Imaging features of 18F-FDG PET/CT in different types of systemic vasculitis.

OBJECTIVE: To analyze 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET)/computed tomography (CT) image features in different types of systemic vasculitis and explore its classification and diagnostic significance for systemic vasculitis. METHODS: Clinical and 18F-FDG PET/CT imaging data of 71 consecutive patients (34 males, 37 females, mean age 53.4 ± 20.5 years) who were examined at Peking University People's Hospital and diagnosed with active systemic vasculitis were analyzed retrospectively, and 18F-FDG PET/CT imaging features of different types of systemic vasculitis were analyzed in patients with positive 18F-FDG PET/CT findings. RESULTS: The proportions of large-, medium-, small-, and variable-vessel vasculitis were 36.6%, 15.5%, 29.6%, and 18.3%, respectively, and 93.0% of patients had positive findings on PET/CT, of which 63.6% had vascular involvement and 53.0% had extravascular involvement. Different types of vasculitis had different lesion sites, distribution patterns, and morphological changes. Large-vessel vasculitis mainly affected the aorta and its branches to the head and neck. Takayasu arteritis showed segmental involvement of the vascular and morphological changes of the vessel wall. Giant cell arteritis manifested as diffuse vascular involvement, which usually involved the temporal artery and the abdominal aorta and its branches. Medium-vessel vasculitis polyarteritis nodosa manifested as diffuse vascular involvement of both lower extremities. Small-vessel vasculitis anti-neutrophil cytoplasmic antibody-associated vasculitis manifested as granulomatous inflammation of extravascular regions. Variable-vessel vasculitis Behcet's disease involved both blood vessels and extravascular regions. CONCLUSION: Different types of systemic vasculitis show characteristic manifestations in 18F-FDG PET/CT images, which may be useful for the diagnosis and classification of systemic vasculitis. KEY POINTS: • Determining an early diagnosis of systemic vasculitis may be challenging. • Different types of systemic vasculitis show characteristic manifestations in 18F-FDG PET/CT images. • 18F-FDG PET/CT may be useful for the diagnosis and classification of systemic vasculitis.

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.

[Value of (18)F-FDG imaging and serum tumor markers in the diagnosis of recurrent endometrial carcinoma].

OBJECTIVE: Both (18)F-fluorodeoxyglucose (FDG) imaging and serum tumor marker measurements can be used in the post-therapy surveillance of recurrent endometrial carcinoma, but the relationship between those two methods has not been demonstrated yet. The purpose of this study was to compare the diagnostic efficiency of (18)F-FDG imaging and serum tumor marker measurements in the diagnosis of recurrent endometrial carcinoma, as well as to analyze the correlation between those two methods. METHODS: Thirty-five patients with histopathologically confirmed endometrial carcinoma and suspected to have recurrent disease during post-therapy surveillance were included in this study. (18)F-FDG images from the thorax to the pelvis were obtained in all patients by using GE-Millennium VG Hawkeye system, and the abnormal FDG uptake was judged as tumor recurrence. Serum CA-125 and CP-2 were also measured for each patient by enzyme-linked immunoassay, and a cutoff value of 35 U/ml was taken as the criteria for predicting tumor recurrence. Based on the final clinical diagnosis, the efficiency of tumor markers (CA-125, CP-2) and (18)F-FDG imaging in the diagnosis of recurrent tumor was evaluated. RESULTS: According to the histopathological diagnosis or follow-up examinations, tumor recurrence was confirmed in 13 of the 35 patients. Elevated serum level of CA-125 was found in 7 patients, serum CP-2 was increased in 9, and (18)F-FDG imaging was positive in 15. The diagnostic sensitivity, specificity and accuracy were 53.8%, 100% and 82.9% for the serum CA-125; 38.5%, 81.0% and 65.7% for the serum CP-2, and 100%, 90.9% and 94.3% for the (18)F-FDG imaging, respectively. The diagnostic coincidence rate between the (18)F-FDG imaging and serum CA-125 was 77.1% (Kappa = 0.50, P = 0.001), but no significant correlation was found between the (18)F-FDG imaging and serum CP-2. In the patients with true positive (18)F-FDG imaging, a positive correlation between the tumor volume and the serum CA-125 value was found (r = 0.89, P < 0.001), but no correlation was found between the tumor uptake and the serum CA-125 values. CONCLUSION: For the post-therapy surveillance of patients with endometrial carcinoma, serum CA-125 is a high specific tumor marker for diagnosing recurrent disease and better than CP-2, but (18)F-FDG imaging is better than CA-125, and there is a positive correlation between tumor volume and serum CA-125 value.

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.