A method for predicting the effect of treating superficial early-stage gastric tumor lesions

Abstract

The present invention provides a method for predicting the treatment effect of superficial early-stage gastric tumor lesions, which relates to the technical field of treatment effect prediction. The method comprises: performing an initial image scan on a patient to be treated to obtain a preoperative image set; performing parameter sequence extraction on the preoperative image set to obtain an initial parameter sequence set; obtaining an initial derived parameter set based on the initial parameter sequence set; performing surgical treatment on the patient to be tested based on the derived parameter set to obtain a patient to be tested; performing a secondary image scan on the patient to be tested to obtain a postoperative image set; performing parameter sequence extraction on the postoperative image set to obtain a secondary parameter sequence set; obtaining a secondary derived parameter set based on the secondary parameter sequence set; obtaining a first ROI map and a second ROI map based on the derived parameter set and the secondary derived parameter set; and inputting the first ROI map and the second ROI map into a treatment effect prediction model to obtain a prediction result. The present invention solves the problem of low accuracy in treatment effect prediction in the prior art.

Description

Technical Field

[0001] The present invention relates to the technical field of treatment effect prediction, and in particular to a method for predicting the effect of treating superficial early-stage gastric tumor lesions. Background Art

[0002] Gastric cancer is one of the most common malignant tumors with high morbidity and mortality worldwide. Early detection and treatment are crucial for improving patient outcomes. Endoscopic therapies (such as endoscopic mucosal resection) and transarterial chemoembolization (TACE) are widely used in the clinical treatment of early-stage superficial gastric tumors. However, predicting treatment efficacy remains a major challenge in clinical practice, particularly given the complexity of tumor biology and treatment response.

[0003] Currently, traditional imaging methods (such as CT and MRI) have certain limitations in assessing tumor biological characteristics and treatment response. Especially in the early stages, imaging results often fail to fully reflect tumor heterogeneity and microstructural changes. Furthermore, existing treatment response prediction methods often rely on a single imaging parameter and lack a comprehensive assessment of the tumor's overall characteristics, which can lead to inaccurate predictions. Summary of the Invention

[0004] In order to overcome the deficiencies of the prior art, the purpose of the present invention is to provide a method for predicting the effect of treating superficial early gastric tumor lesions. The present invention solves the problem of low accuracy of the effect of treating superficial early gastric tumor lesions in the prior art.

[0005] To achieve the above object, the present invention provides the following solutions:

[0006] A method for predicting the efficacy of treating superficial early-stage gastric tumor lesions, comprising:

[0007] Perform an initial image scan on the patient to be treated to obtain a preoperative image set;

[0008] performing parameter sequence extraction on the preoperative image set to obtain an initial parameter sequence set;

[0009] Obtaining an initial derived parameter set according to the initial parameter sequence set;

[0010] Performing surgical treatment on the patient to be tested according to the derived parameter set at intervals of a first preset number of days to obtain the patient to be tested;

[0011] After a second preset number of days, performing a second image scan on the patient to be tested to obtain a postoperative image set;

[0012] performing parameter sequence extraction on the postoperative image set to obtain a secondary parameter sequence set;

[0013] Obtaining a secondary derivative parameter set according to the secondary parameter sequence set;

[0014] Obtain a first ROI map and a second ROI map according to the derived parameter set and the secondary derived parameter set;

[0015] Inputting the first ROI map and the second ROI map into a treatment effect prediction model to obtain a prediction result;

[0016] Wherein, the method for constructing the treatment effect prediction model is:

[0017] Obtain clinical information and pathological data of each patient;

[0018] Combining the clinical data and pathological data with the features of the initial parameter sequence set and the secondary parameter sequence set to obtain a first feature combination set and a second feature combination set;

[0019] Determining a feature variation set based on the first feature combination set and the second feature combination set;

[0020] Determine the correlation between the parameter features in the first feature combination set and the second feature combination set and the treatment effect using the Pearson correlation coefficient;

[0021] stratifying the features in the feature change set according to the correlation to obtain stratified features;

[0022] A treatment effect prediction model is constructed based on the hierarchical features and the random forest model.

