Method, system, device and medium for non-invasive evaluation of fibrosis MASH remission

Abstract

The invention discloses a non-invasive evaluation method for fibrosis MASH remission. The non-invasive evaluation method is implemented according to the following steps: step 1, acquiring clinical parameters of a to-be-evaluated object at a baseline moment and a follow-up visit moment; step 2, calculating a base line acFibroMASH index and a follow-up visit acFibroMASH index; 3, the acFibroMASH index and the variable quantity of the ALT are calculated; step 4, substituting the baseline acFibroMASH index, the acFibroMASH index and the variation of the ALT into the fibrosis MASH remission prediction model, and calculating a fibrosis MASH remission index Index; and step 5, comparing the index Index with a bilateral cutoff value, and outputting an evaluation result. The invention further discloses a non-invasive evaluation system and device and a storage medium, and the problem that whether histological remission is achieved after fibrosis MASH treatment cannot be reflected through an existing non-invasive diagnosis technology is solved.

Description

Technical Field

[0001] This invention belongs to the field of biomedical information processing technology, specifically relating to a non-invasive assessment method for MASH relief of fibrosis. This invention also relates to a non-invasive assessment system, device and storage medium for MASH relief of fibrosis. Background Technology

[0002] Metabolic fatty liver disease (MAFLD) has become the most common chronic liver disease worldwide. Fibrotic MASH, or fibrotic MASH, is a key subtype of MAFLD, carrying a high risk of progression to cirrhosis, liver decompensation, and hepatocellular carcinoma (HCC). Currently, the focus of clinical diagnosis and treatment for this disease has gradually shifted from simple diagnosis to monitoring treatment effectiveness, i.e., assessing "histochemical remission."

[0003] Currently, liver biopsy remains the "gold standard" for assessing MASH remission in fibrosis. However, liver biopsy has significant limitations: first, it is an invasive procedure with risks of bleeding, pain, and even damage to adjacent organs; second, due to the heterogeneity of liver lesions, biopsy is subject to sampling errors; and third, the high cost and low patient compliance make it difficult to use for large-scale screening or frequent follow-up monitoring.

[0004] While existing noninvasive diagnostic techniques (NITs) have addressed the aforementioned issues to some extent, they also have significant limitations. For example, existing models such as FIB-4, APRI, and FAST scores primarily focus on assessing the "presence" of the disease or the "severity" of fibrosis, rather than assessing the "dynamic remission" or "improvement" of the disease over time.

[0005] Therefore, there is a lack of non-invasive assessment tools in the current technology that are based on routine clinical test indicators, can economically, safely and accurately reflect whether histological remission has been achieved after MASH treatment for fibrosis, and can predict the risk of long-term liver-related events (LREs) accordingly. Summary of the Invention

[0006] The first objective of this invention is to provide a non-invasive method for assessing the remission of MASH fibrosis. This solves the problem that existing non-invasive diagnostic techniques cannot reflect whether histological remission has been achieved after MASH treatment for fibrosis.

[0007] A second objective of this invention is to provide a non-invasive assessment system for MASH relief of fibrosis.

[0008] A third objective of this invention is to provide a non-invasive assessment device for MASH relief of fibrosis.

[0009] A fourth object of the present invention is to provide a computer-readable storage medium.

[0010] The first technical solution adopted in this invention is: a non-invasive assessment method for MASH relief of fibrosis, which is implemented according to the following steps: Step 1: Obtain the clinical parameters of the subjects to be evaluated at baseline time T0 and follow-up time T1; Step 2: Calculate the baseline acFibroMASH index and the follow-up acFibroMASH index respectively; Step 3: Calculate the changes in the acFibroMASH index and ALT, respectively; Step 4: Substitute the baseline acFibroMASH index, the change in the acFibroMASH index, and the change in ALT into the preset fibrosis MASH mitigation prediction model to calculate the fibrosis MASH mitigation index. Step 5: Compare the calculated index with the preset two-sided cutoff value and output the evaluation result.

