CN116808454A - Radiotherapy equipment treatment evaluation system based on NLP - Google Patents
Radiotherapy equipment treatment evaluation system based on NLP Download PDFInfo
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- 238000011282 treatment Methods 0.000 title claims abstract description 73
- 238000011156 evaluation Methods 0.000 title claims abstract description 31
- 238000001959 radiotherapy Methods 0.000 title claims abstract description 16
- 238000003058 natural language processing Methods 0.000 claims abstract description 51
- 238000007405 data analysis Methods 0.000 claims abstract description 18
- 238000000034 method Methods 0.000 claims abstract description 15
- 230000002159 abnormal effect Effects 0.000 claims abstract description 12
- 238000004458 analytical method Methods 0.000 claims description 18
- 230000008451 emotion Effects 0.000 claims description 12
- 238000012545 processing Methods 0.000 claims description 7
- 230000011218 segmentation Effects 0.000 claims description 6
- 238000012800 visualization Methods 0.000 claims description 6
- 238000004590 computer program Methods 0.000 claims description 5
- 238000013473 artificial intelligence Methods 0.000 claims description 3
- 238000013528 artificial neural network Methods 0.000 claims description 3
- 238000004140 cleaning Methods 0.000 claims description 3
- 238000000605 extraction Methods 0.000 claims description 3
- 238000007781 pre-processing Methods 0.000 claims description 3
- 238000002560 therapeutic procedure Methods 0.000 claims 8
- 238000012552 review Methods 0.000 abstract description 2
- 238000004891 communication Methods 0.000 description 6
- 238000005516 engineering process Methods 0.000 description 6
- 238000010586 diagram Methods 0.000 description 4
- 230000000694 effects Effects 0.000 description 3
- 238000012795 verification Methods 0.000 description 3
- 238000005259 measurement Methods 0.000 description 2
- 238000012544 monitoring process Methods 0.000 description 2
- 206010028980 Neoplasm Diseases 0.000 description 1
- 210000003484 anatomy Anatomy 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 201000011510 cancer Diseases 0.000 description 1
- 238000012790 confirmation Methods 0.000 description 1
- 201000010099 disease Diseases 0.000 description 1
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 description 1
- 230000005672 electromagnetic field Effects 0.000 description 1
- 230000005670 electromagnetic radiation Effects 0.000 description 1
- 238000010894 electron beam technology Methods 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 238000011022 operating instruction Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
- A61N5/00—Radiation therapy
- A61N5/10—X-ray therapy; Gamma-ray therapy; Particle-irradiation therapy
- A61N5/1048—Monitoring, verifying, controlling systems and methods
- A61N5/1064—Monitoring, verifying, controlling systems and methods for adjusting radiation treatment in response to monitoring
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
- A61N5/00—Radiation therapy
- A61N5/10—X-ray therapy; Gamma-ray therapy; Particle-irradiation therapy
- A61N5/1048—Monitoring, verifying, controlling systems and methods
- A61N5/1071—Monitoring, verifying, controlling systems and methods for verifying the dose delivered by the treatment plan
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
- A61N5/00—Radiation therapy
- A61N5/10—X-ray therapy; Gamma-ray therapy; Particle-irradiation therapy
- A61N5/1048—Monitoring, verifying, controlling systems and methods
- A61N2005/1074—Details of the control system, e.g. user interfaces
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
- A61N5/00—Radiation therapy
- A61N5/10—X-ray therapy; Gamma-ray therapy; Particle-irradiation therapy
- A61N2005/1085—X-ray therapy; Gamma-ray therapy; Particle-irradiation therapy characterised by the type of particles applied to the patient
- A61N2005/1089—Electrons
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Abstract
The application discloses an NLP-based radiotherapy equipment treatment evaluation system, which comprises a data acquisition unit, a data analysis unit and a treatment unit, wherein the data acquisition unit is used for reading a history log file and a log file to be analyzed of an accelerator, and acquiring and identifying related data of the log file to be analyzed through an NLP natural language processing model trained by the history log file; and the data analysis unit is used for analyzing the related data of the log file to be analyzed to form treatment evaluation of the whole treatment period, and taking corresponding measures to adjust when abnormal data or trend is found. The application can realize comprehensive record and traceability of the treatment process, thereby facilitating subsequent review and evaluation.
