Distinguishing lymphoma from benign lymph node diseases in fever of unknown origin using PET/CT radiomics

作者：Xinchao Zhang, Fenglian Jing, Yujing Hu, Congna Tian, Jianyang Zhang, Shuheng Li, Qiang Wei, Kang Li, Lu Zheng, Jiale Liu, Jingjie Zhang, Yanzhu Bian · 发表于：EJNMMI Research · 年份：2024 · DOI：10.1186/s13550-024-01171-w · 被引用次数：7 · 研究领域：Hematological disorders and diagnostics、Radiomics and Machine Learning in Medical Imaging、Lymphadenopathy Diagnosis and Analysis

BACKGROUND: A considerable portion of patients with fever of unknown origin (FUO) present concomitant lymphadenopathy. Diseases within the spectrum of FUO accompanied by lymphadenopathy include lymphoma, infections, and rheumatic diseases. Particularly, lymphoma has emerged as the most prevalent etiology of FUO with associated lymphadenopathy. Distinguishing between benign and malignant lymph node lesions is a major challenge for physicians and an urgent clinical concern for patients. However, conventional imaging techniques, including PET/CT, often have difficulty accurately distinguishing between malignant and benign lymph node lesions. This study utilizes PET/CT radiomics to differentiate between lymphoma and benign lymph node lesions in patients with FUO, aiming to improve diagnostic accuracy. RESULTS: F-FDG PET/CT examinations for FUO, including 114 lymphoma patients and 90 patients with benign lymph node lesions. Patients were randomly divided into training and testing groups at a ratio of 7:3. A total of 15 effective features were obtained by the least absolute shrinkage and selection operator (LASSO) algorithm. Machine learning models were constructed using logistic regression (LR), support vector machine (SVM), random forest (RF), and k-nearest neighbors (KNN) algorithms. In the training group, the area under the curve (AUC) values for predicting lymphoma and benign cases by LR, SVM, RF, and KNN models were 0.936, 0.930, 0.998, and 0.938, respectively. There were sta...