基于影像组学的肝癌研究进展
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摘要
目的影像组学是指从医学影像中提取定量影像学特征,并将图像特征转化为可挖掘的数据,建立肿瘤预测模型,来定量描述肿瘤表型,在肿瘤的诊断、治疗、预后和评估等方面具有重要的应用价值。影像组学作为一项全新的领域,以其客观、可重现、可挖掘、非侵入的特点,将在肿瘤的诊疗中发挥巨大作用。目前,由于肝癌患者数量的不断增多,影像组学也正逐步应用于肝癌的研究中。本文就影像组学及其在肝癌中的应用研究进展进行综述。
Objective Radiomics refers to the extraction of quantitative imaging features from medical images, the transformation image features into extensible data, establishing descriptive and predictive models for the quantitative description of phenotypes. It has important application value in the diagnosis, treatment, prognosis and evaluation of tumors. As an entirely new field, radiomics plays an important role in the diagnosis and treatment of tumors because of its objective, reproducible, excavable and non-invasive characteristics. At present, due to the increasing number of patients with liver cancer, it is gradually applied to the study of liver cancer. This article reviews the research progress of radiomics and its application in liver cancer.
Objective Radiomics refers to the extraction of quantitative imaging features from medical images, the transformation image features into extensible data, establishing descriptive and predictive models for the quantitative description of phenotypes. It has important application value in the diagnosis, treatment, prognosis and evaluation of tumors. As an entirely new field, radiomics plays an important role in the diagnosis and treatment of tumors because of its objective, reproducible, excavable and non-invasive characteristics. At present, due to the increasing number of patients with liver cancer, it is gradually applied to the study of liver cancer. This article reviews the research progress of radiomics and its application in liver cancer.
引文
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[28] 刘桐桐,董怡,韩红.基于影像组学方法的原发性肝细胞癌微血管侵犯和肿瘤分化等级预测[J].中国医学计算机成像杂志,2018,24(1):83-87.
[29] Bowen SR,Chapman TR,Borgman J,et al.Measuring total liver function on sulfur colloid SPECT/CT for improved risk stratification and outcome prediction of hepatocellular carcinoma patients [J].EJNMMI Res,2016,6(1):57-68.
[30] Fu S,Chen S,Liang C,et al.Texture analysis of intermediate-advanced hepatocellular carcinoma:prognosis and patients' selection of transcatheter arterial chemoembolization and sorafenib [J].Oncotarget,2017,8(23):37855-37865.
[31] Sanada Y,Yoshida K,Itoh H,et al.Comparison of CT enhancement patterns and histologic features in hepatocellular carcinoma up to 2cm:assessment of malignant potential with claudin-10 immunohistochemistry [J].Oncology Reports,2007,17(5):1177-1182.
[32] Hayano K,Yoshida H,Zhu AX,et al.Fractal analysis of contrast-enhanced CT images to predict survival of patients with hepatocellular carcinoma treated with sunitinib [J].Dig Dis Sci,2014,59(8):1996-2003.
[33] Villa E,Critelli R,Lei B,et al.Neoangiogenesis-related genes are hallmarks of fast-growing hepatocellular carcinomas and worst survival.Results from a prospective study [J].Gut,2016,65(5):861-869.
[34] Clarke LP,Sriram RD,Schilling LB.Imaging as a biomarker:standards for change measurements in therapy workshop summary [J].Acad Radiol,2008,15(4):501-530.
[2] Lambin P,Rios-Velazquez 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.
[3] Kumar V,Gu Y,Basu S,et al.Radiomics:the process and the challenges [J].Magn Reson Imaging,2012,30(9):1234-1248.
[4] Mitra S,Shankar BU.Integrating radio imaging with gene expressions toward a personalized management of cancer [J].IEEE Trans Human-Machine Sys,2014,44(5):664 -667.
[5] Levy MA,Freymann JB,Kirby JS,et al.Informatics methods to enable sharing of quantitative imaging research data[J].Magn Reson Imaging,2012,30(9):1249-1256.
[6] Gillies RJ,Kinahan PE,Hricak H.Radiomics:images are more than pictures,they are data [J].Radiology,2016,278(2):563-577.
[7] Sachdeva J,Kumar V,Gupta I,et al.A novel content-based active contour model for brain tumor segmentation [J].Magn Reson Imaging,2012,30(5):694-715.
[8] Gu Y,Kumar V,Hall LO,et al.Automated delineation of lung tumors from CT images using a single click ensemble segmentation approach [J].Pattern Recognit,2013,46(3):692-702.
[9] Ma Z,Tavares JM,Jorge RN,et al.A review of algorithms for medical image segmentation and their applications to the female pelvic cavity [J].Comput Methods Biomech Biomed Engin,2010,13(2):235-246.
[10] Rahmim A,Schmidtlein CR,Jackson A,et al.A novel metric for quantification of homogeneous and heterogeneous tumors in PET for enhanced clinical outcome prediction [J].Phys Med Biol,2016,61(1):227-242.
