肺癌<sup>18</sup>F-FDG PET-CT影像组学研究及展望

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[1]	Lambin P,Rios V E,Leijenaar R,et al.Radiomics:extracting more information from medical images using advanced feature analysis[J].Eur J Cancer,2012,48(4):441-446.
[2]	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.
[3]	Dhara A K,Mukhopadhyay S,Dutta A,et al.A combination of shape and texture features for classification of pulmonary nodules in lung CT images[J].J Digit Imaging,2016,29(4):466-475.
[4]	王敏,宋彬,黄子星,等.基于文献计量学的近5年放射组学研究热点分析[J].中华放射学杂志,2016,50(12):974-977.
[5]	Bae J M,Jeong J Y,Lee H Y,et al.Pathologic stratification of operable lung adenoearcinoma using radiomics features extracted from dual energy CT images[J].Oncotarget,2017,8(1):523-535.
[6]	刘文涓,王全师,吴湖炳,等.以18F-FDG PET-CT和临床信息为基础的孤立性肺结节诊断模型[J].中华核医学与分子影像杂志,2016,36(3):211-215.
[7]	张永学,兰晓莉.分子核医学与多模态影像[M].北京:人民卫生出版社,2019:627-628.
[8]	Krarup M K,Nygard L,Vogelius I R,et al.Heterogeneity in tumours:Validating the use of radiomic features on 18F-FDG PET-CT scans of lung cancer patients as a prognostic tool[J].Radiother Oncol,2020,144(1):72-78.
[9]	马圆,陈斯鹏,田思佳,等.基于PET-CT图像纹理参数的肺结节诊断模型[J].北京生物医学工程,2017,36 (3):257-260.
[10]	朱辉,汪秀玲,李智勇,等.18F-FDG PET-CT图像纹理分析在肺部肿块诊断中的临床价值[J].临床放射学杂志,2017,36(12):1766-1770.
[11]	Kirienko M,Cozzi L,Rossi A,et al.Ability of FDG PET and CT radiomics features to differentiate between primary and metastatic lung lesions[J].Eur J Nucl Med Mol Imaging,2018,45:1649-1660.
[12]	Kim Y I,Paeng J C,Park Y S,et al.Relation of EGFR mutation status to metabolic activity in localized lung adenocarcinoma and its influence on the use of FDG PET-CT parameters in prognosis[J].AJR Am J Roentgenol,2018,210:1346-1351.
[13]	Desseroit M C,Tixier F,Weber W A,et al.Reliability of PET-CT shape and heterogeneity features in functional and morphologic components of non-small cell lung cancer tumors:a repeatability analysis in a prospective multicenter cohort[J].Nucl Med,2017,58:406-11.
[14]	Saad M,Choi T.Deciphering unclassified tumors of non-small-cell lung cancer through radiomics[J].Comput Biol Med,2017,91:222-230.
[15]	Hyun S H,Ahn M S,Koh Y W,et al.A Machine-learning approach using PET-based radiomics to predict the histological subtypes of lung cancer[J].Clin Nucl Med,2019,44(12):956-960.
[16]	Bae J M,Jeong J Y,Lee H Y,et al.Pathologic stratification of operable lung adenoearcinoma using radiomics features extracted from dual energy CT images[J].Oncotarget,2017,8(1):523-535.
[17]	Yuan M,Zhang Y D,Pu X H,et al.Comparison of a radiomic biomarker with volumetric analysis for decoding tumour phenotypes of lung adenocarcinoma with different diseasespecific survival[J].Eur Radiol,2017,27(11):4857-4865.
[18]	Gomez L O,Garcia A M,Honguero A F,et al.Heterogeneity in 18Ffluorodeoxyglucose positron emission tomography/computed tomography of non-small cell lung carcinoma and its relationship to metabolic parametersand pathologic staging[J].Mol Imaging,2014,13(4):1072-1081.
[19]	Flechsig P,Frank P,Kratochwil C,et al.Radiomic analysis using density threshold for FDG PET-CT based N-staging in lung cancer patients[J].Mol Imaging Biol,2017,19(2):315-322.
[20]	Giesel F L,Schneider F,Kratochwil C,et al.Correlation between SUVmax and CT radiomic analysis using lymph node density in PET-CT based lymph node staging[J].J Nucl Med,2017,58(2):282-287.
[21]	Lu W,Chen W.Positron emission tomography/computerized tomography for tumor response assessment a review of clinical practices and radiomics studies[J].Transl Cancer Res,2016,5(4):364-370.
[22]	Lu L,Lv W,Jiang J,et al.Robustness of radiomic features in 11C-Choline and 18F-FDG PET-CT imaging of nasopharyngeal carcinoma:impact of segmentation and discretization[J].Mol Imaging Biol,2016,18(6):935-945.
[23]	Cook G J,Yip C,Siddique M,et al.Are pretreatment 18F-FDG PET tumor textural features in non-small cell lung cancer associated with response and survival after chemoradiotherapy?[J].J Nucl Med,2013,54(1):19-26.
[24]	Coroller T P,Agrawal V,Narayan V,et al.Radiomic phenotype features predict pathological response in non-small cell lung cancer[J].Radiother Oncol,2016,119(3):480-486.
[25]	张利文,方梦捷,臧亚丽,等.影像组学的发展与应用[J].中华放射学杂志,2017,51(1):75-77.
[26]	Lovinfosse P,Janvary Z L,Coucke P,et al.FDG PET-CT texture analysis for predicting the outcome of lung cancer treated by stereotactic body radiation therapy[J].Eur J Nucl Med Mol Imaging,2016,43(8):1453-1460.
[27]	Fried D V,Mawlawi O,Zhang L,et al.Stage Ⅲ non-small cell lung cancer:prognostic value of FDG PET quantitative imaging features combined with clinical prognostic factors[J].Radiology,2016,278(1):214-222.
[28]	Coroller T P,Grossmann P,Hou Y,et al.CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma[J].Radiother Oncol,2015,114(3):345-350.
[29]	罗居东,马长升,赵睿,等.PET特征改变对非小细胞肺癌放射治疗的预测价值[J].中国医学物理学杂志,2019,36(6):677-681.
[30]	Yoon H J,Sohn I,Cho J H,et al.Decoding tumor phenotypes for ALK,ROS1,and RET fusions in lung adenocarcinoma using a radiomics approach[J].Medicine(Baltimore),2015,94(41):e1753.
[31]	杨洋,黄世明,刚波,等.18F-FDG PET-CT对非小细胞肺癌EGFR基因突变诊断价值的Meta分析[J].武警医学,2018,29(5):448-452.
[32]	黄世明,杨洋,尹亮,等.18F-FDG PET-CT代谢参数联合肿瘤标志物对肺腺癌EGFR基因突变的预测价值[J].武警医学,2020,31(6):503-508.
[33]	Liu Y,Kim J,Balagurunathan Y,et al.Radiomic features are associated with EGFR mutation status in lung adenocarcinomas[J].Clin Lung Cancer,2016,17(5):441-448.
[34]	Zhang J Y,Zhao X M,Zhao Y,et al.Value of pre-therapy 18F-FDG PET-CT radiomics in predicting EGFR mutation status in patients with non-small cell lung cancer[J].Eur J Nucl Med Mol Imaging,2019,46(11):1431-1443.