CN113160994A - Construction method, prediction system, device and storage medium for noninvasive screening of non-alcoholic steatohepatitis model - Google Patents

Construction method, prediction system, device and storage medium for noninvasive screening of non-alcoholic steatohepatitis model Download PDF

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CN113160994A
CN113160994A CN202011634864.0A CN202011634864A CN113160994A CN 113160994 A CN113160994 A CN 113160994A CN 202011634864 A CN202011634864 A CN 202011634864A CN 113160994 A CN113160994 A CN 113160994A
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张文杰
王锦程
孙倍成
徐庆祥
李国强
刘巧玉
金康鹏
王飞
张海天
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Nanjing University
Nanjing Medical University
Nanjing Drum Tower Hospital
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Nanjing Drum Tower Hospital
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Abstract

A construction method, a prediction system, equipment and a storage medium for noninvasive screening of a non-alcoholic steatohepatitis model belong to the field of fatty liver detection. The existing method for treating the steatohepatitis has the defects of low accuracy, high cost and the like. A construction method for non-invasive screening of a non-alcoholic steatohepatitis model collects serum, images and pathological data of a steatohepatitis patient; preprocessing serum characteristic data; statistical screening of serum characteristics; b ultrasonic characteristic extraction and screening; integrating effective B-mode ultrasonic features by using a Support Vector Machine (SVM); combining the obtained serum index characteristic with the R-NASH characteristic of B ultrasonic, and obtaining a non-alcoholic steatohepatitis diagnosis model by adopting a multivariate logistic regression method; and confirming the model threshold range of the NASH according to the maximum York index of the ROC curve through model comparison and efficiency analysis, so as to obtain a model with fixed final parameters for prediction.

Description

Construction method, prediction system, device and storage medium for noninvasive screening of non-alcoholic steatohepatitis model
Technical Field
The invention relates to construction of a model for screening steatohepatitis, in particular to a method, a prediction system, equipment and a storage medium for non-invasively screening a non-alcoholic steatohepatitis model.
Background
At present, the diagnosis of steatohepatitis needs to be carried out by methods such as B-ultrasound, CT flat scan liver density or blood detection, B-type ultrasonic examination is an important and practical means for diagnosing steatohepatitis, the diagnosis accuracy is about 70%, the CT flat scan liver density is generally reduced, the ratio of the liver/spleen CT flat scan density is less than or equal to 1, the diagnosis of steatohepatitis can be determined, the degree of steatohepatitis can be judged according to the ratio of the liver/spleen CT flat scan density, the accuracy is slightly superior to that of B-ultrasound, but the method has the defects of high cost, radioactivity and the like.
Disclosure of Invention
The invention provides a construction method, a prediction system, equipment and a storage medium for non-invasive screening of a non-alcoholic steatohepatitis model, aiming at solving the problems of low accuracy, high cost and the like of the existing method for steatohepatitis.
A construction method for non-invasive screening of a non-alcoholic steatohepatitis model is realized by the following steps:
step one, collecting serum, images and pathological data of a steatohepatitis patient;
collecting data including patient basic information, NASH and non-NASH pathological biopsy data, liver ultrasonography B images and serum laboratory examination data; and ultrasound images corresponding to the patient's liver BMP format;
wherein the content of the first and second substances,
NASH has a chinese meaning of nonalcoholic steatohepatitis;
the basic information comprises age, sex, height, weight, BMI calculation, drinking history, diabetes and hepatitis history; BMI means Body Mass Index, also called Body weight for short, Body Mass Index, BMI for short;
serum laboratory test data include white blood cell count, platelet count, glutamic-pyruvic transaminase, glutamic-oxalacetic transaminase, glutamyl transpeptidase, total bilirubin, direct bilirubin, blood coagulation time, alkaline phosphatase, albumin, blood cholesterol, blood coagulation international normalized ratio, blood ferritin, fasting blood glucose, uric acid, blood lipid, fasting insulin, HOMA index;
step two, preprocessing serum characteristic data;
removing NASH and non-NASH pathological biopsy data, and uniformly carrying out the following standardized treatment on the rest serum variables, wherein the treatment method comprises the following steps:
(1) ranking each numerical variable from small to large, treating less than 2.5% or greater than 97.5% as outliers and changing to 2.5% and 97.5%, respectively;
(2) all numerical variables are normalized, and the formula is as follows: xn normalized＝(Xn–Xmin)/(Xmax–Xmin) (ii) a Wherein, XnRepresenting any numerical variable, Xnnormazed denotes a numerical variable XnNormalized value of (2), XmaxRepresenting the maximum value, X, of a numerical variableminRepresents the minimum value of the numerical variables;
thirdly, statistical screening of serum characteristics;
selecting a part of the collected data as a training set; then, screening out indexes with significant correlation through spearman correlation analysis in a training group; then, carrying out statistical screening by forward conditional multivariate logistic regression to obtain a serum index related to NASH; wherein the serum indexes comprise glutamic-oxaloacetic transaminase, platelet count, blood fat, BMI calculation and uric acid;
step four, B ultrasonic characteristic extraction and screening;
the B ultrasonic feature extraction refers to calculating and extracting the gray texture features of the liver tissues; the gray texture features comprise a gray level co-occurrence matrix, a gray level walking run-length matrix, an intensity histogram and an invariant moment;
the screening operation comprises the following three steps:
(1) the viewer consistency test within and between groups exceeded 0.8;
