CN111430031B - Voxel and CT value multiplication integral algorithm system for evaluating lung cancer curative effect - Google Patents
Voxel and CT value multiplication integral algorithm system for evaluating lung cancer curative effect Download PDFInfo
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- CN111430031B CN111430031B CN202010418278.6A CN202010418278A CN111430031B CN 111430031 B CN111430031 B CN 111430031B CN 202010418278 A CN202010418278 A CN 202010418278A CN 111430031 B CN111430031 B CN 111430031B
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- 206010058467 Lung neoplasm malignant Diseases 0.000 title claims abstract description 25
- 201000005202 lung cancer Diseases 0.000 title claims abstract description 25
- 208000020816 lung neoplasm Diseases 0.000 title claims abstract description 25
- 230000000694 effects Effects 0.000 title claims abstract description 13
- 238000004422 calculation algorithm Methods 0.000 title claims description 9
- 238000004364 calculation method Methods 0.000 claims abstract description 11
- 238000012545 processing Methods 0.000 claims description 9
- 230000005540 biological transmission Effects 0.000 claims description 8
- 238000013500 data storage Methods 0.000 claims description 8
- 238000004891 communication Methods 0.000 claims description 6
- 230000003902 lesion Effects 0.000 claims description 3
- 238000007619 statistical method Methods 0.000 claims description 3
- 230000003211 malignant effect Effects 0.000 abstract description 3
- 230000010354 integration Effects 0.000 abstract description 2
- 238000003745 diagnosis Methods 0.000 description 3
- 210000001519 tissue Anatomy 0.000 description 3
- 206010028980 Neoplasm Diseases 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 2
- 201000011510 cancer Diseases 0.000 description 2
- 239000011159 matrix material Substances 0.000 description 2
- 206010056342 Pulmonary mass Diseases 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 210000000988 bone and bone Anatomy 0.000 description 1
- 208000003362 bronchogenic carcinoma Diseases 0.000 description 1
- 238000002059 diagnostic imaging Methods 0.000 description 1
- 201000010099 disease Diseases 0.000 description 1
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 230000004438 eyesight Effects 0.000 description 1
- 230000005484 gravity Effects 0.000 description 1
- 238000011065 in-situ storage Methods 0.000 description 1
- 238000007726 management method Methods 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000000034 method Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 210000000056 organ Anatomy 0.000 description 1
- 238000009877 rendering Methods 0.000 description 1
- 238000012216 screening Methods 0.000 description 1
- 230000004083 survival effect Effects 0.000 description 1
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
Abstract
The invention provides a voxel and CT value multiplication and integration system for evaluating lung cancer curative effect, which is characterized by comprising the following steps: the data acquisition equipment is used for acquiring data, CT values and voxels obtained by thin-layer CT examination; the local calculation unit is used for calculating a value volume, wherein the value volume is the product of the CT value and the voxel; and the cloud server. The system provided by the invention comprises data and CT values obtained by thin-layer CT examination, uses AI to calculate voxels, multiplies the voxels with the CT values to obtain products (product of the values and the products), and can be used for evaluating the good and malignant effects of lung cancer and the effects of lung cancer after treatment in clinical application.
Description
Technical Field
The invention relates to a voxel and CT value product (volume value product) algorithm system for evaluating lung cancer curative effect, which can be clinically used for evaluating benign and malignant lung cancer curative effect and curative effect after lung cancer treatment.
Background
Primary bronchogenic carcinoma (abbreviated lung cancer) is the malignant tumor with highest morbidity and mortality in the world. The latest cancer cases published by the cancer center in China are 429.16 thousands (251.21 men and 177.95 women), 73.33 thousands (50.93 men and 22.40 women) and 61.02 thousands (43.24 men and 17.78 women) of lung cancer cases, all of which are the first tumors in China. In addition, the survival rate of lung cancer in China is only 19.7% in 5 years due to late diagnosis. In order to change the current situation, the current passive diagnosis and treatment mode is changed into an active diagnosis and treatment mode with a forward port and a downward center of gravity, namely, the eye sight for diagnosing lung cancer is moved to screening, scientific evaluation and standard management of lung nodules, early lung cancer (in-situ and stage 1A lung cancer) is discovered early, and timely treatment is given to enable patients to radically cure or survive for a long time.
Disclosure of Invention
It is an object of the present invention to provide a system employing a voxel-to-CT value product (volumetric value product) algorithm.
