CN117976212A - Method, system, equipment and medium for acquiring osteosarcoma prognosis risk factors - Google Patents
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- 201000008968 osteosarcoma Diseases 0.000 title claims abstract description 106
- 238000004393 prognosis Methods 0.000 title claims abstract description 52
- 238000000034 method Methods 0.000 title claims abstract description 27
- 230000004083 survival effect Effects 0.000 claims abstract description 50
- 206010028980 Neoplasm Diseases 0.000 claims abstract description 49
- 230000000366 juvenile effect Effects 0.000 claims abstract description 48
- 208000009458 Carcinoma in Situ Diseases 0.000 claims abstract description 23
- 238000011156 evaluation Methods 0.000 claims abstract description 14
- 201000004933 in situ carcinoma Diseases 0.000 claims abstract description 7
- 238000004458 analytical method Methods 0.000 claims description 16
- 238000007619 statistical method Methods 0.000 claims description 14
- 238000001356 surgical procedure Methods 0.000 claims description 14
- 238000012549 training Methods 0.000 claims description 14
- 238000004590 computer program Methods 0.000 claims description 13
- 230000007774 longterm Effects 0.000 claims description 12
- 206010027476 Metastases Diseases 0.000 claims description 10
- 230000009401 metastasis Effects 0.000 claims description 10
- 238000012795 verification Methods 0.000 claims description 10
- 238000002266 amputation Methods 0.000 claims description 7
- 238000002512 chemotherapy Methods 0.000 claims description 7
- 238000001959 radiotherapy Methods 0.000 claims description 7
- 238000007405 data analysis Methods 0.000 claims description 4
- 238000009792 diffusion process Methods 0.000 claims description 4
- 238000011119 multifactor regression analysis Methods 0.000 claims description 4
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Abstract
The invention relates to a method, a system, equipment and a medium for acquiring osteosarcoma prognosis risk factors, which relate to the technical field of biology, and a public database and a database of research resources are created by applying a data mining technology, and the following intelligent risk factors for indicating the clinical survival rate of the disease are found from clinical characteristics of osteosarcoma patients of teenagers: sex, surgical mode, site of onset of carcinoma in situ, seer tumor stage, number of tumors. And establishing a prediction model for the prognosis survival of the juvenile osteosarcoma according to the prediction value of the risk factors. The method is favorable for targeted and accurate evaluation of prognosis evaluation of juvenile osteosarcoma patients, is simple to operate and visual in result, integrates a large amount of clinical data into a prediction model, and facilitates evaluation of prognosis survival of juvenile osteosarcoma patients.
Description
Technical Field
The invention relates to the technical field of biomedicine, in particular to a method, a system, equipment and a medium for acquiring osteosarcoma prognosis risk factors.
Background
Osteosarcoma (Osteosarcoma) is a malignant bone tumor that affects primarily children and adolescents. Nevertheless, it is still relatively rare in overall cancer. Worldwide, the annual incidence of osteosarcoma is around 4.4 cases/million children and young childhood. Osteosarcoma affects mainly adolescents aged 10 to 20 years, especially during their growth peaks. Such tumors are less common in children and more rare in adults. It typically occurs in rapidly growing skeletal areas, such as long bones near the knee. Osteosarcoma is a complex pathogenesis involving a variety of genetic and environmental factors. The prognosis of juvenile osteosarcoma depends on a number of factors, including the size, location, whether the tumor has metastasized, the manner in which the patient is being treated, and the like.
Diagnosis of osteosarcoma typically requires a combination of medical history, physical examination, and a series of examinations, including X-ray, magnetic Resonance Imaging (MRI), computed Tomography (CT), and biopsies. The objective lens examination, i.e. sampling from the suspected area and examining under a microscope, is a key step in the diagnosis of osteosarcoma. In terms of treatment, surgery is one of the main methods for treating osteosarcoma. The goal of the procedure is to completely ablate the tumor and preserve as much function as possible. In the past, this has generally meant amputation, but as technology advances, more and more patients may undergo limb care surgery. Chemotherapy plays a key role in the treatment of osteosarcoma. It is usually performed before and after surgery, with the aim of shrinking the tumor, killing the tumor cells, and destroying cancer cells that may have spread to other parts of the body. Radiotherapy is not the primary means in standard treatment of osteosarcoma, but in some cases it may be used as an adjunctive treatment. Despite the continued progress in diagnostic means and therapeutic methods, advanced osteosarcoma prognosis remains poor and there is no targeted prognostic prediction tool for juvenile osteosarcoma.
