Tumor radiotherapy dose optimization method based on biomarker

Abstract

The invention relates to the technical field of medical radiotherapy, in particular to a tumor radiotherapy dose optimization method based on a biomarker, which comprises the following steps: S1, biomarker collection: collecting tumor tissues and blood samples of a patient to obtain biomarker data related to a tumor radiotherapy effect; s2, data preprocessing: preprocessing the collected biomarker data; s3, constructing an initial radiotherapy dose optimization model: constructing the initial radiotherapy dose optimization model; s4, immune marker monitoring and dose adjustment: monitoring the change of the immune marker of the patient in real time, and dynamically adjusting the initial radiotherapy dose; and S5, treatment effect evaluation: comprehensively evaluating the treatment effect, and judging the effectiveness of the current treatment strategy. According to the invention, the accuracy and flexibility of radiotherapy are enhanced, the side effects of an immune system are effectively reduced, and immune regulation in the radiotherapy process is optimized.

Description

Technical Field

[0001] The present invention relates to the technical field of medical radiotherapy, and in particular to a method for optimizing tumor radiotherapy dose based on biomarkers. Background Art

[0002] Tumor radiotherapy (RT) is one of the main means of cancer treatment. It uses high-energy radiation to destroy the DNA of tumor cells and prevent their proliferation and spread. However, the effect of radiotherapy not only depends on the radiation dose, but is also closely related to the patient's tumor cell sensitivity to radiation, the response of the immune system, and the tolerance of normal tissues. In recent years, with the development of biomarker detection technology, the application of biomarkers in evaluating tumor radiotherapy sensitivity, personalized dose optimization, and immune response monitoring has received widespread attention. Biomarker-based radiotherapy dose optimization methods provide a new research direction for improving radiotherapy effects and reducing side effects.

[0003] Although biomarkers are increasingly used in cancer treatment, existing radiotherapy dose optimization methods often rely too much on fixed dose regimens and lack dynamic monitoring and real-time feedback mechanisms for individualized biomarkers. This method makes it difficult to accurately predict the radiotherapy effect of patients and can easily lead to insufficient or excessive radiotherapy doses, affecting the treatment effect and increasing damage to normal tissues. In addition, existing technologies are insufficient in the dynamic regulation of the immune system, making it difficult to adjust the radiotherapy dose in real time according to changes in the patient's immune markers, thereby limiting the application effect of personalized treatment. During radiotherapy, there is a lack of comprehensive monitoring and adjustment of immune responses and normal tissue damage, resulting in unsatisfactory treatment effects.

[0004] The present invention aims to solve the problem of insufficient radiotherapy dose optimization in the prior art, and provide a method for optimizing tumor radiotherapy dose based on biomarkers to ensure the effectiveness and safety of radiotherapy, minimize damage to healthy tissues, and continuously optimize the radiotherapy plan through a feedback mechanism to improve the overall treatment effect. Summary of the invention

[0005] The present invention provides a method for optimizing tumor radiotherapy dose based on biomarkers.

[0006] The method for optimizing tumor radiotherapy dose based on biomarkers includes the following steps:

[0007] S1, biomarker collection: Collect tumor tissue and blood samples from patients to obtain biomarker data related to the effect of tumor radiotherapy, including DNA repair markers and radiation sensitivity markers;

[0008] S2, data preprocessing: preprocessing the collected biomarker data, including data cleaning, missing value processing and data standardization;

[0009] S3, construction of initial radiotherapy dose optimization model: based on DNA repair markers and radiation sensitivity markers after pretreatment, an initial radiotherapy dose optimization model is constructed to provide personalized initial radiotherapy dose recommendations for patients;

[0010] S4, Immune marker monitoring and dose adjustment: During radiotherapy, real-time monitoring of the patient's immune marker changes, including changes in immune cell activity levels and inflammatory factor concentrations, is performed. The initial radiotherapy dose is dynamically adjusted based on changes in immune markers, including:

[0011] S41, dynamic monitoring of immune markers: during each radiotherapy and the recovery process after radiotherapy, the patient's immune marker data are regularly collected, including the activity level of immune cells and the concentration of inflammatory factors;

[0012] S42, immune response assessment: assess the patient's immune system response to radiotherapy by analyzing the changing trends of immune markers;

[0013] S43, dynamic adjustment of radiotherapy dose: dynamic adjustment of initial radiotherapy dose in real time according to changes in immune markers;

[0014] S5. Evaluation of treatment effect: After each radiotherapy, the treatment effect is comprehensively evaluated by analyzing the tumor shrinkage, the intensity of the immune system response, and the degree of damage to normal tissues. The effectiveness of the current treatment strategy is judged based on the evaluation results.

[0015] Optionally, the biomarker collection in S1 includes:

[0016] S11, tumor tissue sample collection: obtain samples from the patient's tumor tissue using standardized tissue biopsy techniques;

[0017] S12, blood sample collection: Under sterile conditions, a blood sample is drawn from the patient's vein;

[0018] S13, DNA repair marker detection: Gene expression analysis of tumor tissue samples was performed to detect markers related to DNA repair, including the gene expression levels of p53, ATM, and BRCA1;

[0019] S14, Radiosensitivity marker detection: By analyzing the radiosensitivity of tumor cells, the response of tumor cells to radiotherapy is evaluated, and markers related to radiosensitivity are detected, including ATM, p53, BRCA1 / BRCA2, EGFR, and Ki-67.

