CN115985497B - System for predicting prognosis of patient with non-operative treatment primary liver cancer based on platelet/spleen aspect ratio value - Google Patents
System for predicting prognosis of patient with non-operative treatment primary liver cancer based on platelet/spleen aspect ratio value Download PDFInfo
- Publication number
- CN115985497B CN115985497B CN202211645117.6A CN202211645117A CN115985497B CN 115985497 B CN115985497 B CN 115985497B CN 202211645117 A CN202211645117 A CN 202211645117A CN 115985497 B CN115985497 B CN 115985497B
- Authority
- CN
- China
- Prior art keywords
- level
- score
- spleen
- patient
- survival probability
- 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
Classifications
-
- 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
- Investigating Or Analysing Biological Materials (AREA)
Abstract
The invention provides a system 1 or a system 2 for predicting prognosis of a patient with primary liver cancer in non-operative treatment based on a platelet/spleen aspect ratio value; the prognosis refers to the survival probability of 1 year and/or the survival probability of 3 years after the patient is diagnosed with the primary liver cancer; the system 1 or the system 2 comprises a data collection module and a module for calculating 1-year survival probability and/or 3-year survival probability, wherein the calculation module of the system 1 is based on an established visual Nomogram1 model, and the calculation module of the system 2 is based on an established visual Nomogram2 model to respectively assign scores to independent factors which obviously influence survival of patients and calculate total scores. In both models, the platelet/spleen aspect ratio value was 0 or more and the platelet/spleen aspect ratio value < 909 score was 100. The prediction model constructed by the invention is superior to the classical prediction model.
Description
Technical Field
The invention belongs to the field of medicine, and particularly relates to a system for predicting prognosis of a patient with primary liver cancer in non-operative treatment based on a platelet/spleen length-diameter ratio value.
Background
According to the latest 2021 global cancer statistics, mortality of primary liver cancer (Hepatocellular Carcinoma, HCC) is the fourth leading stage of global cancer. The number of new cases of HCC worldwide is about 90.6 tens of thousands per year, while the number of cases of death is about 80.3 tens of thousands. The number of HCC deaths in China is half of the total number of deaths worldwide (Torre LA, et al Global cancer statistics [ J ],2012.CA Cancer J Clin.2015,65 (2): 87-108). Although surgery is an effective means of treating HCC, a significant proportion of patients have lost surgical opportunity when discovered and can only be treated with non-surgery. Prognosis and risk stratification of non-surgical HCC patients, treatment of early intervention patients is an effective strategy to improve survival of non-surgical HCC patients. The current prognosis prediction model for HCC patients mainly includes Okuda, TNM, BCLC, child and CLIP, etc. The predictive indicators of these prognostic models are mainly focused on tumor burden, liver function, life status, etc. Meanwhile, the classical models are early in construction time, the models contain single prediction factors, and the prediction effect on certain specific groups is not detailed (Zhou Dongdong, and the like). Therefore, a new method is necessary to screen out more comprehensive prognosis prediction factors and construct a simple visualized prognosis prediction model.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides a system for predicting prognosis of a patient with primary liver cancer in non-operative treatment based on the length-diameter ratio value of platelets/spleen. The system of the invention is superior to the existing model and system in the accuracy of predicting the survival rate of patients for 3 years, thereby providing basis and support for the selection of clinical treatment means and strategies.
In order to achieve the aim of the invention, the invention adopts the following technical scheme:
a system 1 for predicting prognosis of a patient with non-surgical treatment of primary liver cancer based primarily on platelet/spleen aspect ratio values; the prognosis refers to the survival probability of 1 year and/or the survival probability of 3 years after the patient is diagnosed with the primary liver cancer; the system 1 comprises:
and a data collection module: for obtaining data for platelet/spleen aspect ratio values, treatment regimens, ascites, portal cancer plugs, number of tumors in the liver, maximum tumor diameter, red blood cell count, total bilirubin level, gamma-glutamyl transpeptidase level, alkaline phosphatase level, and cholinesterase level of the patient;
a module for calculating 1-year survival probability and/or 3-year survival probability: using an established visual Nomogram1 model for predicting prognosis of a non-surgically treated primary liver cancer patient, performing a treatment on the platelet/spleen aspect ratio value, treatment means, ascites, portal cancer embolism, number of intrahepatic tumors, tumor diameter, red blood cell count, total bilirubin level, gamma-glutamyl transpeptidase level,Assigning scores to the data of alkaline phosphatase level and cholinesterase level, calculating total scores, and obtaining corresponding survival probabilities according to the total scores; in the visual Nomogram1 model, the length-diameter ratio value of the platelets/spleen is more than or equal to 909, the score is 0, and the length-diameter ratio value of the platelets/spleen is less than 909, and the score is 100; the treatment of taking the prescription for nourishing yin, strengthening body resistance and detoxifying is 0 in more than 3 months, and the treatment of taking no prescription for nourishing yin, strengthening body resistance and detoxifying is 74.48659 in less than 3 months; no ascites score was 0 and no ascites score was 21.03655; the score for the non-portal cancer plugs was 0 and the score for the portal cancer plugs was 31.95534; single shot score of 0 and multiple shot score of 20.74318 for intrahepatic tumor; the score of the maximum tumor diameter less than 5cm is 0, the score of the maximum tumor diameter more than or equal to 5cm is 30.84085 red blood cell count more than or equal to 4 multiplied by 10 12 A/L score of 0, a red blood cell count of < 4×10 12 the/L score is 27.52993; the total bilirubin level is less than 18.8 mu mol/L and is scored as 0, and the total bilirubin level is more than or equal to 18.8 mu mol/L and is scored as 14.72212; the gamma-glutamyl transpeptidase level is less than 60U/L and is scored as 0, and the gamma-glutamyl transpeptidase level is more than or equal to 60U/L and is scored as 27.02208; the alkaline phosphatase level is less than or equal to 125U/L and is scored as 0, and the alkaline phosphatase level is more than 125U/L and is scored as 35.13728; the cholinesterase level is less than or equal to 4000U/L and is scored as 0, and the cholinesterase level is more than 4000U/L and is scored as 6.416764.