The prior data of prognosis of teenager osteosarcoma patients are not arranged, and a prediction model for prognosis of teenager osteosarcoma cannot be built from a large amount of prior data, because the disease is rare, a personalized prediction model and a corresponding tool are not implemented, and the teenager period is a peak period of osteosarcoma, and the prior prediction model of osteosarcoma patients in general or the prediction tool of osteosarcoma of limbs is limited to the osteosarcoma prognosis prediction tool used by teenagers, but does not appear. Thus, factors which are obviously related to prognosis survival of teenagers are found in the teenager population, and starting from the factors, the problem of deficiency of the existing clinical prediction tool for the teenager osteosarcoma is solved, and the prediction accuracy is optimized.
Disclosure of Invention
In order to overcome the defect that the prior art does not have a prognosis survival prediction tool for juvenile osteosarcoma patients, the main purpose of the invention is to provide a clinical prediction model for juvenile osteosarcoma prognosis survival and a construction method thereof.
In order to achieve the above purpose, the invention adopts the following technical scheme that the method for acquiring the osteosarcoma prognosis risk factor comprises the following steps:
acquiring clinical data of prognosis of the juvenile osteosarcoma patient, and acquiring relevant factors of long-term survival conditions of the juvenile osteosarcoma patient through relevant factor statistical analysis;
Clinical data are divided into a training group and a verification group, multiple factor regression analysis is carried out on factors related to long-term survival conditions of teenager osteosarcoma patients in the training group, and risk factors affecting prognosis of the teenager osteosarcoma patients are confirmed.
In the statistical analysis of related factors, screening out risk evaluation indexes which obviously influence the survival of juvenile osteosarcoma through single-factor Cox analysis;
Adding a multi-factor cox model into risk evaluation indexes which are screened by single-factor cox analysis and are obviously related to the survival of juvenile osteosarcoma, adopting multi-factor cox analysis, dividing each factor into a risk factor, an intermediate factor and an irrelevant factor through a risk ratio, setting the risk factor as a factor related to the survival of juvenile osteosarcoma, and determining the risk factor influencing the survival of juvenile osteosarcoma through statistical analysis and model fitting of case data.
The risk evaluation indexes comprise sex, operation mode, whether chemotherapy, whether radiotherapy, stage of in-situ cancer onset position seer, grade grading, tumor number and tumor size.
The risk factors include sex, surgical mode, location of carcinoma in situ, seer tumor stage, and number of tumors.
The OR of five independent risk factors obtained by adopting multi-factor cox analysis is as follows: sex, surgical mode, limb-protecting surgery, carcinoma in situ location, seer tumor stage and tumor number.
The sampling correction is carried out on the constructed prognosis prediction model of the juvenile osteosarcoma patient, the prognosis prediction model of the juvenile osteosarcoma patient is verified according to the clinical data of the verification group,
The prediction factors are selected from the risk factors by Lasso regression, wherein sex is used as a first prediction factor, an operation mode is used as a second prediction factor, an in-situ cancer position is used as a third prediction factor, seer stages are used as a fourth prediction factor and the number of tumors is used as a fifth prediction factor.
The gender or male of the first predictive factor corresponds to the score of 0 or 21 of the first factor respectively; the second predictive factor is used for amputation, non-surgery and limb protection in a surgery mode, wherein the score of the second predictive factor corresponds to 20 minutes, 77 minutes or 0 minutes respectively; the third factor score of the third predictive factor in-situ cancer position corresponds to 30 or 0 respectively; the fourth predictor seer carries out distant metastasis, no metastasis and near diffusion in a period corresponding to the fourth factor score of 100 or 0 or 39 respectively; the number of tumors of the fifth predictive factor is equal to 1, and the number of tumors is greater than 1 and corresponds to a score of 0 or 27 of the fifth predictive factor respectively; the survival probability of the osteosarcoma teenager patient groups for 1 year, 3 years and 5 years is calculated according to the scores of the test factors, a Nomogram diagram is drawn, and the calculation formula is as follows:
Predicted survival probability of 1 year = 0 x points 3+ -2e-05 x points 2+0.00255 x points +0.82566;
Predicted survival probability for 3 years = 0 x points 3+ -4e-05 x points 2+0.00075 x points +0.8944;
Predicted survival probability for 5 years = 0 x points 3+ -3e-05 x points 2+ -9e-04 x points +0.8922.
