CN117352046A - Tumor targeting drug delivery treatment positioning needle system and assembly thereof - Google Patents
Tumor targeting drug delivery treatment positioning needle system and assembly thereof Download PDFInfo
- Publication number
- CN117352046A CN117352046A CN202311322902.2A CN202311322902A CN117352046A CN 117352046 A CN117352046 A CN 117352046A CN 202311322902 A CN202311322902 A CN 202311322902A CN 117352046 A CN117352046 A CN 117352046A
- Authority
- CN
- China
- Prior art keywords
- positioning
- curve
- early warning
- drug delivery
- dimensional dynamic
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000012377 drug delivery Methods 0.000 title claims abstract description 55
- 206010028980 Neoplasm Diseases 0.000 title claims abstract description 29
- 238000011282 treatment Methods 0.000 title claims abstract description 23
- 230000008685 targeting Effects 0.000 title claims description 18
- 239000003814 drug Substances 0.000 claims abstract description 72
- 238000004458 analytical method Methods 0.000 claims abstract description 68
- 229940079593 drug Drugs 0.000 claims abstract description 55
- 238000009792 diffusion process Methods 0.000 claims abstract description 32
- 238000012937 correction Methods 0.000 claims abstract description 21
- 238000013461 design Methods 0.000 claims abstract description 12
- 230000000694 effects Effects 0.000 claims abstract description 9
- 230000001133 acceleration Effects 0.000 claims description 27
- 238000011156 evaluation Methods 0.000 claims description 23
- 238000000034 method Methods 0.000 claims description 23
- 239000003550 marker Substances 0.000 claims description 12
- 238000013178 mathematical model Methods 0.000 claims description 12
- 238000007781 pre-processing Methods 0.000 claims description 10
- 238000004364 calculation method Methods 0.000 claims description 9
- 238000005259 measurement Methods 0.000 claims description 8
- 238000012935 Averaging Methods 0.000 claims description 6
- 238000012545 processing Methods 0.000 claims description 5
- 238000005516 engineering process Methods 0.000 claims description 4
- 238000002059 diagnostic imaging Methods 0.000 claims description 3
- 238000002560 therapeutic procedure Methods 0.000 claims 6
- 230000002159 abnormal effect Effects 0.000 description 3
- 230000000007 visual effect Effects 0.000 description 3
- 238000010835 comparative analysis Methods 0.000 description 2
- 238000012854 evaluation process Methods 0.000 description 2
- 229940126585 therapeutic drug Drugs 0.000 description 2
- 206010006187 Breast cancer Diseases 0.000 description 1
- 208000026310 Breast neoplasm Diseases 0.000 description 1
- 206010009944 Colon cancer Diseases 0.000 description 1
- 208000001333 Colorectal Neoplasms Diseases 0.000 description 1
- 206010058467 Lung neoplasm malignant Diseases 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004422 calculation algorithm Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000001024 immunotherapeutic effect Effects 0.000 description 1
- 201000005202 lung cancer Diseases 0.000 description 1
- 208000020816 lung neoplasm Diseases 0.000 description 1
- 238000007726 management method Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 238000010606 normalization Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
- G16B15/00—ICT specially adapted for analysing two-dimensional or three-dimensional molecular structures, e.g. structural or functional relations or structure alignment
- G16B15/30—Drug targeting using structural data; Docking or binding prediction
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
-
- 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
- G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
- G16H20/10—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients
-
- 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
- G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
- G16H40/60—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices
-
- 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
- G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
Landscapes
- Engineering & Computer Science (AREA)
- Health & Medical Sciences (AREA)
- Physics & Mathematics (AREA)
- Medical Informatics (AREA)
- Biomedical Technology (AREA)
- Public Health (AREA)
- General Health & Medical Sciences (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Data Mining & Analysis (AREA)
- Theoretical Computer Science (AREA)
- Chemical & Material Sciences (AREA)
- Primary Health Care (AREA)
- Epidemiology (AREA)
- Medicinal Chemistry (AREA)
- Spectroscopy & Molecular Physics (AREA)
- General Physics & Mathematics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Mathematical Physics (AREA)
- Databases & Information Systems (AREA)
- Crystallography & Structural Chemistry (AREA)
- Pathology (AREA)
- Evolutionary Biology (AREA)
- Biotechnology (AREA)
- Bioinformatics & Computational Biology (AREA)
- Business, Economics & Management (AREA)
- General Business, Economics & Management (AREA)
- Algebra (AREA)
- Computational Mathematics (AREA)
- Biophysics (AREA)
- Mathematical Analysis (AREA)
- Mathematical Optimization (AREA)
- Pharmacology & Pharmacy (AREA)
- Pure & Applied Mathematics (AREA)
- Software Systems (AREA)
- General Engineering & Computer Science (AREA)