[0011] The first technical solution adopted in this invention is further characterized by: Furthermore, the parameters in step 1 include: baseline liver stiffness measurement value LSM_base, baseline aspartate aminotransferase AST_base, baseline serum creatinine SCr_base; and follow-up liver stiffness measurement value LSM_follow, follow-up aspartate aminotransferase AST_follow, follow-up serum creatinine SCr_follow, baseline alanine aminotransferase ALT_base, and follow-up alanine aminotransferase ALT_follow; The liver stiffness value (LSM) of the patient was measured using a vibration-controlled transient elastography device, in kPa. The patient's serum aspartate aminotransferase (AST) (U / L), alanine aminotransferase (ALT) (U / L), and serum creatinine (SCr) (µmol / L) were measured using a blood biochemistry analyzer.

[0012] Further, step 2 is as follows: Calculate the acMASH index based on AST and SCr; combine LSM and acMASH index and use the logistic regression formula to calculate the acFibroMASH index; calculate the baseline acFibroMASH index and the follow-up acFibroMASH index respectively.

[0013] Furthermore, step 2 includes: Step 2.1: Calculate the acMASH index: According to the formula: acMASH = AST / SCr*10; calculate the baseline acMASH_base and the follow-up acMASH_follow respectively; Step 2.2: Calculate the acFibroMASH index: According to the formula: acFibroMASH = e^ Z / (1+ e^ Z ); Where Z = -3.956 + 0.305 * LSM + 0.065 * acMASH; Substituting the baseline and follow-up parameters respectively, the baseline acFibroMASH_base and the follow-up acFibroMASH_follow are calculated.

[0014] Furthermore, in step 3, the change in the acFibroMASH index ΔacFibroMASH = follow-up value - baseline value; the change in ALT ΔALT = follow-up value - baseline value.

[0015] Furthermore, in step 4, the fibrosis MASH mitigation prediction model is as follows: Index = e^ Y / (1+e^ Y ) The formula for calculating Y is: Y = 1.759 - 7.069 * (acFibroMASH_base) - 4.369 * (△acFibroMASH) - 0.012 * (△ALT).

[0016] Furthermore, in step 5, the calculated index is compared with the preset two-sided cutoff value, and the evaluation result is output: if the index is greater than the second threshold, it is determined to be a high probability of mitigation, i.e., low LREs risk; if the index is less than the first threshold, it is determined to be a low probability of mitigation, i.e., high LREs risk. Specifically as follows: (1) First threshold: i.e., the exclusion threshold Cut-off 1: 0.24; if Index < 0.24, it is judged as no relief, and it indicates that the patient has a high risk of liver-related events (LREs) in the future; (2) Second threshold: i.e., the cut-off 2: 0.61; if the index > 0.61, it is considered to be in remission, and it indicates that the patient's risk of developing LREs in the future is significantly reduced; (3) Grey Zone: 0.61 ≥ Index ≥ 0.24; This indicates that the result is uncertain and it is recommended to shorten the follow-up interval or combine it with other examination methods.

[0017] The second technical solution adopted in this invention is: a non-invasive assessment system for MASH relief of fibrosis, based on the above method, comprising: Data acquisition module: used to acquire clinical parameters of the evaluation subjects at baseline and follow-up times; Calculation module: Used for data processing, including: Intermediate variable calculation submodule: used to calculate the baseline acFibroMASH index and the follow-up acFibroMASH index; Change Calculation Submodule: Used to calculate the change in the acFibroMASH exponent ΔacFibroMASH and the change in ALT ΔALT; The mitigation index calculation submodule contains a fibrosis MASH mitigation prediction model, which calculates the fibrosis MASH mitigation index. Results output module: Compares the calculated exponent with the preset two-sided cutoff value and outputs the evaluation result.

[0018] The third technical solution adopted in this invention is: a non-invasive assessment device for fibrosis MASH relief, comprising: a processor; Memory, which stores the processor's executable instructions; The processor is configured to execute the steps of the above method via executable instructions.

[0019] The fourth technical solution adopted in this invention is: a computer-readable storage medium for storing a program, which, when executed, implements the steps of the above-described method.