Description
Technical Field
The application relates to the field of radiotherapy equipment system evaluation, in particular to a radiotherapy equipment treatment evaluation system based on NLP.
Background
Medical electron linac medical devices, which can produce high energy electron beams or photon beams, are used to treat cancer and other diseases. Linacs accelerate or decelerate in high-speed electrons using electromagnetic field technology and produce high-energy electromagnetic radiation. Such devices can modulate the energy and depth of the electron or photon beam to locate and destroy cancerous cells within the body while minimizing damage to surrounding normal tissue. The linear accelerator is one of the most widely used devices in modern radiotherapy, has the characteristics of high precision, adjustability and high efficiency, and can help doctors to provide more personalized and effective treatment schemes and reduce side effects of treatment.
The treatment verification in radiotherapy is an important link for ensuring safe and effective implementation of personalized accurate intensity modulated radiotherapy, and the traditional treatment verification mainly adopts a measurement-based die body verification method, and is generally implemented before the first treatment of a patient. The method cannot monitor and evaluate the planned execution state of all the patients in the treatment process, and certain risks and errors exist in the treatment process.
Disclosure of Invention
Purpose of (one) application
Based on this, the following technical scheme is disclosed for on-line monitoring and dose evaluation of all divided treatments of a patient.
(II) technical scheme
The application discloses a radiotherapy equipment treatment evaluation system based on NLP, comprising:
the data acquisition unit is used for reading a history log file and a log file to be analyzed of the accelerator, and acquiring and identifying related data of the log file to be analyzed through an NLP natural language processing model trained by the history log file;
and the data analysis unit is used for analyzing the related data of the log file to be analyzed to form treatment evaluation of the whole treatment period, and taking corresponding measures to adjust when abnormal data or trend is found.
In one possible embodiment, the history log file and the log file to be analyzed are log information generated by an accelerator for a previous treatment of the same patient.
In one possible embodiment, the data acquisition unit comprises:
the data preprocessing module is used for cleaning and formatting the history log file and the log file to be analyzed, removing useless information and noise, and converting the useless information and noise into processable history text information and text information to be analyzed;
the marking module is used for marking the word segmentation and the part of speech of the historical text information, dividing the text into words and phrases and marking the part of speech and grammar structures of the words and phrases;
the model generation module is used for generating an NLP natural language processing model according to the word segmentation labels and the part-of-speech labels;
and the identification module is used for identifying and collecting the entity and the keyword in the text information to be analyzed through the NLP natural language processing model.
In a possible implementation manner, the data acquisition unit further comprises a relation extraction module for identifying relations and relations in the text information to be analyzed.
In one possible embodiment, the data analysis unit comprises:
the visualization module is used for performing visualization processing based on the entity and the keywords to be used as analysis results of the previous treatment, so that data analysis processing generated by the repeated treatment of the patient is facilitated;
and the comparison analysis module is used for carrying out one-time comparison on the baseline value obtained when the patient is measured during the first treatment as a reference value and the analysis result of the previous treatment so as to monitor and evaluate the treatment process of the whole period of the patient.
In one possible embodiment, the data analysis unit further comprises:
the emotion analysis module is used for carrying out emotion analysis on the text information to be analyzed and judging emotion tendencies and emotion states in the text information to be analyzed;
and the topic modeling module is used for performing topic modeling on the text information to be analyzed and identifying topics and topics in the text information to be analyzed.
In one possible implementation, the NLP natural language processing model employs a neural network architecture.
In one possible implementation, the system further comprises an intelligent decision support unit, which utilizes artificial intelligence technology to evaluate the state of the accelerator for mode calibration and parameter adjustment based on historical data and existing equipment state information.
As a second aspect of the present application, the present application also discloses a computer-readable storage medium having stored thereon a computer program, characterized in that the program when executed by a processor realizes the steps of:
reading a history log file and a log file to be analyzed of an accelerator, and acquiring and identifying related data of the log file to be analyzed through an NLP natural language processing model trained by the history log file;
and analyzing the related data of the log file to be analyzed to form treatment evaluation of the whole treatment period, and taking corresponding measures for adjustment when abnormal data or trend is found.
