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收稿：2020-04-14，
修回：2020-7-3，
录用：2020-7-10，
纸质出版：2020-10-16
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本文综述了体素内不相干运动扩散加权成像（intravoxel incoherent motion diffusion-weighted imaging，IVIM-DWI）及纹理分析技术在术前鉴别宫颈癌组织学亚型及淋巴结转移的临床应用进展。MRI（magnetic resonance imaging）是宫颈癌临床术前最常用的影像学诊断和分期方法。宫颈癌的组织学类型及有无淋巴结转移与患者的生存及预后紧密相关。IVIM-DWI为MRI新型功能成像技术，其D值反映组织内单纯水分子的布朗运动信息、间接反映恶性肿瘤组织细胞密集度；其D
*
值和f值能提供肿瘤组织内血流灌注的信息。影像组学的纹理分析技术（texture analysis，TA）通过提取肿瘤组织的纹理特征进行客观、定量分析，能检测人眼不能识别的肿瘤组织的微观改变，揭示更多肿瘤组织的灰度分布与定量数据特征，为临床术前预测宫颈癌不同组织学类型及转移淋巴结提供了可能。
Cervical cancer is an important public health problem worldwide
and its high incidence and trends in younger generation also attract increasing attention. The biological behavior and prognosis of cervical cancer are closely related to its histological type and lymph node metastasis. Cervical cancer has many histological types: the most common type is squamous cell carcinoma
accounting for about 80%; the second most common type is adenocarcinoma
accounting for about 15%~20%
whose incidence is increasing in recent years because of the prevalence of cervical cancer screening. The rare types are adenosquamous carcinoma and neuroendocrine tumors (small cell carcinoma). Their incidence is about 5% and is increasing in recent years because of the cervical cancer screening. Cervical squamous cell carcinoma is more sensitive to radiotherapy and has a better prognosis
whereas cervical adenocarcinoma is less sensitive to radiotherapy and prone to lymph node and hematogenous metastasis. Small cell carcinomas are sensitive to chemotherapies due to their particular origin
prone to early lymph node metastasis
and have poor prognosis. Determining lymphatic metastasis based on magnetic resonance imaging (MRI) is the main factor to evaluate the prognosis of cervical cancer and formulate treatment plan. In the early diagnosis of cervical cancer
10%~30% of patients have lymph node metastasis. Thus
the evaluation of pelvic and retroperitoneal lymph node metastasis of cervical cancer before treatment will be directly related to the choice of treatment options and prognosis of patients. Although cervical conization and lymph node biopsy are the gold standard for the diagnosis of cervical cancer histopathology and metastatic lymph nodes
the limitations of sampling and the heterogeneity of tumors do not fully reflect the histological information of primary lesions and metastatic lymph nodes of cervical cancer
and biopsy can increase the risk of tumor dissemination.MRI is the commonly used imaging diagnosis and staging method for cervical cancer before surgery. Intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI)
a new functional imaging technique for MRI
is a multi-b-value and bi-exponential model diffusion-weighted technique
which can separate the random diffusion motion of water molecules from the microvascular perfusion effect and reflect the diffusion information of biological tissues. The main parameters are as follows: ADC
D
D
*
and f value. The ADC
stand
value is similar to the ADC(apparent diffusion coefficient) value in a single-value DWI(diffusion weighted imaging) model
which reflects the combined effect of tissue diffusion and perfusion but can simply reflect the diffusion movement of water molecules in tissues. Given the influence of the elevation of microcirculation blood perfusion