[0020] The beneficial effects of this invention are: 1. High accuracy and reliability of judgment: The non-invasive assessment method of this invention has shown excellent diagnostic performance (AUROC approximately 0.80-0.82) in an international multicenter validation cohort. Whether in the push-out cohort or the validation cohort, it is superior to the existing FAST score and individual component parameters of the model, and can accurately identify histological remissions.

[0021] 2. Non-invasive, safe, and dynamically monitorable: The non-invasive assessment method of this invention is based entirely on blood biochemical indicators and non-invasive vibration-controlled transient elastography (VCTE), avoiding the trauma of liver biopsy, and is particularly suitable for long-term efficacy monitoring that requires multiple follow-ups.

[0022] 3. Significant prognostic predictive value: The non-invasive assessment method of this invention can not only assess the current pathological remission, but also effectively stratify and predict the risk of liver-related events (LREs) in patients over the next 5 and 10 years, with an area under the ROC curve exceeding 0.85. Patients with a score >0.61 have a reduced risk of LREs by approximately 96%, which has extremely high clinical guiding significance.

[0023] 4. High applicability: The parameters required by the non-invasive assessment method of this invention are all routine clinical test items, which are easy to calculate and can be easily integrated into fatty liver clinics or medical software. Attached Figure Description

[0024] Figure 1 This is a schematic diagram of the logical flow of the method of the present invention; Figure 2 In the derivation queue, the present invention provides an exponential diagnostic method for MASH-relief of fibrosis in AUROC. Figure 3 In the validation queue, the present invention provides an index diagnosis of fibrotic MASH remission AUROC; Figure 4 Cumulative incidence of liver-related time stratified according to the MASH fibrosis remission prediction model; Figure 5 This is the ROC curve of the invention index for predicting no liver-related events in 5 years; Figure 6 This is the ROC curve of the index of this invention for predicting no liver-related events in 10 years. Detailed Implementation

[0025] The present invention will be further illustrated below through examples.

[0026] This invention provides a method for evaluating MASH relief of fibrosis based on VCTE, comprising the following steps, such as... Figure 1 As shown: 1. Data Acquisition Steps: Acquire the clinical parameters of the subjects to be evaluated at baseline (T0) and follow-up (T1); the parameters include: baseline liver stiffness measurement (LSM_base), baseline aspartate aminotransferase (AST_base), and baseline serum creatinine (SCr_base); as well as follow-up liver stiffness measurement (LSM_follow), follow-up aspartate aminotransferase (AST_follow), follow-up serum creatinine (SCr_follow), baseline alanine aminotransferase (ALT_base), and follow-up alanine aminotransferase (ALT_follow).

[0027] 2. Intermediate variable calculation steps: Calculate the acMASH index based on AST and SCr; combine LSM and acMASH index and use the logistic regression formula to calculate the acFibroMASH index; calculate the baseline acFibroMASH index and the follow-up acFibroMASH index respectively.

[0028] include: Step 2.1: Calculate the acMASH index: According to the formula: acMASH = AST / SCr*10; calculate the baseline acMASH_base and the follow-up acMASH_follow respectively; Step 2.2: Calculate the acFibroMASH index: According to the formula: acFibroMASH = e^ Z / (1+ e^ Z ); Where Z = -3.956 + 0.305 * LSM + 0.065 * acMASH; Substituting the baseline and follow-up parameters respectively, the baseline acFibroMASH_base and the follow-up acFibroMASH_follow are calculated.

[0029] 3. Steps for calculating the change: Calculate the change in the acFibroMASH index (△acFibroMASH = follow-up value - baseline value); calculate the change in ALT (△ALT = follow-up value - baseline value).

[0030] 4. Relief index calculation steps: Substitute the baseline acFibroMASH index, the change in acFibroMASH index, and the change in ALT into the preset Fibrotic MASH relief prediction model to calculate the Fibrotic MASH resolution VCTE index.

[0031] The specific prediction model for MASH mitigation of fibrosis is as follows: Index = e^ Y / (1+e^ Y ) The formula for calculating Y is: Y = 1.759 - 7.069 * (acFibroMASH_base) - 4.369 * (△acFibroMASH) - 0.012 * (△ALT).

