Radiomic Model Associated with Tumor Microenvironment Predicts Immunotherapy Response and Prognosis in Patients with Locoregionally Advanced Nasopharyngeal Carcinoma
Background: No robust biomarkers have been identified to predict the efficacy of programmed cell death protein 1 (PD-1) inhibitors in patients with locoregionally advanced nasopharyngeal carcinoma (LANPC). We aimed to develop radiomic models using pre-immunotherapy MRI to predict the response to PD-...
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American Association for the Advancement of Science (AAAS)
2025-01-01
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| Online Access: | https://spj.science.org/doi/10.34133/research.0749 |
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| author | Jie Sun Xuewei Wu Xiao Zhang Weiyuan Huang Xi Zhong Xueyan Li Kaiming Xue Shuyi Liu Xianjie Chen Wenzhu Li Xin Liu Hui Shen Jingjing You Wenle He Zhe Jin Lijuan Yu Yuange Li Shuixing Zhang Bin Zhang |
| author_facet | Jie Sun Xuewei Wu Xiao Zhang Weiyuan Huang Xi Zhong Xueyan Li Kaiming Xue Shuyi Liu Xianjie Chen Wenzhu Li Xin Liu Hui Shen Jingjing You Wenle He Zhe Jin Lijuan Yu Yuange Li Shuixing Zhang Bin Zhang |
| author_sort | Jie Sun |
| collection | DOAJ |
| description | Background: No robust biomarkers have been identified to predict the efficacy of programmed cell death protein 1 (PD-1) inhibitors in patients with locoregionally advanced nasopharyngeal carcinoma (LANPC). We aimed to develop radiomic models using pre-immunotherapy MRI to predict the response to PD-1 inhibitors and the patient prognosis. Methods: This study included 246 LANPC patients (training cohort, n = 117; external test cohort, n = 129) from 10 centers. The best-performing machine learning classifier was employed to create the radiomic models. A combined model was constructed by integrating clinical and radiomic data. A radiomic interpretability study was performed with whole slide images (WSIs) stained with hematoxylin and eosin (H&E) and immunohistochemistry (IHC). A total of 150 patient-level nuclear morphological features (NMFs) and 12 cell spatial distribution features (CSDFs) were extracted from WSIs. The correlation between the radiomic and pathological features was assessed using Spearman correlation analysis. Results: The radiomic model outperformed the clinical and combined models in predicting treatment response (area under the curve: 0.760 vs. 0.559 vs. 0.652). For overall survival estimation, the combined model performed comparably to the radiomic model but outperformed the clinical model (concordance index: 0.858 vs. 0.812 vs. 0.664). Six treatment response-related radiomic features correlated with 50 H&E-derived (146 pairs, |r|= 0.31 to 0.46) and 2 to 26 IHC-derived NMF, particularly for CD45RO (69 pairs, |r|= 0.31 to 0.48), CD8 (84, |r|= 0.30 to 0.59), PD-L1 (73, |r|= 0.32 to 0.48), and CD163 (53, |r| = 0.32 to 0.59). Eight prognostic radiomic features correlated with 11 H&E-derived (16 pairs, |r|= 0.48 to 0.61) and 2 to 31 IHC-derived NMF, particularly for PD-L1 (80 pairs, |r|= 0.44 to 0.64), CD45RO (65, |r|= 0.42 to 0.67), CD19 (35, |r|= 0.44 to 0.58), CD66b (61, |r| = 0.42 to 0.67), and FOXP3 (21, |r| = 0.41 to 0.71). In contrast, fewer CSDFs exhibited correlations with specific radiomic features. Conclusion: The radiomic model and combined model are feasible in predicting immunotherapy response and outcomes in LANPC patients. The radiology–pathology correlation suggests a potential biological basis for the predictive models. |
| format | Article |
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| publishDate | 2025-01-01 |
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| spelling | doaj-art-a7539ce66b3e40c98f0843ddfb1b1f8d2025-06-24T22:24:14ZengAmerican Association for the Advancement of Science (AAAS)Research2639-52742025-01-01810.34133/research.0749Radiomic Model Associated with Tumor Microenvironment Predicts Immunotherapy Response and Prognosis in Patients with Locoregionally Advanced Nasopharyngeal CarcinomaJie Sun0Xuewei Wu1Xiao Zhang2Weiyuan Huang3Xi Zhong4Xueyan Li5Kaiming Xue6Shuyi Liu7Xianjie Chen8Wenzhu Li9Xin Liu10Hui Shen11Jingjing You12Wenle He13Zhe Jin14Lijuan Yu15Yuange Li16Shuixing Zhang17Bin Zhang18Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China.Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China.Medical AI Lab, The First Hospital of Hebei Medical University, Hebei Medical University, Shijiazhuang, Hebei, China.Department of Radiology, Hainan Affiliated Hospital of Hainan Medical University (Hainan General Hospital), Haikou, Hainan, China.Department of Medical Imaging, Guangzhou Institute of Cancer Research, the Affiliated Cancer Hospital, Guangzhou Medical University, Guangzhou, Guangdong, China.Department of Radiology, Hainan Cancer Hospital, Haikou, Hainan, China.Department of Radiology, The Third Bethune Hospital of Jilin University, Changchun, Jilin, China.Department of Radiology, Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou, Guangdong, China.Department of Radiology, The Affiliated Panyu Central Hospital, Guangzhou Medical University, Guangzhou, Guangdong, China.Department of Radiology, Hainan Affiliated Hospital of Hainan Medical University (Hainan General Hospital), Haikou, Hainan, China.Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China.Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China.Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China.Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China.Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China.Department of Radiology, Hainan Affiliated Hospital of Hainan Medical University (Hainan General Hospital), Haikou, Hainan, China.Department of Radiology, Affiliated Hospital of Guangdong Medical University, Zhanjiang, Guangdong, China.Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China.Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China.Background: No robust biomarkers have been identified to predict the efficacy of programmed cell death protein 1 (PD-1) inhibitors in patients with locoregionally advanced nasopharyngeal carcinoma (LANPC). We aimed to develop radiomic models using pre-immunotherapy MRI to predict the response to PD-1 inhibitors and the patient prognosis. Methods: This study included 246 LANPC patients (training cohort, n = 117; external test cohort, n = 129) from 10 centers. The best-performing machine learning classifier was employed to create the radiomic models. A combined model was constructed by integrating clinical and radiomic data. A radiomic interpretability study was performed with whole slide images (WSIs) stained with hematoxylin and eosin (H&E) and immunohistochemistry (IHC). A total of 150 patient-level nuclear morphological features (NMFs) and 12 cell spatial distribution features (CSDFs) were extracted from WSIs. The correlation between the radiomic and pathological features was assessed using Spearman correlation analysis. Results: The radiomic model outperformed the clinical and combined models in predicting treatment response (area under the curve: 0.760 vs. 0.559 vs. 0.652). For overall survival estimation, the combined model performed comparably to the radiomic model but outperformed the clinical model (concordance index: 0.858 vs. 0.812 vs. 0.664). Six treatment response-related radiomic features correlated with 50 H&E-derived (146 pairs, |r|= 0.31 to 0.46) and 2 to 26 IHC-derived NMF, particularly for CD45RO (69 pairs, |r|= 0.31 to 0.48), CD8 (84, |r|= 0.30 to 0.59), PD-L1 (73, |r|= 0.32 to 0.48), and CD163 (53, |r| = 0.32 to 0.59). Eight prognostic radiomic features correlated with 11 H&E-derived (16 pairs, |r|= 0.48 to 0.61) and 2 to 31 IHC-derived NMF, particularly for PD-L1 (80 pairs, |r|= 0.44 to 0.64), CD45RO (65, |r|= 0.42 to 0.67), CD19 (35, |r|= 0.44 to 0.58), CD66b (61, |r| = 0.42 to 0.67), and FOXP3 (21, |r| = 0.41 to 0.71). In contrast, fewer CSDFs exhibited correlations with specific radiomic features. Conclusion: The radiomic model and combined model are feasible in predicting immunotherapy response and outcomes in LANPC patients. The radiology–pathology correlation suggests a potential biological basis for the predictive models.https://spj.science.org/doi/10.34133/research.0749 |
| spellingShingle | Jie Sun Xuewei Wu Xiao Zhang Weiyuan Huang Xi Zhong Xueyan Li Kaiming Xue Shuyi Liu Xianjie Chen Wenzhu Li Xin Liu Hui Shen Jingjing You Wenle He Zhe Jin Lijuan Yu Yuange Li Shuixing Zhang Bin Zhang Radiomic Model Associated with Tumor Microenvironment Predicts Immunotherapy Response and Prognosis in Patients with Locoregionally Advanced Nasopharyngeal Carcinoma Research |
| title | Radiomic Model Associated with Tumor Microenvironment Predicts Immunotherapy Response and Prognosis in Patients with Locoregionally Advanced Nasopharyngeal Carcinoma |
| title_full | Radiomic Model Associated with Tumor Microenvironment Predicts Immunotherapy Response and Prognosis in Patients with Locoregionally Advanced Nasopharyngeal Carcinoma |
| title_fullStr | Radiomic Model Associated with Tumor Microenvironment Predicts Immunotherapy Response and Prognosis in Patients with Locoregionally Advanced Nasopharyngeal Carcinoma |
| title_full_unstemmed | Radiomic Model Associated with Tumor Microenvironment Predicts Immunotherapy Response and Prognosis in Patients with Locoregionally Advanced Nasopharyngeal Carcinoma |
| title_short | Radiomic Model Associated with Tumor Microenvironment Predicts Immunotherapy Response and Prognosis in Patients with Locoregionally Advanced Nasopharyngeal Carcinoma |
| title_sort | radiomic model associated with tumor microenvironment predicts immunotherapy response and prognosis in patients with locoregionally advanced nasopharyngeal carcinoma |
| url | https://spj.science.org/doi/10.34133/research.0749 |
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