the measured values tend to be high. The D value reflects Brownian motion information of pure water molecules in tissues and indirectly reflects cell density in tumors. The D
*
value and f value provide information of microcirculatory blood flow perfusion in tumors. Moreover
the D
*
value primarily reflects the blood velocity of microcirculation
whereas the f value primarily reflects the blood volume of microcirculation. Therefore
IVIM-DWI based on multiple parameters can play an important role in tumor characterization
staging
typing
and prediction of lymph node metastasis. At present
artificial intelligence (AI) plays a role in all aspects of people's life. Texture analysis (TA) technology is an important branch of AI research. Medically
TA has become a new research hotspot in Radiomics. TA uses corresponding computer software to extract texture features of lesion tissues from images for objective and quantitative analysis based on imaging images
which can detect microscopic changes of tumor tissues that cannot be recognized by the human eyes
and reveal gray distribution and quantitative data features in tumor tissues. To date
TA is based on images such as computed tomography(CT)
MRI
or positron emission tomography(PET)/CT. Three methods are identified to obtain texture features: statistics based
transformation based
and structure based. Among the methods
the most commonly used method is statistics based. Texture features include first-order features
second-order features
and high-order features. First-order features
also known as histogram analysis
describe the gray distribution of individual pixel values in the region of interest
including mean
variance
skewness
kurtosis
and entropy. Second-order features represent local texture features on the basis of the relationship between adjacent 2 pixels. Common methods include gray-level co-occurrence matrix
gray-level run-length matrix
and gray-level area-size matrix. In addition
high-order features analyze local image information by applying gray-level difference matrix of adjacent pixels to reflect the change of local intensity or the distribution of homogeneous regions. TA can quantify the distribution of texture features
such as signal intensity distribution
morphology
and heterogeneity at the lesion site
to reflect the lesion features objectively and comprehensively. Therefore
TA plays an important role in the qualitative
definitive
and differential diagnosis of diseases. Researchers interpret texture information in images on the basis of imaging images
combined with imaging technology and computer AI technology
through the analysis of quantitative data
which provides a possibility for clinical preoperative prediction of different histological types of cervical cancer and metastatic lymph nodes. This article reviews the recent research progress in the clinical application of IVIM-DWI and TA in the preoperative identification of histological subtypes and lymph node metastasis of cervical cancer.
Atri M, Zhang Z, Dehdashti F, Lee S I, Ali S, Marques H, Koh W J, Moore K, Landrum L, Kim J W, DiSilvestro P, Eisenhauer E, Schnell F and Gold M. 2016. Utility of PET-CT to evaluate retroperitoneal lymph node metastasis in advanced cervical cancer:results of ACRIN6671/GOG0233 trial. Gynecologic Oncology, 142(3):413-419[DOI:10.1016/j.ygyno.2016.05.002]
Brenet D L, Mulé S, Tenenhaus A, Piardi T, Sommacale D, Hoeffel C and Thiéfin G. 2019. Hepatocellular carcinoma:CT texture analysis as a predictor of survival after surgical resection. European Radiology, 29(3):1231-1239[DOI:10.1007/s00330-018-5679-5]
