~(18)F-FDG PET/CT影像组学预测非小细胞肺癌亚型的研究
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摘要
目的:探究基于治疗前~(18)F-FDG PET/CT影像组学特征预测非小细胞肺癌(NSCLC)病理亚型的可行性。方法:回顾性分析100例NSCLC患者治疗前的~(18)F-FDG PET/CT图像,其中腺癌60例,鳞癌40例。首先在PET图像上勾画大体肿瘤靶区(GTV),从GTV内提取肿瘤代谢参数和纹理参数。使用Pearson相关系数和ROC曲线评估特征预测NSCLC病理亚型的效能,并计算其敏感性、特异性和最佳阈值。结果:共提取107个特征,有87个特征在鳞癌与腺癌之间差异有统计学意义(P<0.05)。其中,有8个特征与病理类型具有相关性(r>0.4),AUC均高于0.7。逆差矩、同质性、短区域因子作为预测因子,其ROC曲线下面积分别达到0.770、0.768和0.754,其敏感性和特异性分别为0.949和0.475、0.795和0.607、0.821和0.639。结论:腺癌、鳞癌的部分影像组学特征反应的肿瘤异质性有望为病理诊断提供一种高效、无创的检测方法。
Objective To investigate the feasibility of using pretreatment ~(18)F-FDG PET/CT radiomic features to predict the pathological subtypes of non-small-cell lung cancer(NSCLC). Methods The pretreatment ~(18)F-FDG PET/CT images of 100 NSCLC patients, including 60 adenocarcinoma(ADC) patients and 40 squamous cell carcinoma(SqCC) patients, were analyzed retrospectively. After the gross tumor volume was delineated on PET images, the metabolic parameters and texture parameters were extracted from gross tumor volume. Pearson correlation coefficients and receiver operating characteristic curve were used to assess the performances of the predictive features in the prediction of the pathological subtypes of NSCLC, and to calculate the sensitivity, specificity and optimal threshold of these features. Results Of 107 features extracted in this study, 87 features reflected the differences between ADC and Sq CC(P<0.05). Among the 87 features, there were 8 features related to the pathological subtypes(r>0.4), and their AUC values were all higher than 0.7. Three features with the best predictive performance, namely inverse difference moment, homogeneity and short-zone emphasis, were selected as predictive factors. The AUC values of the 3 predictive factors reached 0.770, 0.768 and 0.754, respectively, and their sensitivity and specificity were 0.949 and 0.475, 0.795 and 0.607,0.821 and 0.639, respectively. Conclusion Tumor heterogeneity reflected in the radiomic features of ADC and Sq CC is expected to provide an efficient and non-invasive detection method for the diagnosis of tumor subtypes.
Objective To investigate the feasibility of using pretreatment ~(18)F-FDG PET/CT radiomic features to predict the pathological subtypes of non-small-cell lung cancer(NSCLC). Methods The pretreatment ~(18)F-FDG PET/CT images of 100 NSCLC patients, including 60 adenocarcinoma(ADC) patients and 40 squamous cell carcinoma(SqCC) patients, were analyzed retrospectively. After the gross tumor volume was delineated on PET images, the metabolic parameters and texture parameters were extracted from gross tumor volume. Pearson correlation coefficients and receiver operating characteristic curve were used to assess the performances of the predictive features in the prediction of the pathological subtypes of NSCLC, and to calculate the sensitivity, specificity and optimal threshold of these features. Results Of 107 features extracted in this study, 87 features reflected the differences between ADC and Sq CC(P<0.05). Among the 87 features, there were 8 features related to the pathological subtypes(r>0.4), and their AUC values were all higher than 0.7. Three features with the best predictive performance, namely inverse difference moment, homogeneity and short-zone emphasis, were selected as predictive factors. The AUC values of the 3 predictive factors reached 0.770, 0.768 and 0.754, respectively, and their sensitivity and specificity were 0.949 and 0.475, 0.795 and 0.607,0.821 and 0.639, respectively. Conclusion Tumor heterogeneity reflected in the radiomic features of ADC and Sq CC is expected to provide an efficient and non-invasive detection method for the diagnosis of tumor subtypes.
引文
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[2]刘慧,王小宜,龙学颖.基于CT图像纹理分析肿瘤异质性的研究进展及应用[J].国际医学放射学杂志,2016,39(5):543-548.LIU H,WANG X Y,LONG X Y.Research progress and clinical application of tumor heterogeneity based on CT texture analysis[J].International Journal of Medical Radiology,2016,39(5):543-548.