[0032] 5. Result determination steps: Compare the calculated index with the preset two-sided cutoff value and output the evaluation result: If the index is greater than the second threshold, it is determined to be a high probability mitigation (low LREs risk); if the index is less than the first threshold, it is determined to be a low probability mitigation (high LREs risk).

[0033] Specifically as follows: (1) First threshold: i.e., the exclusion threshold Cut-off 1: 0.24; if Index < 0.24, it is judged as no relief, and it indicates that the patient has a high risk of liver-related events (LREs) in the future; (2) Second threshold: i.e., the cut-off 2: 0.61; if the index > 0.61, it is considered to be in remission, and it indicates that the patient's risk of developing LREs in the future is significantly reduced; (3) Grey Zone: 0.61 ≥ Index ≥ 0.24; This indicates that the result is uncertain and it is recommended to shorten the follow-up interval or combine it with other examination methods.

[0034] This invention also provides a non-invasive assessment system for MASH relief of fibrosis, based on the above method, comprising: Data acquisition module: used to acquire clinical parameters of the evaluation subjects at baseline and follow-up times; Calculation module: Used for data processing, including: Intermediate variable calculation submodule: used to calculate the baseline acFibroMASH index and the follow-up acFibroMASH index; Change Calculation Submodule: Used to calculate the change in the acFibroMASH exponent ΔacFibroMASH and the change in ALT ΔALT; The mitigation index calculation submodule contains a fibrosis MASH mitigation prediction model, which calculates the fibrosis MASH mitigation index. Results output module: Compares the calculated exponent with the preset two-sided cutoff value and outputs the evaluation result.

[0035] The present invention also provides a non-invasive assessment device for MASH relief of fibrosis, comprising: a processor; Memory, which stores the processor's executable instructions; The processor is used to execute the steps of the above method.

[0036] The present invention also provides a computer-readable storage medium for storing a program that, when executed, implements the steps of the method described above.

[0037] The following will provide further explanation using specific implementation examples.

[0038] Example 1 1. Clinical Data Collection The following physiological parameters of the target patient were obtained using medical testing equipment: 1) Imaging parameters: Liver stiffness (LSM) values ​​are measured using a vibration-controlled transient elastography device (such as FibroScan), measured in kPa. The baseline LSM value (before treatment or at the start of monitoring) is recorded as LSM_base, and the follow-up LSM value (after treatment or at the monitoring point) is recorded as LSM_follow.

[0039] 2) Biochemical parameters: Serum aspartate aminotransferase (AST, U / L), alanine aminotransferase (ALT, U / L), and serum creatinine (SCr, µmol / L) were measured using a blood biochemistry analyzer. Baseline data (AST_base, SCr_base, ALT_base) and follow-up data (AST_follow, SCr_follow, ALT_follow) also needed to be collected.

[0040] 2. Calculate the acFibroMASH index. This step consists of two sub-steps: 1) Calculate the acMASH index: According to the formula: acMASH = AST / SCr*10; calculate the baseline acMASH_base and the follow-up acMASH_follow respectively; 2) Calculate the acFibroMASH index: According to the formula: acFibroMASH = e^ Z / (1+ e^ Z ); Where Z = -3.956 + 0.305 * LSM + 0.065 * acMASH; Substituting the baseline and follow-up parameters respectively, the baseline acFibroMASH_base and the follow-up acFibroMASH_follow are calculated.

[0041] 3. Calculate the change (△) Calculate parameters that reflect the dynamic changes of the disease: △acFibroMASH = acFibroMASH_follow - acFibroMASH_base △ALT = ALT_follow - ALT_base 4. Calculate the MASH remission index for fibrosis. Substitute the above parameters into the core prediction model of this invention: Index = e^ Y / (1+e^ Y ) The formula for calculating Y is: Y = 1.759 - 7.069 * (acFibroMASH_base) - 4.369 * (△acFibroMASH) - 0.012 * (△ALT).

[0042] 5. Output diagnostic and prognostic assessment results The calculated Index value is compared with the preset two-sided truncation value: (1) First threshold: i.e., the exclusion threshold Cut-off 1: 0.24; if Index < 0.24, it is judged as no relief, and it indicates that the patient has a high risk of liver-related events (LREs) in the future; (2) Second threshold: i.e., the cut-off 2: 0.61; if the index > 0.61, it is considered to be in remission, and it indicates that the patient's risk of developing LREs in the future is significantly reduced; (3) Grey Zone: 0.61 ≥ Index ≥ 0.24; This indicates that the result is uncertain and it is recommended to shorten the follow-up interval or combine it with other examination methods.