[0023] Preferably, an initial image scan is performed on the patient to be treated to obtain a preoperative image set, including:

[0024] Performing an initial image scan on the patient to be treated using IVIM to obtain a first preoperative image subset;

[0025] Performing an initial image scan on the patient to be treated using T1WI to obtain a second preoperative image subset;

[0026] Performing an initial image scan on the patient to be treated using T2WI to obtain a third preoperative image subset;

[0027] Performing an initial image scan on the patient to be treated using LAVA to obtain a fourth preoperative image subset;

[0028] The first preoperative image subset, the second preoperative image subset, the third preoperative image subset, and the fourth preoperative image subset are integrated to obtain a preoperative image set.

[0029] Preferably, the initial parameter sequence set includes:

[0030] Number of slices, slice thickness, slice spacing, field of view, matrix, echo time, repetition time, diffusion sensitivity coefficient, fat suppression and respiratory triggering mode.

[0031] Preferably, obtaining an initial derived parameter set according to the initial parameter sequence set includes:

[0032] Determine the quantitative range and magnitude range of the diffusion sensitivity coefficient;

[0033] determining a fast diffusion coefficient, a slow diffusion coefficient, a perfusion fraction, and a single exponential parameter according to a quantity threshold of the diffusion sensitivity coefficient and a size threshold of the diffusion sensitivity coefficient;

[0034] An initial set of derived parameters is obtained based on the fast diffusion coefficient, slow diffusion coefficient, perfusion fraction and monoexponential parameter.

[0035] Preferably, the determining of the fast diffusion coefficient, the slow diffusion coefficient, the perfusion fraction and the single exponential parameter according to the quantity threshold of the diffusion sensitivity coefficient and the size threshold of the diffusion sensitivity coefficient comprises:

[0036] Construct a signal strength attenuation curve;

[0037] The fast diffusion coefficient, slow diffusion coefficient, perfusion fraction and single exponential parameter are obtained by fitting the signal intensity decay curve and different diffusion sensitivity coefficients.

[0038] Preferably, the expression of the signal strength attenuation curve is:

[0039] Sb / S0=(1-f)×exp(-bD)+f×exp(-bD*);

[0040] where Sb represents the signal intensity within the voxel corresponding to the corresponding b value; S0 represents the signal intensity within the voxel when the b value is 0, D is the slow diffusion coefficient, D* is the fast diffusion coefficient, f is the perfusion fraction, ADC is the monoexponential parameter, and b is the diffusion sensitivity coefficient.

[0041] Preferably, the expression of the diffusion sensitivity coefficient is:

[0042] b=γ 2 G 2 δ 2 (Δ-δ / 3);

[0043] where γ is the gyromagnetic constant of the nucleus, G is the strength of the diffusion gradient, δ is the duration of the diffusion gradient, and δ is the interval between gradient pulses.

[0044] Preferably, the expression of the treatment effect prediction model is:

[0045]

[0046] Among them, β0 is the model constant term, β1, β2, β3, and β4 are the first regression coefficient, second regression coefficient, third regression coefficient, and fourth regression coefficient of the image-derived parameter change, respectively. i is the i-th clinical feature, P j is the jth pathological feature, and ε is the error term.

[0047] The present invention discloses the following technical effects:

