Adaptive radiotherapy system with biophysical real-time monitoring of tissue response

Abstract

An adaptive radiotherapy system (100) with biophysical real-time monitoring of the tissue response, wherein the system (100) comprises: a module (1) for real-time biophysical tissue response monitoring, configured to acquire live data on the physiological response and tissue response from a treatment area of ​​a patient during irradiation; a module (2) for localizing patient anatomy and motion detection, configured to detect position changes, organ movements and target shifts in real time; an engine (3) for adaptive treatment planning and dose recalculation, configured to dynamically update a radiotherapy treatment plan based on data received from the module (1) for real-time biophysical monitoring of tissue response and from the module (2) for localizing patient anatomy and motion detection; an engine for predictive modeling of tissue response (4) configured to estimate short-term tissue response, tumor response and normal tissue tolerance using patient data from current and previous sessions; an interface for controlling and modulating the beam (5) which is functionally coupled to a radiation delivery device and configured to modify one or more beam parameters in real time; a safety check and treatment lock control (6) configured to compare current treatment conditions with one or more allowable thresholds and selectively interrupt, stop, or release radiation delivery; and a module (7) for clinical review, data logging and treatment evaluation, configured to store treatment response data, generate session-related adjustment protocols and provide one or more recommendations for subsequent fractions, wherein the system (100) adaptively modifies the radiation output in response to continuously monitored biophysical tissue conditions of the patient.

Description

INVENTION AREA

[0001] The present invention relates generally to the field of radiotherapy systems, medical treatment planning, real-time physiological monitoring and computer-aided adaptive oncological treatment.

[0002] In particular, the present invention relates to an adaptive radiotherapy system (100) configured to monitor the biophysical tissue response during irradiation in real time and to dynamically adjust the treatment parameters based on the measured tissue conditions, anatomical changes, movement behavior and predicted biological response. BACKGROUND OF THE INVENTION

[0003] The subject matter discussed in the background section should not be considered prior art solely because it is mentioned therein. Likewise, a problem mentioned in the background section or related to its subject matter should not be considered to be prior art. The subject matter in the background section merely presents various approaches, which could themselves also be inventions.

[0004] Radiation therapy is one of the most frequently used treatment methods for malignant tumors and certain non-malignant tissue changes. In a typical radiation therapy workflow, a treatment plan is created prior to treatment based on simulation data such as computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET), ultrasound, or combinations thereof. Using this pretreatment data, the clinician determines the gross tumor volume, clinical target volume, planning target volume, and adjacent organs at risk, and a radiation plan is developed to deliver a prescribed dose to the intended target while keeping radiation exposure to normal tissue within acceptable limits.

[0005] Modern radiotherapy techniques such as intensity-modulated radiotherapy (IMRT), volumetric modulated arc therapy (VMAT), stereotactic body radiotherapy (SBRT), proton therapy, and image-guided radiotherapy (IGRT) have significantly improved conformity and targeting accuracy. Despite these advances, however, most treatment plans are still essentially based on a static or semi-static representation of the patient's anatomy acquired during the planning phase. The treatment plan is typically delivered in multiple fractions, often spread over several days or weeks, during which the patient's anatomical and physiological condition can change considerably.

[0006] During the actual course of radiation therapy, the patient's anatomy rarely corresponds exactly to that depicted in the original treatment plan. Positional deviations can occur due to daily variations in positioning, changes in the patient's posture, involuntary body movements, or discomfort during treatment. Furthermore, internal movements caused by breathing, heartbeat, bowel activity, bladder filling, swallowing, or muscle contractions can shift the tumor or surrounding organs during radiation. These effects are particularly significant in radiation therapy to the chest, abdomen, pelvis, head and neck, and thorax, where even small shifts can alter the dose delivered to the target tissue and adjacent critical structures.

[0007] Apart from positional and geometric deviations, significant biophysical and biological changes can also occur in the tumor and surrounding healthy tissue during treatment.

