CN113658696B - Prediction system for predicting prognosis of gastric cancer based on combination of patient age, nutrition index, tumor stage and tumor marker - Google Patents
Prediction system for predicting prognosis of gastric cancer based on combination of patient age, nutrition index, tumor stage and tumor marker Download PDFInfo
- Publication number
- CN113658696B CN113658696B CN202110832843.8A CN202110832843A CN113658696B CN 113658696 B CN113658696 B CN 113658696B CN 202110832843 A CN202110832843 A CN 202110832843A CN 113658696 B CN113658696 B CN 113658696B
- Authority
- CN
- China
- Prior art keywords
- scale
- patient
- value
- gastric cancer
- prediction
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- 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
- G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
- Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
Landscapes
- Health & Medical Sciences (AREA)
- Engineering & Computer Science (AREA)
- Medical Informatics (AREA)
- Public Health (AREA)
- Biomedical Technology (AREA)
- Data Mining & Analysis (AREA)
- Databases & Information Systems (AREA)
- Pathology (AREA)
- Epidemiology (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Investigating Or Analysing Biological Materials (AREA)
Abstract
The invention provides a prediction system for predicting gastric cancer prognosis based on the combination of patient age, nutrition index, tumor stage and tumor marker, which is constructed by taking patient age, tumor TNM stage, AFP, CA153, CA724 and nutrition index as prediction indexes, and belongs to the field of prediction models. The prognosis situation of the gastric cancer patient (especially the patient with the gastric cancer excision operation of the I-IV stage) can be accurately predicted by using the prediction system, and the survival rate of the patient can be accurately predicted. The prediction system for predicting the prognosis of the gastric cancer, provided by the invention, has the advantages of simple construction method, high prediction accuracy and discrimination, great significance for clinically assisting in judging the prognosis condition and survival rate of patients with gastric cancer, and contribution to accurate treatment of individuals of clinical patients.
Description
Technical Field
The invention belongs to the field of prediction models, and particularly relates to a prediction system for predicting gastric cancer prognosis based on the combination of patient age, nutrition index, tumor stage and tumor markers.
Background
Gastric cancer is one of the most common malignant tumors at present, and although the incidence rate of gastric cancer is reduced in some countries in recent years, the absolute value of the number of new cases is also increasing under the condition that the global total incidence rate is reduced due to the increase of the general population and the aging of the population of gastric cancer high incidence areas. Currently, surgery is the primary method of treating gastric cancer. However, the long-term efficacy of gastric cancer patients subjected to surgery is not the same. Accurate prognosis prediction of gastric cancer patients is of great importance in guiding patient treatment and improving prognosis.
TNM stage is one of the most important factors affecting gastric cancer prognosis, and it is currently widely believed that TNM stage can objectively and accurately predict gastric cancer prognosis. The TNM stage provides scientific basis for accurately judging prognosis, can better embody the consistency of the stage and the prognosis, is beneficial to preparing a reasonable treatment scheme, evaluates the treatment effect and embodies the treatment level. However, some TNM stages are the same, patients with basically the same treatment means have still different long-term curative effects, some patients can survive for a long time, and some patients cannot.
In fact, it is far from sufficient to infer the prognosis of a patient with one single index in clinical work, since these indices only provide a stratified risk for the population and do not allow a personalized prediction for each patient-specific situation. It was found that prognosis of cancer patients depends not only on tumor-related factors but also on host-related factors. Many gastric cancer prognosis scoring systems involving host-related factors have been reported. These scoring systems include systems constructed based on neutrophil to lymphocyte ratios (NLR), platelet to lymphocyte ratios (PLR), and lymphocyte to monocyte ratios (LMR) to predict patient survival after radical gastric cancer resection, modified glasge prognosis scoring systems (mGPS) based on inflammation indicators, gene-related gastric cancer prognosis risk scoring systems based on HER2 gene amplification, lncRNA profile, and scoring systems based on physical stamina (Eastern Cooperative Oncology Group performance status, ECOG PS), among others. The nutritional index is also considered to be closely related to prognosis of gastric cancer. Xuechao Liu et al (Systemic prognostic score and nomogram based on inflammatory, nutritional and tumor markers predict cancer-specific survival in stageII-III gastric cancer patients with adjuvant chemotherapy, clinical Nutrition, volume 38,Issue 4,August 2019,Pages 1853-1860) constructed a gastric cancer prognosis scoring system based on C-reactive protein/albumin ratio (CRP/Alb), prognosis Nutrition Index (PNI), preoperative weight loss and CA199 in 2018, which was found by internal validation to be able to predict 1, 3 and 5 years Cancer Specific Survival (CSS) of patients undergoing adjuvant chemotherapy following radical surgery for stage II-III gastric cancer in an ideal model. However, the system is a scoring system established by training data of patients subjected to adjuvant chemotherapy after the radical surgery on the II-III gastric cancer, and cannot be widely applied to the survival rate prediction of the patients with the I-IV gastric cancer (especially I, IV); moreover, the C-index of the system is 0.714 (95% CI: 0.680-0.749), and the prediction accuracy and discrimination have yet to be further improved.
Therefore, there is a need to develop a predictive system for a more accurate prognosis of lung cancer patients for stage I-IV gastric cancer patients.
Disclosure of Invention
The invention aims to provide a prediction system for predicting gastric cancer prognosis based on the combination of patient age, nutrition index, tumor stage and tumor markers.
The invention provides a prediction system for predicting prognosis of gastric cancer patients, which is constructed by taking age of patients, TNM stage of tumor, AFP, CA153, CA724 and nutrition index as prediction indexes.
Further, the prediction system is an alignment chart, the alignment chart comprises straight lines 1-11, and the straight lines 1-11 are sequentially arranged from top to bottom and are parallel to each other; each straight line represents a scale, and scales are carved on the scale;
the 1 st scale represents the scales of scores corresponding to scales on the 2 nd to 7 th scales; the scale value of the 1 st scale is 0-100, 0 is at the leftmost end, 100 is at the rightmost end, and the scale of the scale is equally divided;
the 3 rd scale represents tumor TNM stage of the patient;
the 4 th scale represents the patient's AFP;
the 5 th scale represents patient CA724;
The 7 th scale represents the patient's nutritional index;
the 8 th scale represents the sum of scores corresponding to scales on the 2 nd to 7 th scales;
the 9 th scale represents the patient's 3-year survival prediction value, the patient's 3-year survival prediction value = the value on the 8 th scale for which the score on the 9 th scale corresponds;
the 10 th scale represents the patient 5-year survival prediction value, and the patient 5-year survival prediction value=the value on the 8 th scale corresponding to the score on the 10 th scale;
the 11 th scale represents a patient 8-year survival prediction value, and the patient 8-year survival prediction value=the value on the 8 th scale corresponding to the score on the 11 th scale.
