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

Abstract

• We combined radiomics with deep learning to predict the induction therapeutic efficacy of immunotherapy combined with chemotherapy for advanced nasopharyngeal carcinoma (NPC). • Study prospectively enrolled 99 patients with PD-1 inhibitor + GP treatment, the prediction model was constructed by random forest algorithm, receiver operating characteristic (ROC) curve was used to analyze model performance. • Tf_Radiomics+Resnet101 model could accurately predict the efficacy of PD-1 inhibitor + GP treatment. • This model helps physicians to conduct subsequent individualized treatment. 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. 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. 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). 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. 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.

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