(2) and (3) screening the screening result obtained in the step (1) as follows: carrying out primary screening operation by using a variance threshold method, wherein the related threshold is set to be 1.0;
(3) and (3) screening the screening result obtained in the step (2) as follows: performing final screening by adopting a lasso regression method; the method specifically comprises the following steps:
firstly, screening effective B-mode ultrasonic characteristics by adopting a Lasso regression model, wherein the cost function of the Lasso regression model is as follows:
Figure BDA0002880915840000021
wherein x isijRepresents the jth B-mode characteristic variable, y, of the ith patient dataiIs a response variable of the ith patient data, q represents the total number of B-mode characteristic variables, m is the total number of patient data, λ is a penalty function,
Figure BDA0002880915840000022
the optimal result is obtained;
then, using the non-0 coefficient variable screened out by the lasso regression method as the effective B ultrasonic characteristic of the final modeling;
integrating effective B-ultrasonic features by using a Support Vector Machine (SVM);
predicting the effective B ultrasonic image group characteristics by using a Support Vector Machine (SVM) model to obtain a predicted value R-NASH; the prediction step of the SVM model comprises the following steps:
(1) selecting a Gaussian kernel function:
Figure BDA0002880915840000023
x and z in the space are any two points in the space, and sigma is a width parameter of a function;
(2) constructing a support vector machine model:
Figure BDA0002880915840000031
wherein alpha isiAnd b is the coefficient to be solved; alpha is alphaiIs obtained by solving the following dual problem:
Figure BDA0002880915840000032
Figure BDA0002880915840000033
0≤αi,i＝1,2,K,N
yirepresenting for input xiCorresponding R-NASH value yi；
i represents the ith, j represents the jth, aj、aiFor the j, i sample to be solved; x is the number ofi、xjRepresenting the input characteristics of the type-B ultrasonic of the ith and j samples; y isjYi represents the integrated B ultrasonic characteristic R-NASH corresponding to the j and i samples, and N represents the number of samples; obtaining an optimal solution:
Figure BDA0002880915840000034
selection of alpha*A component of
Figure BDA0002880915840000035
Satisfies the conditions
Figure BDA0002880915840000036
Obtaining the optimal:
Figure BDA0002880915840000037
(3) predicting the characteristics of the B-ultrasonic image group by using the obtained SVM model to obtain a predicted value R-NASH:
Figure BDA0002880915840000038
wherein x is a vector formed by the features of the effective B-ultrasonic image group;
combining the obtained serum index characteristic with the R-NASH characteristic of the B ultrasonic wave, and obtaining a non-alcoholic steatohepatitis diagnosis model by adopting a multivariate logistic regression method, wherein the method comprises the following steps:
(1) suppose x1,x2,K,xnFor predictive value R-NASH and other serum index features, g is the fibrosis index, and the regression model is as follows:
Figure BDA0002880915840000039
z＝hθ(x)＝θ0+θ1x1+θ2x2+Λ+θnxn
wherein, theta0,θ1,θ2ΛθnIs a model parameter of multivariate logistic regression, n is the number of variables, and the adopted characteristics comprise: R-NASH, PLT, AST, BMI, Lipid, Uric acid;
(2) obtaining model parameters, and obtaining a diagnosis model as follows:
YNASH＝2.145×R-NASH–0.306×PLT+0.267×AST+1.152×BMI+1.185×Lipid+ 0.114×Uric acid–3.375；
step seven, confirming the model;
and confirming the model threshold range of the NASH according to the maximum York index of the ROC curve through model comparison and efficiency analysis, so as to obtain a model with fixed final parameters for prediction.
Has the advantages that:
the invention relates to a non-alcoholic steatohepatitis screening model, which is a prediction model for non-alcoholic steatohepatitis related data formed by serum, images and pathological data of a plurality of steatohepatitis patients. The non-alcoholic steatohepatitis model is screened based on a plurality of clinical indexes, so that the advantages and disadvantages between serology and imaging are complemented, the prediction speed and accuracy of the model are improved, and reference can be provided for clinical liver fibrosis diagnosis. The NASH primary screening can be rapidly completed under the condition of not performing hepatic puncture, so that the degree of liver pathological changes of a patient can be efficiently monitored at any time, and corresponding clinical intervention measures can be formulated. The non-invasive prediction not only reduces the economic burden of the patient and the time cost of the doctor, but also avoids the pain and complication caused by the invasive examination of redundant patients and lightens the medical burden.
Drawings
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is a schematic diagram showing comparison between the evaluation of the effect using the ROC curve and the design model of the present invention;
FIG. 3 is a schematic diagram of circled drawing of liver tissue and calculation of extracted gray texture features;
FIG. 4 is a diagrammatic illustration of a gray level co-occurrence matrix calculation method;
FIG. 5 is a schematic representation of a gray-scale walk run-length matrix calculation method;
FIG. 6 is a graphical representation of an intensity histogram correlation feature calculation method;
FIG. 7 is a graphical illustration of a method of calculating an invariant moment.
Detailed Description
The first embodiment is as follows:
in the non-invasive screening method for constructing a non-alcoholic steatohepatitis model according to the embodiment, as shown in fig. 1, the detection method is realized by the following steps:
step one, collecting serum, images and pathological data of a steatohepatitis patient;
collecting liver, gall and pancreas center data of a Nanjing drugstore hospital, including basic information of a patient, NASH (non-alcoholic steatohepatitis) and non-NASH pathological biopsy data, liver B ultrasonic images and serum laboratory examination data; and ultrasound images of the liver in BMP format corresponding to the patient, for example, it is the ultrasound department at the buhui hospital, south kyo that copies ultrasound images of the corresponding patient in BMP format.