Further, the nutritional index is CONUT;
the scale value of the 2 nd scale is 20-90, 20 is at the leftmost end, 90 is at the rightmost end, and the scale of the scale is equally divided; when the scale of the 2 nd scale is 20, the corresponding score of the 2 nd scale is 0 on the 1 st scale, and when the scale of the 2 nd scale is 90, the corresponding score of the 2 nd scale is 65 on the 1 st scale;
the scale value of the 3 rd scale is 1-4, 1 is at the leftmost end, 4 is at the rightmost end, and the scale of the scale is equally divided; when the scale of the 3 rd scale is 1, the corresponding score of the 3 rd scale is 0, and when the scale of the 3 rd scale is 4, the corresponding score of the 3 rd scale is 100;
The scale value of the 4 th scale is 0-1000,0 at the leftmost end, 1000 at the rightmost end, and the scale of the scale is equally divided; when the scale of the 4 th scale is 0, the corresponding score of the 4 th scale is 0 on the 1 st scale, and when the scale of the 4 th scale is 1000, the corresponding score of the 4 th scale is 86 on the 1 st scale;
the scale value of the 5 th scale is 0-300,0 at the leftmost end, 300 at the rightmost end, and the scale of the scale is equally divided; when the scale of the 5 th scale is 0, the corresponding score of the scale is 0 on the 1 st scale, and when the scale of the 5 th scale is 300, the corresponding score of the scale is 42.5 on the 1 st scale;
the scale value of the 6 th scale is 0-1000,0 at the leftmost end, 1000 at the rightmost end, and the scale of the scale is equally divided; when the scale of the 6 th scale is 0, the corresponding score of the scale is 0 on the 1 st scale, and when the scale of the 6 th scale is 1000, the corresponding score of the scale is 90 on the 1 st scale;
the scale value of the 7 th scale is 1-12, 1 is at the leftmost end, 12 is at the rightmost end, and the scale of the scale is equally divided; when the scale of the 7 th scale is 1, the corresponding score of the 7 th scale is 0 on the 1 st scale, and when the scale of the 7 th scale is 12, the corresponding score of the 7 th scale is 92.5 on the 1 st scale;
The 8 th scale is 0-260,0 at the leftmost end and 260 at the rightmost end, and the scales of the scale are equally divided; when the scale of the 8 th scale is 0, the scale corresponds to the 0 scale of the first scale, and when the scale of the 8 th scale is 260, the scale corresponds to the 100 scale of the first scale.
Further, when the nutrition index is CONUT, the alignment chart is shown in fig. 1.
In fig. 1:
when the value corresponding to the value on the 8 th scale on the 9 th scale is at the left end of 0.95, the 3-year survival rate predicted value at the moment is more than 0.95, and when the value corresponding to the value on the 8 th scale on the 9 th scale is at the right end of 0.3, the 3-year survival rate predicted value at the moment is less than 0.3;
when the value corresponding to the value on the 8 th scale on the 10 th scale is at the left end of 0.95, the 5-year survival rate predicted value at the moment is more than 0.95, and when the value corresponding to the value on the 8 th scale on the 10 th scale is at the right end of 0.1, the 5-year survival rate predicted value at the moment is less than 0.1;
when the value corresponding to the value on the 8 th scale on the 11 th scale is at the left end of 0.95, the 8-year survival rate predicted value at the moment is more than 0.95, and when the value corresponding to the value on the 8 th scale on the 11 th scale is at the right end of 0.05, the 8-year survival rate predicted value at the moment is less than 0.05.
Further, the nutritional indicator is NRI;
the scale value of the 2 nd scale is 20-90, 20 is at the leftmost end, 90 is at the rightmost end, and the scale of the scale is equally divided; when the scale of the 2 nd scale is 20, the corresponding score on the 1 st scale is 0, and when the scale of the 2 nd scale is 90, the corresponding score on the 1 st scale is 33.5;
the scale value of the 3 rd scale is 1-4, 1 is at the leftmost end, 4 is at the rightmost end, and the scale of the scale is equally divided; when the scale of the 3 rd scale is 1, the corresponding score of the 3 rd scale is 0, and when the scale of the 3 rd scale is 4, the corresponding score of the 3 rd scale is 61;
the scale value of the 4 th scale is 0-1000,0 at the leftmost end, 1000 at the rightmost end, and the scale of the scale is equally divided; when the scale of the 4 th scale is 0, the corresponding score of the 4 th scale is 0 on the 1 st scale, and when the scale of the 4 th scale is 1000, the corresponding score of the 4 th scale is 56 on the 1 st scale;
the scale value of the 5 th scale is 0-300,0 at the leftmost end, 300 at the rightmost end, and the scale of the scale is equally divided; when the scale of the 5 th scale is 0, the corresponding score of the 5 th scale is 0 on the 1 st scale, and when the scale of the 5 th scale is 300, the corresponding score of the 5 th scale is 21.25 on the 1 st scale;
The scale value of the 6 th scale is 0-1000,0 at the leftmost end, 1000 at the rightmost end, and the scale of the scale is equally divided; when the scale of the 6 th scale is 0, the corresponding score of the scale is 0 on the 1 st scale, and when the scale of the 6 th scale is 1000, the corresponding score of the scale is 60 on the 1 st scale;
the scale value of the 7 th scale is 120-62, 120 is at the leftmost end, 65 is at the rightmost end, and the scale of the scale is equally divided; when the scale of the 7 th scale is 120, the corresponding score of the scale is 0 on the 1 st scale, and when the scale of the 7 th scale is 65, the corresponding score of the scale is 100 on the 1 st scale;
the scale of the 8 th scale is 0-200, 0 is at the leftmost end, 200 is at the rightmost end, and the scale of the scale is equally divided; when the scale of the 8 th scale is 0, the scale corresponds to the 0 scale of the first scale, and when the scale of the 8 th scale is 200, the scale corresponds to the 100 scale of the first scale.
Further, when the nutrition index is NRI, the alignment chart is as shown in fig. 2.
In fig. 2:
when the value corresponding to the value on the 8 th scale on the 9 th scale is at the left end of 0.95, the 3-year survival rate predicted value at the moment is more than 0.95, and when the value corresponding to the value on the 8 th scale on the 9 th scale is at the right end of 0.3, the 3-year survival rate predicted value at the moment is less than 0.3;
When the value corresponding to the value on the 8 th scale on the 10 th scale is at the left end of 0.95, the 5-year survival rate predicted value at the moment is more than 0.95, and when the value corresponding to the value on the 8 th scale on the 10 th scale is at the right end of 0.1, the 5-year survival rate predicted value at the moment is less than 0.1;
when the value corresponding to the value on the 8 th scale on the 11 th scale is at the left end of 0.95, the 8-year survival rate predicted value at the moment is more than 0.95, and when the value corresponding to the value on the 8 th scale on the 11 th scale is at the right end of 0.05, the 8-year survival rate predicted value at the moment is less than 0.05.
