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Received：14 April 2020，
Revised：2020-7-3，
Accepted：10 July 2020，
Published：16 October 2020
移动端阅览
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.
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