It is yet another object of the present invention to provide another system 2 for predicting prognosis of a patient with non-surgical treatment of primary liver cancer based primarily on platelet/spleen aspect ratio values; the prognosis refers to the survival probability of 1 year and/or the survival probability of 3 years after the patient is diagnosed with the primary liver cancer; the system 2 comprises:
and a data collection module: obtaining data for platelet/spleen aspect ratio values, treatment regimens, portal cancer plugs, number of tumors in the liver, maximum tumor diameter, alkaline phosphatase levels, creatinine levels, lactate dehydrogenase levels, and gamma-glutamyl transpeptidase levels of the patient;
a module for calculating 1-year survival probability and/or 3-year survival probability: assigning a total score to the platelet/spleen aspect ratio value, treatment means, portal cancer plug, number of tumors in the liver, tumor diameter, alkaline phosphatase level, creatinine level, lactate dehydrogenase level and gamma-glutamyl transpeptidase level data by using an established visual Nomogram2 model for predicting prognosis of a non-surgically treated primary liver cancer patient, and obtaining a corresponding survival probability according to the total score; in the visual Nomogram2 model, the length-diameter ratio value of the platelets/spleen is more than or equal to 909, the score is 0, and the length-diameter ratio value of the platelets/spleen is less than 909, and the score is 100; the treatment of taking the prescription for nourishing yin, strengthening body resistance and detoxifying is 0 in more than 3 months, and the treatment of taking no prescription for nourishing yin, strengthening body resistance and detoxifying is 65.86856 in less than 3 months; the score for the non-portal cancer plugs was 0 and the score for the portal cancer plugs was 24.62139; single shot score of 0 and multiple shot score of 20.62641 for intrahepatic tumor; the score of the maximum tumor diameter less than 5cm is 0, and the score of the maximum tumor diameter more than or equal to 5cm is 27.08241; the alkaline phosphatase level is less than or equal to 125U/L and is scored as 0, and the alkaline phosphatase level is more than 125U/L and is scored as 38.25946; creatinine level less than or equal to 111 mu mol/L score of 0, creatinine level > 111 mu mol/L score of 22.78374; lactate dehydrogenase level less than or equal to 250U/L score of 0, lactate dehydrogenase level > 250U/L score of 36.49597; the gamma-glutamyl transpeptidase level is less than 60U/L and is scored as 0, and the gamma-glutamyl transpeptidase level is more than or equal to 60U/L and is scored as 18.90881.
Preferably, the formula for nourishing yin, strengthening body resistance and detoxifying comprises the following raw materials:
15 parts of radix adenophorae, 15 parts of dwarf lilyturf tuber, 20 parts of raw astragalus root, 9 parts of bupleurum, 9 parts of lightyellow sophora root, 12 parts of paniculate swallowwort root, 9 parts of largehead atractylodes rhizome and 20 parts of oldenlandia diffusa.
The invention also provides a method for predicting prognosis of a patient with primary liver cancer in non-operative treatment; the prognosis refers to the survival probability of 1 year and/or the survival probability of 3 years after the patient is diagnosed with the primary liver cancer; the method is based on the system 1 or the system 2, and comprises the following steps:
s-1, data acquisition step
Obtaining data for platelet/spleen aspect ratio values, treatment options, ascites, portal cancer plugs, number of tumors in the liver, tumor diameter, red blood cell count, total bilirubin level, gamma-glutamyl transpeptidase level, alkaline phosphatase level, and cholinesterase level of the patient, or obtaining data for platelet/spleen aspect ratio values, treatment options, portal cancer plugs, number of tumors in the liver, maximum tumor diameter, alkaline phosphatase level, creatinine level, lactate dehydrogenase level, and gamma-glutamyl transpeptidase level of the patient;
s-2, data input step
Inputting the data acquired in the step S-1 into the data collection module;
s-3.1 year survival probability and/or 3 year survival probability calculation step
Assigning a total score to the platelet/spleen aspect ratio value, treatment means, ascites, portal cancer embolism, intrahepatic tumor number, maximum tumor diameter, red blood cell count, total bilirubin level, gamma-glutamyl transpeptidase level, alkaline phosphatase level and cholinesterase level data by using the established visual Nomogram1 model for predicting prognosis of a non-surgically treated primary liver cancer patient, calculating a total score, and obtaining a corresponding 1-year survival probability and/or 3-year survival probability according to the total score;
or alternatively, the process may be performed,
and utilizing the established visual Nomogram2 model for predicting prognosis of the primary liver cancer patient undergoing non-operative treatment to assign a total score to the data of the platelet/spleen length-diameter ratio value, the treatment means, the portal cancer embolism, the number of tumors in the liver, the maximum tumor diameter, the alkaline phosphatase level, the creatinine level, the lactate dehydrogenase level and the gamma-glutamyl transpeptidase level, calculating the total score, and obtaining the corresponding 1-year survival probability and/or 3-year survival probability according to the total score.
The two systems for predicting prognosis of patients with primary liver cancer in non-operative treatment, which are mainly based on the length-diameter ratio value of platelets/spleen, are established by the invention, and the accuracy is higher than that of traditional prediction models such as Child, BCLC, ALBI, TNM, CLIP, okuda and the like, so that the two systems have good prediction efficiency; of these, system 1 is more advantageous than system 2. Both can provide a simple and quick decision tool for clinic.
Drawings
The invention is further described below with reference to the accompanying drawings.
FIG. 1 is a visual representation of a Nomogram model built in accordance with the present invention; wherein:
a: a visual map of the Nomogram1 model;
b: visual map of the Nomogram2 model.
FIG. 2 shows ROC curves of the Nomogram model and classical predictive model built by the present invention, wherein:
a: ROC curves of the Nomogram model and the classical prediction model established by the invention in a modeling queue;
b: the Nomogram model and the classical prediction model established by the invention are ROC curves in a verification queue.
In the A diagram and the B diagram, 1-Nomogram1;2-Nomogram2;3-CLIP;4-BCLC;5-Okuda;6-Child;7-TNM;8-ALBI.
FIG. 3 shows a calibration curve of the 3 year overall survival of the Nomogram model built in accordance with the present invention; wherein: the x-axis is the actual survival prediction model of the nomogram, and the y-axis is the survival prediction model calculated by the Kaplan-Meier method.
A: calibration curves for Nomogram1 in modeling queues;
b: calibration curve of Nomogram1 in validation queue;
c: calibration curves for Nomogram2 in modeling queues;
d: calibration curve of Nomogram2 in validation queue;
FIG. 4 shows a decision curve of the Nomogram model built in the present invention; wherein:
a: a decision curve for Nomogram1;
b: a decision curve of Nomogram2;
in the figure: 1-Nomogram1;2-Nomogram2;3-All (full benefit); 4-None (no benefit).