[0008] For example, tumors may shrink, swell, necroticize, develop edema, exhibit altered vascular perfusion, or display fluctuating oxygenation patterns during therapy. Similarly, normal tissues may develop acute inflammatory responses, changes in blood flow, tissue hardening, metabolic shifts, and fluctuations in radiosensitivity. Such changes can affect the actual tissue response to radiation but are generally not adequately detected by systems relying solely on periodic anatomical imaging or simple positional verification.

[0009] Conventional image-guided radiotherapy systems primarily aim to correct geometric misalignments. For example, cone-beam CT or portal imaging can be used to verify patient positioning prior to treatment, and some advanced systems may support gating or tracking based on motion signals. However, these approaches largely focus on the target's location rather than how the tissue is biologically responding to the delivered radiation at that moment. Consequently, a treatment session may remain geometrically acceptable while still being biologically suboptimal, for example, if a portion of the tumor becomes temporarily hypoxic, if surrounding normal tissue becomes unusually sensitive, or if perfusion changes affect radiosensitivity.

[0010] Existing approaches to adaptive radiotherapy also have practical limitations. In many clinical settings, adjustments are made only offline or between fractions, after reviewing imaging or dosimetric changes observed in previous sessions. Such adjustments typically require time-consuming contour revisions, replanning, quality assurance, and clinical review, making continuous or intrafractional adjustment difficult. Consequently, biologically significant changes that occur during the actual treatment time may not be immediately addressed. This can lead to underdosing of target tissue, overdosing of healthy tissue, impaired tumor control probability, or an increased risk of toxicity.

[0011] Furthermore, current radiotherapy systems often address tissue response in an indirect and fragmented manner. Geometry monitoring, physiological acquisition, dose recalculation, toxicity estimation, and beam delivery control are typically handled by separate subsystems or independent workflows. A unified mechanism for continuously acquiring real-time tissue response data, correlating this data with anatomical movements, predicting short-term biological behavior, recalculating dose effects, and automatically adjusting beam parameters within the same treatment session is frequently lacking. This lack of integration limits the clinical utility of available monitoring data and prevents the creation of a truly responsive radiotherapy platform.

[0012] Furthermore, conventional treatment systems typically do not adequately utilize real-time or near-real-time biophysical indicators—such as tissue temperature, oxygenation, blood flow, metabolic state, electrical impedance, acoustic response, optical spectral properties, biomarker fluctuations, or other measurable parameters that can provide information about the changing state of the irradiated tissue. Even when some physiological data is available, it is generally not integrated into the decision logic for beam shaping, dose redistribution, gating modification, or safety interlock. Thus, current systems may miss opportunities to personalize therapy according to actual tissue behavior, rather than relying solely on pre-treatment assumptions.

[0013] Another challenge in conventional radiotherapy is that safety decisions are often based on operational limitations of the equipment and positional tolerances, rather than comprehensive thresholds for biological response. A system may pause treatment if patient movement exceeds a geometric boundary, but it may not be configured to pause or modify treatment if indicators of living tissue suggest excessive exposure of normal tissue, rapid inflammatory changes, unexpected radiosensitivity, or an increasing probability of toxicity. Similarly, the ability to generate a patient-specific tissue response map during treatment is often limited to identify areas requiring dose increase, dose decrease, or spatial redistribution of radiation.

[0014] Furthermore, conventional radiotherapy platforms do not adequately support learning from previous fractions in a clinically integrated manner. Although treatment data is stored, the cumulative physiological and biological response of an individual patient across previous treatment sessions is not always effectively utilized to refine real-time decision-making in subsequent fractions. As a result, opportunities for progressive personalization, adaptive toxicity control, and response-guided dose modulation may remain untapped.

