CN104657598A - Method for calculating fractal dimension of microvessels of tissue - Google Patents
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Abstract
本发明属于医学数字图像分析领域，具体涉及一种组织微血管分形维数的计算方法，该方法包括如下步骤：将待检组织进行常规制片得组织切片；将组织切片进行免疫组织化学染色标记血管得染色切片，然后采用图像采集设备摄取染色切片的二维结构图像得数字切片；从数字切片中截取含微血管丰富的“热点区”，命名为图像A；在HSB色彩空间内基于阈值法提取图像A的染色阳性目标区域，得图像A1，然后将图像A1转化为二进制，得到图像B；对图像B进行图像骨骼化处理，得到图像C；采用盒形计数法计算图像C的分形维数mvFD。该方法操作简单、计算简便、误差较小，所得分形维数能完整、有效反映被检测组织微血管形态的复杂性和数量。
The invention belongs to the field of medical digital image analysis, and in particular relates to a method for calculating the fractal dimension of tissue microvessels. The method comprises the following steps: routinely slice the tissue to be examined to obtain tissue slices; perform immunohistochemical staining on the tissue slices to mark blood vessels Obtain stained slices, and then use image acquisition equipment to capture the two-dimensional structural images of the stained slices to obtain digital slices; intercept the "hot spots" rich in microvessels from the digital slices, and name them as image A; extract images based on the threshold method in the HSB color space The stained positive target area of A is obtained as image A 1 , and then image A 1 is converted into binary to obtain image B; image skeletonization is performed on image B to obtain image C; the fractal dimension of image C is calculated by box counting method mvFD. The method is simple to operate, easy to calculate, and has small errors, and the obtained fractal dimension can completely and effectively reflect the complexity and quantity of microvessel morphology in the detected tissue.
Description
技术领域technical field
本发明属于医学数字图像分析领域，具体涉及一种组织微血管分形维数的计算方法。The invention belongs to the field of medical digital image analysis, in particular to a method for calculating the fractal dimension of tissue microvessels.
背景技术Background technique
血管是生物运送血液的管道。人体正常组织或病变组织均含有血管，尤其是某些病变(如恶性肿瘤)组织含有丰富血管，并形成血管网络系统。病理组织切片是进行病理诊断的主要载体，其中，组织血管的形态、数量等特征可以作为某些疾病(如恶性肿瘤)辅助诊断的依据。目前评估组织血管特征的方法包括血管形态描述、血管密度计算、血管特异性分子标记物免疫组化染色等。然而，目前尚缺乏定量判断血管形态复杂程度的指标。Blood vessels are the conduits through which living things transport blood. Both normal and diseased tissues of the human body contain blood vessels, especially some diseased (such as malignant tumor) tissues contain abundant blood vessels and form a vascular network system. Pathological tissue slices are the main carrier for pathological diagnosis, in which the morphology and quantity of tissue blood vessels can be used as the basis for auxiliary diagnosis of certain diseases (such as malignant tumors). Current methods for assessing tissue vascular characteristics include vascular morphology description, vascular density calculation, and immunohistochemical staining of vascular-specific molecular markers. However, there is still a lack of indicators for quantitatively judging the complexity of vascular morphology.
分形维数是分形几何学中的一个指标，已在材料学、建筑学、数字图像分析等领域被广泛应用。在生物医学中，分形维数被用于评价形态特征的复杂性，包括细胞核形态、染色质结构和肿瘤组织边缘粗糙程度等。目前，尚未建立一种组织微血管分形维数的计算方法。Fractal dimension is an index in fractal geometry, which has been widely used in materials science, architecture, digital image analysis and other fields. In biomedicine, fractal dimension is used to evaluate the complexity of morphological features, including cell nucleus morphology, chromatin structure, and the roughness of tumor tissue edges. Currently, there is no established method for calculating the fractal dimension of tissue microvessels.