[0020] Optionally, the data preprocessing in S2 includes:

[0021] S21, data cleaning: The collected biomarker data were cleaned by Z-score standardization method to remove noise and outliers introduced by sampling error and experimental error;

[0022] S22, missing value processing: linear interpolation is used to fill in the missing values;

[0023] S23, data normalization: The data were scaled to the range of 0 to 1 using the Min-Max normalization method.

[0024] Optionally, the initial radiotherapy dose optimization model in S3 includes:

[0025] S31, define key marker nodes and dependencies: In the Bayesian network, set the DNA repair marker D and the radiation sensitivity marker R as nodes in the network, jointly affecting the radiotherapy dose X, and construct the conditional probability distribution P(X|D,R), which means estimating the radiotherapy dose when the DNA repair marker and the radiation sensitivity marker are known;

[0026] S32, calculating the joint conditional probability: using the chain rule of the Bayesian network to decompose the joint conditional probability;

[0027] S33, Radiotherapy dose recommendation by Bayesian network: Based on the calculated joint conditional probability, the optimal initial radiotherapy dose X is obtained by maximum a posteriori estimation (MAP) to maximize P(X|D,R);

[0028] S34, conditional probability calculation: In practical applications, the conditional probabilities of DNA repair markers and radiation sensitivity markers are estimated by clinical data, that is, each node in the Bayesian network is fitted with a conditional probability distribution according to statistical data, assuming that the DNA repair marker D and the radiation sensitivity marker R obey a Gaussian distribution;

[0029] S35, initialization of radiotherapy dose: The initial recommendation of radiotherapy dose X is calculated based on the joint conditional probability distribution of DNA repair markers and radiation sensitivity markers to obtain a personalized dose recommendation, and the recommended value is the initial dose of radiotherapy.

[0030] Optionally, the dynamic monitoring of immune markers in S41 includes:

[0031] S411, Immune cell activity monitoring: During each radiotherapy and the recovery process after radiotherapy, blood samples from patients are collected regularly to detect and analyze the activity levels of immune cells (CD8+T cells, NK cells);

[0032] S412, Monitoring of inflammatory factor concentrations: Regularly measure the concentrations of inflammatory factors, including IL-2 and TNF-α, using ELISA detection technology.

[0033] Optionally, the immune response assessment in S42 includes:

[0034] S421, analysis of immune marker change trends: by regularly monitoring the patient's immune markers, recording immune marker data during radiotherapy and recovery, and analyzing their changing trends over time using an autoregressive integrated moving average model;

[0035] S422, immune response assessment algorithm: Based on the changing trend of immune markers, an exponentially weighted moving average algorithm is used to assess the immune response score of the immune system;

[0036] S423, Immune system status assessment: Evaluate the intensity of the patient's immune system's response to radiotherapy by analyzing the calculated immune response score. When the immune system's response score is higher than the set threshold, it indicates that the patient's immune system responds effectively to radiotherapy. Conversely, when the response score is lower than the set threshold, it indicates that the immune system response is weakened or radiotherapy tolerance is enhanced.

[0037] Optionally, the dynamic adjustment of the radiotherapy dose in S43 includes:

[0038] S431, radiotherapy dose adjustment: according to the dynamic evaluation results of immune markers, the initial radiotherapy dose is adjusted in real time. If the immune response evaluation results show that the immune response is enhanced, the radiotherapy dose is reduced; if the immune response is weakened, the radiotherapy dose is increased;

[0039] S432, Dose feedback and continuous optimization: Apply the adjusted radiotherapy dose to subsequent treatments, and provide feedback based on the changes in immune markers that are continuously monitored to continuously adjust the radiotherapy dose.

[0040] Optionally, the treatment effect evaluation in S5 includes:

[0041] S51, Tumor shrinkage and immune system response assessment: After each radiotherapy, the tumor shrinkage was assessed and the immune system response strength was assessed in combination with immune markers;

[0042] S52, Normal tissue damage assessment and strategy adjustment: Regularly monitor the degree of damage to normal tissues, evaluate the impact of radiotherapy on the patient's healthy tissues, and based on the evaluation results, judge the effectiveness of the current treatment strategy and decide whether the radiotherapy dose needs to be adjusted.

[0043] Optionally, the tumor shrinkage and immune system response assessment in S51 includes:

[0044] S511, evaluation of tumor shrinkage: after each radiotherapy, the tumor volume was measured by imaging techniques (such as CT or MRI scans) and the proportion of tumor shrinkage was calculated;

[0045] S512, Immune system response assessment: The strength of the immune system response is assessed in combination with immune markers, and the strength of the immune system response is calculated by the rate of change of immune marker levels.

[0046] Optionally, the normal tissue damage assessment and strategy adjustment in S52 includes:

[0047] S521, Normal tissue damage monitoring: Regularly monitor the degree of normal tissue damage through imaging techniques (such as CT or MRI), biochemical indicators (such as serum lactate dehydrogenase level) and clinical symptoms of patients;

[0048] S522, Normal tissue damage assessment: Calculate the normal tissue damage rate R by monitoring the healthy tissue volume and biochemical indicators damage ;

[0049] S523, Strategy adjustment and radiotherapy dose decision: According to the injury rate R damage The calculation results are combined with the patient's clinical symptoms to judge the effectiveness of the current treatment strategy. If the injury rate exceeds the preset safety threshold T safe , then adjust the radiotherapy dose.