In order to achieve the above object, the present invention provides a voxel and CT value multiplication algorithm system for evaluating lung cancer efficacy, which is characterized by comprising:
the data acquisition equipment is used for acquiring data, CT values and voxels obtained by thin-layer CT examination;
the local calculation unit is used for calculating a value volume, wherein the value volume is the product of the CT value and the voxel;
the cloud server is used for storing the volume values and the change data of different patients, and for the same patient, the cloud server stores the volume values and the change data of the current patient at a plurality of time points, so that the volume values and the change data of the current patient are obtained, and the cloud server can be used for evaluating the curative effect of lung cancer after treatment.
Preferably, the data acquisition device comprises a thin layer CT examination data acquisition device and an AI calculation voxel device, wherein the thin layer CT examination data acquisition device is used for acquiring the data obtained by the thin layer CT examination and the CT value; the AI computation voxel device computes the voxels using AI.
Preferably, the cloud server comprises one or more data processing units, one or more data storage units and an information transmission module, wherein the cloud server establishes wired data communication or wireless data communication with the local computing unit through the information transmission module, the data are stored in the data storage units, and the data processing units perform statistical analysis on the condition of the patient regularly.
Preferably, the data processing unit performs probability calculation on the data of the current patient, and:
when the sum of the differences from the baseline value is less than-0.3, automatically notice that: focus alleviation;
the sum and difference from baseline values between-0.3 and 0.2 are automatically noted as: the focus is stable;
the sum and difference from the baseline value of greater than 0.2 is automatically noted as: lesion progression is imminent.
The system provided by the invention comprises data and CT values obtained by thin-layer CT examination, uses AI to calculate voxels, multiplies the voxels with the CT values to obtain products (product of the values and the products), and can be used for evaluating the good and malignant effects of lung cancer and the effects of lung cancer after treatment in clinical application.
Detailed Description
The invention will be further illustrated with reference to specific examples. It is to be understood that these examples are illustrative of the present invention and are not intended to limit the scope of the present invention. Further, it is understood that various changes and modifications may be made by those skilled in the art after reading the teachings of the present invention, and such equivalents are intended to fall within the scope of the claims appended hereto.
The voxel and CT value multiplication algorithm system for evaluating lung cancer curative effect provided by the invention is based on the following two concepts:
CT value: is a measurement unit for measuring the density of human tissues, and is commonly called Hennsfield Unit (HU) air of-1000 and compact bone of +1000. The CT value is a corresponding value of the chest tissue in the CT image corresponding to the X-ray attenuation coefficient. Whether the matrix image or the matrix number is representative of the CT value, which is converted from the mu value of the human tissue and organ.
Voxel: is an abbreviation for Volume element (voxel), a Volume containing a voxel may be represented by a Volume rendering or extraction of a polygonal isosurface of a given threshold contour. Voxels are used in the medical imaging field and conceptually resemble the smallest unit of two-dimensional space, pixels, which are used on the image data of a two-dimensional computer image. Some real three-dimensional displays use voxels to describe their resolution, for example: a 512 x 512 voxel display may be displayed.
Based on the above concept, the voxel and CT value multiplication and integration algorithm system for evaluating lung cancer curative effect provided by the invention comprises the following steps:
and the data acquisition equipment is used for acquiring data, CT values and voxels obtained by the thin-layer CT examination. The data acquisition device comprises a thin-layer CT examination data acquisition device and an AI voxel calculation device. The thin-layer CT examination data acquisition device is used for acquiring data and CT values obtained by thin-layer CT examination of a patient. The AI computation voxel device applies AI computation voxels.
The local calculation unit is used for calculating a volume, which is the product of the CT value and the voxel, and the purpose of clinical application of the volume can be used for evaluating the curative effect of lung cancer after treatment.
The cloud server comprises one or more data processing units, one or more data storage units and an information transmission module. The information transmission module is not particularly limited and may be a wireless transmission module or a wired transmission module. And the local calculation unit uploads the calculation result of the volume of the value to the cloud server through a wireless communication link or a wired communication link. All data are stored in the data storage unit of the cloud server, and the volume value and the change data of the patient can be stored to assist in judging the illness state. In this embodiment, the data storage unit stores the volumes of values at a plurality of time points of the patient and can be used to evaluate the efficacy of lung cancer treatment. The data processing unit may periodically perform a statistical analysis of the condition of the patient based on the data stored by the data storage unit. Because the analysis can be carried out based on a large number of shared samples, the method is beneficial to the adjustment of indexes such as standards of the disease types by researchers, and is beneficial to the establishment and perfection of individual standards of patients of different types and ages.