[0015] Accordingly, there is a need for an improved radiotherapy system that can not only monitor patient position and anatomical changes, but also continuously assess the biophysical tissue response during irradiation in real time. Furthermore, there is a need for a system that can combine tissue response monitoring, anatomical localization, motion tracking, predictive modeling, dose recalculation, beam modulation, safety verification, and treatment learning within a unified framework. Additionally, there is a need for a system that can dynamically adjust radiation delivery in real time or near real time to improve target conformity, reduce unnecessary irradiation of normal tissue, and enhance overall treatment precision, safety, and clinical efficacy.

[0016] The use of any examples or exemplary formulations (e.g., "as") in relation to certain embodiments described herein serves only to better illustrate the invention and does not constitute a limitation of the scope of the otherwise claimed invention. No formulation in the description should be interpreted as referring to an unclaimed element that is essential to carrying out the invention.

[0017] The information disclosed above in this "Background" section is provided solely for the purpose of better understanding the background of the invention and may therefore contain information that is not part of the prior art already known to a person skilled in the art in this country. SUMMARY

[0018] Before describing the systems and methods presented here, it should be noted that this application is not limited to the specific systems and methods described, as there may be several possible embodiments not expressly presented in this disclosure. It should also be noted that the terminology used in the description serves only to describe the specific versions or embodiments and is not intended to limit the scope of this application.

[0019] The present invention provides an adaptive radiotherapy system (100) that utilizes real-time biophysical tissue response monitoring, wherein the system comprises a real-time biophysical tissue response monitoring module (1), a patient anatomy localization and motion detection module (2), an adaptive treatment planning and dose recalculation engine (3), a predictive tissue response modeling engine (4), a beam control and modulation interface (5), a safety check and treatment lock control (6), and a clinical review, data logging, and treatment evaluation module (7); wherein the adaptive radiotherapy system (100) is configured to monitor the tissue state in real time and adjust the radiation delivery based on the physiological response, anatomical variations, predicted biological effects, and safety restrictions.

[0020] The present invention discloses a computer-aided and sensor-integrated adaptive radiotherapy system (100) that performs real-time monitoring and treatment adjustment during radiotherapy.

[0021] In one embodiment, the module (1) for real-time biophysical monitoring of the tissue response acquires physiological and tissue-specific response data from the treatment area using one or more sensor technologies. The monitored data can include tissue temperature, oxygenation, blood flow, electrical impedance, metabolic indicators, optical spectra, acoustic responses, biomarker signatures, and similar biophysical parameters that provide information about the tissue condition and the radiation response.

[0022] The patient anatomy localization and motion detection module (2) determines the anatomical position, organ movement, target displacement, and the relative movement of surrounding tissue and organs at risk. This module can utilize image registration, reference point tracking, surface tracking, respiratory gating data, ultrasound localization, optical monitoring, or other motion detection techniques.

[0023] The adaptive treatment planning and dose recalculation engine (3) receives input from modules (1) and (2) and recalculates treatment parameters in real time or near real time. The engine can determine the updated dose distribution, accumulated dose, estimates of biological effects, exposure of organs at risk, and revised beam delivery instructions.

[0024] The predictive tissue response modeling engine (4) evaluates current and historical patient data to estimate the probability of tumor control, tissue tolerance, inflammation, edema, changes in hypoxia, shifts in radiosensitivity, and the potential development of toxicity. The engine can use machine learning, statistical inference, rule-based logic, or hybrid clinical decision algorithms.

[0025] The interface for beam control and modulation (5) communicates with a radiotherapy device and adjusts beam intensity, angle, dose rate, aperture shape, multileaf collimator settings, pulse timing, gating parameters, lying position, particle energy or irradiation duration.

[0026] The control unit for safety verification and treatment interlock (6) checks whether the monitored tissue behavior, the extent of movement, sensor integrity, the planned dose, and the predicted toxicity remain within the permissible limits. If a threshold is exceeded, the control unit can interrupt, block, terminate, or reauthorize the treatment.

[0027] The clinical review, data logging and treatment evaluation module (7) stores treatment records, adjustment events, session-related data, override actions and response trends and can generate reports and recommendations for subsequent fractions.

