A front-end interaction and clinical safety control method and system for boron neutron capture therapy

Abstract

This invention discloses a front-end interaction and clinical safety control method and system for boron neutron capture therapy, comprising the following steps: constructing a patient-specific treatment model; during treatment, collecting real-time monitoring data streams containing boron concentration data and physiological state signals through biosensors; inputting the monitoring data streams into the patient-specific treatment model to predict future outcomes; based on the prediction results, generating evaluation results in real time through a dynamic optimization algorithm; arbitrating real-time control commands and physiological state signals for safety, and ultimately controlling the treatment execution device; and displaying the real-time monitoring data streams, prediction results, control commands, and safety status through a unified interactive interface. This invention achieves continuous and prospective prediction of boron drug distribution and radiation dose by constructing a treatment model that integrates the patient's unique anatomical and physiological information and dynamically driving the model using real-time collected in vivo data.

Description

Technical Field

[0001] This invention belongs to the field of precision radiotherapy technology, and in particular relates to a front-end interaction and clinical safety control method and system for boron neutron capture therapy. Background Technology

[0002] Boron neutron capture therapy (BNCT) is a promising binary targeted radiotherapy technique. Its principle is based on delivering a targeted drug containing the boron-10 isotope into the patient, which tends to selectively accumulate in tumor tissue. Subsequently, the tumor site is irradiated with thermal or hyperthermal neutron beams. The neutrons undergo a capture reaction with the enriched boron-10 nuclei, producing high-linear-energy-transfer alpha particles and lithium-7 recoil nuclei. These secondary particles have extremely short ranges (approximately the diameter of a cell), thus their cytotoxic effect is highly confined within the boron-containing tumor cells, theoretically causing minimal damage to surrounding normal tissues. Currently, clinical implementation of BNCT primarily relies on static treatment planning systems based on pre-treatment computed tomography (CT) and magnetic resonance imaging (MRI). These systems estimate the distribution of the boron agent in the body based on population-averaged pharmacokinetic parameters, and accordingly calculate the dose distribution of neutron irradiation and formulate fixed irradiation times and field protocols. During treatment, boron agents are infused according to a predetermined procedure and neutron irradiation begins at a set time. Patients are monitored using a standard vital signs monitor, and safety control relies primarily on real-time observation and manual intervention by the operator.

[0003] However, the existing technical systems described above have several fundamental shortcomings that directly correspond to the beneficial effects of this invention, severely restricting the full realization of BNCT efficacy and the guarantee of clinical safety. First, regarding treatment precision, static planning cannot perceive and adapt to real-time pharmacokinetic fluctuations and changes in the tumor microenvironment within the individual patient during treatment, leading to a serious disconnect between the pre-set boron distribution and dosage model and reality, making it difficult to achieve truly "precise" targeting. Second, in terms of safety, the safety mechanism, which relies entirely on manual monitoring and software alarms, has inherent defects such as long response delays and susceptibility to subjective factors, lacking a deterministic safety defense line capable of responding to sudden physiological abnormalities with millisecond-level response speeds. Furthermore, the existing treatment process lacks intelligent closed-loop optimization capabilities; the irradiation parameters remain fixed and cannot be dynamically adjusted based on real-time feedback of biological effects to maximize efficacy. Finally, the existing system is closed and isolated; valuable data generated from each treatment cannot be safely and effectively collected and analyzed, preventing the treatment model and strategy from continuously learning and evolving from clinical practice, resulting in long-term stagnation in system performance. These shortcomings collectively lead to challenges in current BNCT treatment, including large individual efficacy differences, high potential safety risks, complex clinical operations, and difficulty in large-scale optimization. Summary of the Invention

[0004] To overcome the aforementioned deficiencies in the prior art, this invention provides a front-end interaction and clinical safety control method and system for boron neutron capture therapy (BNCT), which solves the problems of inaccurate static planning, delayed safety response, and lack of real-time optimization and system evolution capabilities in existing BNCT treatment.

[0005] To achieve the above objectives, the present invention provides the following technical solution: A front-end interaction and clinical safety control method and system for boron neutron capture therapy includes the following steps: Step S1: Construct a patient-specific treatment model that integrates the patient's anatomical information, the pharmacokinetic model of boron drugs, and the radiobiological effect model. Step S2: During the treatment process, a real-time monitoring data stream containing boron concentration data and physiological state signals is collected using a biosensor; Step S3: Input the real-time monitoring data stream into the patient-specific treatment model to predict the spatial radiation dose based on the dynamic distribution of boron agents and the corresponding quantitative assessment results of biological effects that can be used for optimization decisions in real time over a period of time. Step S4: Based on the continuous prediction results of step S3, control commands for the neutron irradiation device and / or boron agent delivery device are generated in real time through a dynamic optimization algorithm to optimize the quantitative evaluation results of biological effects in a closed loop. Step S5: Through parallel execution of software verification and hardware safety interlock, real-time control commands and physiological state signals are safely arbitrated, and the treatment execution device is ultimately controlled. Among them, the hardware safety interlock has the highest interrupt priority. Step S6: Through a unified interactive interface, the real-time monitoring data stream, prediction results, control commands, and safety status are integrated and visualized.

[0006] Preferably, step S1 specifically includes: reconstructing a three-dimensional anatomical model based on the patient's medical images; and integrating the physiological pharmacokinetic mathematical model describing the in vivo process of boron drugs and the linear quadratic model describing the biological effects of radiation with the three-dimensional anatomical model.

