Correlation between contrast-enhanced cone-beam breast computed tomography features and prognostic staging in breast cancer.

OBJECTIVE: To evaluate whether contrast-enhanced cone-beam breast CT (CE-CBBCT) features can risk-stratify prognostic stage in breast cancer. METHODS: Overall, 168 biopsy-proven breast cancer patients were analysed: 115 patients in the training set underwent scanning using v. 1.5 CE-CBBCT between August 2019 and December 2019, whereas 53 patients in the test set underwent scanning using v. 1.0 CE-CBBCT between May 2012 and August 2014. All patients were restaged according to the American Joint Committee on Cancer eighth edition prognostic staging system. Following the combination of CE-CBBCT imaging parameters and clinicopathological factors, predictors that were correlated with stratification of prognostic stage via logistic regression were analysed. Predictive performance was assessed according to the area under the receiver operating characteristic curve (AUC). Goodness-of-fit of the models was assessed using the Hosmer-Lemeshow test. RESULTS: As regards differentiation between prognostic stage (PS) I and II/III, increased tumour-to-breast volume ratio (TBR), rim enhancement pattern, and the presence of penetrating vessels were significant predictors for PS II/III disease (p < 0.05). The AUCs in the training and test sets were 0.967 [95% confidence interval (CI) 0.938-0.996; p < 0.001] and 0.896 (95% CI, 0.809-0.983; p = 0.001), respectively. Two features were selected in the training set of PS II vs III, including tumour volume [odds ratio (OR)=1.817, p = 0.019] and calcification (OR = 4.600, p = 0.040), achieving an AUC of 0.790 (95% CI, 0.636-0.944, p = 0.001). However, there was no significant difference in the test set of PS II vs III (P＞0.05). CONCLUSION: CE-CBBCT imaging biomarkers may provide a large amount of anatomical and radiobiological information for the pre-operative distinction of prognostic stage. ADVANCES IN KNOWLEDGE: CE-CBBCT features have distinctive promise for stratification of prognostic stage in breast cancer.

Association between islet autoantibodies and the prevalence of autoimmune uveitis.

AIM: To evaluate the predictive value of islet autoantibodies for the diagnosis of autoimmune uveitis (AU), as well as to characterize the association bet ween islet autoantibodies and AU. METHODS: Totally 97 patients with AU and 100 healthy persons without any autoimmune diseases as the control group were recruited. Multiple serum islet autoantibodies were measured using commercial enzyme-linked immunosorbent assay kits (ELISA). A supplementary questionnaire was used to complement the subject's demographics and clinical features. The level of glucose concentrations and white blood cells were measured. Conditional logistic regression was performed to estimate odds ratios (ORs), and 95% confidence intervals (CIs) of AU according to islet autoantibodies and to evaluate the predictive value of islet autoantibodies for AU diagnosis. Autoantibodies subgroups and other variables were included into analysis. RESULTS: In AU patients, the prevalence of detecting at least one of the autoantibodies was 31.9% (31/97). The most frequent autoantibody was ZnT8A (30.9%), followed by GADA (11.3%), IA-2A (4.1%), ICA (2.1%) and IAA (2.1%). Islet autoantibodies were found to be correlated positively with AU diagnosis [OR (95%CI): 13.86 (3.28, 58.50), P<0.001]. Moreover, Zn-T8A was remarkably correlated with AU diagnosis [OR (95%CI): 6.13 (1.96, 19.17), P<0.001], In contrast, neither GADA nor other islet antibodies (IA-2A, ICA and IAA) showed any association with AU risk under an additive model. CONCLUSION: The prevalence of islet antibodies, especially ZnT8A, in patients with AU is higher. Islet antibodies as well as novel biomarkers should be included in routine evaluation at AU and is a valuable biological marker to classify newly-diagnosed uveitis.