[0043] Example 2 The difference from Embodiment 1 is that, in terms of LSM acquisition, although the FibroScan device (VCTE technology) is preferred in this embodiment, other elastography techniques that can provide reliable liver stiffness measurement (such as shear wave elastography (SWE) and magnetic resonance elastography (MRE)) can also be applied to the logical framework of this invention after appropriate coefficient correction.

[0044] Example 3 The difference from Example 1 is that, in terms of biochemical detection, AST, ALT and SCr can be detected using routine clinical standard testing methods such as enzymatic methods and kinetic methods. As long as the unit is consistent, this model can be applied.

[0045] Example 4 Low-risk patients identified as "remission" using the method of this invention Patient A, male, 55 years old, was diagnosed with MASLD and received lifestyle intervention treatment.

[0046] Baseline data: LSM = 12.0 kPa, AST = 60 U / L, SCr = 80 µmol / L, ALT = 90 U / L. Calculated baseline acMASH = 7.5; baseline acFibroMASH = 0.54.

[0047] 12-month follow-up data: LSM=6.5kPa, AST=30U / L, SCr=80µmol / L, ALT=20U / L. The calculated follow-up acMASH=3.75; follow-up acFibroMASH=0.15.

[0048] Changes: △acFibroMASH = 0.15 - 0.54 = -0.39; △ALT = 20 - 90 = -70.

[0049] Substituting into the formula, we calculate: Y = 1.759 - 7.069 * (acFibroMASH_base) - 4.369 (△acFibroMASH) - 0.012 (△ALT) = 1.759 - 3.817 + 1.704 + 0.84 = 0.486.

[0050] Index = e^ 0.486 / (1+e^ 0.486 =0.62>0.61.

[0051] Therefore, this patient's condition suggests histological remission.

[0052] Example 5 High-risk patients identified as "not in remission" using the method of this invention Patient B, male, 50 years old, has MASLD with severe liver fibrosis and is receiving drug and lifestyle intervention treatment.

[0053] Baseline data: LSM = 18.0 kPa, AST = 85 U / L, SCr = 80 µmol / L, ALT = 110 U / L. Calculated baseline acMASH = 10.63; baseline acFibroMASH = 0.90.

[0054] 12-month follow-up data: LSM=16.5kPa, AST=78U / L, SCr=82 μmol / L, ALT=95U / L. The calculated follow-up acMASH=9.51; follow-up acFibroMASH=0.85.

[0055] Changes: △acFibroMASH = 0.85 - 0.90 = -0.05; △ALT = 95 - 110 = -15.

[0056] Substituting into the formula, we calculate: Y = 1.759 - 7.069 * (acFibroMASH_base) - 4.369 * (△acFibroMASH) - 0.012 * (△ALT) = 1.759 - 6.362 + 0.218 + 0.18 = -4.205.

[0057] Index=e^ -4.205 / (1+e^ -4.205 =0.015<0.24.

[0058] Conclusion: Therefore, this patient did not achieve histological remission and belongs to the high-risk group for long-term liver-related events (LREs).

[0059] Example 6 The result determined by the method of this invention is uncertain. Patient C, female, 48 years old, was diagnosed with MAFLD and received regular exercise intervention and management of metabolic risk factors.

[0060] Baseline data: LSM = 10.5 kPa, AST = 50 U / L, SCr = 70 µmol / L, ALT = 80 U / L. Calculated baseline acMASH = 7.14; baseline acFibroMASH = 0.43.

[0061] 12-month follow-up data: LSM=8.5kPa, AST=40U / L, SCr=70µmol / L, ALT=65U / L. The calculated follow-up acMASH=5.71; follow-up acFibroMASH=0.27.

[0062] Change in amount: △acFibroMASH = 0.27 0.43= 0.16; △ALT=65 80= 15.