[0050] Beneficial effects of the present invention:

[0051] The present invention can comprehensively evaluate the radiotherapy sensitivity of tumor cells and the immune response of patients by collecting and analyzing biomarker data related to the effect of tumor radiotherapy, including DNA repair markers, radiation sensitivity markers and immune markers. Through dynamic monitoring and real-time analysis of biomarkers, combined with an accurate radiotherapy dose optimization model, personalized initial dose recommendations are provided for each patient, ensuring the accuracy of the radiotherapy plan and significantly improving the tumor cell killing effect.

[0052] The present invention can dynamically adjust the radiotherapy dose during radiotherapy through the monitoring and evaluation of immune markers to ensure real-time optimization of the treatment plan. When the immune system response is enhanced, the radiotherapy dose is timely reduced to avoid over-activation of the immune system. When the immune response is weakened, the dose is appropriately increased to enhance the therapeutic effect on the tumor. This dynamic dose adjustment mechanism enhances the accuracy and flexibility of radiotherapy, while effectively reducing the side effects of the immune system and optimizing immune regulation during radiotherapy.

[0053] The present invention comprehensively measures the efficacy and safety of radiotherapy through treatment effect evaluation combined with monitoring of tumor shrinkage, immune system response intensity and normal tissue damage. It ensures that the radiotherapy dose can achieve a balance between tumor treatment and normal tissue protection based on individualized threshold setting and dynamic adjustment, minimizes damage to healthy tissues, and achieves personalized and precise control of radiotherapy dose through real-time feedback and continuous optimization, thereby improving the overall treatment effect of radiotherapy and the safety of patients. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0055] Figure 1 A schematic diagram of the optimization method flow of an embodiment of the present invention;

[0056] Figure 2 Schematic diagram of immune marker monitoring and dosage adjustment according to an embodiment of the present invention. DETAILED DESCRIPTION

[0057] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. At the same time, it is explained here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments, and those skilled in the art may also adopt other alternatives to implement some known technologies; and the accompanying drawings are only for more specific description of the embodiments, and are not intended to specifically limit the present invention.

[0058] It should be noted that the references to "one embodiment", "an embodiment", "an exemplary embodiment", "some embodiments" and the like in the specification indicate that the embodiments described may include specific features, structures or characteristics, but not every embodiment may include the specific features, structures or characteristics. In addition, when a specific feature, structure or characteristic is described in conjunction with an embodiment, it should be within the knowledge of a person skilled in the art to implement such feature, structure or characteristic in conjunction with other embodiments (whether or not explicitly described).

[0059] In general, a term can be understood, at least in part, from its use in context. For example, depending, at least in part, on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in the singular sense, or can be used to describe a combination of features, structures, or characteristics in the plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey an exclusive set of factors, but can instead, depending, at least in part, on the context, allow for the presence of other factors that are not necessarily explicitly described.

[0060] like Figure 1-Figure 2 As shown, the tumor radiotherapy dose optimization method based on biomarkers includes the following steps:

[0061] S1, biomarker collection: Collect tumor tissue and blood samples from patients to obtain biomarker data related to the effect of tumor radiotherapy, including DNA repair markers and radiation sensitivity markers;

[0062] S2, data preprocessing: preprocessing the collected biomarker data, including data cleaning, missing value processing and data standardization;

[0063] S3, construction of initial radiotherapy dose optimization model: based on DNA repair markers and radiation sensitivity markers after pretreatment, an initial radiotherapy dose optimization model is constructed to provide personalized initial radiotherapy dose recommendations for patients;

[0064] S4, Immune marker monitoring and dose adjustment: During radiotherapy, real-time monitoring of the patient's immune marker changes, including changes in immune cell activity levels and inflammatory factor concentrations, is performed. The initial radiotherapy dose is dynamically adjusted based on changes in immune markers, including:

[0065] S41, dynamic monitoring of immune markers: during each radiotherapy and the recovery process after radiotherapy, the patient's immune marker data are regularly collected, including the activity level of immune cells and the concentration of inflammatory factors;

[0066] S42, immune response assessment: assess the patient's immune system response to radiotherapy by analyzing the changing trends of immune markers;

[0067] S43, dynamic adjustment of radiotherapy dose: dynamic adjustment of initial radiotherapy dose in real time according to changes in immune markers;

[0068] S5, Treatment effect evaluation: After each radiotherapy, the treatment effect is comprehensively evaluated by analyzing the tumor shrinkage, the strength of the immune system response, and the degree of damage to normal tissues. Based on the evaluation results, the effectiveness of the current treatment strategy is judged;

[0069] Through the above steps, dynamic adjustment and optimization of the patient's personalized dose during radiotherapy is achieved, which not only improves the accuracy and effectiveness of radiotherapy, but also can adjust the treatment plan in time according to the patient's immune response, thereby maximizing the tumor cell killing effect and reducing damage to normal tissues, ultimately improving the overall treatment effect and safety.