[0028] Thus, the disclosed system enables biologically based adaptive radiotherapy, thereby improving the personalization and safety of the treatment. BRIEF DESCRIPTION OF THE DRAWING

[0029] To clarify various aspects of some embodiments of the present invention, a more detailed description of the invention is given with reference to specific embodiments shown in the accompanying drawing. It should be noted that this drawing only represents illustrative embodiments of the invention and is therefore not to be considered a limitation of its scope. The invention is described and explained with additional specificity and detail using the accompanying drawing.

[0030] To make the advantages of the present invention easily understandable, a detailed description of the invention is discussed below in conjunction with the accompanying drawing, although it should not be assumed that the scope of the invention is limited to the accompanying drawing, in which: Fig. A block diagram of the adaptive radiotherapy system (100) shows the connections between the module for biophysical real-time tissue response monitoring (1), the module for localizing patient anatomy and motion detection (2), the engine for adaptive treatment planning and dose recalculation (3), the engine for predictive modeling of tissue response (4), the interface for controlling and modulating the beam (5), the control for safety checks and treatment lockout (6), and the module for clinical review, data logging, and treatment evaluation (7). DETAILED DESCRIPTION

[0031] The present invention relates to an adaptive radiotherapy system (100) using real-time biophysical tissue response monitoring.

[0032] Fig.shows a detailed block diagram representation of the adaptive radiotherapy system (100) using real-time biophysical tissue response monitoring.

[0033] The present invention will now be described in detail with reference to exemplary embodiments. It is understood that the following description is illustrative and does not serve to limit the scope of the invention. Various modifications, substitutions, and equivalent arrangements can be made by a person skilled in the art without departing from the spirit and scope of the present invention.

[0034] In one embodiment, the present invention provides an adaptive radiotherapy system (100) configured to operate in conjunction with a radiotherapy device such as a linear accelerator, a proton therapy unit, a heavy ion therapy platform, a brachytherapy controller, or any other therapeutic radiation delivery device. The system (100) is designed to dynamically adapt the radiation delivery to the real-time tissue behavior, anatomical variations, movement characteristics, and the patient's predicted biological response during treatment.

[0035] The adaptive radiotherapy system (100) comprises a module for real-time biophysical monitoring of tissue response (1), a module for patient anatomy localization and motion tracking (2), an engine for adaptive treatment planning and dose recalculation (3), an engine for predictive tissue response modeling (4), an interface for beam path control and modulation (5), a control for safety verification and treatment interlock (6), and a module for clinical review, data logging, and treatment evaluation (7). These modules can be implemented in a centralized workstation, a distributed computing architecture, a cloud-based treatment environment, embedded treatment control hardware, or any combination thereof.The modules are functionally interconnected to enable continuous patient data acquisition, treatment reassessment, beam modification, safety validation, and longitudinal treatment learning.

[0036] The real-time biophysical tissue response monitoring module (1) is configured to acquire live data on physiological and biophysical parameters that reflect the state and response of the radiation-exposed tissue. In various embodiments, the module (1) can incorporate one or more sensor technologies, such as thermal sensors, optical spectroscopy sensors, near-infrared sensors, ultrasound probes, electrical impedance meters, oxygen sensors, perfusion sensors, fluorescence or phosphorescence sensors, surface electrophysiology sensors, implantable physiological sensors, wearable monitoring devices, microfluidic biomarker readers, MRI-derived interfaces for functional data, PET-derived interfaces for metabolic responses, and acoustic emission sensors.By using these sensor arrangements, the module (1) is able to measure tissue temperature, perfusion levels, oxygen saturation, local pH, metabolic activity, tissue conductance, impedance changes, blood flow characteristics, elasticity, stiffness, edema indicators, inflammatory markers, cell viability indicators and other real-time parameters related to the radiation response.