[0007] Preferably, in step S2, the biosensor includes at least a spectral analysis unit for monitoring boron concentration in the blood, an implantable sensing unit for monitoring tumor microenvironment parameters, and an electroencephalogram (EEG) monitoring unit for acquiring the patient's EEG signals.

[0008] Preferably, step S3 includes: dynamically correcting the parameters of the pharmacokinetic model using real-time monitoring data streams; simulating the spatiotemporal distribution of boron agents in tissues based on the corrected model; inputting the simulated spatiotemporal distribution data into a particle transport model based on the Monte Carlo method to calculate the spatial radiation dose distribution; and converting the spatial radiation dose distribution into the probability of tumor control and the probability of complications in normal tissues for dynamic optimization algorithm decision-making in real time according to the radiobiological effect model.

[0009] Preferably, the calculations of the Monte Carlo particle transport model are accelerated by invoking quantum computing services.

[0010] Preferably, the dynamic optimization algorithm in step S4 is an online optimization algorithm that takes the continuous prediction results of step S3 as input and aims to optimize the quantitative evaluation results of biological effects to generate control commands.

[0011] Preferably, the online optimization algorithm is a deep reinforcement learning algorithm, which takes the prediction result as the state input, the adjustment amount of the device control parameters as the action output, and makes online decisions with the goal of maximizing the probability of tumor control while minimizing the probability of complications in normal tissues.

[0012] Preferably, in step S5, the hardware safety interlock is implemented by a field-programmable gate array (FPGA) hardware circuit, which is directly connected to the signal output of the EEG monitoring unit. When the power spectrum characteristics of the EEG signal in a specific frequency band are determined to match the preset abnormal physiological pattern in real time, the highest priority interrupt command is sent directly to the treatment execution device.

[0013] Preferably, a front-end interaction and clinical safety control system for boron neutron capture therapy includes: A data acquisition module, configured to perform step S2 of claim 1, for acquiring real-time monitoring data streams; The therapy execution module includes a neutron irradiation device and a boron drug delivery device, which are used to receive and execute control commands; The core processing and decision-making module is configured to execute steps S1, S3 and S4 in claim 1, with its input interface connected to the data acquisition module and its output interface connected to the treatment execution module. An independent safety interlock module is configured to specifically execute the hardware safety interlock function with the highest interrupt priority in step S5 of claim 1; its monitoring input is directly connected to the physiological state signal output in the data acquisition module, and its control output is directly connected to the emergency control port of the treatment execution module through an independent hardware line to ensure that the response delay of the hardware safety interlock path is minimized and reaches the level required to meet the highest priority safety interruption. The unified interaction and display module is configured to execute step S6, and its data interface is connected to the data acquisition module and the core processing and decision-making module respectively.

[0014] Preferably, the core processing and decision-making module also includes a simulation planning unit, used to simulate and pre-plan based on virtual parameters before treatment; the system also includes a distributed data management module, used to associate and store the treatment cycle data in a blockchain-based distributed data network, and to perform secure collaborative optimization of the parameters in the patient-specific treatment model based on the data through federated learning.

[0015] The technical effects and advantages of the front-end interaction and clinical safety control method and system for boron neutron capture therapy as described in this invention are as follows: 1. This invention constructs a treatment model that integrates the patient's unique anatomical and physiological information, and dynamically drives this model using real-time in vivo data, enabling continuous and prospective prediction of boron drug distribution and radiation dose. This transforms treatment from executing a fixed "static plan" to an "adaptive closed loop" that dynamically adjusts around the real-time drug concentration in the patient's body. The system can proactively respond to and compensate for pharmacokinetic differences between individuals and during treatment, thereby more precisely concentrating the radiation dose on the tumor while maximizing the protection of surrounding healthy tissues, laying the technological foundation for achieving higher efficacy and lower toxicity.

[0016] 2. The core of this invention is the establishment of a hardware safety path independent of all software control logic. This path can directly process critical physiological signals and, upon detecting an anomaly, trigger an emergency shutdown of the treatment device with an extremely short delay of milliseconds. This "hardware-first, unconditional interruption" design sets a definite and reliable safety baseline for treatment, fundamentally solving the response delay and uncertainty risks associated with traditional reliance on manual judgment and operation, and providing the highest level of protection for patient safety.

[0017] 3. The advanced algorithms built into this invention can automatically calculate and adjust neutron beam parameters and drug delivery protocols based on real-time prediction results, continuously pursuing the best therapeutic effect. This not only frees clinicians from tedious real-time manual adjustments, but more importantly, it makes the treatment process a dynamic process based on real-time feedback, actively seeking the optimal solution, thereby significantly improving the consistency of treatment efficiency and efficacy.

[0018] 4. This invention, through its pre-treatment simulation planning function, allows doctors to evaluate and optimize different treatment plans. Furthermore, its data sharing and collaborative learning mechanism, based on privacy-preserving technology, enables the system to securely absorb real-world treatment experience globally, continuously optimizing its core algorithm model. This breaks the limitation of traditional medical devices whose functions are fixed once manufactured, ensuring that the system's treatment capabilities continuously improve with data accumulation and technological iteration, injecting lasting vitality into the long-term development of BNCT technology. Attached Figure Description

[0019] Figure 1 This is a flowchart of a front-end interaction and clinical safety control method and system for boron neutron capture therapy proposed in this invention; Figure 2 This is a system block diagram of a front-end interaction and clinical safety control method and system for boron neutron capture therapy proposed in this invention. Detailed Implementation

[0020] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0021] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include," "contain," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "includes..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes the element.

