【摘要】 目的 总结甲型H1N1流感病毒性肺炎患者的胸部X线和CT表现特征。 方法 回顾分析2009年3月-11月3例经临床表现及病原学检查证实的甲型H1N1流感病毒性肺炎的胸部X线、CT表现。 结果 肺部病灶多呈散在小片状高密度影，边缘模糊，邻近胸膜；病变最常累及肺基底段；病灶多有少量胸腔积液；病灶有扩散迅速，合理用药后消失较快的特点；病灶吸收落后于临床表现。 结论 甲型H1N1流感病毒性肺炎的X线、CT表现具有一定的特点，总结并掌握这些特点，有利于早期诊断。其确诊有赖于实验室检查和流行病学调查。【Abstract】Objective To explore the chest X-ray, CT manifestations of pneumonia of patients with influenza virus A/H1N1 infection. Methods The pulmonary X-ray and CT findings of 3 patients who were confirmed by laboratory results and epidemiology with infection of influenza virus A/H1N1 were retrospectively analyzed between March 2009 to November 2009. Results Both sides of the lung field showed many small cloudy infiltration in chest X-ray and CT film. The lesions of the lung were mostly near the pleural. They were often found in basal segment. Pleural effusion may be observed. Radiology dynamic changes showed the diffusion of the lesions of the lung was quick in a short time, and scattered and disappeared quickly after rational use of drugs. The lesions vanished later than clinical disappearance. The lesions of the lung may appear fibrosis at the period of the end. Conclusion Some radiographic characteristics exist in the pneumonia of patients with influenza virus A/H1N1 infection. It will be helpful for early diagnosis when getting familiar with its X-ray and CT manifestations, but the final diagnosis depends on the laboratory results and epidemiological history.
ObjectiveBy combining biological detection and imaging evaluation, a clinical prediction model is constructed based on a large cohort to improve the accuracy of distinguishing between benign and malignant pulmonary nodules. MethodsA retrospective analysis was conducted on the clinical data of the 32 627 patients with pulmonary nodules who underwent chest CT and testing for 7 types of lung cancer-related serum autoantibodies (7-AABs) at our hospital from January 2020 to April 2024. The univariate and multivariate logistic regression models were performed to screen independent risk factors for benign and malignant pulmonary nodules, based on which a nomogram model was established. The performance of the model was evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). ResultsA total of 1 017 patients with pulmonary nodules were included in the study. The training set consisted of 712 patients, including 291 males and 421 females, with a mean age of (58±12) years. The validation set included 305 patients, comprising 129 males and 176 females, with a mean age of (58±13) years. Univariate ROC curve analysis indicated that the combination of CT and 7-AABs testing achieved the highest area under the curve (AUC) value (0.794), surpassing the diagnostic efficacy of CT alone (AUC=0.667) or 7-AABs alone (AUC=0.514). Multivariate logistic regression analysis showed that radiological nodule diameter, nodule nature, and CT combined with 7-AABs detection were independent predictors, which were used to construct a nomogram prediction model. The AUC values for this model were 0.826 and 0.862 in the training and validation sets, respectively, demonstrating excellent performance in DCA. ConclusionThe combination of 7-AABs with CT significantly enhances the accuracy of distinguishing between benign and malignant pulmonary nodules. The developed predictive model provides strong support for clinical decision-making and contributes to achieving precise diagnosis and treatment of pulmonary nodules.