An acquisition system for osteosarcoma prognosis risk factors, comprising:
the data processing module is used for acquiring clinical data of prognosis of the juvenile osteosarcoma patient and acquiring relevant factors of long-term survival conditions of the juvenile osteosarcoma patient through relevant factor statistical analysis;
The data analysis module divides clinical data into a training group and a verification group, carries out multi-factor regression analysis on factors related to long-term survival conditions of teenager osteosarcoma patients in the training group, and confirms risk factors affecting prognosis of the teenager osteosarcoma patients;
a computer device, comprising:
At least one memory;
At least one processor;
At least one computer program;
The at least one computer program is stored in the at least one memory, the at least one processor executing the at least one computer program to implement: such as a system for establishing a prognosis prediction model of osteosarcoma.
A computer-readable medium storing a computer program for causing a computer to execute: such as a method for obtaining a prognosis risk factor of osteosarcoma.
Compared with the prior art, the invention has the beneficial effects that:
The invention provides a method for preparing clinical data of patients and establishing a prediction model for prognosis of teenager osteosarcoma, which can provide a simpler and more effective mode for a clinician to evaluate the teenager osteosarcoma according to the prediction model and Nomogram diagrams thereof, and parameters required by the model can be quickly obtained through simple physical examination and emergency CT, so that the model has better sensitivity and specificity for prognosis prediction of the teenager osteosarcoma. The invention can rapidly evaluate teenager patients after being admitted by osteosarcoma under the condition of perfecting basic examination, and the method is simple, convenient and economic.
Drawings
FIG. 1 is a forest chart of risk factor results from a multi-factor cox screening assay of juvenile osteosarcoma patient clinical data showing the OR value of risk factors and 95% CI;
FIG. 2 is a diagram of a clinical predictive model Nomogram of a juvenile osteosarcoma patient according to the present invention;
Fig. 3 is a schematic flow chart of the present invention.
Detailed Description
The invention will be further described with reference to the drawings and embodiments.
Example 1:
referring to fig. 1-3, a method, a system, a device and a medium for obtaining a prognosis risk factor of osteosarcoma comprise the following steps:
acquiring clinical data of prognosis of the juvenile osteosarcoma patient, and acquiring relevant factors of long-term survival conditions of the juvenile osteosarcoma patient through relevant factor statistical analysis;
Dividing clinical data into a training group and a verification group, carrying out multi-factor regression analysis on factors related to long-term survival conditions of teenager osteosarcoma patients in the training group, and confirming risk factors affecting prognosis of the teenager osteosarcoma patients;
In this embodiment, the confirmed risk factors are converted into fractions in proportion and then used as predictors to construct a prognosis prediction model of the juvenile osteosarcoma patient, and the prognosis prediction model of the juvenile osteosarcoma patient is verified according to clinical data of a verification group. And sampling and correcting the constructed prognosis prediction model of the juvenile osteosarcoma patient, and verifying the prognosis prediction model of the juvenile osteosarcoma patient according to clinical data of a verification group.
More specifically, in the statistical analysis of relevant factors, screening out risk evaluation indexes which obviously influence the survival of juvenile osteosarcoma in the single factor Cox analysis;
Adding a multi-factor cox model into risk evaluation indexes which are screened by single-factor cox analysis and are obviously related to the survival of juvenile osteosarcoma, adopting multi-factor cox analysis, dividing each factor into a risk factor, an intermediate factor and an irrelevant factor through a risk ratio, setting the risk factor as a factor related to the survival of juvenile osteosarcoma, and determining the risk factor influencing the survival of juvenile osteosarcoma through statistical analysis and model fitting of case data.