[11] Cunliffe A,Armato SG,Castillo R,et al.Lung texture in serial thoracic computed tomography scans:correlation of radiomics-based features with radiation therapy dose and radiation pneumonitis development [J].Int J Radiat Oncol Biol Phys,2015,91(5):1048-1056.
[12] Davnall F,Yip CS,Ljungqvist G,et al.Assessment of tumor heterogeneity:an emerging imaging tool for clinical practice [J].Insights Imaging,2012,3(6):573-589.
[13] Aerts HJ,Velazquez ER,Leijenaar RTH,et al.Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach [J].Nat Commun,2014,5(10):4006-4008.
[14] Griffié J,Shannon M,Bromley CL.A Bayesian cluster analysis method for single-molecule localization microscopy data [J].Nat Protoc,2016,11(12):2499-2514.
[15] Larue RT,Defraene G,De Ruysscher D,et al.Quantitative radiomics studies for tissue characterization:a review of technology and methodological procedures[J].Br J RT,2017,90(10):2016-2017.
[16] Huang YL,Chen JH,Shen WC.Diagnosis of hepatic tumors with texture analysis in nonenhanced computed tomography images [J].Acad Radiol,2006,13(6):713 -720.
[17] 马泽兰,黄燕琪,何兰,等.基于CT图像的纹理分析鉴别肝脏实质性局灶性病变[J].中国医学影像学杂志,2016,24(4):289-292.
[18] Starmans M,Klein S,Voort SRVD,et al.Classification of malignant and benign liver tumors using a radiomics approach [J].Image Processing,2018,10514( 48):1-8.
[19] Chang CC,Chen HH,Chang YC.Computer-aided diagnosis of liver tumors on computed tomography images [J].Comput Methods Programs Biomed,2017,145(6):45-51.
[20] Segal E,Sirin CB,Ooi C,et al.Decoding global gene expression programs in liver cancer by noninvasive imaging [J].Nat Biotechnol,2007,25(6):675-680.
[21] Banerjee S,Wang DS,Kim HJ,et al.A computed tomography radiogenomic biomarker predicts microvascular invasion and clinical outcomes in hepatocellular carcinoma [J].Hepatology,2015,62(3):792-800.
[22] Kuo MD,Gollub J,Sirlin CB,et al.Radiogenomic analysis to identify imaging prototypes associated with drug response gene expression programs in hepatocellular carcinoma[J].J VascInterv Radiol,2007,18(7):821-831.
[23] Dai M,Dong Y,Han H,et al.Predicting the expression of Ki-67 in hepatocellular carcinoma based on radiomics approach [J].Oncoradiology,2018,27(1):7-11.
[24] Li M,Fu S,Zhu Y,et al.Computed tomography texture analysis to facilitate therapeutic decision making in hepatocellular carcinoma [J].Oncotarget,2016,7(11):13248 -13259.
[25] Zhou H,Huang H,Shi J,et al.Prognostic value of interleukin 2 and interleukin 15 in peritumoral hepatic tissues for patients with hepatitis B-related hepatocellular carcinoma after curative resection [J].Gut,2010,59 (12):1699-1708.
[26] Simpson AL,Doussot A,Creasy JM,et al.Computed tomography image texture:a noninvasive prognostic marker of hepatic recurrence after hepatectomy for metastatic colorectal cancer [J].Ann Surg Oncol,2017,24(9):2482-2490.
[27] 祁红琳,胡先玲,李传明.基于MRI纹理特征的早期肝癌术后复发预测[J].中国医学物理学杂志,2017,34(9):908-911.
[28] 刘桐桐,董怡,韩红.基于影像组学方法的原发性肝细胞癌微血管侵犯和肿瘤分化等级预测[J].中国医学计算机成像杂志,2018,24(1):83-87.
[29] Bowen SR,Chapman TR,Borgman J,et al.Measuring total liver function on sulfur colloid SPECT/CT for improved risk stratification and outcome prediction of hepatocellular carcinoma patients [J].EJNMMI Res,2016,6(1):57-68.
[30] Fu S,Chen S,Liang C,et al.Texture analysis of intermediate-advanced hepatocellular carcinoma:prognosis and patients' selection of transcatheter arterial chemoembolization and sorafenib [J].Oncotarget,2017,8(23):37855-37865.
[31] Sanada Y,Yoshida K,Itoh H,et al.Comparison of CT enhancement patterns and histologic features in hepatocellular carcinoma up to 2cm:assessment of malignant potential with claudin-10 immunohistochemistry [J].Oncology Reports,2007,17(5):1177-1182.
[32] Hayano K,Yoshida H,Zhu AX,et al.Fractal analysis of contrast-enhanced CT images to predict survival of patients with hepatocellular carcinoma treated with sunitinib [J].Dig Dis Sci,2014,59(8):1996-2003.
[33] Villa E,Critelli R,Lei B,et al.Neoangiogenesis-related genes are hallmarks of fast-growing hepatocellular carcinomas and worst survival.Results from a prospective study [J].Gut,2016,65(5):861-869.
[34] Clarke LP,Sriram RD,Schilling LB.Imaging as a biomarker:standards for change measurements in therapy workshop summary [J].Acad Radiol,2008,15(4):501-530.