision Medicine, Xiangya Hospital Central South University Department of General Surgery, Changsha, Hunan, ChinaDepartment of Radiology, and National Clinical Research Center for Geriatric Disorders, Xiangya Hospital Central South University, Changsha, Hunan, ChinaDepartment of Radiology, and National Clinical Research Center for Geriatric Disorders, Xiangya Hospital Central South University, Changsha, Hunan, ChinaDepartment of Radiology, and National Clinical Research Center for Geriatric Disorders, Xiangya Hospital Central South University, Changsha, Hunan, ChinaDepartment of Radiology, and National Clinical Research Center for Geriatric Disorders, Xiangya Hospital Central South University, Changsha, Hunan, ChinaDepartment of Nuclear Medicine, Hainan Cancer Hospital, Haikou, Hainan, ChinaDepartment of Radiology, The Second Affiliated Hospital of Hainan Medical University, Haikou, Hainan, ChinaDepartment of Radiology, and National Clinical Research Center for Geriatric Disorders, Xiangya Hospital Central South University, Changsha, Hunan, ChinaDepartment of Radiology, and National Clinical Research Center for Geriatric Disorders, Xiangya Hospital Central South University, Changsha, Hunan, ChinaDepartment of Radiology, and National Clinical Research Center for Geriatric Disorders, Xiangya Hospital Central South University, Changsha, Hunan, ChinaBackground Intratumoral tertiary lymphoid structures (iTLS) in hepatocellular carcinoma (HCC) are associated with improved survival and may influence treatment decisions. However, their non-invasive detection remains challenging in HCC. We aim to develop a non-invasive model using baseline contrast-enhanced MRI to predict the iTLS status.Methods A total of 660 patients with HCC who underwent surgery were retrospectively recruited from four centers between October 2015 and January 2023 and divided into training, internal test, and external validation sets. After features dimensionality and selection, corresponding features were used to construct transfer learning radiomic (TLR) models for diagnosing iTLS, and model interpretability was explored with pathway analysis in The Cancer Genome Atlas-Liver HCC. The performances of models were assessed using the area under the receiver operating characteristic curve (AUC). The log-rank test was used to evaluate the prognostic value of the TLR model. The combination therapy set of 101 patients with advanced HCC treated with first-line anti-programmed death 1 or ligand 1 plus antiangiogenic treatment between January 2021 and January 2024 was used to investigate the value of the TLR model for evaluating the treatment response.Results The presence of iTLS was identified in 46.0% (n=308) patients. The TLR model demonstrated excellent performance in predicting the presence of iTLS in training (AUC=0.91, 95% CI: 0.87, 0.94), internal test (AUC=0.85, 95% CI: 0.77, 0.93) and external validation set (AUC=0.85, 95% CI: 0.81, 0.90). The TLR model-predicted iTLS group has favorable overall survival (HR=0.66; 95% CI: 0.48, 0.90; p=0.007) and relapse-free survival (HR=0.64; 95% CI: 0.48, 0.85; p=0.001) in the external validation set. The model-predicted iTLS status was associated with inflammatory response and specific tumor-associated signaling activation (all p<0.001). The proportion of treatment responders was significantly higher in the model-predicted group with iTLS than in the group without iTLS (36% vs 13.73%, p=0.009).Conclusion The TLR model has indicated accurate prediction of iTLS status, which may assist in the risk stratification for patients with HCC in clinical practice.https://jitc.bmj.com/content/13/3/e011126.full |
| spellingShingle | Juan Chen Xiong Chen Kai Fu Feng Yi Shichao Long Mengsi Li Linhui Zhong Wenguang Liu Deng Pan Ganmian Dai Wenzheng Li Yue Ruan Bocheng Zou Transfer learning radiomic model predicts intratumoral tertiary lymphoid structures in hepatocellular carcinoma: a multicenter study Journal for ImmunoTherapy of Cancer |
| title | Transfer learning radiomic model predicts intratumoral tertiary lymphoid structures in hepatocellular carcinoma: a multicenter study |
| title_full | Transfer learning radiomic model predicts intratumoral tertiary lymphoid structures in hepatocellular carcinoma: a multicenter study |
| title_fullStr | Transfer learning radiomic model predicts intratumoral tertiary lymphoid structures in hepatocellular carcinoma: a multicenter study |
| title_full_unstemmed | Transfer learning radiomic model predicts intratumoral tertiary lymphoid structures in hepatocellular carcinoma: a multicenter study |
| title_short | Transfer learning radiomic model predicts intratumoral tertiary lymphoid structures in hepatocellular carcinoma: a multicenter study |
| title_sort | transfer learning radiomic model predicts intratumoral tertiary lymphoid structures in hepatocellular carcinoma a multicenter study |
| url | https://jitc.bmj.com/content/13/3/e011126.full |
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