Further, the nutritional index is PNI;
the scale value of the 2 nd scale is 20-90, 20 is at the leftmost end, 90 is at the rightmost end, and the scale of the scale is equally divided; when the scale of the 2 nd scale is 20, the corresponding score on the 1 st scale is 0, and when the scale of the 2 nd scale is 90, the corresponding score on the 1 st scale is 21.25;
the scale value of the 3 rd scale is 1-4, 1 is at the leftmost end, 4 is at the rightmost end, and the scale of the scale is equally divided; when the scale of the 3 rd scale is 1, the corresponding score of the 3 rd scale is 0, and when the scale of the 3 rd scale is 4, the corresponding score of the 3 rd scale is 52.5;
The scale value of the 4 th scale is 0-1000,0 at the leftmost end, 1000 at the rightmost end, and the scale of the scale is equally divided; when the scale of the 4 th scale is 0, the corresponding score of the 4 th scale is 0 on the 1 st scale, and when the scale of the 4 th scale is 1000, the corresponding score of the 4 th scale is 45 on the 1 st scale;
the scale value of the 5 th scale is 0-300,0 at the leftmost end, 300 at the rightmost end, and the scale of the scale is equally divided; when the scale of the 5 th scale is 0, the corresponding score of the scale is 0 on the 1 st scale, and when the scale of the 5 th scale is 300, the corresponding score of the scale is 24.5 on the 1 st scale;
the scale value of the 6 th scale is 0-1000,0 at the leftmost end, 1000 at the rightmost end, and the scale of the scale is equally divided; when the scale of the 6 th scale is 0, the corresponding score of the scale is 0 on the 1 st scale, and when the scale of the 6 th scale is 1000, the corresponding score of the scale is 57.5 on the 1 st scale;
the scale value of the 7 th scale is 70-20, 70 is at the leftmost end, 20 is at the rightmost end, and the scale of the scale is equally divided; when the scale of the 7 th scale is 70, the corresponding score of the scale is 0 on the 1 st scale, and when the scale of the 7 th scale is 20, the corresponding score of the scale is 100 on the 1 st scale;
The scale of the 8 th scale is 0-160,0 at the leftmost end and 160 at the rightmost end, and the scale of the scale is equally divided; when the scale of the 8 th scale is 0, the scale corresponds to the 0 scale of the first scale, and when the scale of the 8 th scale is 160, the scale corresponds to the 100 scale of the first scale.
Further, when the nutrition index is PNI, the alignment chart is as shown in fig. 3.
In fig. 3:
when the value corresponding to the value on the 8 th scale on the 9 th scale is at the left end of 0.95, the 3-year survival rate predicted value at the moment is more than 0.95, and when the value corresponding to the value on the 8 th scale on the 9 th scale is at the right end of 0.3, the 3-year survival rate predicted value at the moment is less than 0.3;
when the value corresponding to the value on the 8 th scale on the 10 th scale is at the left end of 0.95, the 5-year survival rate predicted value at the moment is more than 0.95, and when the value corresponding to the value on the 8 th scale on the 10 th scale is at the right end of 0.1, the 5-year survival rate predicted value at the moment is less than 0.1;
when the value corresponding to the value on the 8 th scale on the 11 th scale is at the left end of 0.95, the 8-year survival rate predicted value at the moment is more than 0.95, and when the value corresponding to the value on the 8 th scale on the 11 th scale is at the right end of 0.1, the 8-year survival rate predicted value at the moment is less than 0.1.
Further, the method for constructing the nomogram comprises the following steps:
(1) Collecting indexes of a patient and inputting the indexes into an input module;
the indicators include age, tumor TNM stage, AFP, CA153, CA724, and nutritional indicators of the patient;
(2) And constructing a cox regression model by using indexes in the input module, completing the alignment visualization of the cox regression model by using an RMS operation package, and completing the alignment verification of the cox regression model by using calibration Curve and a precision Curve operation package.
Further, the age is 20-95 years old, the TNM stage is 1, 2, 3 or 4, the AFP is 0-1000 ng/mL, the CA153 is 0-1000U/mL, and the CA724 is 0-300U/mL.
Further, when the nutrition index is CONUT, CONUT is 1-12; when the nutrition index is NRI, the NRI is 120-62; when the nutrition index is PNI, the PNI is 70-20.
The invention also provides equipment for predicting prognosis of gastric cancer patients, which is characterized in that: the device comprises the prediction system.
The invention also provides application of the prediction system in preparing equipment for predicting prognosis of gastric cancer patients.
Further, the gastric cancer patient is a gastric cancer excision operation patient in the stage I-IV;
And/or, the prognosis of the gastric cancer patient is survival rate of the gastric cancer patient in 3, 5 and 8 years.
The invention constructs an alignment chart for predicting gastric cancer prognosis based on multiple indexes of patient age, nutrition indexes (CONUT, NRI or PNI), tumor TNM stage and tumor markers (AFP, CA153 and CA 724). The nomogram can be used for accurately predicting the prognosis situation of a gastric cancer patient (particularly a patient with a gastric cancer excision operation in the I-IV stage) and accurately predicting the survival rate of the patient.
Compared with the existing gastric cancer prognosis prediction model, the nomogram for predicting gastric cancer prognosis provided by the invention has the following advantages:
1. the system is suitable for patients with I-IV stage gastric cancer excision surgery;
2. the indexes adopted by the application are all convenient for a clinician to obtain and use;
3. compared with the gastric cancer prognosis prediction system based on single index (constructed in comparative examples 1-8), the area under ROC curve and C-index of the gastric cancer prognosis prediction system constructed based on multiple indexes of patient age, nutrition index (CONUT, NRI or PNI), tumor TNM stage and tumor markers (AFP, CA153 and CA 724) constructed in the invention are obviously increased, and the prediction result is more accurate.
4. In addition, the gastric cancer prognosis prediction system constructed in example 3 has the best prediction effect among the gastric cancer prognosis prediction systems constructed in examples 1 to 3 based on multiple indexes. The gastric cancer prognosis prediction system with multiple indexes constructed by using PNI as a nutrition index has better prediction effect on gastric cancer prognosis.
The nomogram construction method for predicting the prognosis of the gastric cancer is simple, has high prediction accuracy and discrimination, has important significance for clinically assisting in judging the prognosis condition and survival rate of patients with gastric cancer, and is beneficial to the accurate treatment of individuals of clinical patients.
It should be apparent that, in light of the foregoing, various modifications, substitutions and alterations can be made herein without departing from the spirit and scope of the invention as defined by the appended claims.
The above-described aspects of the present invention will be described in further detail below with reference to specific embodiments in the form of examples. It should not be understood that the scope of the above subject matter of the present invention is limited to the following examples only. All techniques implemented based on the above description of the invention are within the scope of the invention.
Drawings
FIG. 1 is a nomogram of the prognosis of gastric cancer based on a combination of age, nutritional index CONUT, tumor stage, and tumor markers.
FIG. 2 is a nomogram of the prognosis of gastric cancer based on a combination of age, nutritional index NRI, tumor stage and tumor markers.
FIG. 3 is a nomogram of the prognosis of gastric cancer based on age, nutritional index PNI, tumor stage, and tumor marker multi-index combination prediction.
FIG. 4 is a comparison of the ROC curve for the prognosis of each single-index prediction system constructed in example 1 with that of the control example.
FIG. 5 is a comparison of the ROC curve for the prognosis of each single-index prediction system constructed in example 2.
FIG. 6 is a comparison of the ROC curve for the prognosis of each single-index prediction system constructed in example 3.
Fig. 7 shows the results of verification of the multi-index prediction system constructed in examples 1 to 3 of the present invention for predicting the survival rate of patients for 3 years (A, C, E) and the survival rate for 5 years (B, D, F) by using a decision curve analysis method.
FIG. 8 shows the results of verification of the survival rates of patients 3, 5 and 8 years predicted by the multi-index prediction systems constructed in examples 1 to 3 of the present invention using a calibration curve.
Detailed Description
The raw materials and equipment used in the invention are all known products and are obtained by purchasing commercial products.
The patient data adopted in the embodiment of the invention are all from 937 patients with gastric cancer excision operation in stage I-IV (the treatment hospital is Huaxi hospital of Sichuan university, the treatment time is 3 months in 2006 to 7 months in 2020, the affected disease is primary gastric cancer, and the treatment scheme is surgical excision). After the treatment of all patient systems is finished, outpatient follow-up, inpatient follow-up and telephone follow-up are adopted.