In this embodiment, the data processing unit performs probability calculation on the data of the current patient, and:
when the sum of the differences from the baseline value is less than-0.3, automatically notice that: focus alleviation;
the sum and difference from baseline values between-0.3 and 0.2 are automatically noted as: the focus is stable;
the sum and difference from the baseline value of greater than 0.2 is automatically noted as: lesion progression is imminent.
Claims (2)
1. A voxel and CT value multiplication algorithm system for assessing lung cancer efficacy, comprising:
the data acquisition equipment is used for acquiring data, CT values and voxels obtained by thin-layer CT examination;
the local calculation unit is used for calculating a value volume, wherein the value volume is the product of the CT value and the voxel;
the cloud server is used for storing the volume values and the change data of different patients, and for the same patient, the cloud server stores the volume values and the change data of a plurality of time points of the current patient, so that the volume values and the change data of the current patient are obtained, and the cloud server can be used for evaluating the curative effect of lung cancer after treatment, wherein:
the cloud server comprises one or more data processing units, one or more data storage units and an information transmission module, wherein the cloud server establishes wired data communication or wireless data communication with the local computing unit through the information transmission module, the data are stored in the data storage units, and the data processing units perform statistical analysis on the condition of a patient regularly; the data processing unit performs probability calculation on the data of the current patient, and:
when the sum of the differences from the baseline value is less than-0.3, automatically notice that: focus alleviation;
the sum and difference from baseline values between-0.3 and 0.2 are automatically noted as: the focus is stable;
the sum and difference from the baseline value of greater than 0.2 is automatically noted as: lesion progression is imminent.
2. The voxel and CT value multiplication algorithm system for assessing the efficacy of a lung cancer of claim 1, wherein the data acquisition device comprises a thin layer CT examination data acquisition device and an AI computing voxel device, wherein the thin layer CT examination data acquisition device is configured to acquire the thin layer CT examination data and the CT value; the AI computation voxel device computes the voxels using AI.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20030090082A (en) * | 2002-05-21 | 2003-11-28 | 박종원 | A volume calculation method for the target internal organs using computed tomography |
| CN104881568A (en) * | 2015-04-27 | 2015-09-02 | 苏州敏宇医疗科技有限公司 | Cloud computation based early oncotherapy efficacy evaluation system and method |
| CN108538399A (en) * | 2018-03-22 | 2018-09-14 | 复旦大学 | A kind of magnetic resonance liver cancer cosmetic effect evaluating method and system |
| CN108766563A (en) * | 2018-05-25 | 2018-11-06 | 戴建荣 | Radiotherapy prediction of result method and system based on dosage group |
| CN110544534A (en) * | 2019-08-30 | 2019-12-06 | 中国人民解放军联勤保障部队第九〇〇医院 | method and system for automatically evaluating treatment effect of skin disease |
-
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- 2020-05-18 CN CN202010418278.6A patent/CN111430031B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20030090082A (en) * | 2002-05-21 | 2003-11-28 | 박종원 | A volume calculation method for the target internal organs using computed tomography |
| CN104881568A (en) * | 2015-04-27 | 2015-09-02 | 苏州敏宇医疗科技有限公司 | Cloud computation based early oncotherapy efficacy evaluation system and method |
| CN108538399A (en) * | 2018-03-22 | 2018-09-14 | 复旦大学 | A kind of magnetic resonance liver cancer cosmetic effect evaluating method and system |
| CN108766563A (en) * | 2018-05-25 | 2018-11-06 | 戴建荣 | Radiotherapy prediction of result method and system based on dosage group |
| CN110544534A (en) * | 2019-08-30 | 2019-12-06 | 中国人民解放军联勤保障部队第九〇〇医院 | method and system for automatically evaluating treatment effect of skin disease |
Non-Patent Citations (2)
| Title |
|---|
| 尤志军,张学昕,漆家学,石明国.冠状动脉钙化的量化分析方法及临床价值.医疗卫生装备.2005,(08),全文. * |
| 黄晨,高建苑,王晓明,李榕,苏慧,郑敏文,薛富波.电子束CT对判断2型糖尿病患者冠状动脉病变的意义.临床心血管病杂志.2005,(07),全文. * |
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