[0063] Substituting into the formula, we get: Y = 1.759 7.069*(acFibroMASH_base) 4.369*(△acFibroMASH) 0.012 * (△ALT) = 1.759 3.038 + 0.699 + 0.18 = 0.40.

[0064] Index = e^ 0.40 / (1+e^ 0.40 = 0.40, which is between 0.24 and 0.61.

[0065] Therefore, the patient falls into the "gray zone" as determined by this invention.

[0066] The core inventive point of this invention lies in constructing a multi-dimensional evaluation model that includes both "baseline state" and "dynamic changes." Specifically, this is reflected in: 1. For the first time, a specific linear combination (and its weighting coefficient) of “baseline acFibroMASH”, “acFibroMASH change” and “ALT change” was discovered and verified, which can reflect the reversal of liver histology more accurately than a single indicator.

[0067] 2. This combined model was not only used to diagnose current histological remission, but also extended to predict long-term clinical outcomes (LREs) over a period of 5-10 years, establishing a correlation between diagnosis and prognosis.

[0068] From a pathological perspective, the remission of fibrotic MASH involves two key processes: a reduction in hepatocellular inflammation / damage (reflected by a decrease in ALT) and the regression of fibrosis (reflected by acFibroMASH and its dynamic changes). Baseline acFibroMASH determines the severity of the disease at the start of treatment; severe fibrosis is generally more difficult to reverse and is therefore introduced as a negative weight. △acFibroMASH directly reflects the combined improvement in fibrosis and metabolic burden during treatment. △ALT reflects the immediate changes in hepatic necrotizing inflammatory activity. This invention organically integrates information from these three dimensions through mathematical modeling, enabling the capture of disease progression trajectories that cannot be reflected by a single time point or single indicator, thereby achieving high-precision non-invasive assessment.

[0069] Experimental evidence and beneficial effects The effectiveness of this invention was validated by a global multicenter study involving 2017 matched liver biopsy patients: 1. Diagnostic efficacy: In the derivation cohort (N=111), the AUROC of the present invention index for diagnosing MASH remission of fibrosis was 0.82 (95% CI 0.74-0.90, e.g., ...). Figure 2 As shown); in the external validation queue (N=141), the AUROC was 0.80 (95% CI 0.72-0.88, as shown). Figure 3 (As shown). In comparison, the AUROC of the FAST score was only 0.59, and that of the FIB-4 was only 0.68. This invention has a significant statistical advantage (p<0.05).

[0070] 2. Prognostic prediction: In a longitudinal cohort of 8752 patients (median follow-up 3.9 years), patients with an invention index >0.61 had an extremely low risk of developing LREs (e.g., Figure 4 (As shown). Compared with the high-risk group (index <0.24), the low-risk group had a hazard ratio (HR) of 0.043 (adjusted), meaning the risk was reduced by 95.7%.

[0071] 3. Predictive power: The AUROC for predicting the probability of no LREs in 5 and 10 years reached 0.86 respectively (e.g., Figure 5 (as shown) and 0.87 (as shown) Figure 6 As shown in the figure, it demonstrates its excellent long-term prognostic value.

Claims

1. A non-invasive assessment method for MASH relief of fibrosis, characterized in that, The specific steps are as follows: Step 1: Obtain the clinical parameters of the subjects to be evaluated at baseline time T0 and follow-up time T1; Step 2: Calculate the baseline acFibroMASH index and the follow-up acFibroMASH index respectively; Step 3: Calculate the changes in the acFibroMASH index and ALT, respectively; Step 4: Substitute the baseline acFibroMASH index, the change in the acFibroMASH index, and the change in ALT into the preset fibrosis MASH mitigation prediction model to calculate the fibrosis MASH mitigation index. Step 5: Compare the calculated index with the preset two-sided cutoff value and output the evaluation result.

