Bibliographic Details
| 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 |
| Authors: |
Yiru Wang, Fuli Chen, Zhechen Ouyang, Siyi He, Xinling Qin, Xian Liang, Weimei Huang, Rensheng Wang, Kai Hu |
| Source: |
Translational Oncology, Vol 52, Iss , Pp 102245- (2025) |
| Publisher Information: |
Elsevier, 2025. |
| Publication Year: |
2025 |
| Collection: |
LCC:Neoplasms. Tumors. Oncology. Including cancer and carcinogens |
| Subject Terms: |
Nasopharyngeal carcinoma, Immunotherapy, Radiomics, Deep learning, MRI, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282 |
| More Details: |
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. |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
1936-5233 |
| Relation: |
http://www.sciencedirect.com/science/article/pii/S1936523324003711; https://doaj.org/toc/1936-5233 |
| DOI: |
10.1016/j.tranon.2024.102245 |
| Access URL: |
https://doaj.org/article/957523fc9b06408884430b3bea2a14d7 |
| Accession Number: |
edsdoj.957523fc9b06408884430b3bea2a14d7 |
| Database: |
Directory of Open Access Journals |