[0048] The present invention provides a method for predicting the effect of treating superficial early-stage tumorous lesions of the stomach, comprising: performing an initial image scan on a patient to be treated to obtain a preoperative image set; performing parameter sequence extraction on the preoperative image set to obtain an initial parameter sequence set; obtaining an initial derived parameter set based on the initial parameter sequence set; performing surgical treatment on the patient to be treated based on the derived parameter set at an interval of a first preset number of days to obtain a patient to be treated; performing a secondary image scan on the patient to be treated at an interval of a second preset number of days to obtain a postoperative image set; performing parameter sequence extraction on the postoperative image set to obtain a secondary parameter sequence set; obtaining a secondary derived parameter set based on the secondary parameter sequence set; obtaining a first ROI map and a second ROI map based on the derived parameter set and the secondary derived parameter set; and The first ROI map and the second ROI map are input into the treatment effect prediction model to obtain a prediction result; wherein, the method for constructing the treatment effect prediction model is: obtaining clinical information and pathological data of each patient; combining the clinical information and pathological data with the features of the initial parameter sequence set and its secondary parameter sequence set respectively to obtain a first feature combination set and a second feature combination set; determining a feature change set based on the first feature combination set and the second feature combination set; using the Pearson correlation coefficient to determine the correlation between the parameter features in the first feature combination set and the second feature combination set and the treatment effect; stratifying the features in the feature change set based on the correlation to obtain stratified features; constructing a treatment effect prediction model based on the stratified features and the random forest model. The present invention introduces a comparison of preoperative and postoperative parameters, calculates the change in parameters, integrates the patient's clinical information and pathological data, and constructs a prediction model together with imaging parameters. The accuracy of treatment effect prediction is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0050] Figure 1A flow chart of a method for predicting the effectiveness of treating superficial early-stage gastric tumor lesions provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0051] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0052] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0053] like Figure 1 As shown, the present invention provides a method for predicting the effect of treating superficial early-stage gastric tumor lesions, comprising:

[0054] Step 100: Perform an initial image scan on the patient to be treated to obtain a preoperative image set;

[0055] Step 200: extracting parameter sequences from the preoperative image set to obtain an initial parameter sequence set;

[0056] Step 300: Obtain an initial derived parameter set according to the initial parameter sequence set;

[0057] Step 400: performing surgical treatment on the patient to be tested according to the derived parameter set at intervals of a first preset number of days to obtain the patient to be tested;

[0058] Step 500: performing a second image scan on the patient at an interval of a second preset number of days to obtain a postoperative image set;

[0059] Step 600: extracting parameter sequences from the postoperative image set to obtain a secondary parameter sequence set;

[0060] Step 700: Obtain a secondary derivative parameter set according to the secondary parameter sequence set;

[0061] Postoperative Parameter Set: The imaging parameters extracted from the postoperative image set, corresponding to the initial parameter set.

[0062] Step 800: Obtain a first ROI image and a second ROI image according to the derived parameter set and the secondary derived parameter set;

[0063] Step 900: Inputting the first ROI map and the second ROI map into a treatment effect prediction model to obtain a prediction result;

[0064] Wherein, the method for constructing the treatment effect prediction model is:

[0065] Step 901: Obtain clinical information and pathological data of each patient;

[0066] Step 902: Combining the clinical data and pathological data with the features of the initial parameter sequence set and the secondary parameter sequence set to obtain a first feature combination set and a second feature combination set;

[0067] Specifically, Clinical Data: **Collect clinical information of each patient, including but not limited to: age, gender, symptoms (such as abdominal pain, indigestion, etc.), laboratory test results (such as blood routine, tumor marker levels), family history and lifestyle habits, etc. **Pathological Data: **Collect pathological information of each patient: tumor differentiation degree (well differentiated, moderately differentiated, poorly differentiated)

[0068] Tumor size and location, depth of invasion, lymph node metastasis, and other pathological features (such as vascular invasion and neural invasion).

[0069] Step 903: determining a feature variation set based on the first feature combination set and the second feature combination set;

[0070] Step 904: using the Pearson correlation coefficient to determine the correlation between the parameter features in the first feature combination set and the second feature combination set and the treatment effect;

[0071] Step 905: stratifying the features in the feature change set according to the correlation to obtain stratified features;

[0072] Step 906: Construct a treatment effect prediction model based on the hierarchical features and the random forest model.

[0073] More specifically, construct the first feature combination set (Feature Combination Set 1):

[0074] Steps: Merge each patient's clinical and pathological data horizontally to generate a data table containing all patient clinical and pathological information. Merge the imaging features from the initial parameter sequence set with this data table. Ensure that the patient's identity is correctly matched (e.g., by patient ID). Result: Result: The first feature set is generated, containing each patient's clinical data, pathological data, and preoperative imaging features.

