A deep learning-based prognostic model for diffuse large B-cell lymphoma incorporating PET/CT imaging features.

Objective: This study aims to create and validate a prognostic prediction model that leverages deep features derived from PET/CT imaging to support personalized precision treatment for patients with diffuse large B-cell lymphoma (DLBCL). Materials and methods: We retrospectively analyzed clinical and pretreatment PET/CT data from 209 patients with DLBCL. Deep features were extracted from three-dimensional tumor lesions; after dimensionality reduction, a radiomics model was built to predict 3-year overall survival (OS). We evaluated six machine learning algorithms: Logistic Regression (LR), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM). The optimal radiomics model was integrated with clinical features to form a fusion model. The fusion model's performance was assessed on both training and independent test sets using metrics such as accuracy, area under the receiver operating characteristic (ROC) curve (AUC), sensitivity, and specificity. Furthermore, the model's clinical utility was evaluated through Decision Curve Analysis (DCA), and survival analysis was performed using the Kaplan-Meier (KM) method. Results: Univariate and multivariate analyses identified age, AB group, International Prognostic Index (IPI) score, serum β2-microglobulin level, and maximum tumor diameter as independent risk factors for 3-year survival in DLBCL patients. Among the machine learning-based radiomics models, the LR model showed superior predictive performance, achieving an accuracy of 0.865, AUC of 0.950, sensitivity of 0.875, and specificity of 0.863. Integration with clinical features further improved the model's performance. On the test set, the fusion model attained an accuracy of 0.921, an impressive AUC of 0.974, and sensitivities and specificities of 0.846 and 0.940, respectively. DCA revealed that this fusion model offers a significant clinical net benefit for prognostic risk prediction in DLBCL patients over a wide threshold probability range of 0.05-0.900. Conclusion: The fusion model, combining PET/CT deep features with clinical characteristics, is a dependable prognostic tool for DLBCL, holding substantial promise for clinical application and personalized treatment.