[0070] Biomarker collection in S1 includes:

[0071] S11, tumor tissue sample collection: obtain samples from the patient's tumor tissue using standardized tissue biopsy techniques;

[0072] S12, blood sample collection: Under sterile conditions, a blood sample is drawn from the patient's vein;

[0073] S13, DNA repair marker detection: Gene expression analysis of tumor tissue samples was performed to detect markers related to DNA repair, including the gene expression levels of p53, ATM, and BRCA1;

[0074] S14, Radiosensitivity marker detection: The response of tumor cells to radiotherapy is evaluated by analyzing the radiosensitivity of tumor cells and detecting markers related to radiosensitivity, including ATM, p53, BRCA1 / BRCA2, EGFR, and Ki-67;

[0075] Through the above content, the DNA repair ability, radiation sensitivity and cell proliferation status of tumor cells can be comprehensively evaluated. Through multi-dimensional marker analysis, it not only provides a scientific basis for personalized radiotherapy dose optimization, but also can monitor the patient's response to radiotherapy in real time, thereby improving the accuracy and effectiveness of treatment, reducing damage to normal tissues, and improving the overall treatment effect.

[0076] Data preprocessing in S2 includes:

[0077] S21, data cleaning: The collected biomarker data were cleaned by Z-score standardization method to remove noise and outliers introduced by sampling error and experimental error, expressed as:

[0078]

[0079] Where X is the observed value of a marker, μ is the mean of the marker, and σ is the standard deviation;

[0080] S22, missing value processing: linear interpolation is used to fill in the missing values, expressed as:

[0081]

[0082] Among them, X prev and X next The missing data values ​​are estimated for the adjacent valid data points to ensure data integrity;

[0083] S23, data standardization: The Min-Max standardization method is used to scale the data to the interval of 0 to 1, expressed as:

[0084]

[0085] Among them, X is the original data, X min and X max are the minimum and maximum values ​​of the marker, respectively;

[0086] Through the above steps, the high quality and integrity of biomarker data are ensured. Abnormal data are eliminated through Z-score to effectively prevent the influence of extreme data on the analysis results. Linear interpolation method is used to fill missing data to avoid analysis bias caused by incomplete data. Min-Max standardization unifies the data scales of different markers.

[0087] The initial radiation dose optimization model in S3 includes:

[0088] S31, define key marker nodes and dependencies: In the Bayesian network, set the DNA repair marker D and the radiation sensitivity marker R as nodes in the network, jointly affecting the radiotherapy dose X, and construct the conditional probability distribution P(X|D,R), which means estimating the radiotherapy dose when the DNA repair marker and the radiation sensitivity marker are known, expressed as:

[0089]

[0090] Where P(X|D,R) is the posterior probability of the radiotherapy dose X given the markers D and R, P(D,R|X) is the conditional probability of the markers D and R at different radiotherapy doses, P(X) is the prior probability of the radiotherapy dose, and P(D,R) is the joint distribution of DNA repair markers and radiation sensitivity markers;

[0091] S32, calculation of joint conditional probability: In order to accurately reflect the effects of DNA repair markers and radiation sensitivity markers on radiotherapy dose, the linkage rule of the Bayesian network is used to decompose the joint conditional probability, which is expressed as:

[0092] P(D,R|X)=P(D|X)P(R|X,D);

[0093] Among them, P(D|X) represents the probability of DNA repair marker D under a given radiotherapy dose X. Patients with weak DNA repair ability (such as p53 and BRCA1 gene mutations) may be more sensitive to high-dose radiotherapy. P(R|X,D) represents the probability of radiation sensitivity marker R under the conditions of given radiotherapy dose X and DNA repair marker D. Radiation sensitivity (such as high expression of EGFR) will affect the cell's tolerance to radiation and dose response.

[0094] S33, Radiotherapy dose recommendation based on Bayesian network: Based on the calculated joint conditional probability, the optimal initial radiotherapy dose X is obtained through maximum a posteriori estimation (MAP) to maximize P(X|D,R), which is expressed as:

[0095] X MAP = arg max X P(X|D,R);

[0096] This formula means: Given the patient's DNA repair marker D and radiation sensitivity marker R, select the radiation dose X with the maximum posterior probability. MAP , as a personalized initial dosage recommendation;

[0097] S34, conditional probability calculation: In practical applications, the conditional probabilities of DNA repair markers and radiation sensitivity markers are estimated by clinical data, that is, each node in the Bayesian network is fitted with a conditional probability distribution according to statistical data, assuming that the DNA repair marker D and the radiation sensitivity marker R obey a Gaussian distribution, expressed as:

[0098]

[0099] Among them, μ D (X) and μ R (X, D) represent the expected values ​​under given radiotherapy dose X and marker D, respectively. and is the variance of DNA repair markers and radiation sensitivity markers;

[0100] S35, initialization of radiotherapy dose: the initial recommendation of radiotherapy dose X is calculated based on the joint conditional probability distribution of DNA repair markers and radiation sensitivity markers to obtain a personalized dose recommendation, and the recommended value is the initial dose of radiotherapy;

[0101] Through the above steps, optimized recommendations for personalized radiotherapy doses are achieved. The maximum a posteriori estimation method is used to calculate the optimal initial radiotherapy dose based on the specific biomarkers of each patient, ensuring more accurate dose recommendations. The hierarchical structure of the model effectively captures the complex dependencies between markers, making it more flexible and efficient in processing multidimensional data. In addition, through conditional probability decomposition and Gaussian distribution assumptions, the computational efficiency and adaptability of the model are improved, which helps to provide accurate individualized treatment plans in clinical practice.