Figure 5 shows that the Nomogram1 model established by the invention has good distinguishing effect on low-risk, medium-risk and high-risk patients. Wherein:
a: high, medium and low risk stratification of total survival of Nomogram1 in 3 years in the modeled queue;
b: high, medium and low risk stratification of total survival of Nomogram1 in the validation queue for 3 years;
in the figure, 1-low risk group, 2-medium risk group, 3-high risk group.
Fig. 6 shows a schematic diagram of the decision tree established in study 1.
Detailed Description
The invention is described below with reference to specific examples. It will be appreciated by those skilled in the art that these examples are for illustration of the invention only and are not intended to limit the scope of the invention in any way.
The experimental methods in the following examples are conventional methods unless otherwise specified. The raw materials and reagent materials used in the examples below are all commercially available products unless otherwise specified.
Abbreviations for terms:
AFP (α -fetoprotein): alpha fetoprotein;
ALB (serum albumin): serum albumin;
ALP (Alkaline Phosphatase): alkaline phosphatase;
ALT (alanine aminotransferase): alanine aminotransferase;
AST (aspartate aminotransferase): aspartate aminotransferase;
AUC (area under curve): area under the curve;
BCLC (Barcelona clinic liver cancer): the clinical staging system of the barcelona liver cancer;
CA-199 (Carbohydrate antigen 199): carbohydrate antigen 199;
CEA (carcinoembryonic antigen): carcinoembryonic antigen;
CHE (cholinesterase): cholinesterase;
CI (confidence interval): confidence interval;
CIC (clinical impact curve): clinical impact profile;
cr (creatinine): creatinine;
CRP (C-reactive protein) C-reactive protein;
DCA (decision curve analysis): analyzing a decision curve;
gamma-GGT (gamma-Glutamy 1 transfer): gamma-glutamyl transpeptidase
HCC (Hepatocellular carcinoma): primary liver cancer;
HCV: hepatitis c virus;
HbeAg: hepatitis b e antigen;
HBsAg: hepatitis b surface antigen;
HBV-DNA: hepatitis b virus-deoxyribonucleic acid;
HR (hazard ratio): a risk ratio;
IDI (Integrated discrimination improvement): integral identification index
LDH (Lactic acid dehydrogenase): lactate dehydrogenase;
MRI (magnetic resonance imaging): magnetic resonance imaging;
NPV (Negative predictive value): negative predictive rate;
NRI (net reclassification index): net weight new classification index
OR (odds ratio): ratio of ratio;
PLT (platelet): platelets;
PPV (Positive predictive value): positive predictive rate;
PSL (playlet count (PC)/Spleen Diameter (SD) ratio): platelet/spleen aspect ratio values;
PTA (prothrombin time activity): prothrombin activity;
PVTT (Portal vein tumor thrombus): portal vein cancer suppository;
RBC (red blood cell): red blood cells;
RECIST (Response evaluation critecia in solid tumors): evaluation criteria for response to efficacy of solid tumors;
ROC (receiver operating curve): a receiver operating profile;
TCM: treating with yin nourishing, body resistance strengthening and toxic materials removing prescription;
TBIL (Total bilirubin): total cholesterol;
tdROC (time-dependent receiver operating characteristics curve): time dependent receiver operating curves;
WBC (white blood cell): white blood cells.
The following study examples are used for nourishing yin, strengthening body resistance and detoxifying, and the composition of one dose is as follows:
15g of root of straight ladybell, 15g of dwarf lilyturf tuber, 20g of raw astragalus, 9g of bupleurum, 9g of lightyellow sophora root, 12g of paniculate swallowwort root, 9g of largehead atractylodes rhizome and 20g of oldenlandia diffusa.
The above materials are decocted in water for 2-3 times, and the decoctions are combined and taken orally three times a day.
Study example 1Construction of a visual Nomogram model for predicting prognosis of non-surgically treated HCC patient 1. Subject
Retrospectively, 2580 HCC patients admitted to the beijing forum hospital affiliated with the university of capital medical science from 1 st 2012 to 12 th 2017. The study was approved by the ethics committee of the local hospital.
1.1 diagnostic criteria
Diagnostic criteria for western medicine of primary liver cancer: according to the diagnosis and treatment specification of primary liver cancer (2019 edition), the primary liver cancer can be diagnosed when the following conditions are satisfied, namely (1) + (2) (1) or (1) + (2) (2) + (3) are satisfied, or the diagnosis has clear pathology: (1) Evidence of cirrhosis and HBV and/or HCV infection (HBV and/or HCV antigen positive); (2) HCC typical imaging features: contemporaneous multi-row CT scanning and/or dynamic contrast-enhanced MRI examination shows rapid heterogeneous vessel enhancement of liver occupancy in arterial phase, and rapid elution in venous phase or delayed phase. (1) If the liver occupation diameter is more than or equal to 2cm, one of CT or MRI shows the liver occupation characteristic; (2) if the liver is 1-2cm in diameter, both CT and MRI are required to show liver occupancy characteristics to enhance diagnostic specificity. (3) Serum AFP is greater than or equal to 400 mug/L for 1 month or greater than or equal to 200 mug/L for 2 months, and AFP elevation caused by other reasons (gestation, germ line embryogenic tumor, active liver disease, etc.) is eliminated.
1.2 group entry criteria
(1) Age 18-75 years old, unlimited in sex;
(2) Meets the diagnosis standard of Western medicine for primary liver cancer;
(3) The follow-up time is at least 1 month, and the clinical data is complete.
1.3 exclusion criteria
(1) Serious diseases of important organs such as heart, lung, kidney, brain and the like are accompanied with functional insufficiency;
(2) Metastatic liver cancer patients or combined with other tumors;
(3) Past splenectomy patients;
(4) Performing a surgical operation on the patient;
(5) When the medicine is in a group, patients with liver fibrosis resistant medicines such as an Anluohua pill, a compound turtle shell liver softening tablet, a body resistance strengthening and stasis removing tablet and the like are orally taken;
(6) Incomplete clinical data, related examination and examination results.
1.4 treatment
The traditional Chinese medicine is used: patients meeting the nano-row standard take the prescription for treating over 3 months.
Unused traditional Chinese medicine: patients who do not use the yin nourishing, body resistance strengthening and detoxifying formula or take the formula for less than three months.