- Magnetic Resonance Imaging Apparatus (AREA)
Abstract
The invention discloses a tumor targeted drug delivery treatment positioning needle system and components thereof, and relates to the technical field of medical instruments, wherein the tumor targeted drug delivery treatment positioning needle system comprises a model design unit, a comprehensive analysis unit and an early warning correction unit, the comprehensive analysis unit comprises a dynamic path analysis module, a positioning accuracy analysis module and a drug diffusion analysis module, a targeted drug delivery positioning model is built through the model design unit, a preset path of a positioning needle is generated, an actual path of the positioning needle is collected, the preset path and the actual path of the positioning needle are subjected to comparison analysis through the comprehensive analysis unit, the consistency and the accuracy of positioning and the release performance of drugs are evaluated, the positioning efficiency of the targeted drug delivery positioning model is further comprehensively analyzed, and the positioning efficiency of the positioning needle is analyzed and early warned, so that the efficiency of the positioning needle is subjected to positioning correction in advance in tumor targeted drug delivery treatment, and a stable and accurate drug delivery positioning effect is realized.
Description
Technical Field
The invention relates to the technical field of medical instruments, in particular to a tumor targeted drug delivery treatment positioning needle system and components thereof.
Background
The tumor patients can use chemotherapeutics, targeted therapeutic drugs, immunotherapeutic drugs and the like, wherein the targeted therapeutic drugs are used for selecting different targeted drug treatments for different tumor diseases, such as lung cancer, breast cancer, colorectal cancer and the like;
in the existing tumor targeted drug delivery treatment, the positioning efficiency of drug delivery of a positioning needle is difficult to monitor and evaluate, and the positioning effect in a certain time period in the future cannot be early-warned, so that the positioning needle cannot be corrected in advance, the targeted drug delivery effect in the patient treatment process is influenced, and the problems of poor positioning stability and accuracy of tumor targeted drug delivery are caused;
in view of the above technical drawbacks, a solution is now proposed.
Disclosure of Invention
The invention aims at: solves the problem of poor stability and accuracy of tumor targeting drug delivery and realizes stable and accurate drug delivery and positioning effects.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
the tumor targeting drug delivery treatment positioning system comprises a model design unit, a comprehensive analysis unit and an early warning correction unit, wherein the model design unit, the comprehensive analysis unit and the early warning correction unit are connected by signals,
the comprehensive analysis unit comprises a dynamic path analysis module, a positioning accuracy analysis module and a medicine diffusion analysis module, wherein the dynamic path analysis module, the positioning accuracy analysis module and the medicine diffusion analysis module are in signal connection;
the model design unit is used for establishing a targeting drug delivery positioning model, generating and outputting a preset path A of the positioning needle to the comprehensive analysis unit;
the comprehensive analysis unit acquires a preset path A of the positioning needle, acquires an actual path B of the positioning needle through a medical imaging technology, and further analyzes, generates and sends an early warning signal to the early warning correction unit:
the dynamic path analysis module is used for analyzing dynamic changes of the preset path A and the actual path B, curve pretreatment is carried out first, curve comparison is carried out, the stability coefficient of positioning is comprehensively obtained, and the consistency of positioning is evaluated;
analyzing the distance error and the target position error of the preset path A and the actual path B through a positioning accuracy analysis module, and evaluating the positioning accuracy;
and analyzing the diffusion path of the drug molecules by a drug diffusion analysis module: dividing a target area into N1 grids, collecting the concentration of drug molecules of each grid for analysis, and evaluating the release performance of the drug;
the positioning efficiency of the targeted drug delivery positioning model is obtained through comprehensively analyzing the consistency and the accuracy of positioning and the release performance of the drug, the positioning efficiency is fitted into an early warning mathematical model, and the early warning mathematical model is analyzed to generate a corresponding early warning signal;
the early warning correction unit is used for receiving the early warning signal and carrying out corresponding positioning correction operation, and early warning is carried out on the effectiveness of the positioning needle in tumor targeted drug delivery treatment so as to realize stable and accurate drug delivery positioning effect.