[0037] During operation, the module (1) can continuously or periodically acquire data for real-time biophysical monitoring of the tissue response during beam delivery and process the acquired data before transmission to subsequent modules. Such processing can include filtering, noise reduction, normalization, calibration, temporal labeling, spatial correlation, confidence estimation, and signal validation. In certain embodiments, the module (1) can generate a tissue response profile corresponding to a target area or a surrounding normal tissue area and spatially map the monitored response data onto existing anatomical images or treatment coordinates, allowing the biological state of the irradiated tissue to be evaluated in conjunction with the treatment geometry.

[0038] The patient anatomy localization and motion tracking module (2) is configured to determine the geometric and positional status of the treatment target and surrounding anatomical structures in real or near real time. In one embodiment, the module (2) can employ imaging and tracking technologies such as cone-beam CT, ultrasound imaging, MRI guidance, optical surface scanning, infrared marker detection, reference marker tracking, electromagnetic localization, respiratory motion detection, pressure-based body position sensing, and stereoscopic camera systems. Using these technologies, the module (2) determines the patient's current anatomical state and monitors the movement of the target tissue, organs at risk, and external or internal anatomical landmarks.

[0039] The patient anatomy localization and motion detection module (2) can perform target localization, organ at risk localization, deformable image registration, target contour refinement, motion vector estimation, respiratory phase determination, gating signal generation, patient posture verification, and anatomical state updates. In a preferred embodiment, the module (2) registers the patient's current anatomical state using one or more images from the pretreatment planning and generates a motion-compensated anatomical representation. This representation is forwarded to the adaptive treatment planning and dose recalculation engine (3) to reassess treatment accuracy and correct radiation delivery in real time or near real time.

[0040] The adaptive treatment planning and dose recalculation engine (3) is configured to receive data from the real-time biophysical tissue response monitoring module (1) and the patient anatomy localization and motion detection module (2), and determines whether a change to the treatment plan or irradiation parameters is required. The engine (3) can estimate the current target geometry, update tissue property maps, recalculate the dose distribution, evaluate the accumulated dose, determine the biologically effective dose, revise dose limits, assess the exposure of organs at risk, compensate for motion-related deviations, and update fraction-specific treatment settings.In some embodiments, the engine (3) performs the replanning itself during the treatment session, while in other embodiments the engine (3) can make a rapid adjustment between beam segments, gating intervals or partial fractions.

[0041] In an exemplary embodiment, the adaptive treatment planning and dose recalculation engine (3) can calculate a revised beam strategy if the target moves beyond a permissible position limit or if a monitored physiological parameter, such as oxygenation, blood flow, or temperature, indicates a change in tissue radiosensitivity. Such a revised strategy may include dose redistribution, selective dose increase in resistant subregions, dose reduction in sensitive normal tissue, modification of the gating windows, change in beam shaping instructions, revision of the beam intensity profiles, and recalculation of the exposure of organs at risk. Thus, the engine (3) acts as the core of the treatment adaptation, transforming anatomical and biological response data into an updated treatment plan.

[0042] The predictive tissue response modeling engine (4) is configured to analyze current session data and historical patient-specific data to estimate the short- and long-term biological response to treatment. In various embodiments, the engine (4) can employ machine learning models, neural networks, statistical prediction algorithms, probabilistic inference models, digital twin representations, toxicity prediction frameworks, radiobiological models, or rule-based clinical decision systems.The inputs to the predictive modeling engine (4) can include current tissue monitoring data, data from previous irradiation fractions, the cumulative dose history, the history of anatomical deformations, pathological information, tumor classification, patient-specific demographic or clinical characteristics, biomarker profiles, genomic information and parameters of the treatment protocol.

