金志发,陈相猛,冯宝,陈业航,李青,李荣岗,龙晚生.CT纹理特征分析鉴别诊断表现为肺部亚实性结节的微浸润腺癌和浸润性腺癌[J].中国医学影像技术,2019,35(5):691~695 |
CT纹理特征分析鉴别诊断表现为肺部亚实性结节的微浸润腺癌和浸润性腺癌 |
CT texture features in differentiation of minimally invasive and invasive adenocarcinoma manifesting as subsolid pulmonary nodules |
投稿时间:2018-10-22 修订日期:2019-01-16 |
DOI:10.13929/j.1003-3289.201810119 |
中文关键词: 肺肿瘤 纹理分析 影像组学 体层摄影术,X线计算机 |
英文关键词:lung neoplasms texture analysis radiomics tomography, X-ray computed |
基金项目: |
作者 | 单位 | E-mail | 金志发 | 暨南大学附属第一医院医学影像中心, 广东 广州 510060 江门市中心医院 中山大学附属江门医院放射科, 广东 江门 529030 | | 陈相猛 | 江门市中心医院 中山大学附属江门医院放射科, 广东 江门 529030 | | 冯宝 | 江门市中心医院 中山大学附属江门医院放射科, 广东 江门 529030 中山大学生物医学工程学院, 广东 广州 510010 | | 陈业航 | 江门市中心医院 中山大学附属江门医院放射科, 广东 江门 529030 | | 李青 | 江门市中心医院 中山大学附属江门医院病理科, 广东 江门 529030 | | 李荣岗 | 江门市中心医院 中山大学附属江门医院病理科, 广东 江门 529030 | | 龙晚生 | 暨南大学附属第一医院医学影像中心, 广东 广州 510060 江门市中心医院 中山大学附属江门医院放射科, 广东 江门 529030 | jmlws2@163.com |
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中文摘要: |
目的 评估CT纹理特征术前鉴别表现为亚实性肺结节的微浸润腺癌(MIA)和浸润腺癌(IAC)的价值。方法 回顾性收集胸部CT表现为亚实性肺结节、经手术病理证实为MIA或IAC的100例患者,包括43例MIA和57例IAC。选择4个CT主观征象(密度、大小、分叶、形态)构建诊断MIA与IAC的CT主观征象模型。提取896个CT纹理特征,并构建CT纹理特征模型。绘制ROC曲线评估纹理特征模型、CT主观征象模型鉴别诊断MIA和IAC的效能。结果 CT主观征象中,亚实性结节的密度和大小的一致性非常好,选择密度征象[优势比=8.177,95%CI(1.142,58.575)]为CT主观征象模型的独立预测因子;于896个纹理特征中,选择4个纹理特征构建模型。训练集中纹理特征模型诊断MIA与IAC的敏感度为0.85(33/39),特异度为0.90(28/31),AUC为0.94[95%CI(0.88,0.99)];验证集中纹理特征模型的敏感度为0.89(16/18),特异度为1.00(12/12),AUC为0.97[95%CI(0.92,1.00)]。结论 CT纹理特征有助于提高术前鉴别诊断表现为亚实性肺结节的MIA和IAC的效能。 |
英文摘要: |
Objective To assess the value of CT texture features in differentiating minimally invasive adenocarcinoma (MIA) and invasive adenocarcinoma (IAC) manifesting as sub-solid pulmonary nodules. Methods Totally 100 patients with pulmonary adenocarcinoma (43 MIA and 57 IAC lesions) manifesting as sub-solid pulmonary nodules confirmed by pathology underwent CT scanning. The solid presence, lesion size, shape regularity and margins of pulmonary nodules were assessed to construct a subjective finding model, while 896 texture features were extracted with in-house software. Diagnostic performance of prediction models were evaluated using ROC curve analysis. Results The solid presence and lesion size of sub-solid pulmonary nodules manifested very good coherence in subjective finding model. The solid presence (odds ratio=8.177, 95%CI[1.142, 58.575]) was proved to be an independent predictor in the subjective model. Of 896 CT texture features, 4 independent features were identified as risk factors to build the texture based model via multivariate analysis. Compared with the subjective model, the texture based model achieved better discrimination accuracy in the training set, the sensitivity, specificity and AUC of texture based model in differentiating MIA and IAC was 0.85 (33/39), 0.90 (28/31), 0.94 (95%CI[0.88, 0.99]), respectively, while was 0.89 (16/18), 1.00 (12/12) and 0.97 (95%CI[0.92, 1.00]) in validation set, respectively. Conclusion CT texture based model has potential to preoperatively differentiate MIA and IAC in patients with sub-solid pulmonary nodules. |
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