MRI-based deep learning and radiomics for predicting the efficacy of PD-1 inhibitor combined with induction chemotherapy in advanced nasopharyngeal carcinoma: A prospective cohort study
Background: An increasing number of nasopharyngeal carcinoma (NPC) patients benefit from immunotherapy with chemotherapy as an induction treatment. Currently, there isn't a reliable method to assess the efficacy of this regimen, which hinders informed decision-making for follow-up care. Aim: To...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-02-01
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| Series: | Translational Oncology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1936523324003711 |
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| author | Yiru Wang Fuli Chen Zhechen Ouyang Siyi He Xinling Qin Xian Liang Weimei Huang Rensheng Wang Kai Hu |
| author_facet | Yiru Wang Fuli Chen Zhechen Ouyang Siyi He Xinling Qin Xian Liang Weimei Huang Rensheng Wang Kai Hu |
| author_sort | Yiru Wang |
| collection | DOAJ |
| description | Background: An increasing number of nasopharyngeal carcinoma (NPC) patients benefit from immunotherapy with chemotherapy as an induction treatment. Currently, there isn't a reliable method to assess the efficacy of this regimen, which hinders informed decision-making for follow-up care. Aim: To establish and evaluate a model for predicting the efficacy of programmed death-1 (PD-1) inhibitor combined with GP (gemcitabine and cisplatin) induction chemotherapy based on deep learning features (DLFs) and radiomic features. Methods: Ninety-nine patients diagnosed with advanced NPC were enrolled and randomly divided into training set and test set in a 7:3 ratio. From MRI scans, DLFs and conventional radiomic characteristics were recovered. The random forest algorithm was employed to identify the most valuable features. A prediction model was then created using these radiomic characteristics and DLFs to determine the effectiveness of PD-1 inhibitor combined with GP chemotherapy. The model's performance was assessed using Receiver Operating Characteristic (ROC) curve analysis, area under the curve (AUC), accuracy (ACC), and negative predictive value (NPV). Results: Twenty-one prediction models were constructed. The Tf_Radiomics+Resnet101 model, which combines radiomic features and DLFs, demonstrated the best performance. The model's AUC, ACC, and NPV values in the training and test sets were 0.936 (95%CI: 0.827–1.0), 0.9, and 0.923, respectively. Conclusion: The Tf_Radiomics+Resnet101 model, based on MRI and Resnet101 deep learning, shows a high ability to predict the clinically complete response (cCR) efficacy of PD-1 inhibitor combined with GP in advanced NPC. This model can significantly enhance the treatment management of patients with advanced NPC. |
| format | Article |
| id | doaj-art-957523fc9b06408884430b3bea2a14d7 |
| institution | Kabale University |
| issn | 1936-5233 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Translational Oncology |
| spelling | doaj-art-957523fc9b06408884430b3bea2a14d72025-01-22T05:41:27ZengElsevierTranslational Oncology1936-52332025-02-0152102245MRI-based deep learning and radiomics for predicting the efficacy of PD-1 inhibitor combined with induction chemotherapy in advanced nasopharyngeal carcinoma: A prospective cohort studyYiru Wang0Fuli Chen1Zhechen Ouyang2Siyi He3Xinling Qin4Xian Liang5Weimei Huang6Rensheng Wang7Kai Hu8Department of Radiation Oncology, The First Affiliated Hospital of Guangxi Medical University, Nanning 530021, Guangxi, ChinaDepartment of Radiation Oncology, The First Affiliated Hospital of Guangxi Medical University, Nanning 530021, Guangxi, ChinaDepartment of Radiation Oncology, The First Affiliated Hospital of Guangxi Medical University, Nanning 530021, Guangxi, ChinaDepartment of Radiation Oncology, The First Affiliated Hospital of Guangxi Medical University, Nanning 530021, Guangxi, ChinaDepartment of Radiation Oncology, The First Affiliated Hospital of Guangxi Medical University, Nanning 530021, Guangxi, ChinaDepartment of Radiation Oncology, The First Affiliated Hospital of Guangxi Medical University, Nanning 530021, Guangxi, ChinaDepartment of Radiation Oncology, The First Affiliated Hospital of Guangxi Medical University, Nanning 530021, Guangxi, ChinaDepartment of Radiation Oncology, The