Further, the specific process of curve preprocessing by the dynamic path analysis module is as follows:
a1: marking a preset path A of the positioning needle as a three-dimensional dynamic curve S1, marking an actual path B of the positioning needle as a three-dimensional dynamic curve S2, and establishing a curve preprocessing model:
a1-1: inputting a three-dimensional dynamic curve Sm, and marking any point on the curve Sm as M (Xm, ym, zm);
presetting a time node t0, dispersing a curve Sm into n0 position points, performing distance measurement and calculation on two position points in adjacent time to obtain a distance value Lm of the two adjacent points, obtaining a speed vm=the distance value Lm/the time node t0 in the time node, and establishing and outputting a two-dimensional dynamic curve Sn of the speed Vm-the time node t 0;
wherein, the distance measurement formula of two position points is: presetting two adjacent points as (Xi, yi, zi) and (Xj, yj, zj), and then the distance L:
a1-2: then, the slope between two points on the curve Sn in the time node t0 is measured, the acceleration ACm is obtained, and a two-dimensional dynamic curve Sc of the acceleration ACm-time node t0 is established and output;
a2: substituting the three-dimensional dynamic curve S1 and the three-dimensional dynamic curve S2 into a curve preprocessing model respectively, and outputting a corresponding two-dimensional dynamic curve Sn and a corresponding two-dimensional dynamic curve Sc:
for the three-dimensional dynamic curve S1, a two-dimensional dynamic curve Sn1 of a speed Va-time node t0 and a two-dimensional dynamic curve Sc1 of an acceleration ACa-time node t0 are obtained;
for the three-dimensional dynamic curve S2, a two-dimensional dynamic curve Sn2 of the velocity Vb-time node t0 and a two-dimensional dynamic curve Sc2 of the acceleration ACb-time node t0 are obtained.
Further, the specific process of curve comparison by the dynamic path analysis module is as follows:
b1: establishing a curve comparison model, and obtaining a consistency evaluation coefficient by comparing two curves:
b2-1: the abscissa of the marked curve No. 1 is time ti, the ordinate is yi, the abscissa of the marked curve No. 2 is time tj, and the ordinate is yj;
b2-2: the abscissa of the two curves are overlapped, and the deviation degree of the ordinate of the two curves is compared, so that the time ti=tj is calculated, and a formula is establishedObtaining an evaluation coefficient PG of the deviation degree;
b3: substituting the two-dimensional dynamic curve Sn1 and the curve Sn2 of the speed into a curve comparison model to obtain an evaluation coefficient PG1 of the speed deviation;
substituting the two-dimensional dynamic curve Sc1 and the curve Sc2 of the acceleration into a curve comparison model to obtain an evaluation coefficient PG2 of the acceleration deviation;
b4: an evaluation coefficient PG1 of the deviation degree of the integrated speed and an evaluation coefficient PG2 of the deviation degree of the acceleration are established by establishing a formulaObtaining a stability coefficient WD of positioning;
wherein α1 and α2 are weight factors of the evaluation coefficients PG1 and PG2, respectively, and α1 and α2 are both greater than 0.