As a third aspect of the present application, the present application also discloses a computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the following steps when executing the program:
reading a history log file and a log file to be analyzed of an accelerator, and acquiring and identifying related data of the log file to be analyzed through an NLP natural language processing model trained by the history log file;
and analyzing the related data of the log file to be analyzed to form treatment evaluation of the whole treatment period, and taking corresponding measures for adjustment when abnormal data or trend is found.
(III) beneficial effects
According to the NLP-based radiotherapy equipment treatment evaluation system disclosed by the application, the NLP technology is used for detecting abnormal conditions in the log file, evaluating the treatment effect so as to adjust the treatment plan and improve the treatment effect in time, and the operation log records various parameters and operation information of equipment by analyzing the operation log of the accelerator, so that the NLP-based radiotherapy equipment treatment evaluation system has important significance for maintenance and fault removal of the equipment and real-time confirmation of the treatment plan of a patient.
Drawings
The embodiments described below with reference to the drawings are exemplary and intended to illustrate and describe the application and should not be construed as limiting the scope of the application.
Fig. 1 is a block diagram of a treatment evaluation system for NLP-based radiotherapy apparatus of the present disclosure.
Fig. 2 is a schematic structural diagram of a computer device according to the present disclosure.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application become more apparent, the technical solutions in the embodiments of the present application will be described in more detail below with reference to the accompanying drawings in the embodiments of the present application.
An embodiment of the NLP-based radiotherapy apparatus treatment assessment system of the present disclosure is described in detail below with reference to fig. 1. As shown in fig. 1, the system disclosed in this embodiment mainly includes:
the data acquisition unit is used for reading a history log file and a log file to be analyzed of the accelerator, and acquiring and identifying related data of the log file to be analyzed through an NLP natural language processing model trained by the history log file;
and the data analysis unit is used for analyzing the related data of the log file to be analyzed to form treatment evaluation of the whole treatment period, and taking corresponding measures to adjust when abnormal data or trend is found.
According to the NLP-based radiotherapy equipment treatment evaluation system, the NLP technology is utilized, so that time spent by a physical engineer on tedious data analysis can be reduced, meanwhile, the accuracy of the data analysis is improved, meanwhile, the working efficiency and the accuracy of the physical engineer are improved, the error rate is reduced, potential treatment problems can be found and solved in time through analysis and dose evaluation of an accelerator log, the treatment quality and safety are improved, comprehensive recording and traceability of a treatment process can be realized, and further follow-up review and evaluation are facilitated.
Preferably, the history log file and the log file to be analyzed are log information generated by the accelerator on previous treatment of the same patient, so that the planned execution state, the stability of machine output, the positioning error, the change of the anatomical structure of the patient and the like of all the patients in the treatment process are conveniently monitored and evaluated, and the safety and the accuracy of the whole treatment process are further ensured.
Preferably, the data acquisition unit includes:
the data preprocessing module is used for cleaning and formatting the history log file and the log file to be analyzed, removing useless information and noise, and converting the useless information and noise into processable history text information and text information to be analyzed;
the processable historical text information and the text information to be analyzed comprise information such as equipment position, frame angle, collimator angle, dose rate, hop count (MU) and the like.
The marking module is used for marking the word segmentation and the part of speech of the historical text information, dividing the text into words and phrases and marking the part of speech and grammar structures of the words and phrases;
and the model generation module is used for generating an NLP natural language processing model according to the word segmentation labels and the part-of-speech labels.
And the identification module is used for identifying and collecting the entity and the keyword in the text information to be analyzed through the NLP natural language processing model.
The entity comprises a device name, an operator, time and the like; keywords include abnormal conditions, fault information, etc., so that healthcare workers quickly learn about the operation and problems of the device.
Preferably, the data acquisition unit further comprises a relationship extraction module for identifying relationships and links in the text information to be analyzed, so that medical workers can better know the operation condition and problems of the equipment.
Preferably, the data analysis unit includes:
the visualization module is used for performing visualization processing based on the entity and the keywords to be used as analysis results of the previous treatment, so that data analysis processing generated by the repeated treatment of the patient is facilitated;
the analysis results can be displayed through word cloud diagrams, relationship diagrams and the like, so that medical workers can more intuitively know the operation condition and the problem of the equipment.