发明内容Contents of the invention
有鉴于此，本发明的目的在于提供一种组织微血管分形维数的计算方法，该方法操作简单、计算简便、误差较小，所得分形维数能完整、有效反映被检测组织微血管形态的复杂性和数量。In view of this, the purpose of the present invention is to provide a method for calculating the fractal dimension of tissue microvessels, which is simple to operate, easy to calculate, and has small errors, and the obtained fractal dimension can completely and effectively reflect the complexity of the detected tissue microvessel morphology and quantity.
为实现上述目的，本发明的技术方案为：To achieve the above object, the technical solution of the present invention is:
一种组织微血管分形维数的计算方法，包括如下进行的步骤：A method for calculating the fractal dimension of tissue microvessels, comprising the following steps:
(1)将待检组织进行常规制片得组织切片；(1) Tissue slices obtained by routinely making slices of the tissue to be tested;
(2)将步骤(1)所得的组织切片进行免疫组织化学染色标记血管得染色切片，然后采用图像采集设备摄取染色切片的二维结构图像得数字切片；(2) Perform immunohistochemical staining on the tissue section obtained in step (1) to mark blood vessels to obtain a stained section, and then use an image acquisition device to capture a two-dimensional structural image of the stained section to obtain a digital section;
(3)从步骤(2)所得的数字切片中截取含微血管丰富的“热点区”，命名为图像A；(3) From the digital slice obtained in step (2), intercept the "hot spot" containing microvessels, and name it as image A;
(4)在HSB色彩空间内基于阈值法提取步骤(3)所得的图像A的染色阳性目标区域，得图像A1，然后将图像A1转化为二进制，得到图像B；(4) extracting the stained positive target region of the image A obtained in step (3) based on the threshold method in the HSB color space to obtain the image A 1 , and then converting the image A 1 into binary to obtain the image B;
(5)对步骤(4)所得的图像B进行图像骨骼化处理，得到图像C；(5) Carry out image skeletonization processing to the image B of step (4) gained, obtain image C;
(6)采用盒形计数法计算所述步骤(5)中图像C的分形维数mvFD，具体为：以像素值4、8、16、32、64、128、256为正方形盒边长ε，分别计算覆盖图像C中所有目标区域即二进制值为1的区域所需要的最小的非重叠方盒数N(ε)；以logε为横坐标，log N(ε)为纵坐标，作散点图，以最小二乘法作线性回归，得到回归方程：log N(ε)＝a·logε+b，根据此方程，a的相反数即被认为是mvFD。(6) adopt the box-shaped counting method to calculate the fractal dimension mvFD of the image C in the step (5), specifically: take pixel values 4, 8, 16, 32, 64, 128, 256 as the square box side length ε, Calculate the minimum number of non-overlapping boxes N(ε) required to cover all target areas in the image C, that is, the area with a binary value of 1; take logε as the abscissa and log N(ε) as the ordinate, and make a scatter diagram , use the least squares method to do linear regression, and get the regression equation: log N(ε)=a logε+b, according to this equation, the opposite number of a is considered as mvFD.
进一步，所述的一种组织微血管分形维数的计算方法，所述步骤(2)中，所述免疫组织化学染色是抗CD34免疫组化染色。Further, in the method for calculating the fractal dimension of tissue microvessels, in the step (2), the immunohistochemical staining is anti-CD34 immunohistochemical staining.
更进一步，所述的一种组织微血管分形维数的计算方法，所述抗CD34免疫组化染色采用DAB显色。Furthermore, in the method for calculating the fractal dimension of tissue microvessels, the anti-CD34 immunohistochemical staining is developed with DAB.
进一步，所述的一种组织微血管分形维数的计算方法，所述步骤(2)中，所述图像采集设备为切片全景扫描仪。Further, in the method for calculating the fractal dimension of tissue microvessels, in the step (2), the image acquisition device is a panoramic slice scanner.