Wherein the content of the first and second substances,
NASH has a chinese meaning of nonalcoholic steatohepatitis;
the basic information comprises age, sex, height, weight, BMI calculation, drinking history, diabetes and hepatitis history; BMI means Body Mass Index, also called Body weight for short, Body Mass Index, BMI for short;
serum laboratory test data include white blood cell count, platelet count, glutamic-pyruvic transaminase, glutamic-oxalacetic transaminase, glutamyl transpeptidase, total bilirubin, direct bilirubin, blood coagulation time, alkaline phosphatase, albumin, blood cholesterol, blood coagulation international normalized ratio, blood ferritin, fasting blood glucose, uric acid, blood lipid, fasting insulin, HOMA index;
step two, preprocessing serum characteristic data;
except for NASH and non-NASH pathological biopsy data, serum variables were uniformly normalized as follows:
(1) ranking each numerical variable from small to large, treating less than 2.5% or greater than 97.5% as outliers and changing to 2.5% and 97.5%, respectively;
(2) all numerical variables are normalized, and the formula is as follows: xn normalized＝(Xn–Xmin)/(Xmax–Xmin) (ii) a Wherein, XnRepresenting any numerical variable, Xnnormazed denotes a numerical variable XnNormalized value of (2), XmaxRepresenting the maximum value, X, of a numerical variableminRepresents the minimum value of the numerical variables;
thirdly, statistical screening of serum characteristics;
selecting a part of the collected data as a training set; for example, the hepatobiliary pancreas center case of drumbeat hospital, Nanjing was used as the training group. Then, screening out indexes with significant correlation through spearman correlation analysis in a training group; then, carrying out statistical screening by forward conditional multivariate logistic regression to obtain the final meaningful serum index most relevant to NASH; wherein the serum indexes comprise glutamic-oxaloacetic transaminase, platelet count, blood fat, BMI calculation and uric acid; the serum indexes are AST for short, PLT for short, lipid for short, BMI calculation and Uric acid for short;
step four, B ultrasonic characteristic extraction and screening;
the B ultrasonic feature extraction refers to calculating and extracting the gray texture features of the liver tissues; the gray texture features comprise a gray level co-occurrence matrix, a gray level walking run-length matrix, an intensity histogram and an invariant moment;
the screening operation comprises the following three steps:
(1) an intra-and inter-group observer consistency test (ICC) of more than 0.8;
(2) carrying out primary screening operation by using a variance threshold method, wherein the related threshold is set to be 1.0;
(3) performing final screening by adopting a lasso regression method; the method specifically comprises the following steps:
firstly, screening effective B-mode features by adopting a Lasso regression model, thereby eliminating the features with small effect, wherein the cost function of the Lasso regression model is as follows:
Figure BDA0002880915840000061
wherein x isijRepresents the jth B-mode characteristic variable, y, of the ith patient dataiIs a response variable of the ith patient data, q represents the total number of B-mode characteristic variables, m is the total number of patient data, λ is a penalty function,
Figure BDA0002880915840000062
the optimal result is obtained;
then, using the non-0 coefficient variable screened out by the lasso regression method as the effective B ultrasonic characteristic of the final modeling;
integrating effective B-ultrasonic features by using a Support Vector Machine (SVM);
predicting the effective B ultrasonic image group characteristics by using a Support Vector Machine (SVM) model to obtain a predicted value R-NASH; the SVM model prediction step comprises the following steps:
(1) selecting a Gaussian kernel function:
Figure BDA0002880915840000063
x and z in the space are any two points in the space, and sigma is a width parameter of a function;
(2) constructing a support vector machine model:
Figure BDA0002880915840000064
wherein alpha isiAnd b is the coefficient to be solved; alpha is alphaiIs obtained by solving the following dual problem:
Figure BDA0002880915840000065
Figure BDA0002880915840000066
0≤αi,i＝1,2,K,N
yirepresenting for input xiCorresponding R-NASH value yi；
i represents the ith, j represents the jth, aj、aiFor the j, i sample to be solved; x is the number ofi、xjRepresenting the input characteristics of the type-B ultrasonic of the ith and j samples; y isjYi represents the integrated B ultrasonic characteristic R-NASH corresponding to the j and i samples, and N represents the number of samples; obtaining an optimal solution:
Figure BDA0002880915840000067
selection of alpha*A component of
Figure BDA0002880915840000068
Satisfies the conditions
Figure BDA0002880915840000069
Obtaining the optimal:
Figure BDA00028809158400000610
(3) predicting the characteristics of the B-ultrasonic image group by using the obtained SVM model to obtain a predicted value R-NASH:
Figure BDA00028809158400000611
wherein x is a vector formed by the features of the effective B-ultrasonic image group;
combining the obtained serum index characteristic with the R-NASH characteristic of the B ultrasonic wave, and obtaining a non-alcoholic steatohepatitis diagnosis model by adopting a multivariate logistic regression method; wherein the multivariate logistic regression method comprises the following steps:
(1) suppose x1,x2,K,xnFor predictive value R-NASH and other serum index features, g is the fibrosis index, and the regression model is as follows:
Figure BDA0002880915840000071
z＝hθ(x)＝θ0+θ1x1+θ2x2+Λ+θnxn
wherein, theta0,θ1,θ2ΛθnIs a model parameter of multivariate logistic regression, n is the number of variables, and the adopted characteristics comprise: R-NASH, PLT, AST, BMI, Lipid, Uric acid;
(2) obtaining model parameters, and obtaining a diagnosis model as follows:
YNASH＝2.145×R-NASH–0.306×PLT+0.267×AST+1.152×BMI+1.185×Lipid+ 0.114×Uric acid–3.375。
step seven, confirming the model;
confirming a model threshold range for evaluating the NASH according to the maximum Johnson index (sensitivity plus specificity-1) of an ROC curve through model comparison and efficiency analysis, and obtaining a reasonable parameter range of a final model according to the determined threshold value so as to obtain a model with fixed final parameters for prediction; and applied to clinical validation.