2. Endpoint event
All patients were tested periodically every three months for CT, nuclear magnetic resonance, ultrasound or serum AFP. Dynamic CT or MRI is further used to determine if liver cancer has progressed when patient serum AFP is elevated or new intrahepatic nodules are found by ultrasound. With reference to the mRECIST standard (Lenmioni R, et al. Modified RECIST (mRECIST) assessment for hepatocellular carcinoma [ J ]. Semin Liver Dis.2010,30 (1): 52-60), tumor progression is defined as a > 20% increase in the sum of arterial phase-enhanced lesion diameters of Liver cancer patients or the appearance of new lesions. Tumor progression is also considered when vascular metastasis or extrahepatic spread occurs. Progression free survival is defined as progression or death from baseline time to the patient. Survival time is defined as the 12-month 31-day period from the time the patient entered the group until death occurred or the last follow-up time 2020.
3. Data collection and arrangement
Using the retrospective cohort study method, the following information was recorded by consulting the electronic case:
(1) Basic information and demographic characteristics: patient name, sex, age, date of first and last visit, family history, history of illness, history of smoking, history of drinking, treatment, history of medication, etiology, complications, etc.;
smoking Shi Dingyi: smoking more than 10 cigarettes per day for more than one year;
drinking Shi Dingyi: the wine is drunk more than 20g per day, more than 5 times per week for more than one year.
(2) Laboratory examination:
blood convention: white blood cells, red blood cells, platelets, hemoglobin, and the like;
coagulation function: prothrombin activity and the like;
biochemical analysis: liver function, kidney function, etc.;
virology index: HBsAg, HBeAg, HBV-DNA;
tumor index: AFP, CEA, CA-199.
(3) Imaging data: b ultrasonic, CT, MRI, chest radiography.
4. Statistical method
The study was a retrospective study in which study subjects were patients meeting the inclusion criteria, which were randomly divided into a modeling cohort and a validation cohort for analysis based on follow-up results. According to the normal value checked by the clinical laboratory as the cutoff value, the continuous variable is converted into the classified variable, so that the model is more objective and simpler. SPSS 24.0 is adopted for statistical analysis, statistical data and clinical factors of the modeling queue patients and the verification queue patients are compared, and the classification variable adopts χ 2 Testing or Fisher exact probability testing. LASSO Cox regression, nomogram, time dependent ROC curve, calibration curve, decision tree were constructed using R software version 4.0.4 (http:// www.rproject.org /). In a modeling queue, drawing a visual Nomogram1 model by applying factors of LASSO Cox regression screening; the factors of forward stepwise Cox regression screening were applied to construct a visual Nomogram2 model. And comparing the model with the established Nomogram1 and Nomogram2 in a modeling queue and a verification queue respectively, checking the differentiation degree of the model through NRI and IDI scores, C indexes, ROC curve areas and time-dependent ROC curves, drawing the calibration degree of a calibration curve checking model, and comparing the clinical net benefit and the performance improvement of the model by a decision curve. And compared to the classical model (Okuda, TNM, BCLC, child, CLIP) for C-index, ROC curve area and time dependent ROC curve. Drawing a decision tree according to the constructed Nomogram1 and Nomogram2, and showing the incorporation of the modelImportance of the index. R packets of "foreign", "survivinal", "rms", "glmcet", "nrocens", "stdca", "time ROC", etc. are used. ROC curve is drawn by using MedCalc19.2.0, and survival curve (P) of HCC patient is drawn by using Kalplan-Merier (K-M) method according to high, medium and low risk groups of the constructed model<0.05 with statistical differences).
5. Results of the study
5.1 modeling and validating clinical profile characteristics of a queue
The study co-included 1104 HCC patients meeting inclusion and exclusion criteria, and randomly divided the patients into a modeling cohort 772 HCC patients and a validation cohort 332 HCC patients at a ratio of 7:3. The continuous variable is converted into the classified variable, so that the model is more objective and simple, and the cutoff value of the classified variable is the normal value of clinical laboratory examination. Patient clinical profile characteristics of the modeled and validated cohorts are shown in table 1.
Table 1 clinical profile characterization of patients in the study modeling and validation cohorts
Screening for Nomogram inclusion indicators
All available clinical criteria in table 1, including general characteristics, etiology, complications, laboratory criteria, hepatitis b-related characteristics, and tumor-related characteristics of patients, were subjected to LASSO Cox regression analysis. As a result, it was found that 16 indexes such as traditional Chinese medicine, ascites, portal cancer embolism, number of tumors in liver, maximum tumor diameter, red blood cells, hemoglobin, platelet/spleen long diameter, creatinine, total bilirubin, lactate dehydrogenase, gamma-glutamyl transpeptidase, alkaline phosphatase, cholinesterase, total cholesterol, alpha fetoprotein and the like are correlated with the total survival rate of HCC patients for 3 years at the minimum; when 1-fold standard error, 11 indexes of traditional Chinese medicine, ascites, portal cancer embolism, tumor number, tumor diameter, red blood cells, platelet/spleen long diameter, total bilirubin, gamma-glutamyl transpeptidase, alkaline phosphatase and cholinesterase are found to be related to the total survival rate of HCC patients for 3 years.
All clinical indicators were included by stepwise Cox regression (Forward Stepwise Cox regression) single factor analysis, and it was found that 25 indicators of use of traditional Chinese medicine, cirrhosis, spleen hyperactivity, ascites, hepatic encephalopathy, portal cancer embolism, HBV-DNA, number of tumors in liver, maximum tumor diameter, NLR, erythrocytes, hemoglobin, platelets, platelet/spleen long diameter, creatinine, glutamate oxaloacetate, total bilirubin, albumin, lactate dehydrogenase, γ -glutamyl transpeptidase, alkaline phosphatase, cholinesterase, prothrombin activity, alpha fetoprotein, CRP, and the like had a significant correlation with the total survival rate of HCC patients for 3 years. The results after Cox multifactor analysis were then displayed: the 9 indices of traditional Chinese medicine, portal cancer embolism, intrahepatic tumor number, maximum tumor diameter, platelet/spleen long diameter, creatinine, lactate dehydrogenase, gamma-glutamyl transpeptidase and alkaline phosphatase are independent risk factors affecting 3 years prognosis of HCC patients (see table 2).