[0102] Dynamic monitoring of immune markers in S41 includes:

[0103] S411, Immune cell activity monitoring: During each radiotherapy and the recovery process after radiotherapy, blood samples from patients are collected regularly to detect and analyze the activity levels of immune cells (CD8+T cells, NK cells);

[0104] S412, inflammatory factor concentration monitoring: Regularly measure the concentration of inflammatory factors, including IL-2 and TNF-α, by ELISA detection technology;

[0105] Through the above content, a comprehensive assessment of the activity level of immune cells and the concentration changes of inflammatory factors can reflect the patient's immune response status during radiotherapy and recovery in real time. Combined with precise flow cytometry and ELISA detection technology, it can dynamically monitor changes in the immune system and provide a scientific basis for personalized adjustment of radiotherapy plans, thereby improving treatment effects and reducing unnecessary side effects, and effectively optimizing immune regulation during the treatment process.

[0106] The immune response assessment in S42 includes:

[0107] S421, Analysis of immune marker change trends: By regularly monitoring the patient's immune markers, the immune marker data during radiotherapy and recovery period are recorded, and the autoregressive integrated sliding average model is used to analyze the change trend over time, expressed as:

[0108]

[0109] Among them, X t is the immune marker data value at time t (such as CD8+T cell activity or inflammatory factor concentration), is the autoregressive coefficient, reflecting the impact of the marker value at the past p moments on the current moment, θ1,θ2,…,θ q is the moving average coefficient, which indicates the influence of the error of the previous q moments on the current moment, ∈ t is the white noise error term, representing random fluctuations;

[0110] S422, immune response evaluation algorithm: Based on the changing trend of immune markers, an exponentially weighted moving average algorithm is used to evaluate the immune response score of the immune system, expressed as:

[0111] S t =α·M t +(1-α)·S t-1 ;

[0112] Among them, S t is the immune system response score at the current time t, M t is the immune marker monitoring value at time t, α is the smoothing coefficient (0<α<1), which is used to control the weight of current and historical immune response data, S t-1 Score the immune response at the previous moment;

[0113] S423, immune system status assessment: by analyzing the calculated immune response score, the patient's immune system's response to radiotherapy is assessed. When the immune system's response score is higher than the set threshold, it indicates that the patient's immune system responds effectively to radiotherapy. Conversely, when the response score is lower than the set threshold, it indicates that the immune system's response is weakened or radiotherapy tolerance is enhanced.

[0114] The threshold settings include:

[0115] Statistical analysis based on historical data: Analyze the immune response of multiple patients during radiotherapy through statistical methods, calculate the historical mean and standard deviation of immune markers (such as CD8+T cell activity, IL-2, TNF-α concentration), and set the threshold range, expressed as:

[0116] T=μ X +kσ X ;

[0117] Among them, T is the set threshold, μ X is the mean value of immune markers in healthy or well-responding patients, σ X is the standard deviation of the immune marker, reflecting the volatility of the immune marker, k is the adjustment factor, ranging from 1 to 3, reflecting the strictness of setting the threshold, and a larger k value will set a higher threshold, indicating that a stronger immune response can be considered effective;

[0118] Dynamically adjust the threshold: Taking into account the individual differences of each patient, the threshold is dynamically adjusted by observing the patient's immune marker response during the previous radiotherapy and adjusting the threshold based on its historical data. For example, if the patient's immune system shows a significant increase in immune response during the previous radiotherapy, the threshold can be adjusted upward according to the individual situation, expressed as:

[0119]

[0120] Among them, T new is the new threshold, T previous is the threshold set several times before, is the average value of the current immune markers, is the average value of the immune markers in the previous treatments, α is the learning rate or adjustment coefficient, which controls the speed of change of the threshold (usually between 0.1 and 0.5);

[0121] Individualized threshold setting based on immune response: In addition to setting thresholds based on population data, more accurate personalized thresholds are provided based on the patient's baseline immune level. baseline Set the threshold, expressed as:

[0122] T individual =Xbaseline +ΔX;

[0123] Among them, X baseline is the initial level of the patient's immune marker, ΔX is the expected immune response increment during radiotherapy, which is set based on the average increase value of the population data;

[0124] Through the above steps, the changing trends of the patient's immune markers can be accurately tracked, and the patient's immune system's response to radiotherapy can be reflected in a timely manner. Scientific thresholds can be used to determine whether the radiotherapy dose needs to be adjusted, thereby ensuring the effectiveness and safety of the treatment plan, optimizing the treatment effect and reducing potential damage to the patient's immune system.

[0125] Dynamic adjustment of radiotherapy dose in S43 includes:

[0126] S431, radiotherapy dose adjustment: according to the dynamic evaluation results of immune markers, the initial radiotherapy dose is adjusted in real time. If the immune response evaluation results show that the immune response is enhanced, the radiotherapy dose is reduced to avoid overactivation of the immune system. If the immune response is weakened, the radiotherapy dose is increased to enhance the tumor treatment effect.