The NASH preliminary evaluation model based on a plurality of clinical data can quickly complete the NASH preliminary screening under the condition of not performing hepatic puncture, so that the degree of liver pathological changes of a patient can be monitored at any time with high efficiency, and corresponding clinical intervention measures are formulated. The noninvasive diagnosis not only reduces the economic burden of patients and the time cost of doctors, but also avoids the pain and complication caused by invasive examination of redundant patients and lightens the medical burden.
The second embodiment is as follows:
different from the first specific embodiment, in the construction method for non-invasive screening of the non-alcoholic steatohepatitis model according to the first embodiment, a step of model verification and comparison is further added between the sixth step and the seventh step, specifically:
the model of the invention was compared to the existing model for ultrasonic FLI examination. The ROC curve was used for comparison between the effect evaluation and the model, as shown in fig. 2.
The ROC curve abscissa represents specificity and the ordinate represents sensitivity, and the higher the curve is, the better the classification capability of the model is. The solid curve is a new model, the dotted curve is an existing model, the two are put together for comparison, and the advantages and the disadvantages can be obviously distinguished.
The third concrete implementation mode:
different from the first or second specific embodiment, in the fourth step of the construction method for non-invasive screening of the non-alcoholic steatohepatitis model according to the present embodiment, the features used in the step of B-ultrasonic feature extraction are as follows: (type-B ultrasonic characteristics in raw Material)
B ultrasonic characteristic extraction:
reading a B-mode ultrasonic image by using screening software (liveradomics), standardizing the image through a gray level stretching step, and calculating and extracting the gray level texture characteristics of the liver; the gray texture features comprise a gray level co-occurrence matrix, a gray level walking run-length matrix, an intensity histogram and an invariant moment; as shown in fig. 3;
wherein, the algorithm for extracting the features by the screening software is as follows,
the method for calculating the gray level co-occurrence matrix is to describe texture according to the spatial correlation characteristics of gray levels, and belongs to a common method, and comprises the following steps:
autocorrelation characteristics, known in english as Autocorrelation: the method is characterized in that correlation exists among expected values of random error items and is used for evaluating the definition of an image;
and (3) correlation: it measures the degree of similarity of spatial gray level co-occurrence matrix elements in the row or column direction, and thus the magnitude of the correlation value reflects the local gray level correlation in the image. When the matrix element values are uniform and equal, the correlation value is large; conversely, if the matrix pixel values differ greatly then the correlation value is small. If the image has horizontal direction texture, the COR of the horizontal direction matrix is larger than the COR values of the other matrixes; COR is called completely, and Chinese meaning is related;
cluster protrusion: under the condition that an object is abrupt in the image, the larger the contrast between the lines and the patterns is, the larger the prom value is; the prom is called Clusterpromience, and Chinese meaning is cluster protrusion;
cluster shadow, english full clusterslide: shadow size (shade), which may be related to picture quality, is more intuitive to relate to the degree of drape of the garment, the flatter the shade is, the smaller the shade value is; measuring the skewness of the matrix and measuring the concept of uniformity; when the value is high, the image is asymmetric; shadow Chinese meaning shadow;
difference, english is called similarity: when calculating the contrast, the weights increase exponentially with the distance between the matrix elements and the diagonal, and if the weights increase linearly, dissimilarity is obtained.
Energy is called Energy throughout: the sum of squares of the gray level co-occurrence matrix element values is also called energy, and reflects the uniformity degree of the gray level distribution of the image and the thickness of the texture. If all the values of the co-occurrence matrix are equal, the ASM value is small; conversely, if some of the values are large and others are small, the ASM value is large. When the elements in the co-occurrence matrix are distributed in a concentrated manner, the ASM value is large. A large ASM value indicates a more uniform and regularly varying texture pattern.
Homogeneity, english term Homogeneity: the local uniformity of the image is measured, the value of the non-uniform image is lower, and the value of the uniform image is higher. In contrast to contrast or dissimilarity, the weight of homogeneity decreases with the distance of the element value from the diagonal, in an exponential fashion.
Maximum probability, english full name maximummobavailability: representing the texture feature that appears the most frequently in the image.
The calculation method is shown in fig. 4. In the figure, j represents the gray scale value of the pixel y, i represents the gray scale value of the pixel x, p represents the probability, and M, N represents an M × N matrix.
The fourth concrete implementation mode:
different from the third specific embodiment, in the method for constructing a non-invasive screening non-alcoholic steatohepatitis model according to the third specific embodiment, the method for calculating the gray-scale run matrix is shown in fig. 5, the gray-scale run matrix is a matrix formed by lengths of gray-scale run rows, and is only used for measuring and counting image pixel information, and the generated gray-scale run matrix is calculated in the actual use process to obtain image characteristic information based on a gray-scale co-occurrence matrix. Including short term emphasis (SRE), long term emphasis (LRE), run length non-uniformity (RLN), gray level non-uniformity (GLN), Run Percentage (RP), low gray level run emphasis (LGRE), high gray level run emphasis (HGRE), short term low gray level emphasis (SRLGE), short term high gray level emphasis (SRHGE), long term low gray level emphasis (LRLGE), long term high gray level emphasis (LRHGE).