2. The non-invasive assessment method for MASH relief of fibrosis according to claim 1, characterized in that, The parameters in step 1 include: baseline liver stiffness measurement value LSM_base, baseline aspartate aminotransferase AST_base, baseline serum creatinine SCr_base; and follow-up liver stiffness measurement value LSM_follow, follow-up aspartate aminotransferase AST_follow, follow-up serum creatinine SCr_follow, baseline alanine aminotransferase ALT_base, and follow-up alanine aminotransferase ALT_follow; The liver stiffness value (LSM) of the patient was measured using a vibration-controlled transient elastography device, in kPa. The patient's serum aspartate aminotransferase (AST) (U / L), alanine aminotransferase (ALT) (U / L), and serum creatinine (SCr) (µmol / L) were measured using a blood biochemistry analyzer.

3. The non-invasive assessment method for MASH relief of fibrosis according to claim 2, characterized in that, Step 2 is as follows: Calculate the acMASH index based on AST and SCr; combine LSM and acMASH index and use the logistic regression formula to calculate the acFibroMASH index; calculate the baseline acFibroMASH index and the follow-up acFibroMASH index respectively.

4. The non-invasive assessment method for MASH relief of fibrosis according to claim 3, characterized in that, Step 2 includes: Step 2.1: Calculate the acMASH index: According to the formula: acMASH = AST / SCr*10; calculate the baseline acMASH_base and the follow-up acMASH_follow respectively; Step 2.2: Calculate the acFibroMASH index: According to the formula: acFibroMASH = e^ Z / (1+ e^ Z ); Where Z = -3.956 + 0.305 * LSM + 0.065 * acMASH; Substituting the baseline and follow-up parameters respectively, the baseline acFibroMASH_base and the follow-up acFibroMASH_follow are calculated.

5. The non-invasive assessment method for MASH relief of fibrosis according to claim 4, characterized in that, In step 3, the change in the acFibroMASH index, ΔacFibroMASH, is equal to the follow-up value minus the baseline value; the change in ALT, ΔALT, is equal to the follow-up value minus the baseline value.

6. The non-invasive assessment method for MASH relief of fibrosis according to claim 5, characterized in that, In step 4, the fibrosis MASH mitigation prediction model is as follows: Index = e^ Y / (1+e^ Y ) The formula for calculating Y is: Y = 1.759 - 7.069 * (acFibroMASH_base) - 4.369 * (△acFibroMASH) - 0.012 * (△ALT).

7. The non-invasive assessment method for MASH relief of fibrosis according to claim 6, characterized in that, In step 5, the calculated index is compared with the preset two-sided cutoff value, and the evaluation result is output: if the index is greater than the second threshold, it is determined to be a high probability of mitigation, i.e., low LREs risk; if the index is less than the first threshold, it is determined to be a low probability of mitigation, i.e., high LREs risk. Specifically as follows: (1) First threshold: i.e., the exclusion threshold Cut-off 1: 0.24; if Index < 0.24, it is judged as no relief, and it indicates that the patient has a high risk of developing liver-related events (LREs) in the future; (2) Second threshold: i.e., confirmation threshold Cut-off 2: 0.61; if Index > 0.61, it is considered as a remission, and it indicates that the patient's risk of developing LREs in the future is significantly reduced; (3) Gray Zone: 0.61≥ Index≥0.24; The results are uncertain; it is recommended to shorten the follow-up interval or combine other examination methods.

8. A non-invasive assessment system for MASH relief of fibrosis, based on the method described in any one of claims 1-7, characterized in that, include: Data acquisition module: used to acquire clinical parameters of the evaluation subjects at baseline and follow-up times; Calculation module: Used for data processing, including: Intermediate variable calculation submodule: used to calculate the baseline acFibroMASH index and the follow-up acFibroMASH index; Change Calculation Submodule: Used to calculate the change in the acFibroMASH exponent ΔacFibroMASH and the change in ALT ΔALT; The mitigation index calculation submodule contains a fibrosis MASH mitigation prediction model, which calculates the fibrosis MASH mitigation index. Results output module: Compares the calculated exponent with the preset two-sided cutoff value and outputs the evaluation result.

9. A non-invasive assessment device for MASH relief of fibrosis, characterized in that, include: processor; A memory in which executable instructions of the processor are stored; The processor is configured to perform the steps of the method according to any one of claims 1-7 via the executable instructions.

10. A computer-readable storage medium for storing a program, characterized in that, When the program is executed, it performs the steps of the method described in any one of claims 1-7.

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