[0043] By processing the aforementioned data, the predictive modeling engine (4) can estimate the probability of tumor regression, changes in radiosensitivity, edema progression, inflammation development, fibrosis tendency, hypoxia fluctuations, the risk of acute toxicity, the risk of chronic toxicity, normal tissue tolerance, and the probability of target tissue underdosing or treatment failure. In a preferred embodiment, the engine (4) generates a patient-specific digital tissue response map that identifies one or more subregions within the treatment zone that may require different radiation intensities.Such a map can be used by the adaptive treatment planning and dose recalculation engine (3) to perform spatial dose escalation, dose de-escalation or modified dose redistribution according to the predicted biological state of the tissue.

[0044] The interface for controlling and modulating the beam (5) is functionally connected to a radiation delivery device and configured to receive beam modification instructions generated by the adaptive treatment planning and dose recalculation engine (3). Such instructions may only be applied after verification and authorization by the safety check and treatment interlock control unit (6). The interface (5) can control one or more beam delivery parameters, including beam intensity, beam angle, gantry position, collimator angle, aperture geometry, multi-leaf collimator position, dose rate, pulse sequencing, gating timing, patient position, energy level, dwell time, treatment duration, and scan path in the case of particle beam therapy.In some embodiments, the beam control and modulation interface (5) supports continuous beam correction during treatment with the beam switched on, while in other embodiments it supports discrete plan adjustments between segments or subfractions.

[0045] The safety check and treatment interlock controller (6) is configured to ensure that adaptive treatment remains within clinically and technically acceptable limits. The controller (6) can receive motion threshold data, sensor integrity status, current dose rate information, proximity of organs at risk data, predicted toxicity values, beam configuration parameters, device fault status, and clinician-defined override settings. The controller (6) compares one or more monitored, calculated, or predicted states with predefined safety thresholds or dynamically determined safety limits.

[0046] If the control (6) for safety checks and treatment interlocks detects that a critical parameter exceeds an allowable threshold, the control can generate a beam hold signal, a beam pause signal, a treatment stop signal, or a request for clinician confirmation before treatment continues. Such intervention may occur, for example, if patient movement exceeds a tolerance range, if target displacement becomes too large, if a risk organ moves into a high-dose area, if tissue response indicates an abnormal risk of injury, if predicted toxicity exceeds a threshold, if sensor data becomes unreliable, if a communication error is detected, or if machine performance deviates from allowable operating conditions.Accordingly, the control (6) serves as a higher level of safety to prevent clinically unsafe or technically invalid adaptive measures.

[0047] The clinical review, data logging, and treatment evaluation module (7) is configured to store, organize, and display treatment-related data for real-time monitoring and subsequent learning. Module (7) can record sensor measurements, anatomical localization records, motion traces, adjusted plan versions, dose recalculation results, beam parameter changes, safety threshold exceedances, interlock actions, toxicity prediction results, clinician approvals, clinician override actions, and fractional treatment response trends. Module (7) can also generate session adjustment reports, treatment summary reports, disease progression visualizations, dose comparison outputs, alerts, and recommendations for later fractions or subsequent treatment sessions.

[0048] In some embodiments, the module (7) supports clinical review, data logging, and treatment adaptation, facilitating cross-fraction learning by utilizing historical records from the same patient to refine future treatment adjustments. In other embodiments, anonymized data from multiple patients can be used, with appropriate data protection and regulatory safeguards in place, to improve predictive models and adaptive decision rules. In this way, the module (7) not only records the treatment history but also contributes to the progressive personalization of therapy throughout the entire treatment course.

[0049] In an exemplary workflow, the patient is first positioned for radiotherapy, and initial anatomical localization is performed by the Patient Anatomy Localization and Motion Capture Module (2). Baseline tissue response data is acquired simultaneously or subsequently by the Biophysical Real-Time Tissue Response Monitoring Module (1). Once treatment begins, the Monitoring Module (1) continuously or periodically acquires tissue response data, while the Anatomical Localization and Motion Capture Module (2) continues to monitor the position and movement of the target and surrounding anatomical structures. The resulting data streams are transmitted to the Adaptive Treatment Planning and Dose Recalculation Engine (3) and the Predictive Tissue Response Modeling Engine (4).