The 3 nutritional indexes of pre-operative basic conditions (including gender, age, ethnicity, smoking or drinking) of 937 patients, gastric cancer related tumor markers (including AFP, unit ng/mL, CA153, unit U/mL, CA724, unit U/mL), TNM staging conditions and CONUT (Controlling Nutritional Status, control nutritional status), NRI (nutritional risk index ) and PNI (prognostic nutritional index, prognosis nutritional index) are collected for single-factor and multi-factor cox regression analysis, C-index calculation and screening, and indexes which are related to gastric cancer prognosis and are convenient for clinicians are screened out:
wherein TNM stage conditions are comprehensively analyzed and judged by pathology examination and clinicians; CONUT is calculated from albumin, total cholesterol, lymphocyte counts; NRI is calculated from albumin and body weight (preoperative body weight and ideal body weight); PNI was calculated by albumin and lymphocyte counts.
937 patients were randomized into two groups: one 649 cases are taken as model training groups; another group of 288 cases was used as the model validation group.
Example 1: the invention relates to a construction method of a nomogram for predicting gastric cancer prognosis based on age, nutrition index CONUT, tumor stage and tumor marker multi-index combination
1. Input module
Age (Age), tumor TNM stage (stage), tumor markers AFP, CA153 and CA724, and nutrition index CONUT of 649 patients in the model training group were collected as indexes related to prognosis of gastric cancer of patients. These indices are entered into an input module.
2. Establishment of gastric cancer prognosis prediction model
Constructing a cox regression model by using indexes in an input module, completing alignment visualization of the cox regression model by using an RMS operation package, and completing alignment verification of the cox regression model by using calibration Curve and a precision Curve operation package to obtain an alignment 1 shown in FIG. 1.
The alignment chart contains 11 scales:
the 1 st scale represents the scales of scores corresponding to scales on the 2 nd to 7 th scales; the scale value of the 1 st scale is 0-100, 0 is at the leftmost end, 100 is at the rightmost end, and the scale of the scale is equally divided;
the 3 rd scale represents tumor TNM stage of the patient;
the 4 th scale represents the patient's AFP;
the 5 th scale represents patient CA724;
the 7 th scale represents the patient's nutritional index cout;
the 8 th scale represents Total risk scores (Total Points), total risk score = risk score corresponding to age + risk score corresponding to tumor TNM stage + risk score corresponding to AFP + risk score corresponding to CA153 + risk score corresponding to CA724 + risk score corresponding to CONUT, i.e. the sum of scores corresponding to scales on the 2 th to 7 th scales;
The 9 th scale represents a patient 3-year survival predictor, patient 3-year survival predictor = a value on the 8 th scale corresponding to the score on the 9 th scale;
the 10 th scale represents the patient 5-year survival prediction value, and the patient 5-year survival prediction value=the value on the 8 th scale corresponding to the score on the 10 th scale;
the 11 th scale represents a patient 8-year survival prediction value, and the patient 8-year survival prediction value=the value on the 8 th scale corresponding to the score on the 11 th scale;
the scale value of the 2 nd scale is 20-90, 20 is at the leftmost end, 90 is at the rightmost end, and the scale of the scale is equally divided; when the scale of the 2 nd scale is 20, the corresponding score of the 2 nd scale is 0 on the 1 st scale, and when the scale of the 2 nd scale is 90, the corresponding score of the 2 nd scale is 65 on the 1 st scale;
the scale value of the 3 rd scale is 1-4, 1 is at the leftmost end, 4 is at the rightmost end, and the scale of the scale is equally divided; when the scale of the 3 rd scale is 1, the corresponding score of the 3 rd scale is 0, and when the scale of the 3 rd scale is 4, the corresponding score of the 3 rd scale is 100;
the scale value of the 4 th scale is 0-1000,0 at the leftmost end, 1000 at the rightmost end, and the scale of the scale is equally divided; when the scale of the 4 th scale is 0, the corresponding score of the 4 th scale is 0 on the 1 st scale, and when the scale of the 4 th scale is 1000, the corresponding score of the 4 th scale is 86 on the 1 st scale;
The scale value of the 5 th scale is 0-300,0 at the leftmost end, 300 at the rightmost end, and the scale of the scale is equally divided; when the scale of the 5 th scale is 0, the corresponding score of the scale is 0 on the 1 st scale, and when the scale of the 5 th scale is 300, the corresponding score of the scale is 42.5 on the 1 st scale;
the scale value of the 6 th scale is 0-1000,0 at the leftmost end, 1000 at the rightmost end, and the scale of the scale is equally divided; when the scale of the 6 th scale is 0, the corresponding score of the scale is 0 on the 1 st scale, and when the scale of the 6 th scale is 1000, the corresponding score of the scale is 90 on the 1 st scale;
the scale value of the 7 th scale is 1-12, 1 is at the leftmost end, 12 is at the rightmost end, and the scale of the scale is equally divided; when the scale of the 7 th scale is 1, the corresponding score of the 7 th scale is 0 on the 1 st scale, and when the scale of the 7 th scale is 12, the corresponding score of the 7 th scale is 92.5 on the 1 st scale;
the 8 th scale is 0-260,0 at the leftmost end and 260 at the rightmost end, and the scales of the scale are equally divided; when the scale of the 8 th scale is 0, the scale corresponds to the 0 scale of the first scale, and when the scale of the 8 th scale is 260, the scale corresponds to the 100 scale of the first scale.
In fig. 1:
when the value corresponding to the value on the 8 th scale on the 9 th scale is at the left end of 0.95, the 3-year survival rate predicted value at the moment is more than 0.95, and when the value corresponding to the value on the 8 th scale on the 9 th scale is at the right end of 0.3, the 3-year survival rate predicted value at the moment is less than 0.3;
When the value corresponding to the value on the 8 th scale on the 10 th scale is at the left end of 0.95, the 5-year survival rate predicted value at the moment is more than 0.95, and when the value corresponding to the value on the 8 th scale on the 10 th scale is at the right end of 0.1, the 5-year survival rate predicted value at the moment is less than 0.1;
when the value corresponding to the value on the 8 th scale on the 11 th scale is at the left end of 0.95, the 8-year survival rate predicted value at the moment is more than 0.95, and when the value corresponding to the value on the 8 th scale on the 11 th scale is at the right end of 0.05, the 8-year survival rate predicted value at the moment is less than 0.05.
3. Predicting patient survival rate using gastric cancer prognosis prediction model
The age, tumor TNM stage, tumor markers AFP, CA153 and CA724, and nutrition index CONUT of 288 patients in the collection model verification group are obtained according to the alignment chart 1, and the predicted 3-year survival rate, 5-year survival rate and 8-year survival rate of each patient are obtained.
Example 2: the invention relates to a construction method of a nomogram for predicting gastric cancer prognosis based on age, nutrition index NRI, tumor stage and tumor marker multi-index combination
1. Input module
The age, tumor TNM stage, tumor markers AFP, CA153 and CA724, and nutritional index NRI of 649 patients in the model training group were collected as indices related to prognosis of gastric cancer in patients. These indices are entered into an input module.
2. Establishment of gastric cancer prognosis prediction model
Constructing a cox regression model by using indexes in an input module, completing alignment visualization of the cox regression model by using an RMS operation package, and completing alignment verification of the cox regression model by using calibration Curve and a precision Curve operation package to obtain an alignment 2 shown in FIG. 2.