[0127] S432, Dose feedback and continuous optimization: Apply the adjusted radiotherapy dose to subsequent treatments, and provide feedback based on the changes in immune markers that are continuously monitored to continuously adjust the radiotherapy dose to ensure the accuracy and therapeutic effect of personalized radiotherapy doses;

[0128] Through the above content, the radiotherapy dose can be accurately adjusted to ensure that the response of the immune system during treatment is fully considered. When the immune response is enhanced, the radiotherapy dose is appropriately reduced to avoid over-activation of the immune system. When the immune response is weakened, the dose is increased to enhance the treatment effect of the tumor. This realizes a personalized radiotherapy plan and optimizes the treatment effect. At the same time, it reduces the risk of excessive damage to the immune system. Through continuous feedback and optimization, it can adapt to the patient's changing physiological condition throughout the treatment process to ensure the safety and effectiveness of the treatment.

[0129] Treatment efficacy assessment in S5 includes:

[0130] S51, Tumor shrinkage and immune system response assessment: After each radiotherapy, the tumor shrinkage was assessed and the immune system response strength was assessed in combination with immune markers;

[0131] S52, Normal tissue damage assessment and strategy adjustment: Regularly monitor the degree of damage to normal tissues to assess the impact of radiotherapy on the patient's healthy tissues. Based on the assessment results, determine the effectiveness of the current treatment strategy and decide whether the radiotherapy dose needs to be adjusted to balance the treatment effect and safety.

[0132] Through the above content, the effect and safety of radiotherapy can be comprehensively measured, and the treatment effect of tumors and the response of the immune system can be accurately evaluated, ensuring that the treatment is not only effective against tumors, but also can activate the immune system, ensure the safety of treatment, and avoid damage to normal tissues due to excessive doses. It provides a scientific basis for the adjustment of radiotherapy doses and optimization of strategies, and achieves a balance and precise control of personalized treatment.

[0133] Tumor shrinkage and immune system response assessments in S51 include:

[0134] S511, Tumor shrinkage assessment: After each radiotherapy, the tumor volume was measured by imaging techniques (such as CT or MRI scans) and the tumor shrinkage ratio was calculated, expressed as:

[0135]

[0136] Among them, V pre is the volume of the tumor before radiotherapy, V post is the volume of the tumor after radiotherapy, R tumor It indicates the percentage of tumor shrinkage, reflecting the effectiveness of radiotherapy;

[0137] S512, Immune system response assessment: Combined with immune markers, the immune system response strength is assessed and the immune system response strength is calculated by the rate of change of immune marker levels, expressed as:

[0138]

[0139] Among them, M pre is the baseline level of immune markers before radiotherapy, M post is the detection value of immune markers after radiotherapy, R immune It indicates the percentage of changes in immune markers, reflecting the degree of response of the immune system to radiotherapy;

[0140] Through the above content, the changes in tumor volume can be accurately quantified, which directly reflects the control effect of radiotherapy on tumors. At the same time, the immune system response assessment measures the degree of activation of the immune system through changes in the levels of immune markers, ensuring that the immune system plays an active anti-tumor role during radiotherapy, providing more comprehensive treatment feedback, and providing a scientific basis for subsequent personalized radiotherapy dose adjustment and optimization, ensuring the efficacy and patient safety.

[0141] Normal tissue damage assessment and strategy adjustment in S52 include:

[0142] S521, Normal tissue damage monitoring: Regularly monitor the degree of normal tissue damage through imaging techniques (such as CT or MRI), biochemical indicators (such as serum lactate dehydrogenase level) and clinical symptoms of patients;

[0143] S522, Normal tissue damage assessment: Calculate the normal tissue damage rate R by monitoring the healthy tissue volume and biochemical indicators damage , expressed as:

[0144]

[0145] Among them, V damaged V is the volume of normal tissue damaged after radiotherapy. total is the total volume of normal tissue, R damage It indicates the proportion of normal tissue damage, reflecting the impact of radiotherapy on healthy tissue;

[0146] S523, Strategy adjustment and radiotherapy dose decision: According to the injury rate R damage The calculation results are combined with the patient's clinical symptoms to judge the effectiveness of the current treatment strategy. If the injury rate exceeds the preset safety threshold T safe , then adjust the radiotherapy dose, the adjustment of radiotherapy dose is expressed as:

[0147] X new =X current -α(R damage -T safe );

[0148] Among them, X new is the adjusted radiotherapy dose, X current is the current radiotherapy dose, α is the adjustment coefficient, which is used to control the amplitude of dose change, T safe is the safety threshold, reflecting the level of damage that normal tissue can withstand;

[0149] Safety threshold T safe Threshold setting based on statistical and individualized data, including:

[0150] Initial setting based on clinical statistical data: The safety threshold is first derived from clinical big data based on the tolerance of normal tissues during radiotherapy. For example, different types of tissues (such as lungs, liver, kidneys, etc.) have different tolerance to radiotherapy. By analyzing large-scale patient data, the safety damage threshold of each type of tissue during radiotherapy is determined, expressed as:

[0151] T safe,avg =μ D +kσ D ;

[0152] Among them, μ D is the average safe injury value of a specific normal tissue in clinical data, σ D is the standard deviation of the tissue damage value, k is the safety factor, ranging from 1 to 3, reflecting the strictness of setting the safety threshold;