Burzawa J, Gonzales N and Frumovitz M. 2015. Challenges in the diagnosis and management of cervical neuroendocrine carcinoma. Expert Review of Anticancer Therapy, 15(7):805-810[DOI:10.1586/14737140.2015.1047767]
Cai M Y, Huang W S, Lin C S, Li Z R, Qian J S, Huang M S, Zeng Z L, Huang J J, Shan H and Zhu K S. 2016. Partial splenic embolization for thrombocytopenia in liver cirrhosis:predictive factors for platelet increment and risk factors for major complications. European Radiology, 26(2):370-380[DOI:10.1007/s00330-015-3839-4]
Chandarana H, Lee V S, Hecht E, Taouli B and Sigmund E E. 2011. Comparison of biexponential and monoexponential model of diffusion weighted imaging in evaluation of renal lesions:preliminary experience. Investigative Radiology, 46(5):285-291[DOI:10.1097/RLI.0b013e3181ffc485]
Cheng N, Lv X H, Ren K, Lu T and Wang Y F. 2017. Diagnostic value of intravoxel incoherent motion DWI multiple models parameter analyses in cervical cancer. Radiologic Practice, 32(2):157-161
程楠, 吕星海, 任克, 卢涛, 王永峰. 2017. IVIM-DWI多模型参数分析对宫颈癌的诊断价值.放射学实践, 32(2):157-161 [DOI:10.13609/j.cnki.1000-0313.2017.02.013]
Ciolina M, Vinci V, Villani L, Gigli S, Saldari M, Panici P B, Perniola G, Laghi A, Catalano C and Manganaro L. 2019. Texture analysis versus conventional MRI prognostic factors in predicting tumor response to neoadjuvant chemotherapy in patients with locally advanced cancer of the uterine cervix. La Radiologia Medica, 124(10):955-964[DOI:10.1007/s11547-019-01055-3]
Ganeshan B, Panayiotou E, Burnand K, Dizdarevic S and Miles K. 2012. Tumour heterogeneity in non-small cell lung carcinoma assessed by CT texture analysis:a potential marker of survival. European Radiology, 22(4):796-802[DOI:10.1007/s00330-011-2319-8]
Gillies R J, Kinahan P E and Hricak H. 2016. Radiomics:images are more than pictures, they are data. Radiology, 278(2):563-577[DOI:10.1148/radiol.2015151169]
He Z B, Chen S M, Luo Y and Ma F W. 2019. Value of DCE-MRI and IVIM-DWI in diagnosing the pathological grade and clinical stage of cervical cancer. Chinese Journal of CT and MRI, 17(8):110-113
何志兵, 陈首名, 罗鹰, 马方伟. 2019. DCE-MRI和IVIM-DWI诊断宫颈癌病理分级和临床分期的价值分析.中国CT和MRI杂志, 17(8):110-113 [DOI:10.3969/j.issn.1672-5131.2019.08.033]
Jakola A S, Zhang Y H, Skjulsvik A J, Solheim O, Bø H K, Berntsen E M, Reinertsen I, Gulati S, Förander P and Brismar T B. 2018. Quantitative texture analysis in the prediction of IDH status in low-grade gliomas. Clinical Neurology and Neurosurgery, 164:114-120[DOI:10.1016/j.clineuro.2017.12.007]
Jonska-Gmyrek J, Gmyrek L, Zolciak-Siwinska A, Kowalska M and Kotowicz B. 2019. Adenocarcinoma histology is a poor prognostic factor in locally advanced cervical cancer. Current Medical Research and Opinion, 35(4):595-601[DOI:10.1080/03007995.2018.1502166]
Le Bihan D, Breton E, Lallemand D, Grenier P, Cabanis E and Laval-Jeantet M. 1986. MR imaging of intravoxel incoherent motions:application to diffusion and perfusion in neurologic disorders. Radiology, 161(2):401-407[DOI:10.1148/radiology.161.2.3763909]
Lee S I and Atri M. 2019.2018 FIGO staging system for uterine cervical cancer:enter cross-sectional imaging. Radiology, 292(1):15-24[DOI:10.1148/radiol.2019190088]
Li Z M, Yu L, Wang X, Yu H Y, Gao Y X, Ren Y D, Wang G and Zhou X M. 2018. Diagnostic performance of mammographic texture analysis in the differential diagnosis of benign and malignant breast tumors. Clinical Breast Cancer, 18(4):e621-e627[DOI:10.1016/j.clbc.2017.11.004]
