CN113161000A - Mixed cell type liver cancer prognosis scoring model and construction method thereof - Google Patents
Mixed cell type liver cancer prognosis scoring model and construction method thereof Download PDFInfo
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- 201000007270 liver cancer Diseases 0.000 title claims abstract description 47
- 208000014018 liver neoplasm Diseases 0.000 title claims abstract description 46
- 238000004393 prognosis Methods 0.000 title claims abstract description 44
- 238000010276 construction Methods 0.000 title claims abstract description 10
- 210000004027 cell Anatomy 0.000 claims abstract description 31
- 206010028980 Neoplasm Diseases 0.000 claims abstract description 21
- 238000000556 factor analysis Methods 0.000 claims abstract description 19
- 230000004083 survival effect Effects 0.000 claims abstract description 15
- 230000001575 pathological effect Effects 0.000 claims abstract description 10
- INZOTETZQBPBCE-NYLDSJSYSA-N 3-sialyl lewis Chemical compound O[C@H]1[C@H](O)[C@H](O)[C@H](C)O[C@H]1O[C@H]([C@H](O)CO)[C@@H]([C@@H](NC(C)=O)C=O)O[C@H]1[C@H](O)[C@@H](O[C@]2(O[C@H]([C@H](NC(C)=O)[C@@H](O)C2)[C@H](O)[C@H](O)CO)C(O)=O)[C@@H](O)[C@@H](CO)O1 INZOTETZQBPBCE-NYLDSJSYSA-N 0.000 claims abstract description 9
- 108010088751 Albumins Proteins 0.000 claims abstract description 9
- 102000009027 Albumins Human genes 0.000 claims abstract description 9
- 108010022366 Carcinoembryonic Antigen Proteins 0.000 claims abstract description 9
- 102000012406 Carcinoembryonic Antigen Human genes 0.000 claims abstract description 9
- 101710107035 Gamma-glutamyltranspeptidase Proteins 0.000 claims abstract description 9
- 101710173228 Glutathione hydrolase proenzyme Proteins 0.000 claims abstract description 9
- 102000006640 gamma-Glutamyltransferase Human genes 0.000 claims abstract description 9
- 210000001165 lymph node Anatomy 0.000 claims abstract description 9
- 238000012216 screening Methods 0.000 claims abstract description 5
- 238000000034 method Methods 0.000 claims description 18
- 230000009545 invasion Effects 0.000 claims description 12
- 125000001992 L-gamma-glutamyl group Chemical group N[C@@H](CCC(=O)*)C(=O)O 0.000 claims description 3
- 238000012360 testing method Methods 0.000 claims description 3
- 208000006990 cholangiocarcinoma Diseases 0.000 description 13
- 208000009854 congenital contractural arachnodactyly Diseases 0.000 description 11
- 206010073071 hepatocellular carcinoma Diseases 0.000 description 6
- 231100000844 hepatocellular carcinoma Toxicity 0.000 description 6
- 238000004458 analytical method Methods 0.000 description 3
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 208000035977 Rare disease Diseases 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
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- 238000011156 evaluation Methods 0.000 description 1
- 210000003494 hepatocyte Anatomy 0.000 description 1
- 238000001325 log-rank test Methods 0.000 description 1
- 210000004088 microvessel Anatomy 0.000 description 1
- 239000013610 patient sample Substances 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000007619 statistical method Methods 0.000 description 1
Classifications
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
Abstract
The invention has provided a mixed cell type liver cancer prognosis scoring model and its construction method, this construction method includes according to clinical case, collect mixed cell type liver cancer patient and relevant clinical pathological index of survival, including albumin, gamma-glutamyl transpeptidase, carcinoembryonic antigen, carbohydrate antigen 19-9, tumor size, tumor quantity, capillary and lymph node; adopting single factor analysis and screening the indexes related to the prognosis of the mixed cell type liver cancer patient; further bringing the screened indexes into multi-factor analysis to obtain independent prognostic factors related to the survival of the patient; according to the risk ratio of multi-factor analysis, assigning values to each index and establishing a grading table for prognosis prediction; the AUC and the C-index of the mixed cell type liver cancer prognosis scoring model reach 0.72, and are superior to other existing prognosis models, so that the prognosis scoring model has high prediction efficiency, can better guide clinical decision and further improve the prognosis of patients.