[0050] During treatment, the predictive tissue response modeling engine (4) assesses whether the observed tissue behavior indicates increased radiosensitivity, decreased radiosensitivity, the onset of toxicity, abnormal inflammation, hypoxia-induced resistance, or another clinically relevant biological condition. Almost simultaneously, the adaptive treatment planning and dose recalculation engine (3) determines whether the actual delivered or predicted dose remains consistent with the treatment goals in light of the anatomical and physiological changes. If an adjustment is required, the engine (3) generates revised irradiation parameters and forwards them for safety assessment.

[0051] The revised treatment parameters are then reviewed by the safety review and treatment lock controller (6). If the updated plan meets the clinical and technical safety requirements, the beam control and modulation interface (5) transmits the revised instructions to the treatment unit, and treatment continues with the modified beam behavior. If the safety controller (6) detects an exceedance of one or more thresholds, treatment may be interrupted, suspended, terminated, or referred for clinician review before being resumed. All events, data, and outcomes associated with the adjustment are recorded by the clinical review, data logging, and treatment learning module (7) for traceability and future use.

[0052] In an exemplary embodiment, the adaptive radiotherapy system (100) is used to treat a thoracic tumor that is subject to respiratory motion. During treatment, the biophysical tissue response monitoring module (1) measures the oxygen saturation, tissue temperature, and regional blood flow within the target region and surrounding tissue in real time. Simultaneously, the patient anatomy localization and motion detection module (2) tracks respiratory motion and identifies tumor movement relative to the initial treatment coordinates. During radiation delivery, the system detects that the tumor has shifted from the planned position and that some of the adjacent normal lung tissue has moved into an area that would otherwise be exposed to an increased radiation dose.

[0053] Based on the monitored perfusion pattern and tissue response characteristics, the predictive tissue response modeling engine (4) determines that the risk of normal tissue toxicity has increased. In response, the adaptive treatment planning and dose recalculation engine (3) calculates a revised dose distribution and narrows the gating window so that radiation is delivered only during favorable movement intervals. The beam control and modulation interface (5) then adjusts the multileaf collimator position, beam timing, and dose rate according to the revised plan. The safety check and treatment interlock control (6) verifies that the modified treatment parameters remain within acceptable safety limits and allows the treatment to continue.The entire process, including tissue response data, plan revision, beam adjustment and safety check result, is stored in the clinical review, data logging and treatment evaluation module (7) and can be used to improve planning for a subsequent treatment fraction.

[0054] Thus, the disclosed adaptive radiotherapy system (100) provides a unified platform for integrating biological monitoring, anatomical tracking, predictive modeling, dose recalculation, adaptive beam control, safety interlock, and treatment learning. By using the real-time biophysical tissue response as active input for treatment adjustment, the system enhances the ability to personalize radiotherapy according to the patient's actual condition during treatment, thereby improving therapeutic precision, safety, and overall treatment efficacy.

[0055] The figure and the preceding description provide examples of embodiments. Those skilled in the art will recognize that one or more of the described elements can be combined to form a single functional element. Alternatively, certain elements can be divided into several functional elements. Elements from one embodiment can be added to another embodiment. For example, the sequence of the processes described here can be changed and is not limited to the manner shown. Furthermore, the steps of a block diagram need not be implemented in the order shown; nor do all steps necessarily have to be executed. In addition, those steps that are not dependent on other steps can be executed in parallel with the other steps. The scope of embodiments is by no means limited to these specific examples.

[0056] Although embodiments of the invention have been described in language relating to structural features and / or methods, it is to be understood that the appended claims are not necessarily limited to the specific features or methods described. Rather, the specific features and methods are disclosed as examples of embodiments of the invention.