Further, the specific processing procedure of the positioning accuracy analysis module is as follows:
c1: the initial position is taken as a base point O, a preset path A of the positioning needle is marked as a three-dimensional dynamic curve S1, an actual path B of the positioning needle is marked as a three-dimensional dynamic curve S2, and the three-dimensional dynamic curve S1 and the three-dimensional dynamic curve S2 are directly compared;
c2: presetting a time node t1, and respectively dispersing a curve S1 and a curve S2 into n1 position points;
any point on the marker curve S1 is P (Xa, ya, za), and the target position of the marker curve S1 is W1 (X1, Y1, Z1);
any point on the marker curve S2 is Q (Xb, yb, zb), and the target position of the marker curve S2 is W2 (X2, Y2, Z2);
and C3: establishing a formula to obtain a locating fine coefficient JX:
further, the specific processing procedure of the drug diffusion analysis module is as follows:
d1: dividing the target area into N1 grids, collecting the concentration of drug molecules in each grid for analysis,
d2: firstly, marking the concentration of the drug molecules in any grid as Ci, setting the threshold value of the concentration of the drug molecules in the grid as Ch, and then carrying out contrast analysis:
when the concentration of the drug molecules in the grid exceeds a threshold value, judging that the concentration of the drug molecules in the grid reaches a qualified standard, marking the grid as a qualified grid, and measuring and calculating the occupation ratio phi of the qualified grid;
d3: averaging the drug molecule concentrations in N1 grids to obtain average drug minute concentration C0;
d4: establishing a formulaObtaining a medicine diffusion coefficient KS;
wherein μ is a diffusion factor, and μ is greater than 0.
Further, the specific process of comprehensively analyzing the consistency, the accuracy and the release performance of the medicine is as follows:
e1: acquiring a positioning efficiency index Zdw of the targeted drug delivery positioning model;
e1-1: firstly, obtaining a positioned stability coefficient WD, a fine coefficient JX and a medicine diffusion coefficient KS;
e1-2: establishing the formula Zdw =wd e1 +JX e2 +KS e3 Acquiring a positioning efficiency index Zdw of the model;
wherein e1, e2 and e3 are respectively the weight indexes of the stability coefficient WD, the fine coefficient JX and the medicine diffusion coefficient KS, and e1, e2 and e3 are all larger than 0;
e2: fitting the positioning efficiency into an early warning mathematical model:
e2-1: the time node t2 is preset, a dynamic graph of the positioning efficiency index Zdw-the time node t2 is established, and the growth rate K of the graph is obtained, wherein the specific process is as follows:
e2-11: selecting any point of the curve as d0, determining the coordinate value d0 (xd 0, yd 0) of the point, selecting the adjacent point d1 of the point, determining the coordinate value d1 (xd 1, yd 1) of the point, calculating the average growth rate Kp of the curve between the two points,
e2-12: repeating the calculation through two continuous points to obtain N2 average growth rates, and averaging the average growth rate Kp to obtain the growth rate K of the curve:
e2-2: by combining the positioning efficiency index Zdw with the curve growth rate K, an early warning index Zyj of the positioning efficiency is established, and the formula of an early warning index Zyj of the preset positioning efficiency is as follows: zyj =τk Zdw;
where τ is the time value from the current time node to the predicted time node, and τ is greater than 0.
Further, the specific process of analyzing the early warning mathematical model to generate the corresponding early warning signal is as follows:
setting the preset interval of the early warning index Zyj of the positioning efficiency as (H1, H2);
comparing and analyzing the early warning index Zyj with a preset interval (H1, H2):
when the early warning index Zyj is more than or equal to H2, generating a first-level early warning signal; when the early warning index Zyj is located in a preset interval, generating a secondary early warning signal; when the early warning index Zyj is smaller than or equal to H1, generating a three-level early warning signal;
and marking the generated primary early warning signals, secondary early warning signals and tertiary early warning signals as early warning signal groups, and sending the early warning signal groups to an early warning correction unit.
Tumor targeting drug delivery treatment positioning needle component, and positioning needle component is applied to the tumor targeting drug delivery treatment positioning system.
In summary, due to the adoption of the technical scheme, the beneficial effects of the invention are as follows:
according to the invention, a targeting drug delivery positioning model is established through a model design unit, a preset path of a positioning needle is generated, an actual path of the positioning needle is acquired, the preset path and the actual path of the positioning needle are subjected to comparative analysis through a comprehensive analysis unit, the consistency and the accuracy of positioning and the release performance of drugs are evaluated, the positioning efficiency of the targeting drug delivery positioning model is comprehensively analyzed, and then the positioning efficiency of the positioning needle is analyzed and early-warned, so that the efficiency of the positioning needle is subjected to positioning correction in advance in tumor targeting drug delivery treatment, and a stable and accurate drug delivery positioning effect is realized.