The risk evaluation index comprises sex, operation mode, chemotherapy, radiotherapy, stage seer of in situ cancer incidence position, grade, tumor number and tumor size.
The risk factors include sex, operation mode, in situ cancer position, seer tumor stage and tumor number.
And (3) adopting multi-factor cox analysis to obtain OR of five independent risk factors: sex (male risk ratio HR 1.38495% ci 1.086-1.764), surgical mode (non-surgical risk ratio HR 2.384, 95% ci 1.61-3.692; amputation surgical risk ratio HR 0.733, 95% ci 0.577-0.965), in situ cancer location (limb bone risk ratio HR 0.629, 95% ci 0.433-0.915), seer tumor stage (in situ risk ratio HR 0.208, 95% ci 0.148-0.293; proximal metastasis risk ratio HR 0.386, 95% ci 0.297-0.500), tumor number (tumor number=1 risk ratio HR 0.655, 95% ci 0.433-0.967).
The prediction factors are selected from the risk factors by Lasso regression, wherein sex is used as a first prediction factor, an operation mode is used as a second prediction factor, an in-situ cancer position is used as a third prediction factor, seer stages are used as a fourth prediction factor and the number of tumors is used as a fifth prediction factor.
The gender or male of the first predictive factor corresponds to the score of 0 or 21 of the first factor respectively; the second predictive factor is used for amputation, non-surgery and limb protection in a surgery mode, wherein the score of the second predictive factor corresponds to 20 minutes, 77 minutes or 0 minutes respectively; the third factor score of the third predictive factor in-situ cancer position corresponds to 30 or 0 respectively; the fourth predictor seer carries out distant metastasis, no metastasis and near diffusion in a period corresponding to the fourth factor score of 100 or 0 or 39 respectively; the number of tumors of the fifth predictive factor is equal to 1, and the number of tumors is greater than 1 and corresponds to a score of 0 or 27 of the fifth predictive factor respectively; the survival probability of the osteosarcoma teenager patient groups for 1 year, 3 years and 5 years is calculated according to the scores of the test factors, a Nomogram diagram is drawn, and the calculation formula is as follows:
Predicted survival probability of 1 year = 0 x points 3+ -2e-05 x points 2+0.00255 x points +0.82566;
Predicted survival probability for 3 years = 0 x points 3+ -4e-05 x points 2+0.00075 x points +0.8944;
Predicted survival probability for 5 years = 0 x points 3+ -3e-05 x points 2+ -9e-04 x points +0.8922.
An acquisition system for osteosarcoma prognosis risk factors, comprising:
the data processing module is used for acquiring clinical data of prognosis of the juvenile osteosarcoma patient and acquiring relevant factors of long-term survival conditions of the juvenile osteosarcoma patient through relevant factor statistical analysis;
The data analysis module divides clinical data into a training group and a verification group, carries out multi-factor regression analysis on factors related to long-term survival conditions of teenager osteosarcoma patients in the training group, and confirms risk factors affecting prognosis of the teenager osteosarcoma patients;
When the model is used for actual clinical data analysis, a model building module can be further arranged for converting the confirmed risk factors into fractions according to proportion and then taking the fractions as prediction factors to build a prognosis prediction model of the juvenile osteosarcoma patient, and verifying the prognosis prediction model of the juvenile osteosarcoma patient according to clinical data of a verification group.
A computer device, comprising:
At least one memory;
At least one processor;
At least one computer program;
The at least one computer program is stored in the at least one memory, the at least one processor executing the at least one computer program to implement: such as a system for establishing a prognosis prediction model of osteosarcoma.
A computer-readable medium comprising a computer program product,
The computer readable medium stores a computer program for causing a computer to execute:
Such as a method for obtaining a prognosis risk factor of osteosarcoma.
Example 2
Referring to FIGS. 1-3, on the basis of example 1, the data used and analyzed in the study of the model construction can be obtained in a free download in the national cancer institute's monitoring, epidemiology and end result (SEER) database (http:// SEER cancer. Gov). No approval by ethics committee is required, and no risk of benefit conflicts.
