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This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made. AbstractBackground: A-kinase-interacting protein 1 (AKIP1) has been reported as an oncogenetic factor in multiple cancers; however, no study has reported its role in nonsmall cell lung cancer (NSCLC) yet. This study aimed to evaluate the expression of AKIP1, and its correlation with tumor characteristics as well as prognosis in patients with NSCLC.Methods: Four hundred and ninety patients with NSCLC who underwent resection were reviewed, and baseline clinical data were collected. AKIP1 expression in tumor tissue/paired adjacent tissue was detected by immunohistochemistry. Disease-free survival (DFS) and overall survival (OS) were calculated. Results:A-kinase-interacting protein 1 expression was elevated in tumor tissue compared with paired adjacent tissue (P < .001), and high AKIP1 tumor tissue expression was correlated with poor pathological differentiation (P < .001), tumor size >5 cm (P = .001), lymph node metastasis (P = .016), higher TNM stages (P < .001), and abnormal CEA level (>5 ng/mL) (P = .035). DFS was worse in patients with tumor tissue AKIP1 high expression compared with patients who had AKIP1 low expression in total patients (P < .001), TNM stage I (P < .001) and TNM stage III (P < .001) patients. And the OS was also decreased in patients with AKIP1 high expression in total patients (P < .001), TNM stage I patients (P = .001) and TNM stage III patients (P = .004). Moreover, multivariate Cox's proportional hazards regression model analysis revealed that AKIP1 high expression was an independent predictive factor for worse DFS (P < .001) and OS (P < .001).

Objective: To investigate the value of Doppler ultrasound in evaluating the efficacy of high intensity focused ultrasound (HIFU) in treatment of cesarean section scar pregnancy.

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Background The ability of lung cancer screening to manage pulmonary nodules was limited because of the high false‐positive rate in the current mainstream screening method, low‐dose computed tomography (LDCT). We aimed to reduce overdiagnosis in Chinese population. Methods Lung cancer risk prediction models were constructed using data from a population‐based cohort in China. Independent clinical data from two programs performed in Beijing and Shandong, respectively, were used as the external validation set. Multivariable logistic regression models were used to estimate the probability of lung cancer incidence in the whole population and in smokers and nonsmokers. Results In our cohort, 1,016,740 participants were enrolled between 2013 and 2018. Of 79,581 who received LDCT screening, 5165 participants with suspected pulmonary nodules were allocated into the training set, of which, 149 lung cancer cases were diagnosed. In the validation set, 1815 patients were included, and 800 developed lung cancer. The ages of patients and radiologic factors of nodules (calcification, density, mean diameter, edge, and pleural involvement) were included in our model. The area under the curve (AUC) values of the model were 0.868 (95% CI: 0.839–0.894) in the training set and 0.751 (95% CI: 0.727–0.774) in the validation set. The sensitivity and specificity were 70.5% and 70.9%, respectively, which could reduce the 68.8% false‐positive rate in simulated LDCT screening. There was no substantial difference between smokers' and nonsmokers' prediction models. Conclusion Our models could facilitate the diagnosis of suspected pulmonary nodules, effectively reducing the false‐positive rate of LDCT for lung cancer screening.