[0075] Constructing the second feature combination set (Feature Combination Set 2): Repeat the above process and merge the postoperative imaging features in the secondary parameter sequence set with the patient's clinical and pathological data. Result: The second feature combination set was obtained, which contained each patient's clinical data, pathological data, and postoperative imaging features.

[0076] Furthermore, an initial image scan is performed on the patient to be treated to obtain a preoperative image set, including:

[0077] Performing an initial image scan on the patient to be treated using IVIM to obtain a first preoperative image subset;

[0078] Performing an initial image scan on the patient to be treated using T1WI to obtain a second preoperative image subset;

[0079] Performing an initial image scan on the patient to be treated using T2WI to obtain a third preoperative image subset;

[0080] Performing an initial image scan on the patient to be treated using LAVA to obtain a fourth preoperative image subset;

[0081] The first preoperative image subset, the second preoperative image subset, the third preoperative image subset, and the fourth preoperative image subset are integrated to obtain a preoperative image set.

[0082] Specifically, all enrolled patients underwent abdominal MRI dynamic contrast-enhanced examinations including IVIM-DWI sequences using a 3.0T superconducting magnetic resonance imaging (GE Discovery MR750, GE Healthcare, Milwaukee, WI, USA). (1) A 32-channel body coil was used, with the xiphoid process as the center for positioning, and a respiratory gating hose was placed. (2) Because the postprandial and fasting states have a significant impact on the blood volume of the liver, this study required fasting for 4 to 6 hours before IVIM-DWI examinations. (3) Before the scan, professional MRI staff conducted respiratory training for the patients (requiring them to master the rhythm of even breathing and the timing of breath holding at the end of expiration). (4) The scanning range was required to cover the diaphragm at the upper edge and the lower edge to encompass the entire lower edge of the liver.

[0083] The specific MRI scanning sequences are as follows: transverse axial fat-suppressed T2-weighted sequence (PROPELLER FST2WI), breath-hold transverse axial fast three-dimensional spoiled gradient echo T1-weighted sequence (LAVA-Flex); IVIM-DWI uses a respiratory-triggered spin echo planar imaging sequence. To improve the accuracy of the rapid diffusion coefficient (D*), we used an optimized b-value distribution, including 6 low b-values ​​(≤200 s / mm2) and 5 high b-values ​​(>200 s / mm2), for a total of 11 b-values, namely (0, 10, 20, 40, 80, 200, 400, 600, 800, 1000, 1200 s / mm2). The total acquisition time for the scan protocol was approximately 17 minutes; the IVIM sequence took 5 minutes and 30 seconds. Scanning parameters remained constant throughout the study. For enhanced scanning, paramagnetic contrast agent (Magnevist, Bayer, Berlin, Germany) was injected using a high-pressure syringe. Arterial phase images were acquired approximately 18 seconds after contrast injection, portal venous phase images approximately 70 seconds after injection, and delayed phase images approximately 180 seconds after injection. The contrast agent dose was 0.1 mmol / kg, and the injection rate was 1.5 to 2 ml / s. The median interval between preoperative IVIM-DWI and TACE was 5.5 days (range, 1 to 16 days), and the median interval between TACE and postoperative follow-up was 42 days (range, 28 to 146 days).

[0084] Specifically, the initial parameter sequence set includes:

[0085] Number of slices, slice thickness, slice spacing, field of view, matrix, echo time, repetition time, diffusion sensitivity coefficient, fat suppression and respiratory triggering mode.