进一步，所述的一种组织微血管分形维数的计算方法，所述步骤(3)中，从所述数字切片中截取3-5个含微血管最丰富的“热点区”，命名为图像A；Further, in the method for calculating the fractal dimension of tissue microvessels, in the step (3), 3-5 "hot spots" containing the most abundant microvessels are intercepted from the digital slice, and named as image A;
进一步，所述的一种组织微血管分形维数的计算方法，所述步骤(3)中，所述图像A的像素不低于1920×1080。Further, in the method for calculating the fractal dimension of tissue microvessels, in the step (3), the pixels of the image A are not lower than 1920×1080.
进一步，所述的一种组织微血管分形维数的计算方法，所述步骤(4)中，所述阈值法设定的参数为：H：0-90和215-255，S：25-255或S：38-255，B：0-255。Further, in the method for calculating the fractal dimension of tissue microvessels, in the step (4), the parameters set by the threshold method are: H: 0-90 and 215-255, S: 25-255 or S: 38-255, B: 0-255.
本发明的一种组织微血管分形维数的计算方法，该方法操作简单、计算简便、误差较小，血管分形维数能完整、有效反映被检测组织微血管形态的复杂性、数量及分布状态。The method for calculating the fractal dimension of tissue microvessels of the present invention has the advantages of simple operation, convenient calculation and small error, and the fractal dimension of blood vessels can completely and effectively reflect the complexity, quantity and distribution state of the detected tissue microvessel morphology.
脑胶质瘤是中枢神经系统最常见的肿瘤之一。胶质母细胞瘤是恶性程度最高的胶质瘤(WHO IV级)，占所有胶质瘤的50％，治疗困难、预后差且复发率高。临床上主要基于病理诊断，依据包括瘤细胞异型性、坏死和血管增生等特征，其中，血管增生最具特征性，表现为血管具有多种复杂形态，即呈现“异型血管”，其在同一瘤体内、不同瘤体之间分布具有较大异质性。血管形态的复杂性和分布的异质性对于辅助胶质母细胞瘤诊断具有重要意义。现有反映胶质瘤血管形态和分布特性的指标包括定性指标(将异型血管分为血管簇样、肾小球样和花环样等)和定量指标如微血管密度和血管直径、分支数等。但是，定性指标统计主观性较大，缺乏统一标准；微血管密度仅能反映血管数量，且由于异型血管形态的复杂性其计数误差较大；血管直径和分支数统计容易造成选择误差。因此，需要一个能反映胶质母细胞瘤微血管形态复杂性和数量且计算简便、误差较小的新指标，微血管分形维数有望符合上述要求。Glioma is one of the most common tumors of the central nervous system. Glioblastoma is the most malignant glioma (WHO grade IV), accounting for 50% of all gliomas, with difficult treatment, poor prognosis and high recurrence rate. Clinically, it is mainly based on pathological diagnosis, which includes features such as tumor cell atypia, necrosis, and vascular hyperplasia. Among them, vascular hyperplasia is the most characteristic, showing that blood vessels have various complex shapes, that is, presenting "abnormal blood vessels". The distribution in vivo and among different tumors has great heterogeneity. The complexity of vascular morphology and the heterogeneity of distribution are of great significance in assisting the diagnosis of glioblastoma. Existing indicators reflecting the morphology and distribution of glioma vessels include qualitative indicators (dividing abnormal vessels into cluster-like, glomerulus-like, and rosette-like, etc.) and quantitative indicators such as microvessel density, vessel diameter, and number of branches. However, the statistics of qualitative indicators are relatively subjective and lack uniform standards; microvessel density can only reflect the number of vessels, and the counting error is relatively large due to the complexity of abnormal vessel morphology; the statistics of vessel diameter and branch number are likely to cause selection errors. Therefore, there is a need for a new indicator that can reflect the complexity and quantity of glioblastoma microvessels, and is simple to calculate and has less error. The fractal dimension of microvessels is expected to meet the above requirements.