In fig. 5, the (i, j) point represents the count of j consecutive occurrences of a pixel with a gray level i in a certain direction of the image, p represents the probability, and M represents the gray level on the imageThe number of degree stages; n represents the number of different runs on the image; n isrRepresenting the number of pixel points on the image;
the fifth concrete implementation mode:
different from the third or fourth embodiment, in the method for constructing a non-invasive screening non-alcoholic steatohepatitis model according to this embodiment, as shown in fig. 6, the relevant features of the intensity histogram are first-order features calculated from the intensity histogram, and the first-order features are the simplest first-order features, including Mean: statistical mean, Variance of intensity histogram: statistical variance of intensity histogram, Entropy: statistical entropy of intensity histogram, energy: the sum of the statistical energies of the intensity histograms; kurtosis (Kurtosis): and characterizing the characteristic number of the peak value height of the probability density distribution curve at the average value, describing whether the peak value of the distribution formed by Sample is abrupt or flat, and visually showing that the kurtosis reflects the sharpness of the peak. The kurtosis of the sample is a statistic compared with normal distribution, the kurtosis of the time series x is calculated, the kurtosis is used for measuring the condition that x deviates from certain distribution, and the kurtosis of the normal distribution is 3. If the kurtosis is larger than 3, the shape of the peak is sharper than that of the normal distribution peak. And vice versa; in statistics, kurtosis measures the kurtosis of a real random variable probability distribution, and high kurtosis means that the variance increase is caused by extreme differences at low frequencies that are greater or less than the mean. Skewness (Skewness): the situation of the symmetry of the distribution constituted by samples is described.
Wherein, the situation describing the symmetry of the distribution composed of samples is that the skewness of the time series x is calculated, and the skewness is used for measuring the symmetry of the x; if the skewness is negative, the dispersion degree on the left side of the x mean value is stronger than that on the right side; if the skewness is positive, the dispersion degree on the left side of the x-mean is weaker than that on the right side; for a normal distribution (or strictly symmetric distribution), skewness is equal to O.
The sixth specific implementation mode:
unlike the fifth embodiment, in the method for constructing a non-invasive screening non-alcoholic steatohepatitis model according to the fifth embodiment, as shown in fig. 7, the moment is a concept of probability and statistics, and is a digital feature of a random variable, the first-order origin moment is expected, the first-order central moment μ 1 is 0, and the second-order central moment μ 2 is the variance var (X) of X, and statistically, moments higher than 4 orders are rarely used. The third-order central moment mu 3 is used for measuring whether the distribution is biased or not, and the fourth-order central moment mu 4 is used for measuring how steep the distribution (or density) is near the mean value;
7 invariant moment groups (phi 1 phi 7) are derived by utilizing second-order and third-order specification central moments, and the invariant moment groups (phi 1 phi 7) are kept invariant during image translation, rotation and scale change.
The seventh embodiment:
the prediction system for the construction method for non-invasive screening of the non-alcoholic steatohepatitis model of the embodiment comprises:
the data acquisition module is used for preprocessing the collected serum, image and pathological data of the steatohepatitis patient;
the serum characteristic data preprocessing module is used for removing NASH and non-NASH pathological biopsy data and uniformly carrying out the following standardized processing on the rest serum variables; the specific treatment method comprises the following steps:
(1) ranking each numerical variable from small to large, treating less than 2.5% or greater than 97.5% as outliers and changing to 2.5% and 97.5%, respectively;
(2) all numerical variables are normalized, and the formula is as follows: xn normalized＝(Xn–Xmin)/(Xmax–Xmin) (ii) a Wherein, XnRepresenting any numerical variable, Xnnormazed denotes a numerical variable XnNormalized value of (2), XmaxRepresenting the maximum value, X, of a numerical variableminRepresents the minimum value of the numerical variables;
serum characteristic statistical screening, which is used for serum characteristic statistical screening; specifically, a part of the collected data is selected as a training set; then, screening out indexes with significant correlation through spearman correlation analysis in a training group; then, carrying out statistical screening by forward conditional multivariate logistic regression to obtain a serum index related to NASH; wherein the serum indexes comprise glutamic-oxaloacetic transaminase, platelet count, blood fat, BMI calculation and uric acid;
the B ultrasonic characteristic extraction and screening module is used for extracting and screening B ultrasonic characteristics, and non-0 coefficient variables screened out by a lasso regression method are used as effective B ultrasonic characteristics of final modeling, so that the gray texture characteristics of liver tissues are calculated and extracted; the effective B-mode ultrasonic characteristics are screened out by adopting a Lasso regression model, wherein the cost function of the Lasso regression model is as follows:
Figure BDA0002880915840000101
wherein x isijRepresents the jth B-mode characteristic variable, y, of the ith patient dataiIs a response variable of the ith patient data, q represents the total number of B-mode characteristic variables, m is the total number of patient data, λ is a penalty function,
Figure BDA0002880915840000102
the optimal result is obtained;
the characteristic integration module is used for predicting the effective B ultrasonic image group characteristics by utilizing a Support Vector Machine (SVM) model to obtain a predicted value R-NASH and integrating the effective B ultrasonic characteristics;
the prediction model establishing module is used for combining the obtained serum index characteristics and the R-NASH characteristics of the B ultrasonic wave and obtaining a non-alcoholic steatohepatitis screening model by adopting a multivariate logistic regression method;
the prediction model confirmation module is used for confirming the model threshold range of the NASH to be evaluated so as to obtain a model with fixed final parameters; the model threshold range for confirming and evaluating NASH is confirmed according to the maximum York index of an ROC curve through model comparison and efficiency analysis and is finally used for prediction.
The specific implementation mode is eight:
the detection device for the construction method for non-invasive screening of the non-alcoholic steatohepatitis model comprises the following steps:
a processor;
a memory having stored therein executable instructions of the processor;
wherein the processor is configured to execute the steps of the method of constructing a non-invasive screening non-alcoholic steatohepatitis model by executing the executable instructions.
The specific implementation method nine:
a computer readable storage medium of the present embodiment stores a program that, when executed, implements the steps of the method of constructing a non-invasive screening non-alcoholic steatohepatitis model.