[0153] Individualized adjustment: Each patient has a different physiological condition, and the initial statistical safety threshold T safe,avg It needs to be adjusted according to the individualized data of the patient. For example, the patient's age, health status, tumor type and its impact on normal tissue tolerance need to be considered, and the individual correction factor F is introduced. individual Reflecting the individual differences of patients, expressed as:

[0154] T safe =T safe,avg ×F individual ;

[0155] Among them, F individual It is a correction factor based on the patient's physiological state, ranging from 0.8 to 1.2, and is adjusted according to the patient's age, basic health status, organ function, etc. For example, for elderly patients or patients with a history of organ damage, F individual It may be lower than 1, reflecting their lower tolerance to radiation therapy, while younger, healthier patients may allow for an appropriate higher threshold;

[0156] Dynamic adjustment: During radiotherapy, the patient's tissue tolerance to radiotherapy may change, so the safety threshold T safe Dynamic adjustment is also required. According to the real-time monitoring data of normal tissue damage after each radiotherapy, the threshold is adjusted dynamically. For example, if the patient's tissue damage is found to be less than expected, the safety threshold can be appropriately increased, otherwise it can be reduced, which is expressed as:

[0157] T safe,new =T safe,prev +γ(R damage,prev -T safe,prev );

[0158] Among them, T safe,new is the new safety threshold, T safe,prev is the previously set safety threshold, R damage,prev is the previous actual damage rate, γ is the adjustment coefficient, which is between 0.1 and 0.5, and controls the dynamic change range of the threshold;

[0159] Through the above content, combined with individualized adjustment and dynamic adjustment mechanisms, the safety of normal tissues during radiotherapy is ensured, and safety thresholds are set based on clinical statistical data and individualized factors. Personalized treatment protection can be provided according to the specific physiological conditions of each patient to prevent excessive damage. At the same time, through real-time monitoring and dynamic adjustment, the treatment plan can flexibly adapt to changes in the patient's condition to ensure a balance between radiotherapy effect and safety, which helps to maximize the protection of healthy tissues while ensuring the effectiveness of tumor treatment.

[0160] The present invention covers any substitution, modification, equivalent method and scheme made on the essence and scope of the present invention. In order to make the public have a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments of the present invention, but those skilled in the art can fully understand the present invention without the description of these details. In addition, in order to avoid unnecessary confusion about the essence of the present invention, well-known methods, processes, procedures, components and circuits are not described in detail.

[0161] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for optimizing tumor radiotherapy dose based on biomarkers, characterized in that: The following steps are involved: S1, biomarker collection: Collect tumor tissue and blood samples from patients to obtain biomarker data related to the effect of tumor radiotherapy, including DNA repair markers and radiation sensitivity markers; S2, data preprocessing: preprocessing the collected biomarker data, including data cleaning, missing value processing and data standardization; S3, construction of initial radiotherapy dose optimization model: based on DNA repair markers and radiation sensitivity markers after pretreatment, an initial radiotherapy dose optimization model is constructed to provide personalized initial radiotherapy dose recommendations for patients; S4, Immune marker monitoring and dose adjustment: During radiotherapy, real-time monitoring of the patient's immune marker changes, including changes in immune cell activity levels and inflammatory factor concentrations, is performed. The initial radiotherapy dose is dynamically adjusted based on changes in immune markers, including: S41, dynamic monitoring of immune markers: during each radiotherapy and the recovery process after radiotherapy, the patient's immune marker data are regularly collected, including the activity level of immune cells and the concentration of inflammatory factors; S42, immune response assessment: assess the patient's immune system response to radiotherapy by analyzing the changing trends of immune markers; S43, dynamic adjustment of radiotherapy dose: dynamic adjustment of initial radiotherapy dose in real time according to changes in immune markers; S5. Evaluation of treatment effect: After each radiotherapy, the treatment effect is comprehensively evaluated by analyzing the tumor shrinkage, the intensity of the immune system response, and the degree of damage to normal tissues. The effectiveness of the current treatment strategy is judged based on the evaluation results.

2. The method for optimizing tumor radiotherapy dose based on biomarkers according to claim 1, characterized in that: The biomarker collection in S1 includes: S11, tumor tissue sample collection: obtain samples from the patient's tumor tissue using standardized tissue biopsy techniques; S12, blood sample collection: Under sterile conditions, a blood sample is drawn from the patient's vein; S13, DNA repair marker detection: Gene expression analysis of tumor tissue samples was performed to detect markers related to DNA repair, including the gene expression levels of p53, ATM, and BRCA1; S14, Radiosensitivity marker detection: By analyzing the radiosensitivity of tumor cells, the response of tumor cells to radiotherapy is evaluated, and markers related to radiosensitivity are detected, including ATM, p53, BRCA1 / BRCA2, EGFR, and Ki-67.

3. The method for optimizing tumor radiotherapy dose based on biomarkers according to claim 1, characterized in that: The data preprocessing in S2 includes: S21, data cleaning: The collected biomarker data were cleaned by Z-score standardization method to remove noise and outliers introduced by sampling error and experimental error; S22, missing value processing: linear interpolation is used to fill in the missing values; S23, data normalization: The data were scaled to the range of 0 to 1 using the Min-Max normalization method.