有鉴于此，本发明的另一目的在于提供一种脑胶质瘤组织微血管分形维数的计算方法，包括如下进行的步骤：In view of this, another object of the present invention is to provide a method for calculating the fractal dimension of microvessels in glioma tissue, comprising the following steps:
(1)将待检组织进行切片得组织切片；(1) Slicing the tissue to be inspected to obtain a tissue slice;
(2)将步骤(1)所得的组织切片进行进行抗CD34免疫组化染色标记血管得染色切片，然后使用切片全景扫描仪扫描获得数字切片；(2) Perform anti-CD34 immunohistochemical staining on the tissue section obtained in step (1) to mark blood vessels to obtain a stained section, and then use a section panoramic scanner to scan to obtain a digital section;
(3)从步骤(2)所得的数字切片中截取3-5个含微血管最丰富的“热点区”，图像像素为1920×1080，命名为图像A；(3) Intercept 3-5 "hot spots" containing the most abundant microvessels from the digital slice obtained in step (2), the image pixels are 1920 * 1080, and named as image A;
(4)对步骤(3)所得的图像A使用图像分析软件在HSB色彩空间内基于阈值法提取免疫组化染色阳性目标区域，得图像A1，所述阈值法所设定的参数为：H：0-90和215-255，S：25-255，B：0-255；然后使用所述的图像分析软件将图像A1转化为二进制，得到图像B；(4) Use image analysis software on the image A obtained in step (3) to extract the positive target area of immunohistochemical staining based on the threshold method in the HSB color space to obtain image A 1 , and the parameters set by the threshold method are: H : 0-90 and 215-255, S: 25-255, B: 0-255; then use the image analysis software to convert image A 1 into binary to obtain image B;
(5)对步骤(4)所得的图像B使用步骤(4)所述的图像分析软件进行图像骨骼化处理，得到图像C；(5) image skeletonization processing is carried out using the image analysis software described in step (4) to the image B obtained in step (4), to obtain image C;
(6)采用盒形计数法计算所述步骤(5)中图像C的分形维数mvFD，具体为：以像素值4、8、16、32、64、128、256为正方形盒边长ε，分别计算覆盖图像C中所有目标区域即二进制值为1的区域所需要的最小的非重叠方盒数N(ε)；以logε为横坐标，log N(ε)为纵坐标，作散点图，以最小二乘法作线性回归，得到回归方程：log N(ε)＝a·logε+b，根据此方程，a的相反数即被认为是mvFD。(6) adopt the box-shaped counting method to calculate the fractal dimension mvFD of the image C in the step (5), specifically: take pixel values 4, 8, 16, 32, 64, 128, 256 as the square box side length ε, Calculate the minimum number of non-overlapping boxes N(ε) required to cover all target areas in the image C, that is, the area with a binary value of 1; take logε as the abscissa and log N(ε) as the ordinate, and make a scatter diagram , use the least squares method to do linear regression, and get the regression equation: log N(ε)=a logε+b, according to this equation, the opposite number of a is considered as mvFD.
本发明的一种脑胶质瘤组织微血管分形维数的计算方法，该方法操作简单、计算简便、误差较小，胶质瘤组织微血管分形维数能完整、有效反映胶质母细胞瘤微血管复杂程度。A method for calculating the fractal dimension of microvessels of brain glioma tissue according to the present invention has the advantages of simple operation, simple calculation and small error, and the fractal dimension of microvessels of glioma tissue can be complete and effectively reflect the complexity of glioblastoma microvessels degree.
附图说明Description of drawings
图1本发明的一种组织微血管分形维数的计算方法的流程图。Fig. 1 is a flowchart of a method for calculating the fractal dimension of tissue microvessels in the present invention.
图2胶质瘤不同血管形态微血管分形维数比较(A:毛细血管样；B:血管簇样；C:肾小球样；D:花环样；E：A-D四种血管形态mvFD比较。**,P<0.01)。Figure 2 Comparison of fractal dimensions of microvessels in different vascular shapes of glioma (A: capillary-like; B: blood vessel cluster-like; C: glomerulus-like; D: rosette-like; E: comparison of mvFD of four vascular shapes in A-D.** , P<0.01).