The alignment chart contains 11 scales:
the 1 st scale represents the scales of scores corresponding to scales on the 2 nd to 7 th scales; the scale value of the 1 st scale is 0-100, 0 is at the leftmost end, 100 is at the rightmost end, and the scale of the scale is equally divided;
the 3 rd scale represents tumor TNM stage of the patient;
the 4 th scale represents the patient's AFP;
the 5 th scale represents patient CA724;
the 7 th scale represents the patient's nutritional index NRI;
the 8 th represents Total risk score (Total Points), total risk score = risk score corresponding to age + risk score corresponding to tumor TNM stage + risk score corresponding to AFP + risk score corresponding to CA153 + risk score corresponding to CA724 + risk score corresponding to NRI, i.e. the sum of scores corresponding to scales on the 2 nd to 7 th scale;
the 9 th scale represents a patient 3-year survival predictor, patient 3-year survival predictor = a value on the 8 th scale corresponding to the score on the 9 th scale;
The 10 th scale represents the patient 5-year survival prediction value, and the patient 5-year survival prediction value=the value on the 8 th scale corresponding to the score on the 10 th scale;
the 11 th scale represents a patient 8-year survival prediction value, and the patient 8-year survival prediction value=the value on the 8 th scale corresponding to the score on the 11 th scale.
The scale value of the 2 nd scale is 20-90, 20 is at the leftmost end, 90 is at the rightmost end, and the scale of the scale is equally divided; when the scale of the 2 nd scale is 20, the corresponding score on the 1 st scale is 0, and when the scale of the 2 nd scale is 90, the corresponding score on the 1 st scale is 33.5;
the scale value of the 3 rd scale is 1-4, 1 is at the leftmost end, 4 is at the rightmost end, and the scale of the scale is equally divided; when the scale of the 3 rd scale is 1, the corresponding score of the 3 rd scale is 0, and when the scale of the 3 rd scale is 4, the corresponding score of the 3 rd scale is 61;
the scale value of the 4 th scale is 0-1000,0 at the leftmost end, 1000 at the rightmost end, and the scale of the scale is equally divided; when the scale of the 4 th scale is 0, the corresponding score of the 4 th scale is 0 on the 1 st scale, and when the scale of the 4 th scale is 1000, the corresponding score of the 4 th scale is 56 on the 1 st scale;
The scale value of the 5 th scale is 0-300,0 at the leftmost end, 300 at the rightmost end, and the scale of the scale is equally divided; when the scale of the 5 th scale is 0, the corresponding score of the 5 th scale is 0 on the 1 st scale, and when the scale of the 5 th scale is 300, the corresponding score of the 5 th scale is 21.25 on the 1 st scale;
the scale value of the 6 th scale is 0-1000,0 at the leftmost end, 1000 at the rightmost end, and the scale of the scale is equally divided; when the scale of the 6 th scale is 0, the corresponding score of the scale is 0 on the 1 st scale, and when the scale of the 6 th scale is 1000, the corresponding score of the scale is 60 on the 1 st scale;
the scale value of the 7 th scale is 120-62, 120 is at the leftmost end, 65 is at the rightmost end, and the scale of the scale is equally divided; when the scale of the 7 th scale is 120, the corresponding score of the scale is 0 on the 1 st scale, and when the scale of the 7 th scale is 65, the corresponding score of the scale is 100 on the 1 st scale;
the scale of the 8 th scale is 0-200, 0 is at the leftmost end, 200 is at the rightmost end, and the scale of the scale is equally divided; when the scale of the 8 th scale is 0, the scale corresponds to the 0 scale of the first scale, and when the scale of the 8 th scale is 200, the scale corresponds to the 100 scale of the first scale.
In fig. 2:
When the value corresponding to the value on the 8 th scale on the 9 th scale is at the left end of 0.95, the 3-year survival rate predicted value at the moment is more than 0.95, and when the value corresponding to the value on the 8 th scale on the 9 th scale is at the right end of 0.3, the 3-year survival rate predicted value at the moment is less than 0.3;
when the value corresponding to the value on the 8 th scale on the 10 th scale is at the left end of 0.95, the 5-year survival rate predicted value at the moment is more than 0.95, and when the value corresponding to the value on the 8 th scale on the 10 th scale is at the right end of 0.1, the 5-year survival rate predicted value at the moment is less than 0.1;
when the value corresponding to the value on the 8 th scale on the 11 th scale is at the left end of 0.95, the 8-year survival rate predicted value at the moment is more than 0.95, and when the value corresponding to the value on the 8 th scale on the 11 th scale is at the right end of 0.05, the 8-year survival rate predicted value at the moment is less than 0.05.
3. Predicting patient survival rate using gastric cancer prognosis prediction model
The age, tumor TNM stage, tumor markers AFP, CA153 and CA724, and nutrition index NRI of 288 patients in the collection model verification group are obtained according to the alignment chart 2, and the predicted 3-year survival rate, 5-year survival rate and 8-year survival rate of each patient are obtained.
Example 3: the invention relates to a construction method of a nomogram for predicting gastric cancer prognosis based on age, nutrition index PNI, tumor stage and tumor marker multi-index combination
1. Input module
The age, tumor TNM stage, tumor markers AFP, CA153 and CA724, and nutritional index PNI of 649 patients in the model training group were collected as indices related to prognosis of gastric cancer in patients. These indices are entered into an input module.
2. Establishment of gastric cancer prognosis prediction model
Constructing a cox regression model by using indexes in an input module, completing alignment visualization of the cox regression model by using an RMS operation package, and completing alignment verification of the cox regression model by using calibration Curve and a precision Curve operation package to obtain an alignment 3 shown in FIG. 3.
The alignment chart contains 11 scales:
the 1 st scale represents the scales of scores corresponding to scales on the 2 nd to 7 th scales; the scale value of the 1 st scale is 0-100, 0 is at the leftmost end, 100 is at the rightmost end, and the scale of the scale is equally divided;
the 3 rd scale represents tumor TNM stage of the patient;
the 4 th scale represents the patient's AFP;
the 5 th scale represents patient CA724;
the 7 th scale represents the patient's nutritional index PNI;
the 8 th scale represents Total risk scores (Total Points), total risk score = risk score corresponding to age + risk score corresponding to tumor TNM stage + risk score corresponding to AFP + risk score corresponding to CA153 + risk score corresponding to CA724 + risk score corresponding to PNI, i.e. the sum of scores corresponding to scales on the 2 th to 7 th scales;
The 9 th scale represents a patient 3-year survival predictor, patient 3-year survival predictor = a value on the 8 th scale corresponding to the score on the 9 th scale;
the 10 th scale represents the patient 5-year survival prediction value, and the patient 5-year survival prediction value=the value on the 8 th scale corresponding to the score on the 10 th scale;
the 11 th scale represents a patient 8-year survival prediction value, and the patient 8-year survival prediction value=the value on the 8 th scale corresponding to the score on the 11 th scale.