Claims (8)

1. A construction method for non-invasive screening of a non-alcoholic steatohepatitis model is characterized by comprising the following steps: the detection method is realized by the following steps:
step one, collecting serum, images and pathological data of a steatohepatitis patient;
collecting data including patient basic information, NASH and non-NASH pathological biopsy data, liver ultrasonography B images and serum laboratory examination data; and ultrasound images corresponding to the patient's liver BMP format;
wherein the content of the first and second substances,
NASH has a chinese meaning of nonalcoholic steatohepatitis;
the basic information comprises age, sex, height, weight, BMI calculation, drinking history, diabetes and hepatitis history; BMI means Body Mass Index, also called Body weight for short, Body Mass Index, BMI for short;
serum laboratory test data include white blood cell count, platelet count, glutamic-pyruvic transaminase, glutamic-oxalacetic transaminase, glutamyl transpeptidase, total bilirubin, direct bilirubin, blood coagulation time, alkaline phosphatase, albumin, blood cholesterol, blood coagulation international normalized ratio, blood ferritin, fasting blood glucose, uric acid, blood lipid, fasting insulin, HOMA index;
step two, preprocessing serum characteristic data;
removing NASH and non-NASH pathological biopsy data, and uniformly carrying out the following standardized treatment on the rest serum variables, wherein the treatment method comprises the following steps:
(1) ranking each numerical variable from small to large, treating less than 2.5% or greater than 97.5% as outliers and changing to 2.5% and 97.5%, respectively;
(2) all numerical variables are normalized, and the formula is as follows: xn normalized＝(Xn–Xmin)/(Xmax–Xmin) (ii) a Wherein, XnRepresenting any numerical variable, Xnnormazed denotes a numerical variable XnNormalized value of (2), XmaxRepresenting the maximum value, X, of a numerical variableminRepresents the minimum value of the numerical variables;
thirdly, statistical screening of serum characteristics;
selecting a part of the collected data as a training set; then, screening out indexes with significant correlation through spearman correlation analysis in a training group; then, carrying out statistical screening by forward conditional multivariate logistic regression to obtain a serum index related to NASH; wherein the serum indexes comprise glutamic-oxaloacetic transaminase, platelet count, blood fat, BMI calculation and uric acid;
step four, B ultrasonic characteristic extraction and screening;
the B ultrasonic feature extraction refers to calculating and extracting the gray texture features of the liver tissues; the gray texture features comprise a gray level co-occurrence matrix, a gray level walking run-length matrix, an intensity histogram and an invariant moment;
the screening operation comprises the following three steps:
(1) the viewer consistency test within and between groups exceeded 0.8;
(2) and (3) screening the screening result obtained in the step (1) as follows: carrying out primary screening operation by using a variance threshold method, wherein the related threshold is set to be 1.0;
(3) and (3) screening the screening result obtained in the step (2) as follows: performing final screening by adopting a lasso regression method; the method specifically comprises the following steps:
firstly, screening effective B-mode ultrasonic characteristics by adopting a Lasso regression model, wherein the cost function of the Lasso regression model is as follows:
Figure FDA0002880915830000021
wherein x isijRepresents the jth B-mode characteristic variable, y, of the ith patient dataiIs a response variable of the ith patient data, q represents the total number of B-mode characteristic variables, m is the total number of patient data, λ is a penalty function,
Figure FDA0002880915830000022
the optimal result is obtained;
then, using the non-0 coefficient variable screened out by the lasso regression method as the effective B ultrasonic characteristic of the final modeling;
integrating effective B-ultrasonic features by using a Support Vector Machine (SVM);
predicting the effective B ultrasonic image group characteristics by using a Support Vector Machine (SVM) model to obtain a predicted value R-NASH; the prediction step of the SVM model comprises the following steps:
(1) selecting a Gaussian kernel function:
Figure FDA0002880915830000023
x and z in the space are any two points in the space, and sigma is a width parameter of a function;
(2) constructing a support vector machine model:
Figure FDA0002880915830000024
wherein alpha isiAnd b is the coefficient to be solved; alpha is alphaiIs obtained by solving the following dual problem:
Figure FDA0002880915830000025
Figure FDA0002880915830000026
0≤αi,i＝1,2,K,N
yirepresenting for input xiCorresponding R-NASH value yi；
i represents the ith, j represents the jth, aj、aiFor the j, i sample to be solved; x is the number ofi、xjRepresenting the input characteristics of the type-B ultrasonic of the ith and j samples; y isjYi represents the integrated B ultrasonic characteristic R-NASH corresponding to the j and i samples, and N represents the number of samples; obtaining an optimal solution:
Figure FDA0002880915830000027
selection of alpha*A component of
Figure FDA0002880915830000028
Satisfies the conditions
Figure FDA0002880915830000029
Obtaining the optimal:
Figure FDA0002880915830000031
(3) predicting the characteristics of the B-ultrasonic image group by using the obtained SVM model to obtain a predicted value R-NASH:
Figure FDA0002880915830000032
wherein x is a vector formed by the features of the effective B-ultrasonic image group;
combining the obtained serum index characteristic with the R-NASH characteristic of the B ultrasonic wave, and obtaining a non-alcoholic steatohepatitis diagnosis model by adopting a multivariate logistic regression method, wherein the method comprises the following steps:
(1) suppose x1,x2,K,xnFor predictive value R-NASH and other serum index features, g is the fibrosis index, and the regression model is as follows:
Figure FDA0002880915830000033
z＝hθ(x)＝θ0+θ1x1+θ2x2+Λ+θnxn
wherein, theta0,θ1,θ2ΛθnIs a model parameter of multivariate logistic regression, n is the number of variables, and the adopted characteristics comprise: R-NASH, PLT, AST, BMI, Lipid, Uric acid;
(2) obtaining model parameters, and obtaining a diagnosis model as follows:
YNASH＝2.145×R-NASH–0.306×PLT+0.267×AST+1.152×BMI+1.185×Lipid+0.114×Uric acid–3.375；
step seven, confirming the model;
and confirming the model threshold range of the NASH according to the maximum York index of the ROC curve through model comparison and efficiency analysis, so as to obtain a model with fixed final parameters for prediction.