图3不同级别胶质瘤微血管分形维数比较(I，II：低级别胶质瘤；III，IV：高级别胶质瘤。*,P<0.05；**,P<0.01)。Figure 3 Comparison of fractal dimensions of microvessels in different grades of gliomas (I, II: low-grade gliomas; III, IV: high-grade gliomas. *, P<0.05; **, P<0.01).
图4基于mvFD表达水平差异的胶质母细胞瘤患者Kaplan-Meier生存曲线(A：总生存期；B：无进展生存期)。Figure 4 Kaplan-Meier survival curves of glioblastoma patients based on differences in mvFD expression levels (A: overall survival; B: progression-free survival).
图5化疗和非化疗胶质母细胞瘤患者基于mvFD表达水平差异的Kaplan-Meier生存曲线对比(A：化疗患者组；B：非化疗患者组)。Figure 5 Comparison of Kaplan-Meier survival curves between chemotherapy and non-chemotherapy glioblastoma patients based on the difference in mvFD expression level (A: chemotherapy patients group; B: non-chemotherapy patients group).
具体实施方式Detailed ways
下面结合具体实施例来进一步描述本发明，本发明的优点和特点将会随着描述而更为清楚。但这些实施例仅是范例性的，并不对本发明的范围构成任何限制。本领域技术人员应该理解的是，在不偏离本发明的精神和范围下可以对本发明技术方案的细节和形式进行修改或替换，但这些修改和替换均落入本发明的保护范围内。The present invention will be further described below in conjunction with specific embodiments, and the advantages and characteristics of the present invention will become clearer along with the description. However, these embodiments are only exemplary and do not constitute any limitation to the scope of the present invention. Those skilled in the art should understand that the details and forms of the technical solutions of the present invention can be modified or replaced without departing from the spirit and scope of the present invention, but these modifications and replacements all fall within the protection scope of the present invention.
以下实施例中，以胶质母细胞瘤样本(来自重庆市西南医院和北京市天坛医院)为例，具体阐述本发明的组织微血管分形维数的计算方法，其中以Image J 1.46s图像分析软件进行处理分析。In the following examples, taking a glioblastoma sample (from Chongqing Southwest Hospital and Beijing Tiantan Hospital) as an example, the method for calculating the fractal dimension of tissue microvessels of the present invention is specifically described, wherein ImageJ 1.46s image analysis software is used Perform processing analysis.
实施例1胶质瘤组织微血管分形维数的计算方法Example 1 Calculation method of fractal dimension of microvessels in glioma tissue
1、胶质瘤组织微血管分形维数的计算1. Calculation of fractal dimension of microvessels in glioma tissue
按照图1所示的流程图进行胶质瘤组织微血管分形维数的计算，具体步骤如下：According to the flow chart shown in Figure 1, the calculation of the fractal dimension of microvessels in glioma tissue is performed, and the specific steps are as follows:
(1)将待检组织采用切片机进行常规制片得组织切片；(1) Use a microtome to perform routine slices of the tissue to be inspected to obtain tissue slices;
(2)将步骤(1)所得的组织切片进行抗CD34免疫组化染色标记血管(采用DAB显色法)得染色切片，然后使用切片全景扫描仪扫描获得数字切片；(2) Perform anti-CD34 immunohistochemical staining on the tissue section obtained in step (1) to mark blood vessels (using DAB chromogenic method) to obtain a stained section, and then use a slice panoramic scanner to scan to obtain a digital section;
(3)从步骤(2)所得的数字切片中截取3-5个含微血管最丰富的“热点区”，图像像素为1920×1080，命名为图像A；(3) Intercept 3-5 "hot spots" containing the most abundant microvessels from the digital slice obtained in step (2), the image pixels are 1920 * 1080, and named as image A;