Claims

[1] An adaptive radiotherapy system (100) with biophysical real-time monitoring of the tissue response, wherein the system (100) comprises: a module (1) for real-time biophysical tissue response monitoring, configured to acquire live data on the physiological response and tissue response from a treatment area of ​​a patient during irradiation; a module (2) for localizing patient anatomy and motion detection, configured to detect position changes, organ movements and target shifts in real time; an engine (3) for adaptive treatment planning and dose recalculation, configured to dynamically update a radiotherapy treatment plan based on data received from the module (1) for real-time biophysical monitoring of tissue response and from the module (2) for localizing patient anatomy and motion detection; an engine for predictive modeling of tissue response (4) configured to estimate short-term tissue response, tumor response and normal tissue tolerance using patient data from current and previous sessions; an interface for controlling and modulating the beam (5) which is functionally coupled to a radiation delivery device and configured to modify one or more beam parameters in real time; a safety check and treatment lock control (6) configured to compare current treatment conditions with one or more allowable thresholds and selectively interrupt, stop, or release radiation delivery; and a module (7) for clinical review, data logging and treatment evaluation, configured to store treatment response data, generate session-related adjustment protocols and provide one or more recommendations for subsequent fractions, wherein the system (100) adaptively modifies the radiation output in response to continuously monitored biophysical tissue conditions of the patient. [2] The adaptive radiotherapy system (100) according to claim 1, wherein the module (1) for real-time monitoring of the biophysical tissue response comprises one or more sensing subsystems selected from functional imaging sensors, thermal sensors, optical spectroscopy sensors, ultrasound sensors, electrical impedance sensors, oxygen sensors, perfusion sensors, interfaces for sensing biomarkers and combinations thereof. [3] The adaptive radiotherapy system (100) according to claim 1, wherein the module (2) for localizing the patient anatomy and motion detection is configured to compare a current anatomical state of the patient with an image from the pretreatment planning and generates a motion-compensated target localization output for real-time beam correction. [4] The adaptive radiotherapy system (100) according to claim 1, wherein the adaptive treatment planning and dose recalculation engine (3) is configured to generate an updated dose distribution by recalculating at least one of the following parameters: Target coverage, exposure of the organ at risk, biologically effective dose, deformable accumulated dose and fractional dose compensation. [5] The adaptive radiotherapy system (100) according to claim 1, wherein the predictive tissue response modeling engine (4) comprises a machine learning or rule-based prediction model trained to identify one or more conditions, including tumor shrinkage at radiation dose, tissue inflammation, hypoxia variation, edema development, change in radiosensitivity and toxicity risk. [6] The adaptive radiotherapy system (100) according to claim 1, wherein the interface for controlling and modulating the beam (5) is configured to automatically change one or more beam delivery parameters selected from beam intensity, beam angle, aperture shape, position of the multileaf collimator, dose rate, pulse timing, gating window, table position, particle energy and irradiation time. [7] The adaptive radiotherapy system (100) according to claim 1, wherein the control (6) for safety check and treatment interlock is configured to trigger an immediate pause in the radiation if at least one of the following factors - detected tissue response, patient movement, deviation of an organ at risk, sensor error or predicted toxicity - exceeds a predefined threshold. [8] The adaptive radiotherapy system (100) according to claim 1, wherein the module (7) for clinical review, data logging and treatment evaluation is configured to generate a treatment adaptation report that includes session-specific sensor data, dose changes, beam adaptation events, threshold exceedances, predicted response results and records of clinical overrides. [9] The adaptive radiotherapy system (100) according to claim 1, wherein the predictive tissue response modeling engine (4) and the adaptive treatment planning and dose recalculation engine (3) work together to generate a patient-specific digital tissue response map that identifies subregions that require dose escalation, dose escalation or spatial redistribution of radiation. [10] The adaptive radiotherapy system (100) according to claim 1, wherein the system (100) is configured to perform fraction-to-fraction learning by using historical treatment data stored in the treatment learning module (7) for clinical review, data logging and treatment evaluation to refine future plan adjustments, toxicity predictions and beam control decisions for the same patient.

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