Table 2 one-factor, multi-factor analysis of the total survival of the modeled queue for 3 years
3. Construction of visual Nomogram
The data of the selected related factors are imported into an R software version 4.0.4 (http:// www.rproject.org /) for open source data analysis, and the assigned values of 11 indexes such as treatment means (whether traditional Chinese medicines are taken), ascites, portal cancer embolism, the number of tumors in the liver, the maximum tumor diameter, red blood cells, platelet/spleen long diameter, total bilirubin, gamma-glutamyl transpeptidase, alkaline phosphatase, cholinesterase and the like, and the total survival probability of HCC patients corresponding to the total assigned values for 1 year and 3 years are obtained. From this, a visual map of Nomogram1 is drawn, including the score of each relevant factor, the total score axis, and the total survival probability axis for 1 year and 3 years, as shown by a in fig. 1. Meanwhile, a Nomogram2 model containing 9 indexes of treatment means (whether traditional Chinese medicines are taken or not), portal cancer suppositories, the number of tumors in the liver, the maximum tumor diameter, platelet/spleen long diameter, creatinine level, lactate dehydrogenase level, gamma-glutamyl transpeptidase level and alkaline phosphatase level is established, and a visual diagram is shown as B in figure 1.
In fig. 1, the uppermost horizontal axis is a one-factor fractional axis (0 to 100). The score axis is below the score axis, and the score axis, the total score axis (0-450), the 1-year survival probability axis and the 3-year survival probability axis of each single factor are sequentially arranged in parallel. Wherein in figure a:
platelet/spleen aspect ratio value (PSL): the score is more than or equal to 909 and is less than 909 and is 100;
the Treatment (TCM) of the prescription for nourishing yin, strengthening body resistance and detoxifying is taken: the score of the medicine exceeds 3 months and is 0, and the medicine is not taken for nourishing yin and strengthening body resistance and detoxifying or the taking time is less than 3 months and is 74.48659;
ascites (Ascites): no score of 0, with score 21.03655;
portal vein cancer suppository (PVTT): no score of 0, with score 31.95534;
number of intrahepatic tumors (tuner number): single shot score of 0 and multiple shot score of 20.74318;
maximum Tumor diameter (tuner size): a score of < 5cm is 0 and a score of 5cm or more is 30.84085 red blood cell count (RBC): not less than 4 x 10 12 the/L score is 0, < 4×10 12 the/L score is 27.52993;
total bilirubin level (TBIL): a score of less than 18.8 mu mol/L is 0, and a score of more than or equal to 18.8 mu mol/L is 14.72212;
gamma-glutamyl transpeptidase level (gamma-GGT): the score of less than 60U/L is 0, and the score of more than or equal to 60U/L is 27.02208;
alkaline phosphatase level (ALP): a score of less than or equal to 125U/L of 0, a score of more than 125U/L of 35.13728;
cholinesterase level (CHE): a score of 0 less than or equal to 4000U/L, a score of 6.416764 greater than 4000U/L;
in the B diagram:
platelet/spleen aspect ratio value (PSL): the score is more than or equal to 909 and is less than 909 and is 100;
the Treatment (TCM) of the prescription for nourishing yin, strengthening body resistance and detoxifying is taken: the score of the medicine exceeds 3 months and is 0, and the medicine is not taken for nourishing yin and strengthening body resistance and detoxifying or the taking time is less than 3 months and is 65.86856;
portal vein cancer suppository (PVTT): no score of 0, with score 23.62139;
intrahepatic Tumor (tuner number): single shot score of 0 and multiple shot score of 20.62641;
maximum Tumor diameter (tuner size): score less than 5cm is 0, score more than or equal to 5cm is 27.08241;
alkaline phosphatase level (ALP): a score of less than or equal to 125U/L of 0, a score of more than 125U/L of 38.25946;
creatinine level (Cr): a score of less than or equal to 111 mu mol/L of 0 and a score of more than 111 mu mol/L of 22.78374;
lactate dehydrogenase Level (LDH): a score of less than or equal to 250U/L of 0 and a score of more than 250U/L of 36.49597;
gamma-glutamyl transpeptidase level (gamma-GGT): a score of less than 60U/L is 0, and a score of more than or equal to 60U/L is 18.90881.
When the constructed visualized nomogram1 and the visualized nomogram2 are used, the corresponding score of each factor is found on the assigned axis, then the scores of all the factors are added, the corresponding point is found on the total score axis, a straight line perpendicular to the total score axis is drawn through the point and is intersected with the following 1-year survival probability axis and/or 3-year survival probability axis, and the value of the intersection point is the 1-year survival probability and/or 3-year survival probability.
Sensitivity of Nomogram1 in modeling cohorts was 82.68%, specificity was 78.75%, PPV was 86.8%, NPV was 72.9%; the sensitivity of Nomogram2 was 87.01%, the specificity was 68.29%, the PPV was 82.3% and the NPV was 75.7%. Compared with Nomogram1, nomogram2 cuts 0.05NRI (-0.089), classifies 0.5NRI (-0.02), IDI 0.017 (p=0.001). The former was slightly better than the latter, although the difference was not large, indicating that Nomogram1 and Nomogram2.
4. Visual assessment of Nomogram
4.1 model discrimination evaluation
The C-indices and AUCs of the visual Nomogram1 model, the Nomogram2 model, the other classical models (Child, BCLC, ALBI, TNM, CLIP and Okuda, etc.) constructed according to the present invention were compared in the modeling and validation queues to evaluate model discrimination, see in particular tables 3 and 4.
Table 3C index of each model in modeling queue and validation queue
Table 4 AUC of each model in modeling and validation queues
Table 3 shows that the C index of Nomogram1 (0.792, 95% CI: 0.772-0.812) is better than Nomogram2 (0.788, 95% CI: 0.768-0.808) and that both Nomogram1 and Nomogram2 are better than other classical models, both in the modeling queue and in the validation queue.
Table 4 shows that in both the modeling and validation queues, the AUC of Nomogram1 (0.866, 95% CI: 0.840-0.889) was better than Nomogram2 (0.854, 95% CI: 0.827-0.878), and that the AUC of Nomogram1 and Nomogram2 were better than other classical models.
As shown in panels a and B of fig. 2, the time-dependent ROC curves show that in both the modeling and validation queues, nomogram1 is better than Nomogram2, and that both models constructed by the present invention are better than the other classical model.
In summary, the degree of discrimination of the two models constructed by the present invention is better than that of the classical model, and Nomogram1 is better than Nomogram2.
4.2 evaluation of model calibration degree
In the modeling queue, the calibration curve for the 3 year overall survival shows that the predictions for Nomogram1 are most consistent with the actual observations (as shown in figures a and C of figure 3); the same results were obtained in the validation queue (as shown in figures B and D of fig. 3). Thus, the calibration degree of Nomogram1 is higher than that of Nomogram2, and the accuracy of Nomogram1 is higher.