First Affiliated Hospital of Guangxi Medical University, Nanning 530021, Guangxi, China; Corresponding authors.Department of Radiation Oncology, The First Affiliated Hospital of Guangxi Medical University, Nanning 530021, Guangxi, China; Key Laboratory of Early Prevention and Treatment for Regional High Frequency Tumor (Guangxi Medical University), Ministry of Education, Nanning 530021, Guangxi, China; Guangxi Key Laboratory of Immunology and Metabolism for Liver Diseases, Nanning 530021, Guangxi, China; State Key Laboratory of Targeting Oncology, Guangxi Medical University, Nanning 530021, Guangxi, China; Corresponding authors.Background: An increasing number of nasopharyngeal carcinoma (NPC) patients benefit from immunotherapy with chemotherapy as an induction treatment. Currently, there isn't a reliable method to assess the efficacy of this regimen, which hinders informed decision-making for follow-up care. Aim: To establish and evaluate a model for predicting the efficacy of programmed death-1 (PD-1) inhibitor combined with GP (gemcitabine and cisplatin) induction chemotherapy based on deep learning features (DLFs) and radiomic features. Methods: Ninety-nine patients diagnosed with advanced NPC were enrolled and randomly divided into training set and test set in a 7:3 ratio. From MRI scans, DLFs and conventional radiomic characteristics were recovered. The random forest algorithm was employed to identify the most valuable features. A prediction model was then created using these radiomic characteristics and DLFs to determine the effectiveness of PD-1 inhibitor combined with GP chemotherapy. The model's performance was assessed using Receiver Operating Characteristic (ROC) curve analysis, area under the curve (AUC), accuracy (ACC), and negative predictive value (NPV). Results: Twenty-one prediction models were constructed. The Tf_Radiomics+Resnet101 model, which combines radiomic features and DLFs, demonstrated the best performance. The model's AUC, ACC, and NPV values in the training and test sets were 0.936 (95%CI: 0.827–1.0), 0.9, and 0.923, respectively. Conclusion: The Tf_Radiomics+Resnet101 model, based on MRI and Resnet101 deep learning, shows a high ability to predict the clinically complete response (cCR) efficacy of PD-1 inhibitor combined with GP in advanced NPC. This model can significantly enhance the treatment management of patients with advanced NPC.http://www.sciencedirect.com/science/article/pii/S1936523324003711Nasopharyngeal carcinomaImmunotherapyRadiomicsDeep learningMRI |
| spellingShingle | Yiru Wang Fuli Chen Zhechen Ouyang Siyi He Xinling Qin Xian Liang Weimei Huang Rensheng Wang Kai Hu MRI-based deep learning and radiomics for predicting the efficacy of PD-1 inhibitor combined with induction chemotherapy in advanced nasopharyngeal carcinoma: A prospective cohort study Translational Oncology Nasopharyngeal carcinoma Immunotherapy Radiomics Deep learning MRI |
| title | MRI-based deep learning and radiomics for predicting the efficacy of PD-1 inhibitor combined with induction chemotherapy in advanced nasopharyngeal carcinoma: A prospective cohort study |
| title_full | MRI-based deep learning and radiomics for predicting the efficacy of PD-1 inhibitor combined with induction chemotherapy in advanced nasopharyngeal carcinoma: A prospective cohort study |
| title_fullStr | MRI-based deep learning and radiomics for predicting the efficacy of PD-1 inhibitor combined with induction chemotherapy in advanced nasopharyngeal carcinoma: A prospective cohort study |
| title_full_unstemmed | MRI-based deep learning and radiomics for predicting the efficacy of PD-1 inhibitor combined with induction chemotherapy in advanced nasopharyngeal carcinoma: A prospective cohort study |
| title_short | MRI-based deep learning and radiomics for predicting the efficacy of PD-1 inhibitor combined with induction chemotherapy in advanced nasopharyngeal carcinoma: A prospective cohort study |
| title_sort | mri based deep learning and radiomics for predicting the efficacy of pd 1 inhibitor combined with induction chemotherapy in advanced nasopharyngeal carcinoma a prospective cohort study |
| topic | Nasopharyngeal carcinoma Immunotherapy Radiomics Deep learning MRI |
| url | http://www.sciencedirect.com/science/article/pii/S1936523324003711 |
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