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Purpose We hypothesized that different quantitative ultrasound (US) parameters may be used as complementary diagnostic criteria and aimed to develop a simple classification algorithm to distinguish benign from malignant breast lesions and aid in the decision to perform biopsy or not. Procedures One hundred twenty-four patients, each with one biopsy-proven, sonographically evident breast lesion, were included in this prospective, IRB-approved study. Each lesion was examined with Bmode US, Color/Power Doppler US and elastography (Acoustic Radiation Force Impulse-ARFI). Different quantitative parameters were recorded for each technique, including pulsatility (PI) and resistive Index (RI) for Doppler US and lesion maximum, intermediate, and minimum shear wave velocity (SWVmax, SWVinterm, and SWVmin) as well as lesion-to-fat SWV ratio for ARFI. Receiver operating characteristic curve (ROC) analysis was used to evaluate the diagnostic performance of each quantitative parameter. Classification analysis was performed using the exhaustive chisquared automatic interaction detection method. Results include the probability for malignancy for every descriptor combination in the classification algorithm. Results Sixty-five lesions were malignant and 59 benign. Out of all quantitative indices, maximum SWV (SWVmax), and RI were included in the classification algorithm, which showed a depth of three ramifications (SWVmax ≤ or > 3.16; if SWVmax ≤ 3.16 then RI ≤ 0.66, 0.66 – 0.77 or > 0.77; if RI ≤ 0.66 then SWVmax ≤ or > 2.71). The classification algorithm leads to an AUC of 0.887 (95% CI 0.818 – 0.937, p < 0.0001), a sensitivity of 98.46% (95% CI 91.7 – 100 %), and a specificity of 61.02 % (95% CI 47.4 –73.5 %). By applying the proposed algorithm, a false-positive biopsy could have been avoided in 61 % of the cases. Conclusions A simple classification algorithm incorporating two quantitative US parameters (SWVmax and RI) shows a high diagnostic performance, being able to accurately differentiate benign from malignant breast lesions and lower the number of unnecessary breast biopsies in up to 60% of all cases, avoiding any subjective interpretation bias. Teh J1, McQueen F2, Eshed I3, Plagou A4, Klauser A5. Advanced Imaging in the Diagnosis of Gout and Other Crystal Arthropathies. Semin Musculoskelet Radiol. 2018 Apr; 22(2): 225–236. doi: 10.1055/s-00381639484. Epub 2018 Apr 19. 1 Department of Radiology, Nuffield Orthopaedic Centre, Oxford University Hospitals NHS Trust, Oxford, United Kingdom. 2 Department of Molecular Medicine and Pathology, University of Auckland, Auckland, New Zealand. 3 Department of Diagnostic Imaging, Sheba Medical Center, Tel Hashomer, Israel. 4 Department of Radiology, Private Institution of Ultrasonography, Athens, Greece. 5 Department of Radiology, Medical University of Innsbruck, Innsbruck, Austria.

Bemerkenswerte Publikationen von ÖGUM-Mitgliedern: Ultraschall mit österreichischem Beitrag …