The scale value of the 2 nd scale is 20-90, 20 is at the leftmost end, 90 is at the rightmost end, and the scale of the scale is equally divided; when the scale of the 2 nd scale is 20, the corresponding score on the 1 st scale is 0, and when the scale of the 2 nd scale is 90, the corresponding score on the 1 st scale is 21.25;
the scale value of the 3 rd scale is 1-4, 1 is at the leftmost end, 4 is at the rightmost end, and the scale of the scale is equally divided; when the scale of the 3 rd scale is 1, the corresponding score of the 3 rd scale is 0, and when the scale of the 3 rd scale is 4, the corresponding score of the 3 rd scale is 52.5;
the scale value of the 4 th scale is 0-1000,0 at the leftmost end, 1000 at the rightmost end, and the scale of the scale is equally divided; when the scale of the 4 th scale is 0, the corresponding score of the 4 th scale is 0 on the 1 st scale, and when the scale of the 4 th scale is 1000, the corresponding score of the 4 th scale is 45 on the 1 st scale;
The scale value of the 5 th scale is 0-300,0 at the leftmost end, 300 at the rightmost end, and the scale of the scale is equally divided; when the scale of the 5 th scale is 0, the corresponding score of the scale is 0 on the 1 st scale, and when the scale of the 5 th scale is 300, the corresponding score of the scale is 24.5 on the 1 st scale;
the scale value of the 6 th scale is 0-1000,0 at the leftmost end, 1000 at the rightmost end, and the scale of the scale is equally divided; when the scale of the 6 th scale is 0, the corresponding score of the scale is 0 on the 1 st scale, and when the scale of the 6 th scale is 1000, the corresponding score of the scale is 57.5 on the 1 st scale;
the scale value of the 7 th scale is 70-20, 70 is at the leftmost end, 20 is at the rightmost end, and the scale of the scale is equally divided; when the scale of the 7 th scale is 70, the corresponding score of the scale is 0 on the 1 st scale, and when the scale of the 7 th scale is 20, the corresponding score of the scale is 100 on the 1 st scale;
the scale of the 8 th scale is 0-160,0 at the leftmost end and 160 at the rightmost end, and the scale of the scale is equally divided; when the scale of the 8 th scale is 0, the scale corresponds to the 0 scale of the first scale, and when the scale of the 8 th scale is 160, the scale corresponds to the 100 scale of the first scale.
In fig. 3:
when the value corresponding to the value on the 8 th scale on the 9 th scale is at the left end of 0.95, the 3-year survival rate predicted value at the moment is more than 0.95, and when the value corresponding to the value on the 8 th scale on the 9 th scale is at the right end of 0.3, the 3-year survival rate predicted value at the moment is less than 0.3;
When the value corresponding to the value on the 8 th scale on the 10 th scale is at the left end of 0.95, the 5-year survival rate predicted value at the moment is more than 0.95, and when the value corresponding to the value on the 8 th scale on the 10 th scale is at the right end of 0.1, the 5-year survival rate predicted value at the moment is less than 0.1;
when the value corresponding to the value on the 8 th scale on the 11 th scale is at the left end of 0.95, the 8-year survival rate predicted value at the moment is more than 0.95, and when the value corresponding to the value on the 8 th scale on the 11 th scale is at the right end of 0.1, the 8-year survival rate predicted value at the moment is less than 0.1.
3. Predicting patient survival rate using gastric cancer prognosis prediction model
The age, tumor TNM stage, tumor markers AFP, CA153 and CA724, and nutrition index PNI of 288 patients in the collection model verification group, and the 3-year survival rate, 5-year survival rate and 8-year survival rate predicted by each patient are obtained according to the alignment chart 3.
The following is a method for establishing a gastric cancer prognosis control prediction system.
Comparative example 1: establishment of single-index gastric cancer prognosis prediction system based on Age (Age)
An Age (Age) -based single index gastric cancer prognosis prediction system was established with reference to the method of example 1, except that the collected index related to patient gastric cancer prognosis was replaced with Age 1 from 6 of Age, tumor TNM stage, tumor markers AFP, CA153 and CA724, and nutritional index NRI.
Comparative example 2: establishment of single-index gastric cancer prognosis prediction system based on tumor TNM stage
The method of reference example 1 was used to establish a single index gastric cancer prognosis prediction system based on tumor TNM stage, with the only difference that the index collected in relation to patient gastric cancer prognosis was replaced by 1 item of tumor TNM stage from 6 items of age, tumor TNM stage, tumor markers AFP, CA153 and CA724, and nutritional index NRI.
Comparative example 3: establishment of single-index gastric cancer prognosis prediction system based on tumor marker AFP
The method of example 1 was used to construct a single index gastric cancer prognosis prediction system based on tumor marker AFP, with the only difference that the collected index related to patient gastric cancer prognosis was replaced with 1 item of tumor marker AFP from 6 items of age, tumor TNM stage, tumor markers AFP, CA153 and CA724, and nutritional index NRI.
Comparative example 4: establishment of single-index gastric cancer prognosis prediction system based on tumor marker CA153
The method of reference example 1 was used to establish a single index gastric cancer prognosis prediction system based on the tumor marker CA153, with the only difference that the collected index related to the prognosis of gastric cancer in patients was replaced by 1 item of tumor marker CA153 from 6 items of age, tumor TNM stage, tumor markers AFP, CA153 and CA724, and nutritional index NRI.
Comparative example 5: establishment of single-index gastric cancer prognosis prediction system based on tumor marker CA724
The method of reference example 1 was used to establish a single index gastric cancer prognosis prediction system based on tumor marker CA724, with the only difference that the collected index related to patient gastric cancer prognosis was replaced by 1 item of tumor marker CA724 from 6 items of age, tumor TNM stage, tumor markers AFP, CA153 and CA724, and nutritional index NRI.
Comparative example 6: establishment of single-index gastric cancer prognosis prediction system based on nutrition index CONUT
The method of example 1 was used to construct a single-index gastric cancer prognosis prediction system based on the nutritional index CONUT, with the only difference that the collected index related to the prognosis of gastric cancer in patients was replaced with the nutritional index CONUT 1 from 6 of age, tumor TNM stage, tumor markers AFP, CA153 and CA724, and nutritional index NRI.
Comparative example 7: establishment of single-index gastric cancer prognosis prediction system based on nutrition index NRI
The method of example 1 was used to construct a single-index gastric cancer prognosis prediction system based on the nutritional index NRI, with the only difference that the collected index related to the prognosis of gastric cancer in patients was replaced with the nutritional index NRI 1 from 6 of age, tumor TNM stage, tumor markers AFP, CA153 and CA724, and nutritional index NRI.
Comparative example 8: establishment of single-index gastric cancer prognosis prediction system based on nutrition index PNI
The method of example 1 was used to construct a single index gastric cancer prognosis prediction system based on the nutritional index PNI, with the only difference that the collected index related to patient gastric cancer prognosis was replaced with the nutritional index PNI 1 from 6 of age, tumor TNM stage, tumor markers AFP, CA153 and CA724, and nutritional index NRI.
The following experiments prove the beneficial effects of the prediction system.
Experimental example 1: efficacy verification of nomogram for predicting prognosis of gastric cancer
1. Verification method
The predicted survival rates of patients 3, 5, and 8 years for the systems described in examples 1-3 were compared to the actual survival rates of patients. The prediction models constructed in comparative examples 1 to 8 were used as a comparison.
Internal verification is performed by training sets, including decision curve analysis (Decision Curve Analysis, DCA), calibration curve (Calibration curve), and analysis of indicators such as AUC, C-index, etc. The validation group performs external validation of the calibration curve.