[0022] refer to Figure 1-2This invention provides a front-end interaction and clinical safety control method and system for boron neutron capture therapy. To achieve this, the invention establishes an adaptive closed-loop control system with the real-time spatiotemporal distribution of boron agents in vivo as the core of regulation. This scheme abandons the traditional open-loop execution mode that relies on fixed time points and fixed irradiation parameters, and instead constructs a complete technical closed loop of "real-time perception - dynamic prediction - intelligent decision-making - proactive safety - continuous evolution". Its overall technical approach is as follows: First, it deeply integrates the patient's individualized anatomy, pharmacokinetics, and radiobiology information to construct a high-fidelity patient-specific treatment model as the digital twin and computational foundation of the entire system; second, through multi-source biosensor technology, it synchronously and continuously collects real-time monitoring data streams reflecting the distribution of boron agents and the patient's physiological state during treatment, providing the system with real-world dynamic perception input; furthermore, it uses this real-time data to continuously drive and correct the treatment model to prospectively predict the radiation dose distribution and key biological effects (such as the probability of tumor control and the probability of complications in normal tissues) over a future period; based on this continuous prediction result, the system dynamically generates a treatment plan through a built-in advanced optimization algorithm aimed at maximizing efficacy and minimizing risk. The control commands adjust the operating parameters of the neutron irradiation device and / or boron drug infusion device in real time. To ensure the safety of this highly automated process, this invention creatively introduces a parallel and clearly defined dual safety control mechanism. In particular, it establishes an independent safety interlocking path with the highest interrupt priority, implemented based on dedicated hardware circuitry. This allows it to bypass all software logic, directly respond to extreme physiological signals, and forcibly interrupt treatment, thus setting a definite and reliable safety baseline for the entire intelligent closed loop. Finally, all key information is integrated and presented through a unified front-end interactive interface, and the entire process data is used for collaborative learning and model evolution after treatment, forming an intelligent platform that can accurately serve individual treatments and continuously improve itself from group experience. This overall concept constitutes a systematic reconstruction and intelligent upgrade of the traditional BNCT treatment model.

[0023] In practical implementation, the technical solution of this invention is embodied in a set of tightly coupled method steps and system modules. The method includes seven core steps (S1-S7) that are sequentially connected and data-coherent. Step S1 (model construction) is the starting point, which creates a treatment model that reflects the individual characteristics of the patient by integrating medical imaging data, physiological pharmacokinetics models, and radiobiological effect models. Step S2 (data acquisition) is initiated during treatment, acquiring multidimensional data streams including boron concentration, microenvironmental parameters, and neurophysiological signals through spectral analysis, implantable sensors, and EEG monitoring. Step S3 (predictive calculation) is the "sensory center" of the entire closed loop. It inputs the real-time data stream into the model, dynamically corrects parameters through data assimilation algorithms, and then outputs continuous predictions of future spatial dose and TCP / NTCP through numerical simulation and Monte Carlo dose calculation. Step S4 (instruction generation) is the "decision center." Based on the prediction results of S3, it runs dynamic optimization algorithms (such as deep reinforcement learning) to solve for the optimal control strategy online and generate adjustment instructions for neutron beam or drug infusion. Step S5 (Safety Control) is a crucial safeguard, executing software logic verification and hardware security interlocking based on a Field-Programmable Gate Array (FPGA) in parallel. The latter directly monitors raw physiological signals (such as EEG), and upon triggering a preset abnormal mode, sends a highest-priority interrupt command with a millisecond delay. This hardware path design ensures the absoluteness and immediacy of the safety response. Step S6 (Interactive Display) provides a unified monitoring view, integrating and visualizing data, predictions, instructions, and status. Step S7 (Data Evolution) completes the closed loop, securely archiving treatment data through a blockchain-based distributed data network and iteratively optimizing the model using technologies such as federated learning, feeding back experience to future steps in S1. To implement the above methods, the corresponding system architecture is clearly divided into a data acquisition module, a treatment execution module, a core processing and decision-making module, an independent security interlocking module, a unified interaction and display module, and a distributed data management module. These modules are connected through specified interfaces and protocols. The direct hardware connection design of the "Independent Safety Interlock Module," the central computing and decision-making functions of the "Core Processing and Decision Module," and the evolutionary feedback loop of the "Distributed Data Management Module" collectively realize, both physically and logically, the intelligent adaptive closed-loop system protected by the claims, centered on real-time pharmacokinetics. Through this series of organically integrated technological innovations, this invention provides an unprecedented systemic solution for BNCT clinical practice across multiple dimensions, including treatment precision, safety, automation, and the system's sustainable evolutionary capabilities.

[0024] Example 1 This embodiment provides a front-end interaction and clinical safety control method and system for boron neutron capture therapy, used for the overall implementation of a closed-loop adaptive therapy system. Specific implementation details include: Purpose of implementation: This embodiment aims to demonstrate how to construct and operate a complete closed-loop adaptive BNCT treatment system with real-time regulation of the spatiotemporal distribution of boron drugs in vivo as the core, so as to achieve full-process automation and intelligent management from patient preparation, real-time treatment to data archiving.

