Quantitative imaging in medicine and surgery · 2022
Differential diagnostic performance of PET/CT in adult-onset still's disease and lymphoma: a retrospective pilot study.
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Wan Liyan, Gao Yuting, Yang Chendie, Gu Jieyu, Liu Tingting, Hu Qiongyi, Tang Zihan, Teng Jialin, Liu Honglei, Cheng Xiaobing, Ye Junna, Su Yutong, Shi Yi, Huang Xinyun, Yang Chengde, Li Biao, Shi Hui, Zhang Min
Аннотация
BACKGROUND: Adult-onset still's disease (AOSD) and lymphoma are the common causes of fever of unknown origin (FUO) and show some similar clinical symptoms. This study aimed to establish a reliable and easy-to-used scoring model based on clinical information, laboratory characteristics and 18F-fluorodeoxyglucose positron emission tomography/computer tomography (18F-FDG PET/CT) images for the differential diagnosis of these two diseases.
METHODS: A development cohort including 70 AOSD and 37 lymphoma patients was used to establish a scoring model based on the features of PET/CT images. The scoring model was then validated in a validation cohort of 15 AOSD and 12 lymphoma patients. The features of involved bone marrow, spleen, lymph nodes, and other organs or tissues displayed on PET/CT images were compared. Multiple logistics regression and decision tree analysis were used to establish the scoring model.
RESULTS: Four features that could significantly differentiate these two diseases were selected to establish a scoring model discriminating AOSD from lymphoma, including (I) white blood cell (WBC) count ≤10×109/L (1 point); (II) ferritin ≤ upper limit of normal (ULN) (1 point); (III) no abnormal bone marrow metabolism (1 point); (IV) total lesion glycolysistotal (TLGtotal) >9.0 (1 point). After decision tree analysis, it showed that a score ≤1 indicates AOSD. A score ≥3 strongly suggested lymphoma, with a sensitivity of 81.1% and specificity of 90.0% in the development cohort, and a sensitivity of 58.3% and specificity of 100% in the validation cohort.
CONCLUSIONS: Our scoring model showed good diagnosis performance in distinguishing AOSD from lymphoma.
METHODS: A development cohort including 70 AOSD and 37 lymphoma patients was used to establish a scoring model based on the features of PET/CT images. The scoring model was then validated in a validation cohort of 15 AOSD and 12 lymphoma patients. The features of involved bone marrow, spleen, lymph nodes, and other organs or tissues displayed on PET/CT images were compared. Multiple logistics regression and decision tree analysis were used to establish the scoring model.
RESULTS: Four features that could significantly differentiate these two diseases were selected to establish a scoring model discriminating AOSD from lymphoma, including (I) white blood cell (WBC) count ≤10×109/L (1 point); (II) ferritin ≤ upper limit of normal (ULN) (1 point); (III) no abnormal bone marrow metabolism (1 point); (IV) total lesion glycolysistotal (TLGtotal) >9.0 (1 point). After decision tree analysis, it showed that a score ≤1 indicates AOSD. A score ≥3 strongly suggested lymphoma, with a sensitivity of 81.1% and specificity of 90.0% in the development cohort, and a sensitivity of 58.3% and specificity of 100% in the validation cohort.
CONCLUSIONS: Our scoring model showed good diagnosis performance in distinguishing AOSD from lymphoma.