4.3 evaluation of decision Curve
The net benefit of Nomogram1 and Nomogram2, both in the modeling and validation queues, is higher than 50% and is not very different (as shown in panels a and B of fig. 4).
Based on the above evaluation, the Nomogram1 and Nomogram2 models constructed in the present invention are both superior to the conventional model, and Nomogram1 is more superior to Nomogram2, which is the preferred model of the present invention.
Application of Nomogram1 in risk stratification
According to Nomogram1 constructed by the invention, patients are divided into three groups of low risk, medium risk and high risk, and good distinguishing effects are shown in a modeling queue and a verification queue. In predicting the overall survival of non-surgically treated HCC patients for 3 years, in the modeling cohort, 194 cases in the low risk group, 385 cases in the medium risk group, 193 cases in the high risk group, the overall survival rates between groups were greater than 30 months, (25.570 ±0.994) months, (5.370 ±0.748) months (p < 0.001), respectively; the risk ratio (HR) values for the overall survival at medium and high risk were 4.46 (95% CI:3.68-5.41; P < 0.001), 14.02 (95% CI:10.38-18.95; P < 0.001), respectively, with reference to the low risk group (as shown in panel A of FIG. 5). In the verification queue, 75 cases of low risk groups, 173 cases of medium risk groups and 84 cases of high risk groups, the total survival rate among the groups is respectively more than 30 months, (25.570 +/-0.960) months, (5.700 +/-1.109) months (p < 0.001); the risk ratio (HR) values for the medium and high risk overall survival were 5.11 (95% CI:3.82-6.83; P < 0.001), 15.08 (95% CI:9.67-23.51; P < 0.001), respectively, with reference to the low risk group (as shown in panel B of FIG. 5).
6. Establishing decision trees shows the overall survival rate of non-surgically treated HCC patients in different risk factors for 3 years
All the metrics of the Nomogram1 model are put into the decision tree in the modeling queue. As shown in fig. 6, in the decision tree of the Nomogram1 model, the PSL is at the first layer, with its column contribution being the largest; secondly, the second position of the prescription for nourishing yin, strengthening body resistance and detoxifying is contributed to the column after more than 3 months of treatment; PSL is more than or equal to 909, and the survival rate of patients taking the traditional Chinese medicine is obviously higher than that of patients with PSL less than 909 and no traditional Chinese medicine.
In summary, the two visualized Nomogram models provided by the invention for predicting the survival rate of patients with primary liver cancer in 1 year and/or 3 years in non-operative treatment are mainly based on the length-diameter ratio value of platelets/spleen. Both models are superior to classical predictive models, thereby providing a new tool for clinical treatment decisions.
Claims (5)
1. A system for predicting prognosis of a patient with non-surgical treatment of primary liver cancer based on platelet/spleen aspect ratio values; the prognosis refers to the survival probability of 1 year and/or the survival probability of 3 years after the patient is diagnosed with the primary liver cancer; the system comprises:
and a data collection module: for obtaining data for platelet/spleen aspect ratio values, treatment regimens, ascites, portal cancer plugs, number of tumors in the liver, maximum tumor diameter, red blood cell count, total bilirubin level, gamma-glutamyl transpeptidase level, alkaline phosphatase level, and cholinesterase level of the patient;
a module for calculating 1-year survival probability and/or 3-year survival probability: assigning a total score to the platelet/spleen aspect ratio value, treatment means, ascites, portal cancer embolism, number of tumors in the liver, tumor diameter, red blood cell count, total bilirubin level, gamma-glutamyl transpeptidase level, alkaline phosphatase level and cholinesterase level data by using an established visual Nomogram1 model for predicting prognosis of a non-surgically treated primary liver cancer patient, calculating a total score, and obtaining a corresponding survival probability according to the total score; in the visual Nomogram1 model, the length-diameter ratio value of the platelets/spleen is more than or equal to 909, the score is 0, and the length-diameter ratio value of the platelets/spleen is less than 909, and the score is 100; the treatment of taking the prescription for nourishing yin, strengthening body resistance and detoxifying is 0 in more than 3 months, and the treatment of taking no prescription for nourishing yin, strengthening body resistance and detoxifying is 74.48659 in less than 3 months; no ascites score was 0 and no ascites score was 21.03655; the score for the non-portal cancer plugs was 0 and the score for the portal cancer plugs was 31.95534; single shot score of 0 and multiple shot score of 20.74318 for intrahepatic tumor; the score of the maximum tumor diameter less than 5cm is 0, the score of the maximum tumor diameter more than or equal to 5cm is 30.84085 red blood cell count more than or equal to 4 multiplied by 10 12 A/L score of 0, a red blood cell count of < 4×10 12 score/L27.52993The method comprises the steps of carrying out a first treatment on the surface of the The total bilirubin level is less than 18.8 mu mol/L and is scored as 0, and the total bilirubin level is more than or equal to 18.8 mu mol/L and is scored as 14.72212; the gamma-glutamyl transpeptidase level is less than 60U/L and is scored as 0, and the gamma-glutamyl transpeptidase level is more than or equal to 60U/L and is scored as 27.02208; the alkaline phosphatase level is less than or equal to 125U/L and is scored as 0, and the alkaline phosphatase level is more than 125U/L and is scored as 35.13728; the cholinesterase level is less than or equal to 4000U/L and is scored as 0, and the cholinesterase level is more than 4000U/L and is scored as 6.416764.
2. The system of claim 1, wherein the yin nourishing, body resistance strengthening and detoxification formula comprises the following raw materials:
15 parts of radix adenophorae, 15 parts of dwarf lilyturf tuber, 20 parts of raw astragalus root, 9 parts of bupleurum, 9 parts of lightyellow sophora root, 12 parts of paniculate swallowwort root, 9 parts of largehead atractylodes rhizome and 20 parts of oldenlandia diffusa.