And the comparison analysis module is used for carrying out one-time comparison on the baseline value obtained when the patient is measured during the first treatment as a reference value and the analysis result of the previous treatment so as to monitor and evaluate the treatment process of the whole period of the patient.
Wherein, the measurement result of any divided treatment can be selected as a reference value according to the actual requirement.
Preferably, the data analysis unit further includes:
and the emotion analysis module is used for carrying out emotion analysis on the text information to be analyzed and judging emotion tendencies and emotion states in the text information to be analyzed so that medical workers can better know the using experience and user feedback of the equipment.
The topic modeling module is used for performing topic modeling on the text information to be analyzed and identifying topics and topics in the text information to be analyzed so that medical workers can better know the running condition and problems of equipment.
Preferably, the NLP natural language processing model adopts a neural network architecture, has strong self-learning capability, and can be successfully applied to the evaluation and prediction of radiotherapy equipment.
Preferably, the application further comprises an intelligent decision support unit, which utilizes an artificial intelligence technology to evaluate the state of the accelerator based on the historical data and the existing equipment state information, and is used for mode calibration and parameter adjustment, so that the time spent by a physical engineer on tedious data analysis is reduced, and the accuracy of the data analysis is improved.
Example 1
An MLC travel log file is generated during treatment plan execution, recording MLC position, gantry angle, collimator angle, dose rate, hop count (MU), etc. The actual dose deviation caused by the MLC position error can be obtained by comparing the machine MLC position information recorded in the log file with the MLC position information in the TPS plan, and the dose deviation of the current treatment of the patient can be obtained by reading the log file and performing corresponding analysis by using an NLP technology. The log file of each treatment is compared with the log file of the first treatment of the patient to monitor the machine operation, thereby performing on-line monitoring and dose evaluation on all the divided treatments of the patient.
Based on the above method as shown in fig. 1, correspondingly, the embodiment of the present application further provides a computer readable storage medium, on which a computer program is stored, which when being executed by a processor, implements the following steps: reading a history log file and a log file to be analyzed of an accelerator, and acquiring and identifying related data of the log file to be analyzed through an NLP natural language processing model trained by the history log file; and analyzing the related data of the log file to be analyzed to form treatment evaluation of the whole treatment period, and taking corresponding measures for adjustment when abnormal data or trend is found.
Based on the above embodiment of the method shown in fig. 1, the embodiment of the present application further provides a computer device, as shown in fig. 2, a processor (processor) 41, a communication interface (Communications Interface) 42, a memory (memory) 43, and a communication bus 44. Wherein: processor 41, communication interface 42, and memory 43 communicate with each other via a communication bus 44. A communication interface 44 for communicating with network elements of other devices, such as clients or other servers. The processor 41 is configured to execute a program, and may specifically perform relevant steps in the embodiment of the method for converting data described above. In particular, the program may include program code including computer-operating instructions. The processor 41 may be a central processing unit CPU, or a specific integrated circuit ASIC (Application Specific IntegratedCircuit), or one or more integrated circuits configured to implement embodiments of the present application.
The one or more processors included in the terminal may be the same type of processor, such as one or more CPUs; but may also be different types of processors such as one or more CPUs and one or more ASICs. A memory 43 for storing programs. The memory 43 may comprise a high-speed RAM memory or may further comprise a non-volatile memory (non-volatile memory), such as at least one disk memory. The program may be specifically for causing the processor 41 to: reading a history log file and a log file to be analyzed of an accelerator, and acquiring and identifying related data of the log file to be analyzed through an NLP natural language processing model trained by the history log file; and analyzing the related data of the log file to be analyzed to form treatment evaluation of the whole treatment period, and taking corresponding measures for adjustment when abnormal data or trend is found.
In the description of the present application, it should be understood that the terms "center," "longitudinal," "lateral," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, are merely for convenience in describing the present application and simplifying the description, and do not indicate or imply that the device or element being referred to must have a specific orientation, be configured and operated in a specific orientation, and therefore should not be construed as limiting the scope of the present application.
Herein, "first", "second", etc. are used merely to distinguish one from another, and do not indicate their importance, order, etc.
