■探讨计算机断层扫描(CT)值(HU)在肺隐球菌病(PC)诊断和鉴别诊断中的应用价值,并构建预测模型。
■回顾性分析2019年1月至2022年5月在我院行肺部CT表现为结节/肿块型职业并经组织病理学证实的73例患者的临床资料,根据病理结果分为PC组(23例)和非PC组(50例)。并测量每位患者肺部病变的CT值。年龄的差异,性别,症状,一个/两个肺的病变受累,肺叶分布,病变的数量,最大病变直径(cm),病变边缘状况,比较两组CT值结果。对PC的独立危险因素进行指标分析,差异有统计学意义。构建临床预测模型,绘制列线图,计算了C(校正)指数,绘制受试者特征(ROC)曲线,进行校正曲线和临床决策曲线分析(DCA)以进一步评估模型的预测功效.
■两组患者数据的比较分析显示,在中央,外周和全局CT值(P<0.05),多元回归分析表明,中心CT值,外周CT值和全局CT值可作为PC诊断和鉴别诊断的独立危险因素。预测PC模型的ROC曲线下面积为0.814(95%CI:0.7011-0.9267),校正C指数(Bootstrap=1000)为0.781;实际曲线与校准曲线吻合良好;DCA结果表明,柱线图模型具有较高的临床应用价值。
■病变的CT值测量可作为PC的独立危险因素,基于以上因素的临床预测模型对PC的诊断和鉴别诊断具有一定的预测性。
UNASSIGNED: To investigate the application value of computed tomography (CT) value (HU) in the diagnosis and differential diagnosis of pulmonary cryptococcosis (PC) and to construct a prediction model.
UNASSIGNED: Retrospective analysis of the clinical data of 73 patients who presented with nodular/mass-type occupations on lung CT and confirmed by histopathology in our hospital from January 2019 to May 2022 were divided into PC group (23 patients) and non-PC group (50 patients) according to the pathological findings, and the CT values of each patient\'s lung lesions were measured. The differences in age, gender, symptoms, lesion involvement in one/both lungs, lung lobe distribution, number of lesions, maximum lesion diameter (cm), lesion margin condition, and CT value results were compared between the two groups. Independent risk factors for PC were analyzed for indicators with statistically significant differences, clinical prediction models were constructed and column line plots were drawn, C (correction) indices were calculated, subject characteristics (ROC) curves were drawn, calibration curves and clinical decision curve analysis (DCA) were performed to further evaluate the predictive efficacy of the models.
UNASSIGNED: Comparative analysis of patient data between the two groups showed statistically significant differences in central, peripheral and global CT values (P < 0.05), and multiple regression analysis indicated that central CT value, peripheral CT value and global CT value could be used as independent risk factors for the diagnosis and differential diagnosis of PC. The area under the ROC curve of the model predicting PC was 0.814 (95 % CI: 0.7011-0.9267), and the corrected C-index (Bootstrap = 1000) was 0.781; the actual curve overlapped well with the calibration curve; the DCA results indicated that the column line graph model has high clinical application value.
UNASSIGNED: CT value measurements of lesions can be used as an independent risk factor for PC, and clinical prediction models based on the above factors are predictive for the diagnosis and differential diagnosis of PC.