2. The method of claim 1, wherein the method comprises the following steps: in the fourth step, the features used in the step of B-mode ultrasonic feature extraction are as follows:
b ultrasonic characteristic extraction:
b ultrasonic images are read by screening software, then the images are normalized through a gray level stretching step, and then the gray level texture features of the liver are calculated and extracted; the gray texture features comprise a gray level co-occurrence matrix, a gray level walking run-length matrix, an intensity histogram and an invariant moment;
wherein, the algorithm for extracting the features by the screening software is as follows,
the method for calculating the gray level co-occurrence matrix is to describe textures according to the spatial correlation characteristics of gray levels, and comprises the following steps:
the autocorrelation characteristic: the method is characterized in that correlation exists among expected values of random error items and is used for evaluating the definition of an image;
and (3) correlation: the similarity degree of the elements of the space gray level co-occurrence matrix in the row or column direction is measured, so that the correlation value reflects the local gray level correlation in the image; when the matrix element values are uniform and equal, the correlation value is large; on the contrary, if the matrix pixel values are greatly different, the correlation value is small; if the image has horizontal direction texture, the COR of the horizontal direction matrix is larger than the COR values of the other matrixes; COR is called completely, and Chinese meaning is related;
cluster protrusion: under the condition that an object is abrupt in the image, the larger the contrast between the lines and the patterns is, the larger the prom value is; the prom is called Clusterpromience, and Chinese meaning is cluster protrusion;
cluster shadow, english full clusterslide: the shadow size is possibly related to the picture quality, more intuitively related to the wrinkle degree of the clothes, and the smoother the shadow value is, the smaller the shadow value is; measuring the skewness of the matrix and measuring the concept of uniformity; when the value is high, the image is asymmetric; shadow Chinese meaning shadow;
difference: when the contrast is calculated, the weight is exponentially increased along with the distance between the matrix elements and the diagonal, and if the distance is linearly increased, the dissimilarity is obtained;
energy: the energy is also called as the energy, and reflects the uniformity degree of the gray level distribution of the image and the thickness of the texture; if all the values of the co-occurrence matrix are equal, the ASM value is small; conversely, if some of the values are large and others are small, the ASM value is large; when elements in the co-occurrence matrix are distributed in a centralized manner, the ASM value is large; a large ASM value indicates a more uniform and regularly varying texture pattern;
homogeneity: measuring the local uniformity of the image, wherein the value of the non-uniform image is lower, and the value of the uniform image is higher; in contrast to contrast or dissimilarity, the weight of homogeneity decreases with the distance of the element value from the diagonal, in an exponential manner;
maximum probability, english full name maximummobavailability: representing the texture feature that appears the most frequently in the image.
3. The method of claim 2, wherein the non-invasive screening of the non-alcoholic steatohepatitis model is as follows: the method for calculating the gray level walking run matrix is that the gray level run matrix is a matrix formed by the lengths of the gray level wandering, the gray level run matrix is only used for measuring and counting image pixel information, and the generated gray level run matrix is required to be calculated in the actual use process to obtain image characteristic information based on the gray level co-occurrence matrix; including short-term emphasis, long-term emphasis, run length non-uniformity, gray level non-uniformity, run percentage, low gray level run emphasis, high gray level run emphasis, short-term low gray level emphasis, short-term high gray level emphasis, long-term low gray level emphasis, long-term high gray level emphasis.
4. The construction method for non-invasive screening of non-alcoholic steatohepatitis model according to claim 2 or 3, wherein: the method for calculating the intensity histogram comprises the following steps that relevant features of the intensity histogram are first-order features calculated according to the intensity histogram, and the first-order features comprise a statistical mean value of the intensity histogram, a statistical variance of the intensity histogram, a statistical entropy value of the intensity histogram and a statistical energy sum of the intensity histogram; the characteristic number of the peak value height of the characteristic probability density distribution curve at the average value describes whether the peak value of the distribution formed by the Sample is abrupt or flat, and describes the condition of the symmetry of the distribution formed by the Sample;
wherein, the situation describing the symmetry of the distribution composed of samples is that the skewness of the time series x is calculated, and the skewness is used for measuring the symmetry of the x; if the skewness is negative, the dispersion degree on the left side of the x mean value is stronger than that on the right side; if the skewness is positive, the dispersion degree on the left side of the x-mean is weaker than that on the right side; skewness is equal to O for a normal distribution.
5. The construction method for non-invasive screening of non-alcoholic steatohepatitis model according to claim 2 or 3, wherein: the method for calculating the moment of invariance is that,
7 invariant moment groups (phi 1 phi 7) are derived by utilizing second-order and third-order specification central moments;
the moment is a digital feature of a random variable, a first-order origin moment is expected, a first-order central moment mu 1 is 0, a second-order central moment mu 2 is a variance var (X) of X, a third-order central moment mu 3 is used for measuring whether the distribution is biased, and a fourth-order central moment mu 4 is used for measuring the degree of steepness of the distribution near the mean value.
