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Bioengineering
By Wen Chen, Mingzhen Xu, Bingxin Gu and 4 others.
To develop and validate a clinical–radiomics nomogram based on 18F-FDG PET/CT for differentiating lymphoma from cancer of unknown primary (CUP) in head and neck metastatic poorly differentiated cancer.
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This retrospective study enrolled 192 patients, including 151 CUP and 41 lymphoma cases. Patients were randomly divided into a training (n = 134, 70%) and test cohorts (n = 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.
Published in Bioengineering. The abstract is shown as published and remains the work of its authors; read the full article at the source.
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