[0086] Furthermore, the specific TACE treatment protocol is as follows: First, the groin area is routinely disinfected and draped. After local anesthesia with 1% lidocaine, the right femoral artery is punctured using a modified Seldinger technique, and a 5F arterial sheath is introduced. Subsequently, a 4F catheter (RH, Terumo Corporation, Japan) is inserted for celiac artery-hepatic artery and superior mesenteric artery angiography. After cannulation of the common hepatic artery, a non-ionic contrast agent (Visipaque, 320 mg / mL; GE Healthcare, Princeton, NJ) is injected via a high-pressure syringe. Hepatic artery angiography allows for preliminary visualization of the tumor vessels and vascular pathways of large primary HCCs. Subsequently, under DSA vascular guidance, a 2.6F Stride microcatheter (Progreat, Terumo Corporation, Tokyo, Japan) is superselectively inserted into the tumor-feeding arteries. If the tumor vessels overlap or are unclear during the procedure, rotational angiography or vertebral CT is performed to further define their blood supply. Embolization therapy is performed after the distal segment of the microcatheter is properly positioned and contrast agent reflux is absent. Furthermore, 30% to 50% of patients with large HCC have collateral blood supply, particularly when the tumor is located in the anterior portion of the liver, adjacent to the diaphragm, or invading the liver capsule. Therefore, after initial embolization, it is important to observe whether the embolization is complete. If lipiodol embolization is incomplete, the possibility of ectopic hepatic artery or other collateral tumor-feeding arteries, such as the inferior phrenic artery, intercostal arteries, gastroduodenal artery, left gastric artery, internal thoracic artery, and adrenal artery, should be actively explored. If collateral blood supply is present, the corresponding collateral arteries should be embolized. Precautions for the preparation of embolic drugs and materials before embolization: (1) Before surgery, the medication regimen is often determined based on the tumor diameter, patient weight, and general condition, including 2 to 4 chemotherapy drugs such as epirubicin (30-50 mg), fluorouracil (500-750 mg), pirarubicin hydrochloride (40-50 mg), platinum (30-60 mg), 5-FU (500-1000 mg), and calcium folinate (75-100 mg); (2) Before surgery, powdered chemotherapy drugs such as epirubicin and platinum are mixed with iodized oil (lipiodol; Laboratoire Guerbet, Roissy, France) to form an emulsion; the total amount of iodized oil in one treatment depends on the tumor size, but generally does not exceed 20 ml; (3) For patients with tumors with rich blood supply, gelatin sponge particles (diameter 1 mm) or polyvinyl alcohol particles (PVA, 300-700 umol / L, Cook Medical, Bloomington, IN, USA) must be given for embolization.

[0087] Furthermore, obtaining an initial derived parameter set according to the initial parameter sequence set includes:

[0088] Determine the quantitative range and magnitude range of the diffusion sensitivity coefficient;

[0089] determining a fast diffusion coefficient, a slow diffusion coefficient, a perfusion fraction, and a single exponential parameter according to a quantity threshold of the diffusion sensitivity coefficient and a size threshold of the diffusion sensitivity coefficient;

[0090] An initial set of derived parameters is obtained based on the fast diffusion coefficient, slow diffusion coefficient, perfusion fraction and monoexponential parameter.

[0091] Furthermore, the determining of the fast diffusion coefficient, the slow diffusion coefficient, the perfusion fraction and the single exponential parameter according to the quantity threshold of the diffusion sensitivity coefficient and the size threshold of the diffusion sensitivity coefficient includes:

[0092] Construct a signal strength attenuation curve;

[0093] The fast diffusion coefficient, slow diffusion coefficient, perfusion fraction and single exponential parameter are obtained by fitting the signal intensity decay curve and different diffusion sensitivity coefficients.

[0094] Furthermore, the expression of the signal strength attenuation curve is:

[0095] Sb / S0=(1-f)×exp(-bD)+f×exp(-bD*);

[0096] where Sb represents the signal intensity within the voxel corresponding to the corresponding b value; S0 represents the signal intensity within the voxel when the b value is 0, D is the slow diffusion coefficient, D* is the fast diffusion coefficient, f is the perfusion fraction, ADC is the monoexponential parameter, and b is the diffusion sensitivity coefficient.

[0097] Furthermore, the expression of the diffusion sensitivity coefficient is:

[0098] b=γ 2 G 2 δ 2 (Δ-δ / 3);

[0099] where γ is the gyromagnetic constant of the nucleus, G is the strength of the diffusion gradient, δ is the duration of the diffusion gradient, and δ is the interval between gradient pulses.

