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
|
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。
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.
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.
引文
[1] Hansell DM,Bankier AA,Macmahon H,et al.Fleischner Society:Glossary ofterms for thoracic imaging.Radiology,2008,246(3):697-722.
[2] Zhao H,Marshall HM,Yang IA,et al.Screen-detected subsolid pulmonary nodules:Long-term follow-up and application of the PanCan lung cancer risk prediction model.Br J Radiol,2016,89(1060):20160016.
[3] Travis WD,Brambilla E,Noguchi M,et al.International Association for the Study of Lung Cancer/American Thoracic Society/European Respiratory Society:International multidisciplinary classification of lung adenocarcinoma.Thorac Oncol,2011,6(2):244-285.
[4] Tsutani Y,Miyata Y,Nakayama H,et al.Appropriate sublobar resection choice for ground glass opacity-dominant clinical stage ⅠA lung adenocarcinoma:Wedge resection or segmentectomy.Chest,2014,145(1):66-71.
[5] Lambin P,Rios-Velazques E,Leijenaar R,et al.Radiomics:Extracting more information from medical images using advaned feature analysis.Eur J Cancer,2012,48(4):441-446.
[6] 冯宝,陈相猛,李浦生,等.小波能量引导下基于活动轮廓模型的部分实性肺结节分割.华南理工大学学报(自然科学版),2019,47(2):00001.
[7] Li C,Kao CY,Gore JC,et al.Minimization of region-scalable fitting energy for image segmentation.IEEE Trans Image Process,2008,17(10):1940-1949.
[8] 吴建强,王平,彭洁,等.孤立性肺结节的CT与病理检查结果对比研究.实用医学杂志,2013,29(2):3733-3735.
[9] Lee SM,Park CM,Goo JM,et al.Invasive pulmonary adenocarcinomas versus preinvasive lesions appearing as ground-glass nodules:Differentiation by using CT features.Radiology,2013,268(1):265-273.
[10] Chae HD,Park CM,Park SJ,et al.Computerized texture analysis of persistent part-solid ground-glass nodules:Differentiation of preinvasive lesions from invasive pulmonary adenocarcinomas.Radiology,2014,273(1):285-293.
[11] Jin C,Cao J,Cai Y,et al.A nomogram for predicting the risk of invasive pulmonary adenocarcinoma for patients with solitary peripheral subsolid nodules.J Thorac Cardiovasc Surg,2017,153(2):462-469.
[12] Zhang Y,Shen Y,Qiang JW,et al.HRCT features distinguishing pre-invasive from invasive pulmonary adenocarcinomas appearing as ground-glass nodules.Eur Radiol,2016,26(9):2921-2928.
[13] Cohen JG,Reymond E,Lederlin M,et al.Differentiating pre- and minimally invasive from invasive adenocarcinoma using CT-features in persistent pulmonary part-solid nodules in Caucasian patients.Eur J Radiol,2015,84(4):738-744.
[14] Kakinuma R,Noguchi M,Ashizawa K,et al.Natural history of pulmonary subsolid nodules:A prospective multicenter study.J Thoracic Oncology,2016,11(7):1012-1028.
[15] Penn A,Ma M,Chou BB,et al.Inter-reader variability when applying the 2013 Fleischner guidelines for potential solitary subsolid lung nodules.Acta Radiol,2015,56(10):1180-1186.
[16] van Riel SJ,Sánchez CI,Bankier AA,et al.Observer variability for classification of pulmonary nodules on low-dose CT images and its effect on nodule management.Radiology,2015,277(3):863-871.
[17] Shen C,Liu Z,Guan M,et al.2D and 3D CT radiomics features prognostic performance comparison in non-small cell lung cancer.Transl Oncol,2017,10(6):886-894.
[18] 罗婷,张峥,李昕,等.CT图像纹理分析鉴别诊断磨玻璃密度肺腺癌的浸润性.中国医学影像技术,2017,33(12):1788-1791.
[2] Zhao H,Marshall HM,Yang IA,et al.Screen-detected subsolid pulmonary nodules:Long-term follow-up and application of the PanCan lung cancer risk prediction model.Br J Radiol,2016,89(1060):20160016.
[3] Travis WD,Brambilla E,Noguchi M,et al.International Association for the Study of Lung Cancer/American Thoracic Society/European Respiratory Society:International multidisciplinary classification of lung adenocarcinoma.Thorac Oncol,2011,6(2):244-285.
[4] Tsutani Y,Miyata Y,Nakayama H,et al.Appropriate sublobar resection choice for ground glass opacity-dominant clinical stage ⅠA lung adenocarcinoma:Wedge resection or segmentectomy.Chest,2014,145(1):66-71.
[5] Lambin P,Rios-Velazques E,Leijenaar R,et al.Radiomics:Extracting more information from medical images using advaned feature analysis.Eur J Cancer,2012,48(4):441-446.
[6] 冯宝,陈相猛,李浦生,等.小波能量引导下基于活动轮廓模型的部分实性肺结节分割.华南理工大学学报(自然科学版),2019,47(2):00001.
[7] Li C,Kao CY,Gore JC,et al.Minimization of region-scalable fitting energy for image segmentation.IEEE Trans Image Process,2008,17(10):1940-1949.
[8] 吴建强,王平,彭洁,等.孤立性肺结节的CT与病理检查结果对比研究.实用医学杂志,2013,29(2):3733-3735.
[9] Lee SM,Park CM,Goo JM,et al.Invasive pulmonary adenocarcinomas versus preinvasive lesions appearing as ground-glass nodules:Differentiation by using CT features.Radiology,2013,268(1):265-273.
[10] Chae HD,Park CM,Park SJ,et al.Computerized texture analysis of persistent part-solid ground-glass nodules:Differentiation of preinvasive lesions from invasive pulmonary adenocarcinomas.Radiology,2014,273(1):285-293.
[11] Jin C,Cao J,Cai Y,et al.A nomogram for predicting the risk of invasive pulmonary adenocarcinoma for patients with solitary peripheral subsolid nodules.J Thorac Cardiovasc Surg,2017,153(2):462-469.
[12] Zhang Y,Shen Y,Qiang JW,et al.HRCT features distinguishing pre-invasive from invasive pulmonary adenocarcinomas appearing as ground-glass nodules.Eur Radiol,2016,26(9):2921-2928.
[13] Cohen JG,Reymond E,Lederlin M,et al.Differentiating pre- and minimally invasive from invasive adenocarcinoma using CT-features in persistent pulmonary part-solid nodules in Caucasian patients.Eur J Radiol,2015,84(4):738-744.
[14] Kakinuma R,Noguchi M,Ashizawa K,et al.Natural history of pulmonary subsolid nodules:A prospective multicenter study.J Thoracic Oncology,2016,11(7):1012-1028.
[15] Penn A,Ma M,Chou BB,et al.Inter-reader variability when applying the 2013 Fleischner guidelines for potential solitary subsolid lung nodules.Acta Radiol,2015,56(10):1180-1186.
[16] van Riel SJ,Sánchez CI,Bankier AA,et al.Observer variability for classification of pulmonary nodules on low-dose CT images and its effect on nodule management.Radiology,2015,277(3):863-871.
[17] Shen C,Liu Z,Guan M,et al.2D and 3D CT radiomics features prognostic performance comparison in non-small cell lung cancer.Transl Oncol,2017,10(6):886-894.
[18] 罗婷,张峥,李昕,等.CT图像纹理分析鉴别诊断磨玻璃密度肺腺癌的浸润性.中国医学影像技术,2017,33(12):1788-1791.