(4)对步骤(3)所得的图像A使用图像分析软件Image J 1.46s，在HSB(Hue-Saturation-Brightness)色彩空间内基于阈值法提取免疫组化染色阳性目标区域，得图像A1，所述阈值法所设定的参数为：H：0-90和215-255，S：25-255，B：0-255；然后在图像分析软件Image J 1.46s中，按照“Process”-“Binary”-“Make Binary”的操作，将图像A1转化为二进制，得到图像B；(4) Use the image analysis software Image J 1.46s on the image A obtained in step (3), extract the positive target area of immunohistochemical staining based on the threshold method in the HSB (Hue-Saturation-Brightness) color space, and obtain the image A 1 , The parameters set by the threshold method are: H: 0-90 and 215-255, S: 25-255, B: 0-255; then in the image analysis software Image J 1.46s, according to "Process"-"Binary"-"MakeBinary" operation, convert image A 1 into binary to get image B;
(5)使用Image J 1.46s软件中的图像骨骼化功能对步骤(4)所得的图像B进行处理，操作为“Process”-“Binary”-“Skeletonize”，得到二进制图像C；(5) Use the image skeletonization function in Image J 1.46s software to process the image B obtained in step (4), and operate as "Process"-"Binary"-"Skeletonize" to obtain binary image C;
(6)采用盒形计数法(box-counting method)计算所述步骤(5)中图像C的分形维数mvFD，具体为：以像素值4、8、16、32、64、128、256为正方形盒边长ε，分别计算覆盖图像C中所有目标区域即二进制值为1的区域所需要的最小的非重叠方盒数N(ε)；以logε为横坐标，log N(ε)为纵坐标，作散点图，以最小二乘法作线性回归，得到回归方程：logN(ε)＝a·logε+b，根据此方程，a的相反数即被认为是mvFD。(6) adopt box-counting method (box-counting method) to calculate the fractal dimension mvFD of image C in the step (5), specifically: take pixel values 4, 8, 16, 32, 64, 128, 256 as Square box side length ε, respectively calculate the minimum number of non-overlapping boxes N(ε) required to cover all target areas in the image C, that is, the area with a binary value of 1; take logε as the abscissa, and log N(ε) as the ordinate Coordinates, make a scatter diagram, and use the least square method for linear regression to obtain the regression equation: logN(ε)=a·logε+b. According to this equation, the opposite number of a is considered to be mvFD.
2、胶质瘤不同血管形态微血管分形维数比较2. Comparison of fractal dimensions of microvessels in different vascular shapes of glioma
将不同血管形态(毛细血管样、血管簇样、肾小球样和花环样)微血管分形维数进行比较，结果如图2所示，微血管分形维数(mvFD)反映病理切片组织微血管复杂程度，以胶质瘤为例，血管形态越复杂，mvFD越大。The microvascular fractal dimensions of different vessel shapes (capillary-like, vascular cluster-like, glomerulus-like, and rosette-like) were compared, and the results are shown in Figure 2. The microvascular fractal dimension (mvFD) reflects the complexity of microvessels in pathological sections. Taking glioma as an example, the more complex the vascular morphology, the larger the mvFD.
3、不同级别胶质瘤微血管分形维数比较3. Comparison of fractal dimensions of microvessels in different grades of glioma
胶质瘤病理分型为Ⅰ级(星形细胞瘤)，Ⅱ级(星形母细胞瘤)，Ⅲ～Ⅳ级(多形胶母细胞瘤)。Ⅰ～Ⅱ级星形细胞瘤为低度恶性，起病缓慢，肿瘤在CT及MR的表现多为实性或囊性，边界不清，肿瘤实性部分或囊性结节均可强化。临床表现与病灶部位不同进行性地出现相应的症状，并最后出现颅高压的症状。Ⅲ～Ⅳ级的多形胶母细胞瘤起病快速，为恶性度最高的肿瘤，多生长于大脑半球，因肿瘤生长迅速，肿瘤中心可有多处坏死及出血，CT及MR均明显强化，周围可伴大片脑组织的水肿。The pathological classification of glioma is grade Ⅰ (astrocytoma), grade Ⅱ (astrocytoma), and grade Ⅲ～Ⅳ (glioblastoma multiforme). Grades Ⅰ-Ⅱ astrocytomas are low-grade malignant tumors with slow onset. On CT and MR, the tumors are mostly solid or cystic, with unclear borders. The solid part of the tumor or cystic nodules can be enhanced. The clinical manifestations are different from the location of the lesion, and the corresponding symptoms will appear progressively, and finally the symptoms of intracranial hypertension will appear. Glioblastoma multiforme of grades III-IV has a rapid onset and is the most malignant tumor. It mostly grows in the cerebral hemisphere. Due to the rapid growth of the tumor, there may be multiple necrosis and hemorrhage in the tumor center, and both CT and MR are significantly enhanced. There may be surrounding edema of a large piece of brain tissue.
