Objective To analyze the imaging features of solitary pulmonary nodules ( SPNs) , and compare the two types of lung cancer prediction models in distinguishing malignancy of SPNs.Methods A retrospective study was performed on the patients admitted to Ruijin Hospital between 2002 and 2009 with newly discovered SPNs. The patients all received pathological diagnosis. The clinical and imaging characteristics were analyzed. Then the diagnostic accuracy of two lung cancer prediction models for distinguishing malignancy of SPNs was evaluated and compared.Results A total of 90 patients were enrolled, of which 32 cases were with benign SPNs, 58 cases were with malignant SPNs. The SPNs could be identified between benign and maligant by the SPN edge features of lobulation ( P lt;0. 05) . The area under ROC curve of VA model was 0. 712 ( 95% CI 0. 606 to 0. 821) . The area under ROC curve of Mayo Clinic model was 0. 753 ( 95% CI 0. 652 to 0. 843) , which was superior to VA model. Conclusions It is meaningful for the identification of benign and maligant SPNs by the obulation sign in CT scan. We can integrate the clinical features and the lung cancer predicting models to guide clinical work.
ObjectiveTo investigate the feasibility of quantitative detection of WBC count and bacteria count with UF-1000i urinary sediment analyzer in rapid screening for urinary tract infection by receiver operator characteristic (ROC) curve. MethodsFrom August to December 2013, we used quantitative bacterial culture and UF-1000i automatic urine sediment analyzer respectively to examine asepsis urine specimens of 218 patients with suspected urinary tract infection. Among them, there were 95 males and 123 females, with an average age of 54.7 years old. ResultsAmong the 218 urinary samples, 65 were culture positive specimens. With positive urine culture as the gold standard for making ROC curve, the area under ROC curve for WBC count and bacterial numbers by UF-1000i urine sediment analyzer were respectively 0.839 and 0.894. The cut-off values of Youden index for optimal WBC cell count and bacterial count were ≥31.0/μL and 38.8/μL, respectively. When the above numbers were used as cut-off values, the WBC count sensitivity and specificity were 78.3% and 80.4%, the positive likelihood ratio was 3.99, and the negative likelihood ratio was 1.11. And the bacterial count sensitivity and specificity were 84.3% and 80.6%, the positive likelihood ratio was 4.30, and the negative likelihood ratio was 0.80. ConclusionUsing white blood cell count ≥31/μL and bacterial count ≥38.8/μL detected by UF-1000i urine sediment analyzer as the cut off values of noninvasive screening indexes has a very important value in screening for urinary tract infection in the early stage, determining whether there is a need for urine culture, and guiding clinical rational application of antibiotics