Background: The study was conducted to investigate the diagnostic performance of infrared (IR) imaging of the breast using an interpretive model derived from a scoring system. Methods: The study was approved by the Institutional Review Board of our hospital. A total of 276 women (mean age = 50.8 years, SD 11.8) with suspicious findings on mammograms or ultrasound received IR imaging of the breast before excisional biopsy. The interpreting radiologists scored the lesions using a scoring system that combines five IR signs. The ROC (receiver operating characteristic) curve and AUC (area under the ROC curve) were analyzed by the univariate logistic regression model for each IR sign and an age-adjusted multivariate logistic regression model including 5 IR signs. The cut-off values and corresponding sensitivity, specificity, Youden's Index (Index = sensitivity+specificity-1), positive predictive value (PPV), negative predictive value (NPV) were estimated from the age-adjusted multivariate model. The most optimal cut-off value was determined by the one with highest Youden's Index. Results: For the univariate model, the AUC of the ROC curve from five IR signs ranged from 0.557 to 0.701, and the AUC of the ROC from the age-adjusted multivariate model was 0.828. From the ROC derived from the multivariate model, the sensitivity of the most optimal cut-off value would be 72.4% with the corresponding specificity 76.6% (Youden's Index = 0.49), PPV 81.3% and NPV 66.4%. Conclusions: We established an interpretive age-adjusted multivariate model for IR imaging of the breast. The cut-off values and the corresponding sensitivity and specificity can be inferred from the model in a subpopulation for diagnostic purpose. Trial Registration: NCT00166998.

Computed tomography pulmonary angiography (CTPA) is the gold standard for the diagnosis of pulmonary embolism (PE). However, contrast is contraindicated in some patients. The purpose of this study was to determine the diagnostic accuracy of unenhanced multidetector CT (MDCT) for diagnosis of central PE using CTPA as the gold standard. The records of patients with suspected PE seen between 2010 and 2013 were retrospectively reviewed. Inclusion criteria were an acute, central PE confirmed by CTPA and non-enhanced MDCT before contrast injection. Patients with a PE ruled out by CTPA served as a control group. MDCT findings studied were high-attenuation emboli in pulmonary artery (PA), main PA dilatation > 33.2 mm, and peripheral wedge-shaped consolidation. Receiver operating characteristic (ROC) analysis was used to determine the sensitivity and specificity of unenhanced MDCT to detect PE. Wells score of all patients were calculated using data extracted from medical records prior to imaging analysis. Thirty-two patients with a PE confirmed by CTPA and 32 with a PE ruled out by CTPA were included. Among the three main MDCT findings, high-attenuation emboli in the PA showed best diagnostic performance (Sensitivity 72.9%; Specificity 100%), followed by main PA dilatation > 33.2 mm (sensitivity 46.9%; specificity 90.6%), and peripheral wedge-shaped consolidation (sensitivity 43.8%; specificity 78.1%). Given any one or more positive findings on unenhanced MDCT, the sensitivity was 96.9% and specificity was 71.9% for a diagnosis of PE in patients. The area under the curve (AUC) of a composite measure of unenhanced MDCT findings (0.909) was significantly higher than that of the Wells score (0.688), indicating unenhanced MDCT was reliable for detecting PE than Wells score. Unenhanced MDCT is an alternative for the diagnosis of acute central PE when CTPA is not available.

Antifibrotic therapy has changed the treatment paradigm for idiopathic pulmonary fibrosis (IPF); however, a subset of patients still experienced rapid disease progression despite treatment. This study aimed to determine whether CT-based radiomic features can predict therapeutic response to antifibrotic agents. In this retrospective study, 35 patients with IPF on antifibrotic treatment enrolled from two centers were divided into training (n = 26) and external validation (n = 9) sets. Clinical and pulmonary function data were collected. The patients were categorized into stable disease (SD) and progressive disease (PD) groups based on functional or radiologic criteria. From pretreatment non-enhanced high-resolution CT (HRCT) images, twenty-six radiomic features were extracted through whole-lung texture analysis, and six parenchymal patterns were quantified using dedicated imaging platforms. The predictive factors for PD were determined via univariate and multivariate logistic regression analyses. In the training set (SD/PD: 12/14), univariate analysis identified eight radiomic features and ground-glass opacity percentage (GGO%) as potential predicators of PD. However, multivariate analysis found that the single independent predictor was the sum entropy (accuracy, 80.77%; AUC, 0.75). The combined sum entropy-GGO% model improved the predictive performance in the training set (accuracy, 88.46%; AUC, 0.77). The overall accuracy of the combined model in the validation set (SD/PD: 7/2) was 66.67%. Our preliminary results demonstrated that radiomic features based on pretreatment HRCT could predict the response of patients with IPF to antifibrotic treatment.

To evaluate whether infrared (IR) imaging findings are associated with prognosis in patients with invasive breast carcinomas. This study was approved by the institutional review board of the research ethics committee of our hospital, and all participants gave written informed consent. From March 2005 to June 2007, we enrolled 143 patients with invasive breast cancer that underwent preoperative IR imaging. We used five IR signs to interpret breast IR imaging. Cox proportional hazards model was used to evaluate the effect of IR signs on long-term mortality. During a median follow-up of 2451 days (6.7 years), 31 patients died. Based on the Cox Proportional Hazards Model, IR1 sign (the temperature of cancer site minus that of the contralateral mirror imaging site) was positively associated with mortality in the univariate analysis (overall mortality hazard ratio [HR], 2.29; p = 0.03; disease-specific mortality HR, 2.57; p = 0.04) as well as the multivariate analysis after controlling for clinicopathological factors (overall mortality HR, 3.85; p = 0.01; disease-specific mortality HR, 3.91, p = 0.02). In patients with clinical stage I and II disease, IR1 was also positively associated with mortality (overall mortality HR, 3.76; p = 0.03; disease-specific mortality HR, 4.59; p = 0.03). Among patients with node-negative disease, IR1 and IR5 (asymmetrical thermographic pattern) were associated with mortality (p = 0.04 for both IR1 and IR5, chi-squared test). Breast IR findings are associated with mortality in patients with invasive breast carcinomas. The association remained in patients with node-negative disease. NCT00166998 .

Vaccine-induced immune thrombotic thrombocytopenia (VITT) is a rare complication after vaccination of Oxford-AstraZeneca coronavirus disease 2019 (COVID-19) vaccine (AZD1222) or Janssen COVID-19 vaccine. It makes a rare complication of thrombosis at common and/or uncommon organs with thrombocytopenia after COVID-19 vaccination four to 28 days later and most patients were younger than 60 years of age. We reported the case of a 75-year-old female with end-stage renal disease who received regular hemodialysis. She received Oxford-AstraZeneca COVID-19 vaccination eight days ago and then she suffered from intermittent chest tightness and epigastric pain with tarry stool passage for two days. Severe thrombocytopenia with elevated D-dimer value was noted and computed tomography of the chest showed azygos vein thrombosis. Elevated cardiac enzyme with ST-T change in 12-lead electrocardiogram was also noted. For positive anti-platelet factor 4 antibodies, VITT with myocardial infarction and azygos vein thrombosis was diagnosed.