Li Z S, Zhang J B, Wang H, Chen X, Qian W L and Qian X. 2019. A study of quantitative intravoxel incoherent motion diffusion weighted imaging in the diagnosis and clinical staging of cervical cancer at 3.0T MR scanner. Radiologic Practice, 34(8):896-900
李志森, 张继斌, 王宏, 陈雪, 钱伟亮, 钱鑫. 2019.3.0T MR IVIM-DWI在宫颈癌诊断及临床分期中的价值.放射学实践, 34(8):896-900 [DOI:10.13609/j.cnki.1000-0313.2019.08.013]
Li Z W, Yuan S S, Huang L, Ma X L and Xia L M. 2013. A preliminary study of magnetic resonance myocardial multi-b values diffusion weighted imaging. Radiologic Practice, 28(3):337-340
李志伟, 袁思殊, 黄璐, 马晓玲, 夏黎明. 2013.心肌磁共振多b值DWI的初步探讨.放射学实践, 28(3):337-340 [DOI:10.13609/j.cnki.1000-0313.2013.03.015]
Liu S Y and Xiao Y. 2017. Challenges and opportunities of artificial intelligence based on deep learning for medical imaging. Chinese Journal of Radiology, 51(12):899-901
刘士远, 萧毅. 2017.基于深度学习的人工智能对医学影像学的挑战和机遇.中华放射学杂志, 51(12):899-901 [DOI:10.3760/cma.j.issn.1005-1201.2017.12.002]
McLaren C E, Chen W P, Nie K and Su M Y. 2009. Prediction of malignant breast lesions from MRI features:a comparison of artificial neural network and logistic regression techniques. Academic Radiology, 16(7):842-851[DOI:10.1016/j.acra.2009.01.029]
MengN, Yue W, Wang S N, Duan J H, Yin H J and Han D M. 2018. Multiple model parameters of intravoxel incoherent motion in differential diagnosis of cervical carcinoma and pre-judgement of pathological types. Chinese Journal of Medical Imaging Technology, 34(3):407-411
孟楠, 岳巍, 王帅娜, 段金辉, 殷慧佳, 韩东明. 2018.体素不相干运动多模型参数鉴别诊断宫颈癌并预判其病理类型.中国医学影像技术, 34(3):407-411 [DOI:10.13929/j.1003-3289.201705003]
Mongula J E, Bakers F, Slangen B F M, Van Kuijk S M J, Kruitwagen R F P M and Mihl C. 2019. Evaluation of various apparent diffusion coefficient measurement techniques in pre-operative staging of early cervical carcinoma. European Journal of Radiology, 118:101-106[DOI:10.1016/j.ejrad.2019.06.021]
Niu W and Luo Y H. 2017. Research progress of DWI and DCE-MRI in evaluating pelvic lymph node metastasis of cervical cancer. Radiologic Practice, 32(4):344-346
牛微, 罗娅红. 2017. DWI和DCE-MRI评价宫颈癌盆腔淋巴结转移的研究进展.放射学实践, 32(4):344-346 [DOI:10.13609/j.cnki.1000-0313.2017.04.010]
Shi Y Q. 2017. The clinical research progress of radiomics. Journal of Practical Radiology, 33(10):1623-1626
施奕倩. 2017.影像组学的临床研究进展.实用放射学杂志, 33(10):1623-1626 [DOI:10.3969/j.issn.1002-1671.2017.10.034]
Sun X R, He B, Luo X, Li Y H, Cao J F, Wang J L, Dong J, Sun X Y and Zhang G X. 2018. Preliminary study on molecular subtypes of breast cancer based on magnetic resonance imaging texture analysis. Journal of Computer Assisted Tomography, 42(4):531-535[DOI:10.1097/RCT.0000000000000738]
Tamura T, Usui S, Murakami S, Arihiro K, Fujimoto T, Yamada T, Naito K and Akiyama M. 2012. Comparisons of multi b -value DWI signal analysis with pathological specimen of breast cancer. Magnetic Resonance in Medicine, 68(3):890-897[DOI:10.1002/mrm.23277]
Vignati A, Mazzetti S, Giannini V, Russo F, Bollito E, Porpiglia F, Stasi M and Regge D. 2015. Texture features on T2-weighted magnetic resonance imaging:new potential biomarkers for prostate cancer aggressiveness. Physics in Medicine and Biology, 60(7):2685-2701[DOI:10.1088/0031-9155/60/7/2685]
Wang M H and Feng Z. 2018. Conventional MRI texture analysis of peritumoral edema in the differential diagnosis of glioblastoma and solitary metastatic brain tumor. Chinese Journal of Radiology, 52(10):756-760
王敏红, 冯湛. 2018.瘤周水肿常规MRI纹理分析鉴别脑胶质母细胞瘤和单发转移瘤的价值.中华放射学杂志, 52(10):756-760 [DOI:10.3760/cma.j.issn.1005-1201.2018.10.007]