The division of modules, units or components herein is merely a division of logic functions, and other manners of division are possible in actual implementation, e.g., multiple modules and/or units may be combined or integrated in another system. The modules, units, and components illustrated as separate components may or may not be physically separate. The components displayed as cells may be physical cells or may not be physical cells, i.e., may be located in a specific place or may be distributed in grid cells. And therefore some or all of the elements may be selected according to actual needs to implement the solution of the embodiment.
The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any changes or substitutions easily contemplated by those skilled in the art within the scope of the present application should be included in the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. NLP-based radiotherapy equipment treatment evaluation system, characterized by comprising:
the data acquisition unit is used for reading a history log file and a log file to be analyzed of the accelerator, and acquiring and identifying related data of the log file to be analyzed through an NLP natural language processing model trained by the history log file;
and the data analysis unit is used for analyzing the related data of the log file to be analyzed to form treatment evaluation of the whole treatment period, and taking corresponding measures to adjust when abnormal data or trend is found.
2. The therapy evaluation system of claim 1, wherein the history log file and the log file to be analyzed are log information generated by an accelerator for a therapy of a same patient.
3. The therapy assessment system of claim 2, wherein said data acquisition unit comprises:
the data preprocessing module is used for cleaning and formatting the history log file and the log file to be analyzed, removing useless information and noise, and converting the useless information and noise into processable history text information and text information to be analyzed;
the marking module is used for marking the word segmentation and the part of speech of the historical text information, dividing the text into words and phrases and marking the part of speech and grammar structures of the words and phrases;
the model generation module is used for generating an NLP natural language processing model according to the word segmentation labels and the part-of-speech labels;
and the identification module is used for identifying and collecting the entity and the keyword in the text information to be analyzed through the NLP natural language processing model.
4. The therapy assessment system of claim 3, wherein said data acquisition unit further comprises a relationship extraction module for identifying relationships and associations in said text information to be analyzed.
5. The therapy assessment system of claim 4, wherein said data analysis unit comprises:
the visualization module is used for performing visualization processing based on the entity and the keywords to be used as analysis results of the previous treatment, so that data analysis processing generated by the repeated treatment of the patient is facilitated;
and the comparison analysis module is used for carrying out one-time comparison on the baseline value obtained when the patient is measured during the first treatment as a reference value and the analysis result of the previous treatment so as to monitor and evaluate the treatment process of the whole period of the patient.
6. The therapy assessment system of claim 5, wherein said data analysis unit further comprises:
the emotion analysis module is used for carrying out emotion analysis on the text information to be analyzed and judging emotion tendencies and emotion states in the text information to be analyzed;
and the topic modeling module is used for performing topic modeling on the text information to be analyzed and identifying topics and topics in the text information to be analyzed.
7. The therapy evaluation system of claim 1, wherein the NLP natural language processing model employs a neural network architecture.
8. The therapy evaluation system of claim 1, further comprising an intelligent decision support unit for performing state evaluation of the accelerator for pattern calibration and parameter adjustment using artificial intelligence techniques based on historical data and existing device state information.
9. A computer readable storage medium having stored thereon a computer program, characterized in that the program when executed by a processor performs the steps of:
reading a history log file and a log file to be analyzed of an accelerator, and acquiring and identifying related data of the log file to be analyzed through an NLP natural language processing model trained by the history log file;
and analyzing the related data of the log file to be analyzed to form treatment evaluation of the whole treatment period, and taking corresponding measures for adjustment when abnormal data or trend is found.
10. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the following steps when executing the program:
reading a history log file and a log file to be analyzed of an accelerator, and acquiring and identifying related data of the log file to be analyzed through an NLP natural language processing model trained by the history log file;
and analyzing the related data of the log file to be analyzed to form treatment evaluation of the whole treatment period, and taking corresponding measures for adjustment when abnormal data or trend is found.
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| CN202311069649.4A CN116808454A (en) | 2023-08-24 | 2023-08-24 | Radiotherapy equipment treatment evaluation system based on NLP |
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| CN202311069649.4A CN116808454A (en) | 2023-08-24 | 2023-08-24 | Radiotherapy equipment treatment evaluation system based on NLP |
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