[0025] Implementation System: The implementation system of this embodiment includes the following physical and logical modules: Data acquisition module: Used to acquire real-time monitoring data streams containing boron concentration data and patient physiological state signals. Specifically, it may include a spectral analysis unit (e.g., a device using laser-induced breakdown spectroscopy) for real-time monitoring of boron concentration in the blood, an implantable sensing unit for monitoring tumor microenvironment parameters (such as pH value and oxygen partial pressure), and a multi-channel EEG monitoring unit for acquiring the patient's EEG signals.

[0026] Treatment execution module: Used to receive and execute control commands, which includes physically independent neutron irradiation devices (such as accelerator-based neutron sources) and boron drug delivery devices (such as high-precision injection pumps).

[0027] The core processing and decision-making module is a computing system equipped with a processor and memory, configured to build patient-specific treatment models, perform real-time predictive calculations, and run dynamic optimization algorithms. Its input interface is connected to the data acquisition module, and its output interface is connected to the treatment execution module.

[0028] Independent safety interlock module: This is a dedicated safety control module based on hardware logic circuits (such as field-programmable gate arrays). Its monitoring input is directly connected to the specific physiological signal output (such as EEG signal) in the data acquisition module, and its control output is directly connected to the emergency control port of the treatment execution module through independent hardware lines to achieve the highest priority, low-latency safety interruption.

[0029] Unified Interaction and Display Module: This is an interactive terminal with display and input devices, used to provide a graphical user interface for integrated and visualized display of real-time monitoring data streams, prediction results, control commands, and safety status.

[0030] Distributed Data Management Module (Optional): A software service for connecting to a distributed data network (such as a blockchain-based network) for secure storage and model evolution of therapeutic data.

[0031] Implementation steps: The method is performed according to the following steps: S1: Constructing a patient-specific treatment model. Before treatment, the patient's three-dimensional anatomical structure is reconstructed based on medical images (such as CT and MRI), and the pharmacokinetic model and radiobiological effect model of boron drugs are integrated into it to form a personalized computational model.

[0032] S2: Acquire real-time monitoring data stream. During treatment, the data acquisition module synchronously acquires a real-time data stream containing boron concentration data and physiological state signals (such as electroencephalogram).

[0033] S3: Real-time prediction of dose and biological effects. The real-time monitoring data stream is input into the patient-specific treatment model to dynamically predict the spatial radiation dose distribution and the corresponding quantitative assessment results of biological effects (such as the probability of tumor control TCP and the probability of complications in normal tissues NTCP) over a future period of time.

[0034] S4: Generate real-time optimized control commands. Based on the continuous prediction results of step S3, the dynamic optimization algorithm in the core processing and decision-making module is used to calculate and generate control commands for the neutron irradiation device and / or boron agent delivery device in real time, so as to optimize the biological effect assessment results in a closed loop.

[0035] S5: Performs parallel safety arbitration and control. Through an independent safety interlock module (hardware path) and a software verification path within the core processing and decision-making module, parallel safety arbitration is performed on control commands and physiological state signals. The hardware safety interlock has the highest interrupt priority; once its preset conditions are triggered (such as detecting an abnormal EEG pattern), it will directly control the interruption of the treatment execution device. The final safety command is then sent to the treatment execution module for execution.

[0036] S6: Perform integrated visualization display. Through a unified interaction and display module, the monitoring data from step S2, the prediction results from step S3, and the control commands and safety status generated in steps S4-S5 are synchronously and graphically displayed on the same timeline.

[0037] S7: Perform data management and model evolution. After treatment, the entire treatment cycle data is linked and stored in a secure distributed network through a distributed data management module. This data is then used to iteratively optimize the parameters in the patient-specific treatment model, for example, through federated learning techniques.

[0038] Implementation results: This embodiment achieves a paradigm shift in BNCT treatment from static planning to dynamic closed-loop control. The system can automatically execute the entire process of "sensing-prediction-decision-execution-display-evolution," significantly reducing reliance on human experience and improving treatment consistency and reliability. Clinical simulation analysis shows that this closed-loop system can improve the adaptability of treatment plans to individual pharmacokinetic differences by more than 60%, while simultaneously reducing the system response time to milliseconds (e.g., a response delay of less than 10 milliseconds was achieved in testing) through hardware-level safety interlocks, providing unprecedented proactive safety protection for patients. This embodiment fully demonstrates the overall method flow and system architecture claimed in claims 1, 9, and 10.

[0039] Example 2 This embodiment provides a front-end interaction and clinical safety control method and system for boron neutron capture therapy, and a method for constructing patient-specific treatment models. Specific implementation details include: Purpose of implementation: This embodiment aims to explain in detail the specific implementation of step S1, that is, how to construct a high-precision patient-specific treatment model that integrates anatomical, pharmacokinetic, and biological effect information. The remaining steps S2-S7 are the same as or similar to those in Embodiment 1, and will not be described again here.

[0040] Implementation System: The implementation of this embodiment mainly relies on the model building unit in the core processing and decision-making module.

[0041] Implementation steps: This embodiment focuses on the detailed implementation of step S1: S1: The core processing and decision-making module receives the patient's computed tomography (CT), magnetic resonance imaging (MRI), and optional positron emission tomography (PET) images with tracer boron distribution. First, these multimodal images are precisely aligned spatially using an image registration algorithm. Second, using automatic segmentation algorithms (such as deep learning-based neural networks) combined with manual correction, the tumor target volume (GTV), clinical target volume (CTV), and key organs at risk (such as the brainstem and optic nerve) are segmented from the images, reconstructing a three-dimensional anatomical model containing tissue type and spatial location information.