Teenager osteosarcoma patients were collected and included 1116 patients with unknown clinical information data by filtering. The self-variable data studied were included: sex, race, surgical mode, whether chemotherapy, whether radiotherapy, location of onset of carcinoma in situ, seer stages, grade classification, number of tumors, tumor size of the patient. And collecting patient survival status information and time to live information. 1116 patients were divided into training and validation groups according to a 7:3 ratio. The training set is used to build the model and the validation set is used for validation.
Clinical data of the teenager osteosarcoma crowd in the year 2004-2015 is downloaded in a seer database, and basic data of a research group for constructing a model foundation are collected, wherein the basic data comprise gender, race, operation mode, chemotherapy, radiotherapy, in-situ cancer incidence position, seer stage, grade stage, tumor number and tumor size.
The collected data are firstly subjected to data filtering to screen out patients with undefined or missing data information, then, the data after the steps are completed are subjected to single-factor cox statistical analysis to screen out risk evaluation indexes which obviously influence the survival of teenager limb osteosarcoma, wherein the evaluation indexes comprise sex, operation mode, chemotherapy, radiotherapy, stage of in-situ cancer onset position seer, grade grading, tumor number and tumor size.
Adding risk evaluation indexes which are screened by single-factor cox analysis and are obviously related to the survival of the juvenile osteosarcoma into a multi-factor cox model, adopting multi-factor cox analysis, and determining risk factors which influence the survival of the juvenile osteosarcoma through statistical analysis and model fitting of case data. The risk factor markers comprise sex, operation mode, in-situ cancer position, seer tumor stage and tumor number, and a prediction model for the prognosis survival of juvenile osteosarcoma is established according to the prediction value of the markers.
According to the predictive value of the marker, a predictive model for the prognosis survival of juvenile osteosarcoma is established, wherein sex is used as a first predictive factor, an operation mode is used as a second predictive factor, an in-situ cancer position is used as a third predictive factor, seer stages are used as a fourth predictive factor, and the number of tumors is used as a fifth predictive factor. And judging the survival probability of the osteosarcoma teenager patient population for 1 year, 3 years and 5 years according to the total score of the predictive factors. And it is suggested by fig. 1 that the OR of five independent risk factors based on multi-factor cox analysis: sex (male risk ratio HR 1.38495% ci 1.086-1.764), surgical mode (non-surgical risk ratio HR 2.384, 95% ci 1.61-3.692; amputation surgical risk ratio HR 0.733, 95% ci 0.577-0.965), in situ cancer location (limb bone risk ratio HR 0.629, 95% ci 0.433-0.915), seer tumor stage (in situ risk ratio HR 0.208, 95% ci 0.148-0.293; proximal metastasis risk ratio HR 0.386, 95% ci 0.297-0.500), tumor number (tumor number=1 risk ratio HR 0.655, 95% ci 0.433-0.967). And judging the survival probability of the juvenile osteosarcoma patient in 1 year, 3 years and 5 years according to the total score of the predictive factors. The specific analysis is as follows: sex women or men respectively correspond to the first factor score of 0 or 21; amputation, no surgery and limb protection in the surgery mode correspond to the score of the second factor by 20 or 77 or 0 respectively; the axial bones and the limb bones in the in-situ cancer position respectively correspond to the third factor score of 30 or 0; seer stages of distant metastasis, no metastasis, near diffusion correspond to a fourth factor score of 100 or 0 or 39 respectively; the number of tumors is equal to 1, and greater than 1 corresponds to a score of 0 or 27 for the fifth factor, respectively. The total scores of the predictors obtained by different levels and combinations of the markers are respectively vertically downward corresponding to the survival probabilities of 1 year, 3 years and 5 years in the figure 2. According to the formula, the total score of five factors in the prediction model is calculated, and the relation between the total score and 1 year, 3 years and 5 years is respectively that the prediction survival probability of 1 year is=0 x points 3+ -2e-05 x points 2+
0.00255 Points+0.82566; predicted survival probability for 3 years = 0x points 3+ -4e-05 x points 2+0.00075 x points +0.8944; predicted survival probability for 5 years = 0x points 3+ -3e-05 x points 2+ -9e-04 x points +0.8922.
