Bioengineering
Clinical–Radiomics Nomogram Based on 18F-FDG PET/CT for Distinguishing Lymphoma from Cancer of Unknown Primary in Head and Neck Metastatic Poorly Differentiated Cancer
Door Wen Chen, Mingzhen Xu, Bingxin Gu en 4 anderen.
Inhoud in het Engels
Abstract
Objective
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.
Methods
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.