Aim: To identify the predictors of in-hospital mortality in patients with coronavirus disease of 2019 (COVID-19) and acute renal impairment (ARI) or chronic kidney disease (CKD), and to evaluate the performance and inter-reader concordance of chest CT total severity scores (TSSs). Methods: This retrospective single-center study was conducted on symptomatic COVID-19 patients with renal impairment (either acute or chronic) and a serum creatinine of >2 mg/dL at the time of admission. The patients’ demographic characteristics, clinical data, and laboratory data were extracted from the clinical computerized medical records. All chest CT images obtained at the time of hospital admission were analyzed. Two radiologists independently assessed the pulmonary abnormalities and scored the severity using CT chest total severity score (TSS). Univariate logistic regression analysis was used to determine factors associated with in-hospital mortality. A receiver operating characteristic (ROC) curve analysis was performed for the TSS in order to identify the cut-off point that predicts mortality. Bland–Altman plots were used to evaluate agreement between the two radiologists assessing TSS. Results: A total of 100 patients were included, with a mean age of 60 years, 54 were males, 53 had ARI, and 47 had CKD. In terms of in-hospital mortality, 60 patients were classified in the non-survivor group and 40 were classified in the survivor group. The mortality rate was higher for those with ARI compared to those with CKD (p = 0.033). The univariate regression analysis showed an increasing odds of in-hospital mortality associated with higher respiratory rate (OR 1.149, 95% CI 1.057–1.248, p = 0.001), total bilirubin (OR 2.532, 95% CI 1.099–5.836, p = 0.029), lactate dehydrogenase (LDH) (OR 1.001, 95% CI 1.000–1.003, p = 0.018), CRP (OR 1.010, 95% CI 1.002–1.017, p = 0.012), invasive mechanical ventilation (MV) (OR 7.667, 95% CI 2.118–27.755, p = 0.002), a predominant pattern of pulmonary consolidation (OR 21.714, 95% CI 4.799–98.261, p < 0.001), and high TSS (OR 2.082, 95% CI 1.579–2.745, p < 0.001). The optimum cut-off value of TSS used to predict in-hospital mortality was 8.5 with a sensitivity of 86.7% and a specificity of 87.5%. There was excellent interobserver agreement (ICC > 0.9) between the two independent radiologists in their quantitative assessment of pulmonary changes using TSS. Conclusions: In-hospital mortality is high in COVID-19 patients with ARI/CKD, especially for those with ARI. High serum bilirubin, a predominant pattern of pulmonary consolidation, and TSS are the most significant predictors of mortality in these patients. Patients with a higher TSS may require more intensive hospital care. TSS is a reliable and helpful auxiliary tool for risk stratification among COVID-19 patients with ARI/CKD.

Breast Imaging: Correlation Between Axillary Lymph Nodes Apparent Diffusion Coefficient and Pathological Lymphovascular Invasion in Patients With Invasive Breast Cancer - European Journal of Breast Health

BACKGROUND Metal artifacts significantly limit the diagnostic quality of computed tomography (CT) algorithm in patients with lower limb metallic prostheses. Single-energy metal artifact reduction (SEMAR) algorithms have been developed to address this limitation. AIM To evaluate the effects of SEMAR algorithm on image quality characteristics of lower limb CT algorithm (CTA) (artifact reduction, signal-to-noise ratio (SNR) and inter-observer agreement) in patients with metallic prostheses. METHODS Twenty-two patients (14 males, 8 females; median age 54 years) with unilateral or bilateral metal lower limb prostheses or screws underwent lower limb CTA on a 128-slice multi-detector CT scanner. Images were reconstructed with and without SEMAR algorithm. Three radiologists independently assessed subjective image quality using standardized scoring systems. Quantitative analysis included measurement of SNR and contrast-to-noise ratio in arterial and soft tissue regions of interest. RESULTS Application of SEMAR significantly improved SNR in two of three observers (P = 0.009 and P = 0.004), with values approaching those in the contralateral reference limb. Overall artifact reduction was statistically significant (median difference 1.5 Hounsfield unit, P < 0.001), as was improvement in overall image quality (median difference 1 Hounsfield unit, P < 0.001). Good inter-observer agreement (intraclass correlation coefficient > 0.75) was demonstrated for SNR and contrast-to-noise ratio assessment with metal artifact reduction (MAR) and reference images, while poor agreement (intraclass correlation coefficient < 0.5) was noted for non-MAR images. Kendall’s W demonstrated significant concordance among observers (W = 0.899, P < 0.001 for study quality with MAR). CONCLUSION SEMAR algorithm significantly reduces metal artifacts from lower limb fixation prostheses without compromising vessel contrast, improving visualization of periprosthetic vascular structures and enhancing diagnostic capability of CTA examinations.