[0100] Specifically, IVIM-DWI data for all b values ​​were input into the GEAW4.6 post-processing workstation. The data were analyzed using MADC software (GE Medical System, Milwaukee, WI, USA). IVIM-derived parameters (ADC, D, D*, and f) were simultaneously extracted and pseudo-color images of the IVIM numbers were generated. The IVIM biexponential fitting formula is: Sb / S0 = (1-f) × exp(-bD) + f × exp(-bD*), where Sb represents the signal intensity within the voxel corresponding to the corresponding b value; S0 represents the signal intensity within the voxel at a b value of 0. D is the slow diffusion coefficient (×10-3 mm2 / s); D* is the fast diffusion coefficient (×10-3 mm2 / s); and f is the perfusion fraction (%). The single exponential parameter ADC was analyzed and measured using a single exponential model, with units of ×10-3 mm2 / s. For image measurements, two senior attending physicians used ITK-SNAP software to select IVIM-DWI images with b=1000 (s / mm²) to delineate the tumor ROI. To more accurately delineate the tumor boundary, T2WI images and dynamic contrast-enhanced images from the patient's concurrent scan series were used as reference. To avoid measurement bias, two observers performed manual delineation of each tumor layer on their respective workstations in a double-blind, randomized manner. During delineation, it was important to ensure that the delineated ROI encompassed all tumor components, such as cystic changes, hemorrhage, and necrosis, to obtain a full-tumor ROI map. Any doubts about the tumor boundary were sought from a third chief physician.

[0101] After completing the above steps, the outlined ROI needs to be synchronously copied to other IVIM parameter images, such as D, D*, f, and ADC images. The whole tumor volume voxel of each IVIM parameter is automatically analyzed using Matlab software to generate the mean (mean), minimum value (min), 5th percentile, 10th percentile, 25th percentile, 50th percentile, 75th percentile, 90th percentile, and maximum value (max), as well as skewness and kurtosis values, and record them in sequence; skewness and kurtosis values ​​can reflect the shape of the histogram and can also analyze the distribution status of ADC value, D*, D value, and f value.

[0102] Furthermore, the expression of the treatment effect prediction model is:

[0103]

[0104] Among them, β0 is the model constant term, β1, β2, β3, and β4 are the first regression coefficient, second regression coefficient, third regression coefficient, and fourth regression coefficient of the image-derived parameter change, respectively. i is the i-th clinical feature, Pj is the jth pathological feature, and ε is the error term.

[0105] Specifically, clinical characteristics: age (years) = 60, sex (male = 1, female = 0) = 1; pathological characteristics: tumor size (cm) = 2.5, degree of differentiation (high = 1, moderate = 2, low = 3) = 2; known regression coefficient β values ​​(hypothetical): β0 = -2.5, β1 = 10 × 103, β2 = 8 × 103, β3 = 0.5 × 103, β4 = 20, β5 = 0.05 corresponding to age, β6 = -1.0 corresponding to sex, β7 = -0.8 (corresponding to tumor size), β8 = 1.5 (corresponding to degree of differentiation);

[0106] TE=-2.5+10×103×(0.25×10-3)+8×103×(0.15×10-3)+0.5×103×(5×10-3)+20 ×0.05+0.05×60-1.0×1-0.8×2.5+1.5×2=-2.5+2.5+1.2+2.5+1+3-1-2+3=4.7;

[0107] If TE is greater than a certain threshold (e.g., 0), then the treatment is predicted to be effective. In this example, TE=4.7>0,

[0108] Therefore, the treatment effect is predicted to be good.

[0109] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0110] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The above examples are only intended to help understand the method and core concept of the present invention. At the same time, those skilled in the art will find that the specific implementation methods and application scopes may vary based on the concept of the present invention. In summary, the contents of this specification should not be construed as limiting the present invention.