It is noted that in the present invention, relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
The above embodiments are merely illustrative of the present invention and are not to be construed as limiting the scope of the present invention, and all designs which are the same or similar to the present invention are within the scope of the present invention.
Claims (10)
1. The method for acquiring the osteosarcoma prognosis risk factor is characterized by comprising the following steps of:
Acquiring clinical data of prognosis of the juvenile osteosarcoma patient, and obtaining relevant factors of the juvenile osteosarcoma patient in long-term survival condition through relevant factor statistical analysis;
Clinical data are divided into a training group and a verification group, multiple factor regression analysis is carried out on factors related to long-term survival conditions of teenager osteosarcoma patients in the training group, and risk factors affecting prognosis of the teenager osteosarcoma patients are confirmed.
2. The method for obtaining osteosarcoma prognosis risk factors according to claim 1, wherein the risk evaluation index that significantly affects the survival of juvenile osteosarcoma is selected from the single factor cox analysis in the statistical analysis of the related factors;
Adding a multi-factor cox model into risk evaluation indexes which are screened by single-factor cox analysis and are obviously related to the survival of juvenile osteosarcoma, adopting multi-factor cox analysis, dividing each factor into a risk factor, an intermediate factor and an irrelevant factor through a risk ratio, setting the risk factor as a factor related to the survival of juvenile osteosarcoma, and determining the risk factor influencing the survival of juvenile osteosarcoma through statistical analysis and model fitting of case data.
3. The method of claim 2, wherein the risk assessment indicator comprises sex, surgical mode, whether chemotherapy, whether radiotherapy, stage of in situ cancer onset position seer, grade, number of tumors, tumor size.
4. The method of claim 2, wherein the risk factors include sex, surgical method, location of carcinoma in situ, seer tumor stage, number of tumors.
5. The method for obtaining prognosis risk factors for osteosarcoma according to claim 2, wherein the five independent risk factors OR obtained by the multi-factor cox analysis: sex and surgical mode; limb protection surgery, carcinoma in situ location, number of tumors.
6. The method of claim 5, wherein the selecting predictors from the risk factors using Lasso regression is performed using gender as the first predictor, surgery as the second predictor, carcinoma in situ as the third predictor, seer stages as the fourth predictor, and tumor number as the fifth predictor.
7. The method for obtaining a prognosis risk factor for osteosarcoma according to claim 6, wherein the sex or male of the first predictor corresponds to a score of 0 or 21, respectively; the second predictive factor is used for amputation, non-surgery and limb protection in a surgery mode, wherein the score of the second predictive factor corresponds to 20 minutes, 77 minutes or 0 minutes respectively; the third factor score of the third predictive factor in-situ cancer position corresponds to 30 or 0 respectively; the fourth predictor seer carries out distant metastasis, no metastasis and near diffusion in a period corresponding to the fourth factor score of 100 or 0 or 39 respectively; the number of tumors of the fifth predictive factor is equal to 1, and the number of tumors is greater than 1 and corresponds to a score of 0 or 27 of the fifth predictive factor respectively.
8. A system for obtaining a prognosis risk factor for osteosarcoma, the prediction system comprising:
the data processing module is used for acquiring clinical data of prognosis of the juvenile osteosarcoma patient and acquiring relevant factors of long-term survival conditions of the juvenile osteosarcoma patient through relevant factor statistical analysis;
The data analysis module divides the clinical data into a training group and a verification group, carries out multi-factor regression analysis on factors related to the long-term survival condition of the juvenile osteosarcoma patients in the training group, and confirms risk factors affecting the prognosis of the juvenile osteosarcoma patients.
9. A computer device, comprising:
At least one memory;
At least one processor;
At least one computer program;
The at least one computer program is stored in the at least one memory, the at least one processor executing the at least one computer program to implement: a method of obtaining a prognostic risk factor for osteosarcoma according to any of claims 1 to 6.
10. A computer-readable medium comprising,
The computer readable medium stores a computer program for causing a computer to execute:
a method of obtaining a prognostic risk factor for osteosarcoma according to any of claims 1 to 6.
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