The alignment chart representation is evaluated based on the ability to distinguish between patients with and without events and calibration (accuracy of alignment chart prediction probability). Area under test (AUC) and C-index were quantified. The 95% confidence interval for each AUC and C-index was calculated. Generally, the AUC is considered to have lower accuracy when 0.5-0.7, a certain accuracy when 0.7-0.9, higher accuracy when more than 0.9, and auc=0.5, which indicates that the diagnosis method is completely ineffective and has no diagnostic value; in general, C-index greater than 0.75 is considered a better differentiation. The alignment ability of the alignment chart is evaluated by plotting the observed prediction probability and the actual occurrence probability using the cox regression model. The curve along the 45 line illustrates that the predicted probability is the same as the observed probability, indicating perfect calibration. Hosmer-Lemeshow (H-L) P > 0.05 was considered well calibrated, meaning that there was no significant difference between actual and predicted prognosis. To fairly evaluate the predicted performance of the nomograms of new patients in the future, 1000 bootstrap evaluations were used to obtain model performance.
The statistical significance was set to p-value < 0.05 in the two-tailed test. Statistical normal distribution data is represented by mean ± standard deviation, and non-normal distribution data is represented by median (quartile spacing). The counting data adopts pearson chi-square test or continuous correction chi-square test or fisher accurate test.
2. Verification result
Table 1 comparison of prediction Capacity of gastric cancer prognosis prediction systems
Table 1 shows the areas under ROC curves of gastric cancer prognosis prediction systems constructed in examples 1 to 3 and comparative examples 1 to 8 of the present invention and their corresponding 95% confidence intervals, C-index and their corresponding 95% confidence intervals, sensitivity, positive predictive values. FIGS. 4-6 show a comparison of the prognostic ROC curves for each predictive system. Compared with the gastric cancer prognosis prediction systems based on single index constructed in comparative examples 1-8, the gastric cancer prognosis prediction systems based on age, nutrition index, tumor stage and tumor marker multi-index constructed in examples 1-3 of the invention have the advantages that the area under ROC curve and C-index are obviously increased, the prediction result is more accurate, and the discrimination is higher.
FIG. 7 is a calibration curve verification result, and it can be seen that the 3 and 5 year survival rate curve (red) is close to the ideal curve (blue); fig. 8 shows the results of the decision curve analysis, and it can be seen that the 3, 5, and 8 year survival curves are far from the two extremum curves (blue and green diagonal lines). The results show that the gastric cancer prognosis prediction system based on multiple indexes constructed in the invention examples 1-3 has high accuracy rate of prediction results of survival rates of patients in 3, 5 and 8 years.
In addition, comparing the c-index values, it can be seen that the gastric cancer prognosis prediction system constructed in example 3 has the best prediction effect in the gastric cancer prognosis prediction system constructed based on multiple indexes. The gastric cancer prognosis prediction system with multiple indexes constructed by using PNI as a nutrition index has the best prediction effect on gastric cancer prognosis.
In summary, the invention provides a prediction system for predicting gastric cancer prognosis based on the combination of patient age, nutrition index, tumor stage and tumor markers and a construction method thereof. The prognosis situation of the gastric cancer patient (especially the patient with the gastric cancer excision operation of the I-IV stage) can be accurately predicted by using the prediction system, and the survival rate of the patient can be accurately predicted. The prediction system for predicting the prognosis of the gastric cancer, provided by the invention, has the advantages of simple construction method, high prediction accuracy and discrimination, great significance for clinically assisting in judging the prognosis condition and survival rate of patients with gastric cancer, and contribution to accurate treatment of individuals of clinical patients.
Claims (6)
1. A prediction system for predicting prognosis of a gastric cancer patient, characterized in that: the prediction system is constructed by taking the age of a patient, the TNM stage of a tumor, AFP, CA153, CA724 and a nutrition index as prediction indexes;
The prediction system is an alignment chart, wherein the alignment chart comprises straight lines 1-11, and the straight lines 1-11 are sequentially arranged from top to bottom and are parallel to each other; each straight line represents a scale, and scales are carved on the scale;
the 1 st scale represents the scale of the score corresponding to the scales on the 2 nd to 7 th scales; the scale value of the 1 st scale is 0-100,0 is at the leftmost end, 100 is at the rightmost end, and the scale of the scale is equally divided;
scale 2 indicates age of patient;
the 3 rd scale represents tumor TNM stage of the patient;
the 4 th scale represents the patient's AFP;
the 5 th scale represents patient CA724;
scale 6 represents CA153 for the patient;
the 7 th scale represents the patient's nutritional index;
the 8 th scale represents the sum of scores corresponding to scales on the 2 nd to 7 th scales;
the 9 th scale represents a patient 3-year survival predictor, patient 3-year survival predictor = a value on the 8 th scale corresponding to the score on the 9 th scale;
the 10 th scale represents the patient 5-year survival prediction value, and the patient 5-year survival prediction value=the value on the 8 th scale corresponding to the score on the 10 th scale;
the 11 th scale represents a patient 8-year survival prediction value, and the patient 8-year survival prediction value=the value on the 8 th scale corresponding to the score on the 11 th scale;
The nutrition index is NRI;
the scale value of the 2 nd scale is 20-90, 20 is at the leftmost end, 90 is at the rightmost end, and the scale of the scale is equally divided; when the scale of the 2 nd scale is 20, the corresponding score on the 1 st scale is 0, and when the scale of the 2 nd scale is 90, the corresponding score on the 1 st scale is 33.5;
the scale value of the 3 rd scale is 1-4,1 is at the leftmost end, 4 is at the rightmost end, and the scale of the scale is equally divided; when the scale of the 3 rd scale is 1, the corresponding score of the 3 rd scale is 0, and when the scale of the 3 rd scale is 4, the corresponding score of the 3 rd scale is 61;
the scale value of the 4 th scale is 0-1000,0 at the leftmost end, 1000 at the rightmost end, and the scale of the scale is equally divided; when the scale of the 4 th scale is 0, the corresponding score of the 4 th scale is 0 on the 1 st scale, and when the scale of the 4 th scale is 1000, the corresponding score of the 4 th scale is 56 on the 1 st scale; the scale value of the 5 th scale is 0-300,0 at the leftmost end, 300 at the rightmost end, and the scale of the scale is equally divided; when the scale of the 5 th scale is 0, the corresponding score of the 5 th scale is 0 on the 1 st scale, and when the scale of the 5 th scale is 300, the corresponding score of the 5 th scale is 21.25 on the 1 st scale;
The scale value of the 6 th scale is 0-1000,0 at the leftmost end and 1000 at the rightmost end, and the scale of the scale is equally divided; when the scale of the 6 th scale is 0, the corresponding score of the scale is 0 on the 1 st scale, and when the scale of the 6 th scale is 1000, the corresponding score of the scale is 60 on the 1 st scale;
the scale value of the 7 th scale is 120-62, 120 is at the leftmost end, 65 is at the rightmost end, and the scale of the scale is equally divided; when the scale of the 7 th scale is 120, the corresponding score of the scale is 0 on the 1 st scale, and when the scale of the 7 th scale is 65, the corresponding score of the scale is 100 on the 1 st scale;
the scale of the 8 th scale is 0-200,0 is at the leftmost end, 200 is at the rightmost end, and the scale of the scale is equally divided; when the scale of the 8 th scale is 0, the scale corresponds to the 0 scale of the first scale, and when the scale of the 8 th scale is 200, the scale corresponds to the 100 scale of the first scale.
2. The prediction system of claim 1, wherein: the construction method of the prediction system comprises the following steps:
(1) Collecting prediction indexes and recording the prediction indexes into an input module;
(2) And constructing a cox regression model by using indexes in the input module, completing the alignment visualization of the cox regression model by using an RMS operation package, and completing the alignment verification of the cox regression model by using calibration Curve and a precision Curve operation package.