6. A prediction system applied to the construction method for non-invasive screening of the non-alcoholic steatohepatitis model according to any one of claims 1 to 5, characterized in that the system comprises:
the data acquisition module is used for preprocessing the collected serum, image and pathological data of the steatohepatitis patient;
the serum characteristic data preprocessing module is used for removing NASH and non-NASH pathological biopsy data and uniformly carrying out the following standardized processing on the rest serum variables; the specific treatment method comprises the following steps:
(1) ranking each numerical variable from small to large, treating less than 2.5% or greater than 97.5% as outliers and changing to 2.5% and 97.5%, respectively;
(2) all numerical variables are normalized, and the formula is as follows: xn normalized＝(Xn–Xmin)/(Xmax–Xmin) (ii) a Wherein, XnRepresenting any numerical variable, Xnnormazed denotes a numerical variable XnNormalized value of (2), XmaxRepresenting the maximum value, X, of a numerical variableminRepresents the minimum value of the numerical variables;
serum characteristic statistical screening, which is used for serum characteristic statistical screening; specifically, a part of the collected data is selected as a training set; then, screening out indexes with significant correlation through spearman correlation analysis in a training group; then, carrying out statistical screening by forward conditional multivariate logistic regression to obtain a serum index related to NASH; wherein the serum indexes comprise glutamic-oxaloacetic transaminase, platelet count, blood fat, BMI calculation and uric acid;
the B ultrasonic characteristic extraction and screening module is used for extracting and screening B ultrasonic characteristics, and non-0 coefficient variables screened out by a lasso regression method are used as effective B ultrasonic characteristics of final modeling, so that the gray texture characteristics of liver tissues are calculated and extracted; the effective B-mode ultrasonic characteristics are screened out by adopting a Lasso regression model, wherein the cost function of the Lasso regression model is as follows:
Figure FDA0002880915830000051
wherein x isijRepresents the jth B-mode characteristic variable, y, of the ith patient dataiIs a response variable of the ith patient data, q represents the total number of B-mode characteristic variables, m is the total number of patient data, λ is a penalty function,
Figure FDA0002880915830000061
the optimal result is obtained;
the characteristic integration module is used for predicting the effective B ultrasonic image group characteristics by utilizing a Support Vector Machine (SVM) model to obtain a predicted value R-NASH and integrating the effective B ultrasonic characteristics;
the prediction model establishing module is used for combining the obtained serum index characteristics and the R-NASH characteristics of the B ultrasonic wave and obtaining a non-alcoholic steatohepatitis screening model by adopting a multivariate logistic regression method;
the prediction model confirmation module is used for confirming the model threshold range of the NASH to be evaluated so as to obtain a model with fixed final parameters; the model threshold range for confirming and evaluating NASH is confirmed according to the maximum York index of an ROC curve through model comparison and efficiency analysis and is finally used for prediction.
7. A detection device for noninvasive screening of a construction method of a non-alcoholic steatohepatitis model is characterized by comprising the following steps:
a processor;
a memory having stored therein executable instructions of the processor;
wherein the processor is configured to perform the steps of a method of construction for non-invasive screening of a non-alcoholic steatohepatitis model according to any one of claims 1 to 5 via execution of the executable instructions.
8. A computer readable storage medium storing a program which when executed performs the steps of a method of constructing a non-invasive screening model for non-alcoholic steatohepatitis as claimed in any one of claims 1 to 5.
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Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113611410A (en) * 2021-09-08 2021-11-05 温州医科大学附属第一医院 Steatohepatitis risk diagnosis equipment and system and training method of residual error network of steatohepatitis risk diagnosis equipment and system
CN114141363A (en) * 2021-12-07 2022-03-04 川北医学院附属医院 Severe pancreatitis prediction model construction method based on machine learning method
CN114242247A (en) * 2021-12-30 2022-03-25 吉林大学第一医院 Non-obese MAFLD prediction system, device and storage medium
CN116825362A (en) * 2023-08-29 2023-09-29 北京回龙观医院(北京心理危机研究与干预中心) Diagnostic prediction model for alcoholic liver injury and construction method and application method thereof

Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20060172286A1 (en) * 2005-02-03 2006-08-03 Thierry Poynard Diagnosis method of alcholic or non-alcoholic steato-hepatitis using biochemical markers

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20060172286A1 (en) * 2005-02-03 2006-08-03 Thierry Poynard Diagnosis method of alcholic or non-alcoholic steato-hepatitis using biochemical markers

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
冯巩等: "基于LASSO回归的非酒精性脂肪性肝病进展性肝纤维化预测模型的构建及分析", 临床肝胆病杂志, vol. 36, no. 10, 15 October 2020 (2020-10-15) *
周玮等: "基于钆塞酸二钠增强磁共振成像影像组学定量评估肝硬化患者肝脏储备功能的应用价值", 中国医学科学院学报, vol. 42, no. 04, 3 September 2020 (2020-09-03) *
胡志军等: "非酒精性脂肪性肝炎无创诊断模型的构建", 解放军医学杂志, vol. 45, no. 07, 28 July 2020 (2020-07-28) *

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113611410A (en) * 2021-09-08 2021-11-05 温州医科大学附属第一医院 Steatohepatitis risk diagnosis equipment and system and training method of residual error network of steatohepatitis risk diagnosis equipment and system
CN114141363A (en) * 2021-12-07 2022-03-04 川北医学院附属医院 Severe pancreatitis prediction model construction method based on machine learning method
CN114141363B (en) * 2021-12-07 2023-09-12 川北医学院附属医院 Machine learning method-based severe pancreatitis prediction model construction method
CN114242247A (en) * 2021-12-30 2022-03-25 吉林大学第一医院 Non-obese MAFLD prediction system, device and storage medium
CN116825362A (en) * 2023-08-29 2023-09-29 北京回龙观医院(北京心理危机研究与干预中心) Diagnostic prediction model for alcoholic liver injury and construction method and application method thereof
CN116825362B (en) * 2023-08-29 2024-01-02 北京回龙观医院(北京心理危机研究与干预中心) Diagnostic prediction model for alcoholic liver injury and construction method and application method thereof

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