Work

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

Year: 2024

Type: article

Abstract: Background: More and more nasopharyngeal carcinoma (NPC) patients benefit from immunotherapy with chemotherapy as an induction treatment. We established and evaluated a model for predicting the efficacy of programmed death-1 (PD-1) inhibitor + 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. Random forest algorithm was employed to identify the most valuable features. Prediction model was created with radiomic characteristics and DLFs to determine the effectiveness of PD-1 inhibitor + 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). Findings: 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. Interpretation: The Tf_Radiomics+Resnet101 model, based on MRI and Resnet101 deep learning, shows a high ability to predict the effect of PD-1 inhibitor + GP in advanced NPC. This model can significantly enhance the treatment management of patients with advanced NPC. Fundings: Joint Project on Regional High-Incidence Diseases Research of Guangxi Natural Science Foundation (2024GXNSFDA010042).

Source: Translational Oncology

DOI: 10.1016/j.tranon.2024.102245