A Dual-Center, Interpretable Deep Learning-Radiomics Fusion Model for Distinguishing Nasopharyngeal Carcinoma from Nasopharyngeal Lymphoma Using FDG PET/CT.

Authors

MeiNa Liang, ChengMao Guo, Xiaoyu Gao, HaiWen Li, Ming Yang, Yang Jing, DanRu Hu, XiaoLin Li, JingXing Xiao

Published in

Academic radiology. Aug 15, 2026. Epub Aug 15, 2026.

Abstract

Accurate differentiation between Nasopharyngeal Carcinoma (NPC) and Nasopharyngeal Lymphoma (NPL) is critical for clinical management. We aimed to develop an interpretable deep learning-radiomics fusion model using positron emission tomography/computed tomography (PET/CT) and validate it externally.
In this retrospective dual-center study, we enrolled 434 patients (349 internal, 85 external) with pathologically confirmed, treatment-naive NPC or NPL who underwent baseline FDG PET/CT. We extracted 1688 radiomics features and deep learning features from 3D-ResNet50. A three-step feature selection was applied, and a multimodal logistic regression model was constructed. Model performance was evaluated on an external test set, and SHapley Additive exPlanations (SHAP) analysis was used for interpretation.
In this retrospective dual-center study, a total of 434 patients (434 primary lesions) were enrolled, including 349 from the internal cohort and 85 from the external cohort. Among them, 329 patients (75.8%) had NPC ( internal: 266, external: 63) and 105 patients (24.2%) had NPL( internal: 83, external: 22). The mean age of NPC patients was 50.5 ± 13.8 years, while that of NPL patients was 59.0 ± 16.5 years. The final fusion model (denoted as the ALL model) achieved an AUC of 0.833, specificity of 95.5%, and accuracy of 75.3% in external validation, outperforming all single-modality models (all P < 0.05). SHAP analysis identified key predictors: original_shape_MinorAxisLength_PET and lbp-3D-k_glrlm_RunLengthNonUniformity_PET for NPL, and wavelet-LLL_firstorder_Maximum_CT features for NPC.
We developed a robust, interpretable fusion model for NPC/NPL differentiation, offering high specificity and transparent decision logic, with potential as a non-invasive clinical decision-support tool.

PMID:
42603752
Bibliographic data and abstract were imported from PubMed on 16 Aug 2026.

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