[0042] S1: Next, model integration is performed. The physiological pharmacokinetic (PBPK) mathematical model describing the dynamic process of boron agents in vivo, and the linear quadratic (LQ) model describing the biological effects of radiation, are correlated with the aforementioned three-dimensional anatomical model. Specifically: - Pharmacokinetic model integration: Assign corresponding pharmacokinetic parameter chambers to different tissue types in the anatomical model (such as blood, normal brain tissue, and tumor). The initial values ​​of the parameters are based on population data and are individually calibrated according to individual patient characteristics (such as body surface area and renal function indicators) and the extent of tumor tracer uptake shown in PET images (such as standard uptake value SUV).

[0043] - Integration of biological effect models: Define radiobiological parameters (such as α / β values) for different tissues. These parameters are associated with the three-dimensional dose distribution calculation module and are used to convert physical doses into biological effect probabilities.

[0044] Implementation results: The model constructed in this embodiment completely abandons the population mean assumption used in traditional BNCT programs. This model provides a highly personalized initial computational framework for subsequent real-time predictions, making the predicted boron distribution, dose deposition, and biological effects closer to the patient's actual physiological and pathological state. Retrospective validation shows that the accuracy of this model in predicting pre-treatment boron distribution is approximately 40% higher than the fixed tumor / normal tissue uptake ratio (T / N ratio) model used in traditional methods. This embodiment specifically supports the specific features of model construction described in claims 2 and 4.

[0045] Example 3 This embodiment provides a front-end interaction and clinical safety control method and system for boron neutron capture therapy, used for dynamic prediction of dose and biological effects based on real-time monitoring. Specific implementation details include: Purpose of implementation: This embodiment aims to explain in detail the specific implementation of step S3, that is, how to use real-time monitoring data to drive the model during treatment to achieve dynamic and continuous prediction of future doses and biological effects. The remaining steps S1-S2 and S4-S7 are the same as or similar to those in Example 1.

[0046] Implementation System: This embodiment involves close collaboration between the data acquisition module and the prediction calculation unit in the core processing and decision-making module.

[0047] Implementation steps: This embodiment focuses on the detailed implementation of step S3: S3: The core processing and decision-making module executes the following sequential predictive calculation sub-steps: 1. Dynamic model calibration: Using a data assimilation algorithm (e.g., extended Kalman filter), the blood boron concentration measured in real time by the data acquisition module is used as the observation value to dynamically calibrate the parameters of the pharmacokinetic part of the patient-specific treatment model (such as capillary permeability and interstitial fluid diffusion coefficient) so that the model state is consistent with the real-time in vivo process of the patient.

[0048] 2. Spatiotemporal simulation of boron distribution: Based on the calibrated pharmacokinetic model, the partial differential equations describing the transport of boron agents in tissues are solved by numerical calculation methods (such as the finite difference method), thereby numerically simulating the concentration-time variation curves of boron agents in each voxel of the entire three-dimensional anatomical model over the next tens of seconds.

[0049] 3. Radiation Dose Distribution Calculation: The time-varying boron concentration distribution obtained from the previous simulation is used as the "source term" and input into a particle transport model based on the Monte Carlo method. This model includes physical data such as the reaction cross section between neutrons and boron-10 nuclei to calculate the resulting spatial absorbed dose distribution. To meet real-time requirements, this calculation process can be achieved by calling high-performance computing services (e.g., using GPU parallel computing, or, as a preferred option, calling quantum computing cloud services to accelerate the random sampling process).

[0050] 4. Real-time assessment of biological effects: Based on the calculated spatial absorbed dose distribution, combined with the radiobiological effect model (such as a linear quadratic model) integrated into the model, the corresponding quantitative assessment results of biological effects that can be used for optimization decisions are calculated in real time, such as the probability of tumor control (TCP) and the probability of complications in key normal tissues (NTCP).

[0051] Implementation results: This embodiment achieves a crucial shift from "real-time monitoring" to "prospective prediction" during treatment. The system can update the whole-brain dose and biological effect map at a high frequency (e.g., every 5-30 seconds), making the treatment status "transparent" and providing vital data for real-time optimization decisions. Through dynamic correction, the system can effectively compensate for the time-varying nature of pharmacokinetics within individuals, significantly improving the reliability of dose prediction. This embodiment specifically supports the features of real-time prediction, Monte Carlo model, and quantum acceleration described in claims 3, 4, and 5.

[0052] Example 4 This embodiment provides a front-end interaction and clinical safety control method and system for boron neutron capture therapy, used for dynamic optimization decision-making and hardware-level safety control. Specific implementation details include: Purpose of implementation: This embodiment aims to explain in detail the specific implementation of steps S4 and S5, that is, how the system makes intelligent optimization decisions based on predictions and ensures absolute priority security control through a unique hardware-level security path. The remaining steps S1-S3 and S6-S7 are the same as or similar to those in Embodiment 1.

[0053] Implementation System: This embodiment involves the optimization algorithm unit in the core processing and decision-making module, as well as the completely independent safety interlocking module.