3. The prediction system of claim 2, wherein: the age is 20-95 years old, TNM stage is 1, 2, 3 or 4, AFP is 0-1000ng/mL, CA153 is 0-1000U/mL, CA724 is 0-300U/mL, and NRI is 120-62.
4. An apparatus for predicting prognosis of a gastric cancer patient, characterized in that: the apparatus comprising the prediction system of any of claims 1-3.
5. Use of the prediction system of any one of claims 1-3 in the manufacture of a device for predicting prognosis of a gastric cancer patient.
6. Use according to claim 5, characterized in that: the gastric cancer patient is a patient with a gastric cancer excision operation in the I-IV stage;
and/or, the prognosis of the gastric cancer patient is survival rate of the gastric cancer patient of 3, 5, 8 years or survival rate of the gastric cancer patient of 3, 5, 8 years.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110832843.8A CN113658696B (en) | 2021-07-22 | 2021-07-22 | Prediction system for predicting prognosis of gastric cancer based on combination of patient age, nutrition index, tumor stage and tumor marker |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110832843.8A CN113658696B (en) | 2021-07-22 | 2021-07-22 | Prediction system for predicting prognosis of gastric cancer based on combination of patient age, nutrition index, tumor stage and tumor marker |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113658696A CN113658696A (en) | 2021-11-16 |
| CN113658696B true CN113658696B (en) | 2023-07-14 |
Family
ID=78477713
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110832843.8A Active CN113658696B (en) | 2021-07-22 | 2021-07-22 | Prediction system for predicting prognosis of gastric cancer based on combination of patient age, nutrition index, tumor stage and tumor marker |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113658696B (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114141360A (en) * | 2021-12-02 | 2022-03-04 | 山东大学 | Breast cancer prediction method based on punished COX regression |
| CN115295126B (en) * | 2022-09-29 | 2023-03-24 | 浙江省肿瘤医院 | Model for predicting mismatch repair gene defect of gastric cancer |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101965514A (en) * | 2008-01-03 | 2011-02-02 | 艾博特生物技术有限公司 | Predicting long-term efficacy of a compound in the treatment of psoriasis |
| CN112185569A (en) * | 2020-09-11 | 2021-01-05 | 中山大学孙逸仙纪念医院 | Breast cancer patient disease-free survival period prediction model and construction method thereof |
Family Cites Families (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR101565080B1 (en) * | 2007-01-16 | 2015-11-03 | 무스크 파운데이션 포 리서치 디벨롭먼트 | Compositions and methods for diagnosing treating and preventing prostate conditions |
| CN106055898A (en) * | 2016-05-27 | 2016-10-26 | 石河子大学 | Prognosis method for patients with gastric carcinoma |
| CN107563134A (en) * | 2017-08-30 | 2018-01-09 | 中山大学 | A kind of system for being used to precisely predict patients with gastric cancer prognosis |
| CN108676881A (en) * | 2018-05-24 | 2018-10-19 | 江苏大学附属医院 | Purposes of the reagent of specific recognition CHAF1A in preparing gastric cancer prognosis evaluation reagent kit |
| CN110305964B (en) * | 2019-07-26 | 2023-03-21 | 安徽医科大学第一附属医院 | Tool for prognostic recurrence risk prediction marker of prostate cancer patient and establishment of risk assessment model of tool |
| CN110931134A (en) * | 2019-12-03 | 2020-03-27 | 南通大学 | Method for predicting hospitalization pressure injury healing based on big data mining model |
| CN112259231A (en) * | 2020-10-20 | 2021-01-22 | 华中科技大学同济医学院附属协和医院 | High-risk gastrointestinal stromal tumor patient postoperative recurrence risk assessment method and system |
-
2021
- 2021-07-22 CN CN202110832843.8A patent/CN113658696B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101965514A (en) * | 2008-01-03 | 2011-02-02 | 艾博特生物技术有限公司 | Predicting long-term efficacy of a compound in the treatment of psoriasis |
| CN112185569A (en) * | 2020-09-11 | 2021-01-05 | 中山大学孙逸仙纪念医院 | Breast cancer patient disease-free survival period prediction model and construction method thereof |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113658696A (en) | 2021-11-16 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Hyder et al. | A nomogram to predict long-term survival after resection for intrahepatic cholangiocarcinoma: an Eastern and Western experience | |
| Zhang et al. | Loss of heterozygosity (LOH) profiles—validated risk predictors for progression to oral cancer | |
| Koh et al. | Utility of pre-treatment neutrophil–lymphocyte ratio and platelet–lymphocyte ratio as prognostic factors in breast cancer | |
| Li et al. | A large cohort study identifying a novel prognosis prediction model for lung adenocarcinoma through machine learning strategies | |
| CN113658696B (en) | Prediction system for predicting prognosis of gastric cancer based on combination of patient age, nutrition index, tumor stage and tumor marker | |
| Liao et al. | Machine‐learning based radiogenomics analysis of MRI features and metagenes in glioblastoma multiforme patients with different survival time | |
| Zhao et al. | Twenty-gene-based prognostic model predicts lung adenocarcinoma survival | |
| CN103205495A (en) | Gene for colorectal cancer prognosis and application thereof | |
| CN114891887A (en) | Method for screening triple negative breast cancer prognosis gene marker | |
| Wang et al. | A three-lncRNA expression signature predicts survival in head and neck squamous cell carcinoma (HNSCC) | |
| Gomez-Rueda et al. | A robust biomarker of differential correlations improves the diagnosis of cytologically indeterminate thyroid cancers | |
| CN112626218A (en) | Gene expression classifier and in-vitro diagnosis kit for predicting pancreatic cancer metastasis risk | |
| Zhou et al. | Constructing a new prognostic signature of gastric cancer based on multiple data sets | |
| Shi et al. | Application of an autophagy-related gene prognostic risk model based on TCGA database in cervical cancer | |
| Ouyang et al. | Construction of a five-super-enhancer-associated-genes prognostic model for osteosarcoma patients | |
| CN114203256B (en) | MIBC typing and prognosis prediction model construction method based on microbial abundance | |
| Zhao et al. | Identification of peripheral blood immune infiltration signatures and construction of monocyte-associated signatures in ovarian cancer and Alzheimer's disease using single-cell sequencing | |
| Yalimaimaiti et al. | Establishment of a prognostic signature for lung adenocarcinoma using cuproptosis-related lncRNAs | |
| Zhu et al. | Comprehensive analysis of N6‐methyladenosine‐related long non‐coding RNAs for prognosis prediction in liver hepatocellular carcinoma | |
| CN116525005A (en) | Application of macrophage marker gene in liver cancer prognosis and risk assessment model thereof | |
| CN110570951A (en) | Method for constructing classification model of new auxiliary chemotherapy curative effect of breast cancer | |
| Xia et al. | Identifying TME signatures for cervical cancer prognosis based on GEO and TCGA databases | |
| Ye et al. | An online survival predictor in glioma patients using machine learning based on WHO CNS5 data | |
| Li et al. | A novel long non-coding RNA-based prognostic signature for renal cell carcinoma patients with stage IV and histological grade G4 | |
| Zhao et al. | Identification Sixteen Metabolic Genes as Potential Biomarkers for Colon Adenocarcinoma |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |