Current situation and cost-effectiveness of 18F-FDG PET/CT for the diagnosis of fever of unknown origin and inflammation of unknown origin: A single-center, large-sample study from China.

PURPOSE: 18F-FDG PET/CT has an important role in the evaluation of fever of unknown origin (FUO) and inflammation of unknown origin (IUO). Our study was to investigate the current status of the inclusion of 18F-FDG PET/CT within FUO/ IUO diagnostic work-up and evaluate the cost-effectiveness of it in China. METHODS: A total of 741 FUO/IUO patients admitted to our hospital from January 2012 to December 2019 were retrospectively reviewed. The clinical characteristic, medical expenses to reach diagnosis and the proportion of definite etiological diagnosis achieved upon hospital discharge were compared between patients examined by 18F-FDG PET/CT (18F-FDG PET/CT group) and patients not examined by 18F-FDG PET/CT (non-18F-FDG PET/CT group). RESULTS: The mean age, proportion of critically-ill patients, proportion of rheumatologic diseases, the number of examinations and hospitalisation days to reach diagnosis in the 18F-FDG PET/CT group were significantly higher than those in the non-18F-FDG PET/CT group. The mean medical costs of 18F-FDG PET/CT group were significantly higher than those of non-18F-FDG PET/CT group, whereas the proportion of definite etiological diagnosis achieved upon hospital discharge of 18F-FDG PET/CT group was significantly higher than that of non-18F-FDG PET/CT group. The mean hospitalisation days and mean medical costs before diagnosis were significantly lower in patients who undertook 18F-FDG PET/CT ≤ 7 days after hospital admission than those in patients who undertook 18F-FDG PET/CT > 7 days after hospital admission. CONCLUSIONS: 18F-FDG PET/CT is mostly used in critically-ill and hard-to-diagnose FUO/IUO patients currently in China, which may conceal its cost-effective advantage. While the early use of 18F-FDG PET/CT according to patient characteristics and etiological clues could help to reduce hospitalization stay, limit medical costs, thus producing its diagnostic effect to the great extent.

18F-FDG PET/CT Radiomic Analysis with Machine Learning for Identifying Bone Marrow Involvement in the Patients with Suspected Relapsed Acute Leukemia.

18F-FDG PET / CT is used clinically for the detection of extramedullary lesions in patients with relapsed acute leukemia (AL). However, the visual analysis of 18F-FDG diffuse bone marrow uptake in detecting bone marrow involvement (BMI) in routine clinical practice is still challenging. This study aims to improve the diagnostic performance of 18F-FDG PET/CT in detecting BMI for patients with suspected relapsed AL. Methods: Forty-one patients (35 in training group and 6 in independent validation group) with suspected relapsed AL were retrospectively included in this study. All patients underwent both bone marrow biopsy (BMB) and 18F-FDG PET/CT within one week. The BMB results were used as the gold standard or real "truth" for BMI. The bone marrow 18F-FDG uptake was visually diagnosed as positive and negative by three nuclear medicine physicians. The skeletal volumes of interest were manually drawn on PET/CT images. A total of 781 PET and 1045 CT radiomic features were automatically extracted to provide a more comprehensive understanding of the embedded pattern. To select the most important and predictive features, an unsupervised consensus clustering method was first performed to analyze the feature correlations and then used to guide a random forest supervised machine learning model for feature importance analysis. Cross-validation and independent validation were conducted to justify the performance of our model. Results: The training group involved 16 BMB positive and 19 BMB negative patients. Based on the visual analysis of 18F-FDG PET, 3 patients had focal uptake, 8 patients had normal uptake, and 24 patients had diffuse uptake. The sensitivity, specificity, and accuracy of visual analysis for BMI diagnosis were 62.5%, 73.7%, and 68.6%, respectively. With the cross-validation on the training group, the machine learning model correctly predicted 31 patients in BMI. The sensitivity, specificity, and accuracy of the machine learning model in BMI detection were 87.5%, 89.5%, and 88.6%, respectively, significantly higher than the ones in visual analysis (P < 0.05). The evaluation on the independent validation group showed that the machine learning model could achieve 83.3% accuracy. Conclusions:18F-FDG PET/CT radiomic analysis with machine learning model provided a quantitative, objective and efficient mechanism for identifying BMI in the patients with suspected relapsed AL. It is suggested in particular for the diagnosis of BMI in the patients with 18F-FDG diffuse uptake patterns.

Reversal effect of ST6GAL 1 on multidrug resistance in human leukemia by regulating the PI3K/Akt pathway and the expression of P-gp and MRP1.

ß-Galactoside α2, 6-sialyltransferse gene (ST6GAL) family has two members, which encode corresponding enzymes ST6Gal I and ST6Gal II. The present atudy was to investigate whether and how ST6GAL family involved in multidrug resistance (MDR) in human leukemia cell lines and bone marrow mononuclear cells (BMMC) of leukemia patients. Real-time PCR showed a high expression level of ST6GAL1 gene in both MDR cells and BMMCs (*P<0.05). Alternation of ST6GAL1 levels had a significant impact on drug-resistant phenotype changing of K562 and K562/ADR cells both in vitro and in vivo. However, no significant changes were observed of ST6GAL2 gene. Further data revealed that manipulation of ST6GAL1 modulated the activity of phosphoinositide 3 kinase (PI3K)/Akt signaling and consequently regulated the expression of P-glycoprotein (P-gp, *P<0.05) and multidrug resistance related protein 1 (MRP1, *P<0.05), which are both known to be associated with MDR. Therefore we postulate that ST6GAL1 is responsible for the development of MDR in human leukemia cells probably through medicating the activity of PI3K/Akt signaling and the expression of P-gp and MRP1.