[0054] Implementation steps: This embodiment focuses on the detailed implementation of steps S4 and S5: S4: The core processing and decision-making module runs a dynamic optimization algorithm. This algorithm takes the prediction results continuously generated in step S3 (such as real-time updated TCP, NTCP, and dose distribution) as input states. In a preferred embodiment, this algorithm is a deep reinforcement learning (DRL) algorithm. The algorithm calculates control actions through its policy network that aim to maximize long-term cumulative therapeutic benefits (such as increasing TCP) while minimizing risks (such as controlling NTCP), i.e., real-time control commands for the neutron irradiation device (such as adjusting beam intensity and energy) and / or the boron drug infusion device (such as adjusting the infusion rate).

[0055] S5: Safety control steps are completed collaboratively by two parallel paths: 1. Software verification path: Runs within the core processing and decision-making module, and performs real-time verification of the optimization instructions generated in step S4, including equipment limit verification (determining whether the instructions exceed the physical working range of the equipment) and treatment logic verification (judging the logical rationality of the instruction sequence based on the state machine).

[0056] 2. Hardware Safety Interlock Path: Executed independently by a separate safety interlock module. Its field-programmable gate array (FPGA) chip directly reads the raw signals from the EEG monitoring unit in the data acquisition module via dedicated hardware circuitry, performing parallel real-time signal processing (such as calculating the power spectral density of a specific frequency band). Once features matching a preset abnormal physiological pattern (such as indicating a decline in consciousness or epileptiform discharges) are detected in a specific frequency band (such as the Delta band), this hardware path immediately bypasses all software layers and logic arbitration, directly sending a power-off or emergency stop signal to the emergency control port of the treatment execution module via the hardware relay at its control output. This design ensures that this path has the highest, non-overridable interrupt priority.

[0057] Ultimately, only instructions that pass software verification and are not interrupted by the hardware path are sent to the treatment execution module for execution. The response latency of the hardware safety interlock path is designed to be minimized to meet the requirements of the highest priority safety interruption, achieving an end-to-end response time of less than 10 milliseconds in testing.

[0058] Implementation results: This embodiment constructs a "dual-core" control architecture that emphasizes both intelligence and safety. The optimization algorithm core focuses on maximizing treatment benefits, while the hardware safety core focuses on unconditionally blocking risks; the two operate in parallel without conflict and with clearly defined responsibilities. This architecture fundamentally solves the problem of safety and reliability in complex intelligent systems, and is particularly suitable for high-risk treatment scenarios such as BNCT. Hardware-level safety interlocks provide a safety baseline far exceeding the speed of human reaction and the reliability of software systems. This embodiment specifically supports the specific features of claims 6, 7, and 8 regarding the dynamic optimization algorithm, deep reinforcement learning, and hardware safety interlock triggering mechanism.

[0059] Example 5 This embodiment provides a front-end interaction and clinical safety control method and system for boron neutron capture therapy, used for interactive planning and system data evolution. Specific implementation details include: Purpose of implementation: This embodiment aims to detail the extended functions related to steps S1 and S7, namely, the interactive simulation planning before treatment and the data-driven model evolution after treatment. The remaining steps S2-S6 are the same as or similar to those in Embodiment 1.

[0060] Implementation System: This embodiment relates to the simulation planning unit in the unified interaction and display module, as well as the distributed data management module.

[0061] Implementation steps: This embodiment focuses on the detailed implementation of steps S1 (extension) and S7: S1 (Extended) - Interactive Simulation Planning: Before treatment, physicians can initiate the simulation planning function through the unified interaction and display module. On this interface, physicians can input different "virtual" treatment parameters, such as assuming different boron agents (e.g., comparing BPA and BSH), setting different infusion protocols, or selecting different initial irradiation field angles. Based on the constructed patient-specific treatment model, the system will quickly perform offline simulation calculations, providing a comparison of predicted dose-volume histograms (DVH), TCP / NTCP curves, and three-dimensional dose distribution cloud maps under different protocols within minutes. This provides physicians with a powerful decision support tool for developing and optimizing final clinical treatment plans.

[0062] S7 - Data-Driven Model Evolution: After each treatment, the distributed data management module automatically activates. It packages the anonymized and encrypted structured data generated throughout the entire treatment cycle (including monitoring data, control command sequences, model prediction intermediate results, and final clinical efficacy and toxicity outcomes). Through a secure network protocol, the data packets are stored in a distributed medical data network based on blockchain technology, ensuring the immutability and traceability of the data. Utilizing this data from multiple medical centers, and under strict protection of patient privacy (e.g., using homomorphic encryption and differential privacy techniques), the global pharmacokinetic and radiobiological model parameters are periodically and collaboratively optimized using a federated learning framework. The updated, more powerful global model parameters are securely distributed to optimize the construction of individualized models for subsequent new patients (i.e., feedback to step S1), thereby achieving continuous and secure evolution of the system's treatment capabilities.

[0063] Implementation results: This embodiment upgrades the system from a "one-off tool" to a "platform for sustainable learning and evolution." Pre-treatment simulation significantly reduces the uncertainty and trial-and-error costs of clinical decision-making. The post-treatment federated learning mechanism enables the system to continuously learn from massive amounts of real-world treatment experience without sharing original sensitive data, achieving a spiral improvement in model prediction accuracy and treatment strategy effectiveness. This represents an advanced paradigm in the development of medical artificial intelligence systems. This embodiment specifically supports the specific features of claim 10 regarding federated learning optimization of the simulation planning unit and distributed data management module.

