Clinical-Radiomics Nomogram Based on <sup>18</sup>F-FDG PET/CT for Distinguishing Lymphoma from Cancer of Unknown Primary in Head and Neck Metastatic Poorly Differentiated Cancer.
Authors
Affiliations (7)
Affiliations (7)
- Department of Nuclear Medicine, Fudan University Shanghai Cancer Center, Fudan University, Shanghai 200032, China.
- College of Biomedical Engineering, Yiwu Research Institute, Fudan University, Shanghai 200433, China.
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China.
- Center for Biomedical Imaging, Fudan University, Shanghai 200032, China.
- Shanghai Engineering Research Center of Molecular Imaging Probes, Shanghai 200032, China.
- Key Laboratory of Nuclear Physics and Ion-Beam Application (MOE), Fudan University, Shanghai 200433, China.
- Department of Nuclear Medicine, Huadong Hospital Affiliated to Fudan University, Shanghai 200040, China.
Abstract
To develop and validate a clinical-radiomics nomogram based on <sup>18</sup>F-FDG PET/CT for differentiating lymphoma from cancer of unknown primary (CUP) in head and neck metastatic poorly differentiated cancer. This retrospective study enrolled 192 patients, including 151 CUP and 41 lymphoma cases. Patients were randomly divided into a training (<i>n</i> = 134, 70%) and test cohorts (<i>n</i> = 58, 30%) using a fixed random seed for partitioning. Radiomic features were separately extracted from PET/CT images, and feature selection was performed using the least absolute shrinkage and selection operator algorithm. The optimal radiomics model was constructed via ten-fold cross-validation and comparative modeling with multiple machine learning methods. The clinical-radiomics nomogram was established by combining the radiomics model score and the clinical model score derived from gender and the metabolic heterogeneity index. The class distribution was preserved to reflect real-world clinical prevalence, consistent with recommendations for clinical prediction models. The lymphoma group had a significantly higher female proportion (53.7%) than the CUP group (17.9%). The radiomics model yielded areas under the curve (AUC) of 0.916 in the training cohort and 0.812 in the test cohort. The final clinical-radiomics nomogram achieved higher and more robust performance, with AUCs of 0.924 (95% CI: 0.868-0.980) in the training set and 0.912 (95% CI: 0.837-0.987) in the test set, respectively. The standalone radiomics model exhibited a drop in sensitivity when evaluated in the test cohort, which directly demonstrates the incremental predictive value of integrating well-selected clinical parameters into the final model. The clinical-radiomics nomogram shows promising potential for differentiating lymphoma from CUP in poorly differentiated cervical lymph node malignancies. As a preliminary, single-center study, these findings warrant further validation in larger, multi-center cohorts before clinical implementation.