[3]任红亮,徐文贵,尤健,等.探讨PET/CT原发灶SUVmax在肺鳞癌患者术后预后中的意义及与临床病理特征的关系[J].中国肺癌杂志,2016,19(4):192-199.REN H L,XU W G,YOU J,et al.Analysis of the role of PET/CTSUVmaxin prognosis and its correlation with clinicopathological characteristics in resectable lung squamous cell carcinoma[J].Chinese Journal of Lung Cancer,2016,19(4):192-199.
[4]FANG Y H,LIN C Y,SHIH M J,et al.Development and evaluation of an open-source software package"CGITA"for quantifying tumor heterogeneity with molecular images[J].Biomed Res Int,2014(5):248505.
[5]LIANG C,HUANG Y,HE L,et al.The development and validation of a CT-based radiomics signature for the preoperative discrimination of stage I-II and stage III-IV colorectal cancer[J].Oncotarget,2016,7(21):31401-31412.
[6]HUI L,YITIAN Z,BURNSIDE E S,et al.Quantitative MRI radiomics in the prediction of molecular classifications of breast cancer subtypes in the TCGA/TCIA data set[J].NPJ Breast Cancer,2016,2(1):16012.
[7]PERIGAUD C,BRIDJI B,ROUSSEL J C,et al.Prospective preoperative mediastinal lymph node staging by integrated positron emission tomography computerised tomography in patients with nonsmall-cell lung cancer[J].Eur J Cardiothorac Surg,2009,36(4):731-736.
[8]LEE B E,VON HAAG D,LOWN T,et al.Advances in positron emission tomography technology have increased the need for surgical staging in non-small cell lung cancer[J].J Thorac Cardiovasc Surg,2007,133(3):746-752.
[9]HATT M,TIXIER F,PIERCE L,et al.Characterization of PET/CTimages using texture analysis:the past,the present any future[J].Eur J Nucl Med Mol Imaging,2017,44(1):151-165.
[10]SIGEL C S,MOREIRAA L,TRAVIS W D,et al.Subtyping of nonsmall cell lung carcinoma:a comparison of small biopsy and cytology specimens[J].J Thorac Oncol,2011,6(11):1849-1856.
[11]TSUJIKAWA T,YAMAMOTO M,SHONO K,et al.Assessment of intratumor heterogeneity in mesenchymal uterine tumor by an18F-FDGPET/CT texture analysis[J].Ann Nucl Med,2017,31(10):752-757.
[12]王敏,宋彬,黄子星,等.大数据时代的精准影像医学:放射组学[J].中国普外基础与临床杂志,2016,23(6):752-755.WANG M,SONG B,HUANG Z X,et al.Precision medical imaging in big data:radiomics[J].Chinese Journal of Bases and Clinics in General Surgery,2016,23(6):752-755.
[13]PYKA T,BUNDSCHUH R A,ANDRATSCHKE N,et al.Textural features in pre-treatment[F18]-FDG-PET/CT are correlated with risk of local recurrence and disease-specific survival in early stage NSCLCpatients receiving primary stereotactic radiation therapy[J].Radiat Oncol,2015,10(1):100.
[14]MIWA K,INUBUSHI M,WAGATSUMA K,et al.FDG uptake heterogeneity evaluated by fractal analysis improves the differential diagnosis of pulmonary nodules[J].Eur J Radiol,2014,83(4):715-719.
[15]COROLLER T P,AGRAWAL V,HUYNH E,et al.Radiomic-based pathological response prediction from primary tumors and lymph nodes in NSCLC[J].Thorac Oncol,2017,12(3):467-476.
[16]STEINFORT D P,RUSSELL P A,TSUI A,et al.Interobserver agreement in determining non-small cell lung cancer subtype in specimens acquired by EBUS-TBNA[J].Eur Respir J,2012,40(3):699-705.
[17]PRIOLA A M,CATALDI A,CATALDI A,et al.Accuracy of CT-guided transthoracic needle biopsy of lung lesions:factors affecting diagnostic yield[J].Radiol Med,2007,112(8):1142-1159.
[18]NYFLOT M J,YANG F,BYRD D,et al.Quantitative radiomics:impact of stochastic effects on textural feature analysis implies the need for standards[J].J Med Imaging,2015,2(4):041002.
[19]NYFLOT M J,YANG F,BYRD D,et al.TU-AB-BRA-04:quantitative radiomics:sensitivity of PET textural features to image acquisition and reconstruction parameters implies the need for standards[J].Med Phys,2015,42(6 Part 31):3587.
[20]LEE J W,LEE S M.Radiomics in oncological PET/CT:clinical applications[J].Nucl Med Mol Imaging,2018,52(3):1-20.
[21]GJR C,AZAD G,OWCZARCZYK K,et al.Challenges and promises of PET radiomics[J].Int J Radiat Oncol Biol Phys,2018,102(4):1083-1089.