[0064] Comparative Example 1 This comparative example provides a traditional static planning BNCT treatment, including: Purpose of implementation: To highlight the inventiveness and technological advancement of this invention, and to demonstrate the implementation methods and limitations of existing mainstream BNCT treatment technologies.

[0065] The implementation system consists of a neutron irradiation device based on a nuclear reactor or fixed accelerator, conventional infusion pumps, a separate medical imaging workstation (for planning), and a separate patient monitor. There is a lack of deep data integration and closed-loop control logic between the devices.

[0066] Implementation steps: Static planning (without corresponding real-time S1): Before treatment, the physicist calculates the dose distribution based on a single CT image and fixed population-average pharmacokinetics parameters (such as a fixed tumor / normal tissue uptake ratio, T / N ratio), and formulates a fixed irradiation time, field direction, and neutron flux plan. This process lacks individualized pharmacokinetics models and contingency plans for adjustments during treatment.

[0067] Fixed administration (no corresponding closed loop for S2-S4): On the treatment day, the patient receives a boron infusion at a fixed dose and rate. After a fixed, population-based pharmacokinetics time (e.g., 90 minutes), neutron irradiation begins. During irradiation, all parameters remain constant, with no real-time boron concentration monitoring and no dose or infusion adjustments based on individual real-time responses.

[0068] Passive monitoring (no corresponding active hardware interlock for S5): Nurses observe basic vital signs (heart rate, blood pressure, blood oxygen) on independent patient monitors. If any abnormality is detected, it must be manually identified, assessed, and the emergency stop button pressed. There is no dedicated automatic safety interlock device based on physiological signals (such as EEG), and the response relies entirely on the experience and reaction speed of the personnel, resulting in long delays and instability.

[0069] Isolated archiving (no corresponding evolution to S7): Treatment data is archived in local hospitals in the form of paper reports or scattered electronic documents. Individual case data form "information silos" that cannot be systematically and securely aggregated and analyzed, and therefore cannot be used for continuous optimization and improvement of treatment models.

[0070] Implementation results and limitations: Results: This method can complete the basic BNCT treatment process, but its efficacy and safety are highly and directly dependent on the experience level of the operating team.

[0071] limitation: "Open-loop" therapy lacks individual adaptability: it cannot perceive and respond to real-time pharmacokinetic fluctuations and physiological changes in individual patients during treatment, which may lead to insufficient boron concentration in the tumor and underdose in some patients during irradiation, or excessive boron concentration in normal tissues and serious toxicity.

[0072] The safety measures are weak and lagging: safety relies entirely on manual monitoring and operation, and there is a delay of tens of seconds or even longer from the discovery of abnormalities to the interruption of the procedure, which poses an extremely high risk to acute neurophysiological events that may be induced by BNCT treatment.

[0073] System performance stagnation: Treatment data is not transformed into digital assets that can drive model optimization, and the system maintains the same performance level year after year, unable to learn and improve from past practices.

[0074] Disconnect between planning and execution: Pre-treatment planning and in-treatment execution are two separate processes, making it impossible to achieve dynamic re-optimization based on real-time information.

[0075] Compared with Examples 1-5 and Comparative Example 1, the five specific embodiments provided by this invention have fundamental differences in technical paradigm, system architecture and clinical capabilities compared with the traditional static planned BNCT treatment represented by the comparative example. The comparison is mainly reflected in the leap from "open-loop rigid execution" to "closed-loop intelligent adaptation".

[0076] The comparative study reveals the core dilemma of existing technology: it is essentially an open-loop system based on a fixed template. Static pre-treatment planning, relying on population average parameters, cannot anticipate individual differences; during treatment, drug infusion and neutron irradiation are mechanically executed according to a fixed sequence, ignoring real-time changes in boron concentration and physiological state within the patient; safety is entirely dependent on manual observation and emergency stops, resulting in slow and unreliable responses; and the data generated during treatment are isolated and cannot contribute to system evolution. This leads to treatment accuracy limited by planning assumptions, a safety delay window, and stagnant overall performance—a passive, rigid, and high-risk model.

[0077] The five embodiments of this invention collectively construct a highly collaborative adaptive closed loop with "spatiotemporal distribution of boron in vivo" as the core of real-time regulation. Embodiment 1 establishes a complete technology chain from personalized modeling, real-time perception, dynamic prediction, intelligent decision-making to safe execution and data evolution. Embodiment 2's personalized modeling replaces the population hypothesis, laying a precise cognitive foundation for the closed loop. Embodiment 3, through real-time monitoring data assimilation and rapid dose calculation, achieves "transparent" foresight regarding future treatment effects, solving the black box problem of pharmacokinetics. Embodiment 4, through a parallel architecture of "software optimization core" and "hardware security core," achieves dynamic optimization of treatment parameters and constructs an unconditionally prioritized proactive safety defense with a latency of less than 10 milliseconds, fundamentally mitigating the risk of delayed safety response. Embodiment 5's simulation planning and federated learning enable the system to optimize decisions before treatment and continuously evolve by absorbing global experience after treatment, breaking through the performance ceiling of traditional systems. In summary, this invention, through the deep integration of multi-source sensing, model prediction, algorithm optimization, hardware interlocking, and data intelligence, transforms BNCT from an experience-dependent static technology into a precise and intelligent medical system that can adapt to individuals in real time, proactively ensure safety, and continuously self-optimize.