3. A system for predicting prognosis of a patient with non-surgical treatment of primary liver cancer based on platelet/spleen aspect ratio values; the prognosis refers to the survival probability of 1 year and/or the survival probability of 3 years after the patient is diagnosed with the primary liver cancer; the system comprises:
and a data collection module: obtaining data for platelet/spleen aspect ratio values, treatment regimens, portal cancer plugs, number of tumors in the liver, maximum tumor diameter, alkaline phosphatase levels, creatinine levels, lactate dehydrogenase levels, and gamma-glutamyl transpeptidase levels of the patient;
a module for calculating 1-year survival probability and/or 3-year survival probability: assigning a total score to the platelet/spleen aspect ratio value, treatment means, portal cancer plug, number of tumors in the liver, tumor diameter, alkaline phosphatase level, creatinine level, lactate dehydrogenase level and gamma-glutamyl transpeptidase level data by using an established visual Nomogram2 model for predicting prognosis of a non-surgically treated primary liver cancer patient, and obtaining a corresponding survival probability according to the total score; in the visual Nomogram2 model, the length-diameter ratio value of the platelets/spleen is more than or equal to 909, the score is 0, and the length-diameter ratio value of the platelets/spleen is less than 909, and the score is 100; the treatment of taking the prescription for nourishing yin, strengthening body resistance and detoxifying is 0 in more than 3 months, and the treatment of taking no prescription for nourishing yin, strengthening body resistance and detoxifying is 65.86856 in less than 3 months; the score for the non-portal cancer plugs was 0 and the score for the portal cancer plugs was 24.62139; single shot score of 0 and multiple shot score of 20.62641 for intrahepatic tumor; the score of the maximum tumor diameter less than 5cm is 0, and the score of the maximum tumor diameter more than or equal to 5cm is 27.08241; the alkaline phosphatase level is less than or equal to 125U/L and is scored as 0, and the alkaline phosphatase level is more than 125U/L and is scored as 38.25946; creatinine level less than or equal to 111 mu mol/L score of 0, creatinine level > 111 mu mol/L score of 22.78374; lactate dehydrogenase level less than or equal to 250U/L score of 0, lactate dehydrogenase level > 250U/L score of 36.49597; the gamma-glutamyl transpeptidase level is less than 60U/L and is scored as 0, and the gamma-glutamyl transpeptidase level is more than or equal to 60U/L and is scored as 18.90881.
4. The system of claim 3, wherein the yin nourishing, body resistance strengthening and detoxification formula comprises the following raw materials:
15 parts of radix adenophorae, 15 parts of dwarf lilyturf tuber, 20 parts of raw astragalus root, 9 parts of bupleurum, 9 parts of lightyellow sophora root, 12 parts of paniculate swallowwort root, 9 parts of largehead atractylodes rhizome and 20 parts of oldenlandia diffusa.
5. A method for predicting prognosis of a patient with non-operative treatment of primary liver cancer; the prognosis refers to the survival probability of 1 year and/or the survival probability of 3 years after the patient is diagnosed with the primary liver cancer; the method is based on the system of claim 1 or claim 3, comprising the steps of:
s-1, data acquisition step
Obtaining data for platelet/spleen aspect ratio values, treatment options, ascites, portal cancer plugs, number of tumors in the liver, tumor diameter, red blood cell count, total bilirubin level, gamma-glutamyl transpeptidase level, alkaline phosphatase level, and cholinesterase level of the patient, or obtaining data for platelet/spleen aspect ratio values, treatment options, portal cancer plugs, number of tumors in the liver, maximum tumor diameter, alkaline phosphatase level, creatinine level, lactate dehydrogenase level, and gamma-glutamyl transpeptidase level of the patient;
s-2, data input step
Inputting the data acquired in the step S-1 into the data collection module;
s-3.1 year survival probability and/or 3 year survival probability calculation step
Assigning a total score to the platelet/spleen aspect ratio value, treatment means, ascites, portal cancer embolism, intrahepatic tumor number, maximum tumor diameter, red blood cell count, total bilirubin level, gamma-glutamyl transpeptidase level, alkaline phosphatase level and cholinesterase level data by using the established visual Nomogram1 model for predicting prognosis of a non-surgically treated primary liver cancer patient, calculating a total score, and obtaining a corresponding 1-year survival probability and/or 3-year survival probability according to the total score;
or alternatively, the process may be performed,
and utilizing the established visual Nomogram2 model for predicting prognosis of the primary liver cancer patient undergoing non-operative treatment to assign a total score to the data of the platelet/spleen length-diameter ratio value, the treatment means, the portal cancer embolism, the number of tumors in the liver, the maximum tumor diameter, the alkaline phosphatase level, the creatinine level, the lactate dehydrogenase level and the gamma-glutamyl transpeptidase level, calculating the total score, and obtaining the corresponding 1-year survival probability and/or 3-year survival probability according to the total score.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211645117.6A CN115985497B (en) | 2022-12-16 | 2022-12-16 | System for predicting prognosis of patient with non-operative treatment primary liver cancer based on platelet/spleen aspect ratio value |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211645117.6A CN115985497B (en) | 2022-12-16 | 2022-12-16 | System for predicting prognosis of patient with non-operative treatment primary liver cancer based on platelet/spleen aspect ratio value |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115985497A CN115985497A (en) | 2023-04-18 |
| CN115985497B true CN115985497B (en) | 2023-08-08 |
Family
ID=85967472
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211645117.6A Active CN115985497B (en) | 2022-12-16 | 2022-12-16 | System for predicting prognosis of patient with non-operative treatment primary liver cancer based on platelet/spleen aspect ratio value |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115985497B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117131468B (en) * | 2023-09-12 | 2024-01-23 | 中山大学孙逸仙纪念医院 | Intrahepatic cholangiocellular carcinoma screening index and analysis and evaluation method of prognosis factors thereof |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106018640A (en) * | 2016-01-27 | 2016-10-12 | 中国药科大学 | Method for rapid screening and identification of tumor biomarkers and application |
| CN106868077A (en) * | 2016-12-29 | 2017-06-20 | 河南科技学院 | A kind of preparation method of the phloridzin triethyl with hepatoma Hep G 2 cells inhibitory activity |
