Ultrasound detection of subclinical synovitis in rheumatoid arthritis patients in clinical remission: a new reduced-joint assessment in 3 target joints.

OBJECTIVES: The ability of ultrasound (US) to identify subclinical joint inflammation in rheumatoid arthritis (RA) patients in remission has been already reported. Nonetheless, current studies present a lack of homogeneity in patient's characteristics and number of joints assessed by US. The aim of this study was to identify a reduced set of target joints to be scanned in RA patients in clinical remission in order to detect subclinical synovitis. METHODS: Forty RA patients in clinical remission (DAS28 ≤2.6) for at least 3 months underwent an US examination of 18 joints: wrist, II-III-IV-V metacarpophalangeal (MCP) and II-III-IV-V metatarsophalangeal joints bilaterally. The presence of synovial hypertrophy (SH) and power-Doppler (PD) signal was registered following the OMERACT definitions and was graded according to a 4-point scale (0-3). Then, by applying a process of data reduction based on the frequency of joint involvement, a reduced assessment was obtained. RESULTS: Twenty (50%) subjects had at least one joint affected by active synovitis; 17.5% presented grade 1 PD and 32.5% grade 2 PD. The joints most frequently affected by active synovitis were the wrists (75%) and the II MCP joints (55%). After data reduction, the evaluation of 3 joints (both wrists and the II MCP of the dominant hand) obtained a sensitivity of 90% for the detection of subclinical synovitis. CONCLUSIONS: The US scan of 3 target joints showed a high sensitivity in detecting subclinical active synovitis in RA patients in clinical remission and can be feasible in the routine assessment of these patients.

Machine Learning-Based Texture Analysis in the Characterization of Cortisol Secreting vs. Non-Secreting Adrenocortical Incidentalomas in CT Scan.

New radioimaging techniques, exploiting the quantitative variables of imaging, permit to identify an hypothetical pathological tissue. We have applied this potential in a series of 72 adrenal incidentalomas (AIs) followed at our center, subdivided in functioning and non-functioning using laboratory findings. Each AI was studied in the preliminary non-contrast phase with a specific software (Mazda), surrounding a region of interest within each lesion. A total of 314 features were extrapolated. Mean and standard deviations of features were obtained and the difference in means between the two groups was statistically analyzed. Receiver Operating Characteristic (ROC) curves were used to identify an optimal cutoff for each variable and a prediction model was constructed via multivariate logistic regression with backward and stepwise selection. A 11-variable prediction model was constructed, and a ROC curve was used to differentiate patients with high probability of functioning AI. Using a threshold value of >-275.147, we obtained a sensitivity of 93.75% and a specificity of 100% in diagnosing functioning AI. On the basis of these results, computed tomography (CT) texture analysis appears a promising tool in the diagnostic definition of AIs.