Claims

1. A method for predicting the effect of treating superficial early-stage gastric tumor lesions, characterized in that: include: Perform an initial image scan on the patient to be treated to obtain a preoperative image set; performing parameter sequence extraction on the preoperative image set to obtain an initial parameter sequence set; Obtaining an initial derived parameter set according to the initial parameter sequence set; Performing surgical treatment on the patient to be treated according to the derived parameter set at intervals of a first preset number of days to obtain a patient to be tested; After a second preset number of days, performing a second image scan on the patient to be treated to obtain a postoperative image set; performing parameter sequence extraction on the postoperative image set to obtain a secondary parameter sequence set; Obtaining a secondary derivative parameter set according to the secondary parameter sequence set; Obtain a first ROI map and a second ROI map according to the derived parameter set and the secondary derived parameter set; Inputting the first ROI map and the second ROI map into a treatment effect prediction model to obtain a prediction result; Wherein, the method for constructing the treatment effect prediction model is: Obtain clinical information and pathological data of each patient; Combining the clinical data and pathological data with the features of the initial parameter sequence set and the secondary parameter sequence set to obtain a first feature combination set and a second feature combination set; Determining a feature variation set based on the first feature combination set and the second feature combination set; Determine the correlation between the parameter features in the first feature combination set and the second feature combination set and the treatment effect using the Pearson correlation coefficient; stratifying the features in the feature change set according to the correlation to obtain stratified features; Constructing a treatment effect prediction model based on the hierarchical features and the random forest model; The initial parameter sequence set includes: Number of slices, slice thickness, slice spacing, field of view, matrix, echo time, repetition time, diffusion sensitivity coefficient, fat suppression and respiratory triggering mode; Obtaining an initial derived parameter set according to the initial parameter sequence set includes: Determine the quantitative range and magnitude range of the diffusion sensitivity coefficient; determining a fast diffusion coefficient, a slow diffusion coefficient, a perfusion fraction, and a single exponential parameter according to a quantity threshold of the diffusion sensitivity coefficient and a size threshold of the diffusion sensitivity coefficient; obtaining an initial derived parameter set according to the fast diffusion coefficient, slow diffusion coefficient, perfusion fraction, and monoexponential parameter; The determining of the fast diffusion coefficient, the slow diffusion coefficient, the perfusion fraction and the single exponential parameter according to the quantity threshold of the diffusion sensitivity coefficient and the size threshold of the diffusion sensitivity coefficient comprises: Construct a signal strength attenuation curve; The fast diffusion coefficient, slow diffusion coefficient, perfusion fraction and single exponential parameter are obtained by fitting the signal intensity decay curve and different diffusion sensitivity coefficients.

2. The method for predicting the effect of treating superficial early-stage gastric tumor lesions according to claim 1, characterized in that: Perform an initial image scan on the patient to be treated to obtain a preoperative image set, including: Performing an initial image scan on the patient to be treated using IVIM to obtain a first preoperative image subset; Performing an initial image scan on the patient to be treated using T1WI to obtain a second preoperative image subset; Performing an initial image scan on the patient to be treated using T2WI to obtain a third preoperative image subset; Performing an initial image scan on the patient to be treated using LAVA to obtain a fourth preoperative image subset; The first preoperative image subset, the second preoperative image subset, the third preoperative image subset, and the fourth preoperative image subset are integrated to obtain a preoperative image set.

3. The method for predicting the effect of treating superficial early-stage gastric tumor lesions according to claim 1, characterized in that: The expression of the signal strength attenuation curve is: Sb / S0=(1-f)×exp(-bD)+f×exp(-bD*); where Sb represents the signal intensity within the voxel corresponding to the corresponding b value; S0 represents the signal intensity within the voxel when the b value is 0, D is the slow diffusion coefficient, D* is the fast diffusion coefficient, f is the perfusion fraction, ADC is the monoexponential parameter, and b is the diffusion sensitivity coefficient.

4. A method for predicting the effect of treating superficial early-stage gastric tumor lesions according to claim 3, characterized in that: The expression of the diffusion sensitivity coefficient is: ； in, is the gyromagnetic ratio constant of the nucleus, is the strength of the diffusion gradient, is the duration of the diffusion gradient, is the interval between gradient pulses.

5. The method for predicting the effect of treating superficial early-stage gastric tumor lesions according to claim 4, characterized in that: The expression of the treatment effect prediction model is: ； in, is the model constant term, 、 、 、 are the first regression coefficient, second regression coefficient, third regression coefficient and fourth regression coefficient of the image-derived parameter changes, is the i-th clinical feature, is the jth pathological feature, is the error term.

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