| CN108254557A (en) * | 2017-12-26 | 2018-07-06 | 首都医科大学附属北京地坛医院 | The system that the related primary carcinoma of liver prognosis of HBV is determined with PD-1 in the T cell of the CD8 positives and TIGIT |
| CN111407872A (en) * | 2020-06-02 | 2020-07-14 | 广西中医药大学附属瑞康医院 | Traditional Chinese medicine composition for treating liver cancer, medicine for treating liver cancer and preparation method of medicine |
| CN111788228A (en) * | 2018-05-18 | 2020-10-16 | 礼新医药科技(上海)有限公司 | Anti-claudin 18.2 antibodies and uses thereof |
| CN112017791A (en) * | 2020-04-24 | 2020-12-01 | 首都医科大学附属北京地坛医院 | System for determining prognosis condition of liver cancer patient based on artificial neural network model |
| CN113851217A (en) * | 2021-10-12 | 2021-12-28 | 宁波市医疗中心李惠利医院 | Prediction method for survival rate of HCC patient without relapse in early postoperative period |
| CN114141371A (en) * | 2021-12-02 | 2022-03-04 | 首都医科大学附属北京地坛医院 | System for predicting prognosis of liver cancer associated with tumor-related anemia patient caused by hepatitis B virus infection |
| CN114155956A (en) * | 2021-12-02 | 2022-03-08 | 首都医科大学附属北京地坛医院 | System for predicting blood vessel invasion probability of primary liver cancer patient incapable of being resected by surgery |
-
2022
- 2022-12-16 CN CN202211645117.6A patent/CN115985497B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106018640A (en) * | 2016-01-27 | 2016-10-12 | 中国药科大学 | Method for rapid screening and identification of tumor biomarkers and application |
| CN106868077A (en) * | 2016-12-29 | 2017-06-20 | 河南科技学院 | A kind of preparation method of the phloridzin triethyl with hepatoma Hep G 2 cells inhibitory activity |
| CN108254557A (en) * | 2017-12-26 | 2018-07-06 | 首都医科大学附属北京地坛医院 | The system that the related primary carcinoma of liver prognosis of HBV is determined with PD-1 in the T cell of the CD8 positives and TIGIT |
| CN111788228A (en) * | 2018-05-18 | 2020-10-16 | 礼新医药科技(上海)有限公司 | Anti-claudin 18.2 antibodies and uses thereof |
| CN112017791A (en) * | 2020-04-24 | 2020-12-01 | 首都医科大学附属北京地坛医院 | System for determining prognosis condition of liver cancer patient based on artificial neural network model |
| CN111407872A (en) * | 2020-06-02 | 2020-07-14 | 广西中医药大学附属瑞康医院 | Traditional Chinese medicine composition for treating liver cancer, medicine for treating liver cancer and preparation method of medicine |
| CN113851217A (en) * | 2021-10-12 | 2021-12-28 | 宁波市医疗中心李惠利医院 | Prediction method for survival rate of HCC patient without relapse in early postoperative period |
| CN114141371A (en) * | 2021-12-02 | 2022-03-04 | 首都医科大学附属北京地坛医院 | System for predicting prognosis of liver cancer associated with tumor-related anemia patient caused by hepatitis B virus infection |
| CN114155956A (en) * | 2021-12-02 | 2022-03-08 | 首都医科大学附属北京地坛医院 | System for predicting blood vessel invasion probability of primary liver cancer patient incapable of being resected by surgery |
Non-Patent Citations (1)
| Title |
|---|
| Dongdong zhou 等.A prognostic nomogram in AFP-negative hepatocellular carcinoma based on LASSO Cox regression.《research square》.2022,第1-28页. * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115985497A (en) | 2023-04-18 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Sanchez et al. | Staging | |
| Yang et al. | Model to estimate survival in ambulatory patients with hepatocellular carcinoma | |
| Mohamadnejad et al. | Noninvasive markers of liver fibrosis and inflammation in chronic hepatitis B-virus related liver disease | |
| Wang et al. | Comparison of FIB‐4 and APRI in Chinese HBV‐infected patients with persistently normal ALT and mildly elevated ALT | |
| Fabrizi et al. | Hepatitis C virus and mortality among patients on dialysis: A systematic review and meta-analysis | |
| Chrysanthos et al. | Aspartate aminotransferase to platelet ratio index for fibrosis evaluation in chronic viral hepatitis | |
| CN112017791B (en) | System for determining prognosis condition of liver cancer patient based on artificial neural network model | |
| Omran et al. | Application of data mining techniques to explore predictors of HCC in Egyptian patients with HCV-related chronic liver disease | |
| CN115985497B (en) | System for predicting prognosis of patient with non-operative treatment primary liver cancer based on platelet/spleen aspect ratio value | |
| Duan et al. | Multicenter prospective study to validate a new transient elastography device for staging liver fibrosis in patients with chronic hepatitis B | |
| Yan et al. | Total bile acid-to-cholesterol ratio as a novel noninvasive marker for significant liver fibrosis and cirrhosis in patients with non-cholestatic chronic hepatitis B virus infection | |
| Chang et al. | A novel nomogram to predict evident histological liver injury in patients with HBeAg-positive chronic hepatitis B virus infection | |
| Jun et al. | External validation of the non‐alcoholic fatty liver disease fibrosis score for assessing advanced fibrosis in Korean patients | |
| Ren et al. | Development and validation of a scoring system to predict progression to acute‐on‐chronic liver failure in patients with acute exacerbation of chronic hepatitis B | |
| Lee et al. | Obesity and hepatocellular carcinoma in patients receiving entecavir for chronic hepatitis B | |
| CN114155956B (en) | System and method for predicting blood vessel invasion probability of primary liver cancer patient incapable of being resected by surgery | |
| Wang et al. | The gamma-glutamyl transpeptidase to platelet ratio predicts liver inflammation in chronic hepatitis B with normal or mildly elevated alanine transaminase | |
| CN114141371B (en) | System for predicting prognosis of liver cancer associated with tumor-related anemia patient caused by hepatitis B virus infection | |
| EP3365818A1 (en) | Method to determine inter- and intra-subject variation in biomarker signals | |
| Liu et al. | Design and validation of a noninvasive diagnostic criteria for biliary atresia in infants based on the STROBE compliant | |
| Chen et al. | Prognostic significance of platelet-to-lymphocyte ratio (PLR) in extrahepatic metastasis of hepatocellular carcinoma after curative resection | |
| Shen et al. | Prediction of hepatic necroinflammatory activity in patients with chronic hepatitis B by a simple noninvasive model | |
| Wu et al. | Differential characteristics and prognosis of patients with hepatitis B virus‐related acute‐on‐chronic liver failure defined by European Association for the Study of the Liver–Chronic Liver Failure criteria | |
| Geng et al. | A scoring model predicts hepatitis B e antigen seroconversion in chronic hepatitis B patients treated with nucleos (t) ide analogs: real-world clinical practice | |
| Wang et al. | Retrospective evaluation of non-invasive assessment based on routine laboratory markers for assessing advanced liver fibrosis in chronic hepatitis B patients |
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 |