| 樊梦思,赵红,曹捍波,余业洲,邹立巍,段绍峰.基于CT平扫影像组学模型鉴别结节/肿块型 肺隐球菌病及肺腺癌与肺结核[J].中国医学影像技术,2020,36(6): |
| 基于CT平扫影像组学模型鉴别结节/肿块型 肺隐球菌病及肺腺癌与肺结核 |
| Differentiation of nodule or mass type pulmonary cryptococcosis and lung adenocarcinoma, tuberculosis based on plain CT scanning radiomics models |
| 投稿时间：2019-06-18 修订日期：2020-06-14 |
| DOI： |
| 中文关键词: 肺 隐球菌病 肺肿瘤 肺结核 人工智能'影像组学 体层摄影术,X线计算机 |
| 英文关键词:lung cryptococcosis lung neoplasms tuberculosis artificial intelligence radiomics tomography, X-ray computed |
| 基金项目:无 |
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| 中文摘要: |
| 目的 探讨基于CT平扫影像组学预测模型鉴别诊断结节/肿块型肺隐球菌(PC)与肺腺癌、肺结核(TB)的可行性。方法 回顾性分析28例结节/肿块型PC和30例肺腺癌、26例TB的平扫CT资料，提取病灶纹理特征，对其进行特征选择，获得PC组与肺腺癌组、PC组与肺TB组之间存在显著差异的特征参数。按7:3比例将所有样本分为训练集和测试集，采用随机森林法以较优特征参数建立预测模型，对训练集数据进行评估，之后于测试集数据进行验证；绘制相应ROC曲线，评估模型的鉴别诊断效能。结果 针对PC和肺腺癌、PC和肺TB分别获得7个和4个较优纹理特征参数。测试集验证结果显示模型鉴别PC与肺腺癌以及PC与肺TB的AUC、敏感度、特异度、准确率分别为0.96、1.00、0.78、0.89及0.99、0.88、0.89、0.88。结论 基于CT平扫图像影像组学可用于鉴别诊断结节/肿块型PC与肺腺癌、肺TB。 |
| 英文摘要: |
| Objective To explore the feasibility of differential diagnosis of nodule or mass type pulmonary cryptococcosis (PC), lung adenocarcinoma and lung tuberculosis (TB) based on plain CT scanning radiomics models. Methods Plain CT data of 28 patients with nodule or mass type PC, 30 with pulmonary adenocarcinoma and 26 with lung TB were retrospectively analyzed. The texture feature of lesions on CT images were extracted and selected to establish the optimized texture parameters between PC and lung adenocarcinoma,also between pc and TB. Then all samlpes were divided into training set and testing set according to ratio of 7:3. The random forest method was used to establish prediction model with the optimized texture parameters,and the model was used to evaluate training set data and verified with testing set data. The corresponding ROC curve was drawn, so as to evaluate the model's diferential diagnosis efficiency. Results After screening, 7 optimized feature parameters were obtained between PC and lung adenocarcinoma, while 4 were obtained between PC and lung TB. The AUC, sensitivity, specificity, accuracy of model for differentiating PC from lung adenocarcinoma was 0.96, 1.00, 0.78, and 0.89, respectively, while for differentiating PC from lung TB was 0.99, 0.88, 0.89, and 0.88,respectively. Conclusion Radiomics models based on plain CT scanning can be used for differentiating and diagnosing nodule or mass PC from lung adenocarcinoma and lung TB. |
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