4. The method for optimizing tumor radiotherapy dose based on biomarkers according to claim 1, characterized in that: The initial radiotherapy dose optimization model in S3 includes: S31, define key marker nodes and dependencies: In the Bayesian network, set the DNA repair marker D and the radiation sensitivity marker R as nodes in the network, jointly affecting the radiotherapy dose X, and construct the conditional probability distribution P(X|D,R), which means estimating the radiotherapy dose when the DNA repair marker and the radiation sensitivity marker are known; S32, calculating the joint conditional probability: using the chain rule of the Bayesian network to decompose the joint conditional probability; S33, Radiotherapy dose recommendation by Bayesian network: Based on the calculated joint conditional probability, the optimal initial radiotherapy dose X is obtained by maximum a posteriori estimation, so that P(X|D,R) is maximized; S34, conditional probability calculation: In practical applications, the conditional probabilities of DNA repair markers and radiation sensitivity markers are estimated by clinical data, that is, each node in the Bayesian network is fitted with a conditional probability distribution according to statistical data, assuming that the DNA repair marker D and the radiation sensitivity marker R obey a Gaussian distribution; S35, initialization of radiotherapy dose: The initial recommendation of radiotherapy dose X is calculated based on the joint conditional probability distribution of DNA repair markers and radiation sensitivity markers to obtain a personalized dose recommendation, and the recommended value is the initial dose of radiotherapy.

5. The method for optimizing tumor radiotherapy dose based on biomarkers according to claim 1, characterized in that: The dynamic monitoring of immune markers in S41 includes: S411, Immune cell activity monitoring: During each radiotherapy period and the recovery process after radiotherapy, blood samples from patients are collected regularly to detect and analyze the activity level of immune cells; S412, Monitoring of inflammatory factor concentrations: Regularly measure the concentrations of inflammatory factors, including IL-2 and TNF-α, using ELISA detection technology.

6. The method for optimizing tumor radiotherapy dose based on biomarkers according to claim 5, characterized in that: The immune response assessment in S42 includes: S421, analysis of immune marker change trends: by regularly monitoring the patient's immune markers, recording immune marker data during radiotherapy and recovery, and analyzing their changing trends over time using an autoregressive integrated moving average model; S422, immune response assessment algorithm: Based on the changing trend of immune markers, an exponentially weighted moving average algorithm is used to assess the immune response score of the immune system; S423, Immune system status assessment: Evaluate the intensity of the patient's immune system's response to radiotherapy by analyzing the calculated immune response score. When the immune system's response score is higher than the set threshold, it indicates that the patient's immune system responds effectively to radiotherapy. Conversely, when the response score is lower than the set threshold, it indicates that the immune system response is weakened or radiotherapy tolerance is enhanced.

7. The method for optimizing tumor radiotherapy dose based on biomarkers according to claim 6, characterized in that: The dynamic adjustment of the radiotherapy dose in S43 includes: S431, radiotherapy dose adjustment: according to the dynamic evaluation results of immune markers, the initial radiotherapy dose is adjusted in real time. If the immune response evaluation results show that the immune response is enhanced, the radiotherapy dose is reduced; if the immune response is weakened, the radiotherapy dose is increased; S432, Dose feedback and continuous optimization: Apply the adjusted radiotherapy dose to subsequent treatments, and provide feedback based on the changes in immune markers that are continuously monitored to continuously adjust the radiotherapy dose.

8. The method for optimizing tumor radiotherapy dose based on biomarkers according to claim 1, characterized in that: The treatment effect evaluation in S5 includes: S51, Tumor shrinkage and immune system response assessment: After each radiotherapy, the tumor shrinkage was assessed and the immune system response strength was assessed in combination with immune markers; S52, Normal tissue damage assessment and strategy adjustment: Regularly monitor the degree of damage to normal tissues, evaluate the impact of radiotherapy on the patient's healthy tissues, and based on the evaluation results, judge the effectiveness of the current treatment strategy and decide whether the radiotherapy dose needs to be adjusted.

9. The method for optimizing tumor radiotherapy dose based on biomarkers according to claim 8, characterized in that: The tumor shrinkage and immune system response assessment in S51 include: S511, evaluation of tumor shrinkage: after each radiotherapy, the tumor volume was measured by imaging technology, and the proportion of tumor shrinkage was calculated; S512, Immune system response assessment: The strength of the immune system response is assessed in combination with immune markers, and the strength of the immune system response is calculated by the rate of change of immune marker levels.

10. The method for optimizing tumor radiotherapy dose based on biomarkers according to claim 9, characterized in that: The normal tissue damage assessment and strategy adjustment in S52 include: S521, Normal tissue damage monitoring: Regularly monitor the degree of normal tissue damage through imaging technology, biochemical indicators and clinical symptoms of patients; S522, Normal tissue damage assessment: Calculate the normal tissue damage rate R by monitoring the healthy tissue volume and biochemical indicators damage ; S523, Strategy adjustment and radiotherapy dose decision: According to the injury rate R damage The calculation results are combined with the patient's clinical symptoms to judge the effectiveness of the current treatment strategy. If the injury rate exceeds the preset safety threshold T safe , then adjust the radiotherapy dose.

Images