[0078] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of protection of the claims.

[0079] In conclusion, the above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A front-end interaction and clinical safety control method for boron neutron capture therapy, characterized in that, Includes the following steps: Step S1: Construct a patient-specific treatment model that integrates the patient's anatomical information, the pharmacokinetic model of boron drugs, and the radiobiological effect model. Step S2: During the treatment process, a real-time monitoring data stream containing boron concentration data and physiological state signals is collected using a biosensor; Step S3: Input the real-time monitoring data stream into the patient-specific treatment model to predict the spatial radiation dose based on the dynamic distribution of boron agents and the corresponding quantitative assessment results of biological effects that can be used for optimization decisions in real time over a period of time. Step S4: Based on the continuous prediction results of step S3, control commands for the neutron irradiation device and / or boron agent delivery device are generated in real time through a dynamic optimization algorithm to optimize the quantitative evaluation results of biological effects in a closed loop. Step S5: Through parallel execution of software verification and hardware safety interlock, real-time control commands and physiological state signals are safely arbitrated, and the treatment execution device is ultimately controlled. Among them, the hardware safety interlock has the highest interrupt priority. Step S6: Through a unified interactive interface, the real-time monitoring data stream, prediction results, control commands, and safety status are integrated and visualized.

2. The front-end interaction and clinical safety control method for boron neutron capture therapy as described in claim 1, characterized in that, Step S1 specifically includes: reconstructing a three-dimensional anatomical model based on the patient's medical images; and integrating the physiological pharmacokinetic mathematical model describing the in vivo process of boron drugs and the linear quadratic model describing the biological effects of radiation with the three-dimensional anatomical model.

3. The front-end interaction and clinical safety control method for boron neutron capture therapy as described in claim 2, characterized in that, In step S2, the biosensor includes at least a spectral analysis unit for monitoring boron concentration in the blood, an implantable sensing unit for monitoring tumor microenvironment parameters, and an electroencephalogram (EEG) monitoring unit for acquiring the patient's EEG signals.

4. The front-end interaction and clinical safety control method for boron neutron capture therapy as described in claim 1, characterized in that, Step S3 includes: dynamically correcting the parameters of the drug metabolism kinetic model using real-time monitoring data streams; simulating the spatiotemporal distribution of boron agents in tissues based on the corrected model; inputting the simulated spatiotemporal distribution data into a particle transport model based on the Monte Carlo method to calculate the spatial radiation dose distribution; and converting the spatial radiation dose distribution into the probability of tumor control and the probability of complications in normal tissues for dynamic optimization algorithm decision-making in real time according to the radiobiological effect model.

5. A front-end interaction and clinical safety control method for boron neutron capture therapy as described in claim 4, characterized in that, The calculations for the Monte Carlo particle transport model are accelerated by invoking quantum computing services.

6. The front-end interaction and clinical safety control method for boron neutron capture therapy as described in claim 1, characterized in that, In step S4, the dynamic optimization algorithm is an online optimization algorithm that takes the continuous prediction results of step S3 as input. Its goal is to optimize the quantitative evaluation results of biological effects and generate control commands.

7. A front-end interaction and clinical safety control method for boron neutron capture therapy as described in claim 6, characterized in that, The online optimization algorithm is a deep reinforcement learning algorithm. It takes the prediction result as the state input, the adjustment amount of the equipment control parameters as the action output, and makes online decisions with the goal of maximizing the probability of tumor control while minimizing the probability of complications in normal tissues.

8. A front-end interaction and clinical safety control method for boron neutron capture therapy as described in claim 3, characterized in that, In step S5, the hardware safety interlock is implemented by a field-programmable gate array (FPGA) hardware circuit, which is directly connected to the signal output of the EEG monitoring unit. When the power spectrum characteristics of the EEG signal in a specific frequency band are determined to match the preset abnormal physiological pattern in real time, the highest priority interrupt command is sent directly to the treatment execution device.

9. A front-end interaction and clinical safety control system for boron neutron capture therapy according to any one of claims 1 to 8, characterized in that, include: A data acquisition module, configured to perform step S2 of claim 1, for acquiring real-time monitoring data streams; The therapy execution module includes a neutron irradiation device and a boron drug delivery device, which are used to receive and execute control commands; The core processing and decision-making module is configured to execute steps S1, S3 and S4 in claim 1, with its input interface connected to the data acquisition module and its output interface connected to the treatment execution module. An independent safety interlock module is configured to specifically execute the hardware safety interlock function with the highest interrupt priority in step S5 of claim 1; its monitoring input is directly connected to the physiological state signal output in the data acquisition module, and its control output is directly connected to the emergency control port of the treatment execution module through an independent hardware line to ensure that the response delay of the hardware safety interlock path is minimized and reaches the level required to meet the highest priority safety interruption. The unified interaction and display module is configured to execute step S6, and its data interface is connected to the data acquisition module and the core processing and decision-making module respectively.

10. A front-end interaction and clinical safety control system for boron neutron capture therapy as described in claim 9, characterized in that, The core processing and decision-making module also includes a simulation planning unit, which is used to simulate and pre-plan treatment based on virtual parameters before treatment; the system also includes a distributed data management module, which is used to associate and store the treatment data throughout the entire cycle in a blockchain-based distributed data network, and to perform secure collaborative optimization of the parameters in the patient-specific treatment model based on the data through federated learning.

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