Wang Y C, Zhang J, Zhang L Y and Chen Y. 2018. Whole-lesion MRI texture analysis in differentiating different histopathological subtype of renal cell carcinoma. Radiologic Practice, 33(8):785-788
王绎忱, 张瑾, 张连宇, 陈雁. 2018.全病灶MRI纹理分析鉴别不同病理类型肾细胞癌.放射学实践, 33(8):785-788 [DOI:10.13609/j.cnki.1000-0313.2018.08.004]
Xie T, Chen X, Fang J Q, Kang H Y, Xue W, Tong H P, Cao P, Wang S M, Yang Y Z and Zhang W G. 2018. Textural features of dynamic contrast-enhanced MRI derived model-free and model-based parameter maps in glioma grading. Journal of Magnetic Resonance Imaging, 47(4):1099-1111[DOI:10.1002/jmri.25835]
Xie Y L, Du D, Xie W, Wang X and Jiang Y P. 2019. The value of texture analysis based on dynamic contrast-enhanced MRI for differentiating cervical adenocarcinoma from squamous cell carcinoma and its prediction of stages. Radiologic Practice, 34(8):835-840
谢元亮, 杜丹, 谢伟, 王翔, 江燕萍. 2019. DCE-MRI纹理分析鉴别宫颈鳞癌与腺癌及预测分级的价值.放射学实践, 34(8):835-840 [DOI:10.13609/j.cnki.1000-0313.2019.08.002]
Ye X H, Zhou C, Wang H, Zhang C and Chen M. 2016. Preliminary study of intravoxel incoherent motion MR imaging in assessing histological characters of cervical cancer. Journal of Clinical Radiology, 35(7):1048-1052
叶晓华, 周诚, 王宏, 张晨, 陈敏. 2016. MR体素内不相干运动成像评价宫颈癌组织学特征的初步研究.临床放射学杂志, 35(7):1048-1052 [DOI:10.13437/j.cnki.jcr.2016.07.016]
Yue Q, Chen J, He M, Wang H and Du Y. 2019. Application of MRI texture analysis in the hepatocellular carcinoma. Journal of Practical Radiology, 35(11):1856-1858, 1877
岳茜, 陈娇, 何淼, 王红, 杜勇. 2019. MRI纹理分析在肝癌中的应用.实用放射学杂志, 35(11):1856-1858, 1877 [DOI:10.3969/j.issn.1002-1671.2019.11.035]
Zhang H M, Zhang C D, Zheng Z X, Ye F, Liu Y, Zou S M and Zhou C W. 2017. Chemical shift effect predicting lymph node status in rectal cancer using high-resolution MR imaging with node-for-node matched histopathological validation. European Radiology, 27(9):3845-3855[DOI:10.1007/s00330-017-4738-7]
Zhang L W, Fang M J, Zang Y L, Zhun Y B, Dong D, Liu X and Tian J. 2017. Development and application of radiomics. Chinese Journal of Radiology, 51(1):75-77
张利文, 方梦捷, 臧亚丽, 朱永北, 董迪, 刘侠, 田捷. 2017.影像组学的发展与应用.中华放射学杂志, 51(1):75-77 [DOI:10.3760/cma.j.issn.1005-1201.2017.01.017]
Zhang L X. 2018. The Value of Intravoxel Incoherent Motion Diffusion Weighted Imaging in the Preoperative Evaluation Benign and Malignant Tumor of Uterine and Prostate. Xinxiang: Xinxiang Medical University
张利祥. 2018.体素内不相干运动扩散加权成像对子宫和前列腺良恶性肿瘤术前评估的价值.新乡: 新乡医学院
Zhao X Y, Zhao X, Zhang XA and Wang X Y. 2018. The diagnostic value and correlation study between IVIM-DWI and DCE-MRI in cervical cancer. Journal of Practical Radiology, 34(5):717-720
赵晓艳, 赵鑫, 张小安, 王雪源. 2018. IVIM-DWI与DCE-MRI对宫颈癌的诊断价值和相关性研究.实用放射学杂志, 34(5):717-720 [DOI:10.3969/j.issn.1002-1671.2018.05.018]
Zhou Z, Chen J, Pan L, Zhou F F and Xing W. 2019. Application of texture analysis in dynamic contrast-enhanced MRI for differentiation of renal cell carcinoma subtypes. Chinese Journal of Magnetic Resonance Imaging, 10(7):525-529
周智, 陈杰, 潘靓, 周菲菲, 邢伟. 2019.纹理分析在MRI动态增强扫描鉴别肾细胞癌亚型中的应用研究.磁共振成像, 10(7):525-529 [DOI:10.12015/issn.1674-8034.2019.07.009]
Zhu X Z, Dong D, Chen Z D, Fang M J, Zhang L W, Song J D, Yu D D, Zang Y L, Liu Z Y, Shi J Y and Tian J. 2018. Radiomic signature as a diagnostic factor for histologic subtype classification of non-small cell lung cancer. European Radiology, 28(7):2772-2778[DOI:10.1007/s00330-017-5221-1]
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