Description
Technical Field
The invention belongs to the technical field of biology, and particularly relates to a prognostic scoring model for mixed cell type liver cancer (the prognostic scoring model is named as PSM-CHCC, PSM is a prognosic score model acronym, and is a prognostic scoring model; CHCC is combined HCC, and is mixed cell type liver cancer) and a construction method thereof.
Background
Mixed cell liver cancer (cHCC-CCA), which is a rare disease that originates from the differentiation of hepatocytes and cholangiocytes, accounts for about 2-5% of primary liver cancer. Allen and Lisa first described the features of cHCC-CCA 70 years ago, and in recent years, it has attracted increasing attention because of its unique biological, pathological and clinical behavior.
Studies have shown that the clinical pathological features of cHCC-CCA are intermediate between hepatocellular carcinoma (HCC) and cholangiocarcinoma (CC). Nevertheless, cHCC-CCA has heterogeneity in clinical behavior and prognosis due to its mixed pathological features and distinct subtypes. The prognostic influencing factors for cHCC-CCA are still unclear and vary widely among different studies. This difference may be due to different etiological backgrounds of patients in different studies, different histopathological features and limited patient samples.
Due to the rarity of cHCC-CCA and its similarity to HCC, the prognostic evaluation system of cHCC-CCA is largely inherited from HCC. Currently widely accepted staging systems for prognostic assessment of cHCC-CCA include the american cancer association eighth edition TNM staging (AJCC TNM-8); the Barcelona liver cancer clinical staging system (BCLC), the Ottabang male staging system (OKUDA), the Italy liver cancer tumor staging and comprehensive prognosis staging system (ITA. LI. CA), and the Italy liver cancer cooperative group score (CLIP). However, most of these staging systems are established in the population of hepatocellular carcinoma, and thus have limitations in clinical decision making and prognosis prediction for patients with mixed-cell liver cancer. Due to the rarity of the cHCC-CCA, almost no prognostic predictive model specifically for the cHCC-CCA is developed at present.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide a method for constructing a prognosis score model of mixed cell type liver cancer, so as to fill the clinical blank and hope that the individual condition can be accurately predicted and clinical decision can be guided.
The invention also aims to provide a mixed cell type liver cancer prognosis scoring model PSM-CHCC.
In order to achieve one of the above purposes, the solution of the invention is as follows:
a method for constructing a prognosis score model of mixed cell type liver cancer comprises the following steps:
(1) collecting clinical pathological indexes of the mixed cell type liver cancer patient according to clinical cases;
(2) adopting single factor analysis and screening the indexes related to the prognosis of the mixed cell type liver cancer patient;
(3) adopting multi-factor analysis in the screened indexes to obtain independent prognostic factors;
(4) and assigning values to each index and establishing a scoring table according to the risk ratio of the multi-factor analysis.
Further, in step (1), the clinical pathological indicators include albumin, gamma-glutamyl transpeptidase, carcinoembryonic antigen, carbohydrate antigen 19-9, tumor size, tumor number, microvessels, and lymph nodes.
Further, in the step (2), the single factor analysis is based on nonparametric test of Log-rank method, the influence of the single factor on the survival time is analyzed, and the index with P <0.1 related to the patient prognosis is screened.
Further, in step (3), the independent prognostic factors include 35g/L albumin, 50U/L γ -glutamyl transpeptidase, 5ug/L carcinoembryonic antigen, 37ku/L carbohydrate antigen 19-9, 5cm tumor, single or multiple tumors, presence or absence of microvascular invasion and presence or absence of lymph node invasion.
Further, in the step (3), the influence of the Cox proportional risk regression model on the survival time by 2 or more factors is used for multi-factor analysis, so that an independent prognostic factor with P <0.05 is obtained.
Further, in the step (4), the index assignment process is as follows: and assigning the index with the lowest risk for 1 point, assigning the index with 2 times of risk for 2 points, and establishing a scoring table.
In order to achieve the second purpose, the solution of the invention is as follows:
a prognosis scoring model of the mixed cell type liver cancer is obtained by the construction method.
Due to the adoption of the scheme, the invention has the beneficial effects that:
the AUC and the C-index (C-index is widely used for comparing the prediction efficiency of different prognosis models) of the established prognosis scoring model PSM-CHCC reach 0.72, and are superior to other prognosis models (comprising a TNM staging (AJCC TNM-8) (C-index: 0.62), a Barcelona liver cancer clinical staging system (BCLC) (C-index: 0.59), an OkUDA (C-index: 0.57), an Italian liver cancer tumor staging, a comprehensive prognosis staging system (ITA. LI. CA) (C-index: 0.61) and an Italian liver cancer cooperative group scoring (CLIP) (C-index: 0.58)) which are clinically used at present. Therefore, in the mixed cell type liver cancer patient population, the PSM-CHCC has better prediction capability on the prognosis of patients, relatively speaking, can better guide clinical decision and improve the prognosis of patients.
Detailed Description
The invention provides a mixed cell type liver cancer prognosis scoring model and a construction method thereof.
Because no prognosis model specially applied to the mixed cell type liver cancer patient exists clinically at present, the invention analyzes the 296 pieces of clinical information of the mixed cell type liver cancer patient with pathologically confirmed diagnosis in the hospital, discovers effective prognostic factors (including albumin, gamma-glutamyl transpeptidase, carcinoembryonic antigen, carbohydrate antigen 19-9, tumor size, tumor number, microvascular invasion and lymph node invasion), and establishes a prognosis scoring model for the cHCC-CCA patient. The statistical analysis in the process of establishing the prognosis model adopts Stata version 15.1(StataCorp, College station, TX), and all related statistics are survival analysis methods commonly applied and accepted in the research field at present, including single-factor analysis, multi-factor analysis, KM function and Log-rank test. Specifically, the method comprises the following steps:
< method for constructing prognosis score model for Mixed-cell type liver cancer >
The construction method of the mixed cell type liver cancer prognosis scoring model comprises the following steps:
(1) and according to clinical cases, collecting clinical pathological indexes of the mixed cell type liver cancer patients, namely dividing potential prognostic factors (including sex, age, laboratory examination results, tumor characteristics and the like) of the mixed cell type liver cancer patients into two classification variables according to clinical common thresholds. The clinical pathological indexes specifically comprise albumin, gamma-glutamyl transpeptidase, carcinoembryonic antigen, carbohydrate antigen 19-9, tumor size, tumor number, microvascular invasion and lymph node invasion, and the patient is followed up and the survival time and the recurrence time of the patient are recorded.
(2) Adopting single factor analysis (non-parameter test based on Log-rank method, analyzing the influence of single factor on survival time), and screening the index related to mixed cell type liver cancer patient prognosis, namely P is less than 0.1.
(3) And obtaining independent prognosis factors by adopting multi-factor analysis (namely a Cox proportional risk regression model which is widely applied to a survival regression model aiming at the influence of 2 or more than 2 factors on survival time) in the screened indexes.
Wherein, in the step (3), the independent prognostic factors include 35g/L albumin, 50U/L gamma-glutamyl transpeptidase, 5ug/L carcinoembryonic antigen, 37ku/L carbohydrate antigen 19-9, 5cm tumor, single or multiple tumor, and the presence or absence of microvascular invasion and lymph node invasion.
In the step (3), the analysis method can consider the mixed bias among different indexes, and the index corresponding to the P value less than 0.05 in the result is the independent prognosis factor of the mixed cell type liver cancer patient, and obtains the risk ratio (HR) of each index. As shown in Table 1, the prognostic scoring model includes the corresponding indexes and the risk ratios, P values and scores corresponding to the indexes.
TABLE 1 Risk ratio, P-value and score for each index
(4) And assigning values to all indexes (if the index with the lowest risk is assigned 1 point, and the index with 2 times of risk is assigned 2 points) according to the risk ratio (HR) of the multi-factor analysis, and establishing a grading table. And divided into 3 stages with significantly different prognosis according to the scores, as shown in table 2.
TABLE 2 survival time in different stages
< model of prognosis score for Mixed-cell type liver cancer >
The mixed cell type liver cancer prognosis scoring model is obtained by the construction method.
The present invention will be further described with reference to the following examples.
Example (b):
the method for constructing the mixed cell type liver cancer prognosis score model comprises the following steps:
(1) and clinically, after a mixed cell type liver cancer patient is determined by a case, laboratory examination results of the patient are collected, wherein the laboratory examination results comprise albumin, gamma-glutamyltranspeptidase, carcinoembryonic antigen, carbohydrate antigen 19-9, and the size and the number of tumors, whether microvascular invasion and lymph node invasion exist.
(2) And adopting single factor analysis and screening the index related to the mixed cell type liver cancer patient prognosis, namely P is less than 0.1.
(3) And adopting multifactorial analysis in the screened indexes to obtain an independent prognostic factor, namely P is less than 0.05.
(4) Determining a score according to the scoring table, and classifying the score into a corresponding stage, wherein if the score is in the A stage, the prognosis is the best, and the predicted survival time is 59 months; the prognosis of the B stage is medium, and the predicted survival time is 17 months; the prognosis of stage C is the worst, with a predicted survival time of 6 months.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. It will be readily apparent to those skilled in the art that various modifications to these embodiments and the generic principles defined herein may be applied to other embodiments without the use of the inventive faculty. Therefore, the present invention is not limited to the above-described embodiments. Those skilled in the art should appreciate that many modifications and variations are possible in light of the above teaching without departing from the scope of the invention.
Claims (7)
1. A method for constructing a prognosis score model of mixed cell type liver cancer is characterized in that: which comprises the following steps:
(1) collecting clinical pathological indexes of the mixed cell type liver cancer patient according to clinical cases;
(2) adopting single factor analysis and screening the indexes related to the prognosis of the mixed cell type liver cancer patient;
(3) adopting multi-factor analysis in the screened indexes to obtain independent prognostic factors;
(4) and assigning values to each index and establishing a scoring table according to the risk ratio of the multi-factor analysis.
2. The method for constructing a mixed-cell liver cancer prognostic score model according to claim 1, wherein: in the step (1), the clinical pathological indexes comprise albumin, gamma-glutamyl transpeptidase, carcinoembryonic antigen, carbohydrate antigen 19-9, tumor size, tumor number, capillary vessel and lymph node.
3. The method for constructing a mixed-cell liver cancer prognostic score model according to claim 1, wherein: in the step (2), the single factor analysis is based on non-parameter test of Log-rank method, the influence of the single factor on the survival time is analyzed, and the index with P <0.1 related to the patient prognosis is screened.
4. The method for constructing a mixed-cell liver cancer prognostic score model according to claim 1, wherein: in step (3), the independent prognostic factors include 35g/L albumin, 50U/L gamma-glutamyl transpeptidase, 5ug/L carcinoembryonic antigen, 37ku/L carbohydrate antigen 19-9, 5cm tumor, single or multiple tumor, presence or absence of microvascular invasion and presence or absence of lymph node invasion.
5. The method for constructing a mixed-cell liver cancer prognostic score model according to claim 1, wherein: in the step (3), the influence of a Cox proportional risk regression model on the survival time by 2 or more factors is adopted in the multi-factor analysis, and an independent prognostic factor with P <0.05 is obtained.
6. The method for constructing a mixed-cell liver cancer prognostic score model according to claim 1, wherein: in the step (4), the index assignment process is as follows: and assigning the index with the lowest risk for 1 point, assigning the index with 2 times of risk for 2 points, and establishing a scoring table.
7. A mixed cell type liver cancer prognostic score model characterized by: which is obtained by the method of construction according to any one of claims 1 to 6.
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