将不同级别胶质瘤微血管分形维数进行统计分析，结果如图3所示，表明高级别胶质瘤mvFD值高于低级别胶质瘤mvFD值。The fractal dimension of microvessels of different grades of gliomas was statistically analyzed, and the results are shown in Figure 3, which indicated that the mvFD value of high-grade gliomas was higher than that of low-grade gliomas.
实施例2胶质瘤组织微血管分形维数的应用Example 2 Application of Microvessel Fractal Dimension in Glioma Tissue
收集2006-2012年83例胶质母细胞瘤患者(56例来自重庆市西南医院，27例来自北京天坛医院)病理组织切片和临床随访资料，统计分析并计算mvFD值并进行生存分析，结果如图4和图5所示，mvFD>1.06胶质母细胞瘤患者总生存期(图4A)和无进展生存期(图4B)均显著大于mvFD≤1.06患者。在接受化疗的胶质母细胞瘤患者中，mvFD>1.06患者无进展生存期显著大于mvFD≤1.06患者，提示mvFD>1.06可作为胶质母细胞瘤患者化疗反应佳的标准(图5A)。在未接受化疗的患者中，mvFD高低患者生存期无差异(图5B)，进一步说明mvFD和化疗反应的相关性。总的来说，对于胶质母细胞瘤，mvFD≤1.06，患者预后不良，化疗反应差，mvFD>1.06，患者预后良好，化疗反应好。From 2006 to 2012, 83 patients with glioblastoma (56 cases from Chongqing Southwest Hospital, 27 cases from Beijing Tiantan Hospital) pathological tissue sections and clinical follow-up data were collected, statistically analyzed and mvFD values were calculated and survival analysis was performed. The results are as follows: As shown in Figures 4 and 5, the overall survival (Figure 4A) and progression-free survival (Figure 4B) of patients with mvFD>1.06 glioblastoma were significantly longer than those of patients with mvFD≤1.06. Among glioblastoma patients receiving chemotherapy, the progression-free survival of patients with mvFD > 1.06 was significantly longer than that of patients with mvFD ≤ 1.06, suggesting that mvFD > 1.06 can be used as a standard for good chemotherapy response in glioblastoma patients (Fig. 5A). Among patients who did not receive chemotherapy, there was no difference in survival between patients with high and low mvFD (Fig. 5B), further illustrating the correlation between mvFD and chemotherapy response. In general, for glioblastoma, mvFD ≤ 1.06, patients with poor prognosis and poor chemotherapy response, mvFD > 1.06, patients with good prognosis and good chemotherapy response.
因此，胶质母细胞瘤微血管分形维数可以反映胶质母细胞瘤微血管形态复杂性，其具有预测胶质母细胞瘤患者的预后和化疗反应效果。Therefore, the fractal dimension of glioblastoma microvessels can reflect the morphological complexity of glioblastoma microvessels, which has the effect of predicting the prognosis and chemotherapy response of glioblastoma patients.
最后说明的是，以上实施例仅用以说明本发明的技术方案而非限制，尽管参照较佳实施例对本发明进行了详细说明，本领域的普通技术人员应当理解，可以对本发明的技术方案进行修改或者等同替换，而不脱离本发明技术方案的宗旨和范围，其均应涵盖在本发明的权利要求范围当中。Finally, it is noted that the above embodiments are only used to illustrate the technical solutions of the present invention without limitation. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be carried out Modifications or equivalent replacements without departing from the spirit and scope of the technical solution of the present invention shall be covered by the claims of the present invention.
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