Drawings
For a clearer description of embodiments of the present application or of the solutions of the prior art, the following brief description will be given of the drawings required to be used in the embodiments, it being evident that the drawings in the following description are only some embodiments described in the present invention, and that other drawings can be obtained from these drawings by a person skilled in the art;
fig. 1 shows a schematic block diagram of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1:
as shown in figure 1, the tumor targeted drug delivery treatment positioning needle assembly and the positioning system thereof comprise a model design unit, a comprehensive analysis unit and an early warning correction unit, wherein the model design unit, the comprehensive analysis unit and the early warning correction unit are connected by signals,
the comprehensive analysis unit comprises a dynamic path analysis module, a positioning accuracy analysis module and a medicine diffusion analysis module, wherein the dynamic path analysis module, the positioning accuracy analysis module and the medicine diffusion analysis module are in signal connection;
the working steps are as follows:
s1: the model design unit is used for establishing a targeting drug delivery positioning model, generating and outputting a preset path A of the positioning needle to the comprehensive analysis unit;
the target drug delivery positioning model performs three-dimensional virtual modeling through a physical modeling technology, and inputs the initial position and the target position of the positioning needle, so as to generate and output a preset path of the positioning needle;
s2: the comprehensive analysis unit firstly acquires a preset path A of the positioning needle, then acquires an actual path B of the positioning needle through a medical imaging technology, and further analyzes, generates and sends an early warning signal to the early warning correction unit;
s2-1: analyzing the dynamic changes of the preset path A and the actual path B through a dynamic path analysis module, preprocessing a curve, comparing the curve, comprehensively acquiring a stability coefficient of positioning, and evaluating the consistency of positioning;
s2-11: the specific process of curve preprocessing by the dynamic path analysis module is as follows:
a1: marking a preset path A of the positioning needle as a three-dimensional dynamic curve S1, marking an actual path B of the positioning needle as a three-dimensional dynamic curve S2, and establishing a curve preprocessing model:
a1-1: inputting a three-dimensional dynamic curve Sm, and marking any point on the curve Sm as M (Xm, ym, zm);
presetting a time node t0, dispersing a curve Sm into n0 position points, performing distance measurement and calculation on two position points in adjacent time to obtain a distance value Lm of the two adjacent points, obtaining a speed vm=the distance value Lm/the time node t0 in the time node, and establishing and outputting a two-dimensional dynamic curve Sn of the speed Vm-the time node t 0;
wherein, the distance measurement formula of two position points is: presetting two adjacent points as (Xi, yi, zi) and (Xj, yj, zj), and then the distance L:
a1-2: then, the slope between two points on the curve Sn in the time node t0 is measured, the acceleration ACm is obtained, and a two-dimensional dynamic curve Sc of the acceleration ACm-time node t0 is established and output;
a2: substituting the three-dimensional dynamic curve S1 and the three-dimensional dynamic curve S2 into a curve preprocessing model respectively, and outputting a corresponding two-dimensional dynamic curve Sn and a corresponding two-dimensional dynamic curve Sc:
for the three-dimensional dynamic curve S1, a two-dimensional dynamic curve Sn1 of a speed Va-time node t0 and a two-dimensional dynamic curve Sc1 of an acceleration ACa-time node t0 are obtained;
for the three-dimensional dynamic curve S2, a two-dimensional dynamic curve Sn2 of a speed Vb-time node t0 and a two-dimensional dynamic curve Sc2 of an acceleration ACb-time node t0 are obtained;
for example, the preset path A of the positioning needle is marked as a three-dimensional dynamic curve S1, and any point on the curve S1 is marked as P (Xa, ya, za); marking the actual path B of the positioning needle as a three-dimensional dynamic curve S2, and marking any point on the curve S2 as Q (Xb, yb, zb);
for a preset path a of the positioning needle: presetting a time node t, dispersing a curve S1 into n position points, performing distance measurement and calculation on two position points in adjacent time, acquiring a distance value La of the two adjacent points, and acquiring a speed Va=the distance value La/the time node t in the time node;
for the actual path B of the positioning needle: presetting a time node t, dispersing a curve S2 into n position points, performing distance measurement and calculation on two position points in adjacent time to obtain a distance value Lb of the two adjacent points, and then obtaining a speed vb=the distance value Lb/the time node t in the time node;
establishing a two-dimensional dynamic curve Sn1 of a speed Va-time node t, measuring and calculating the slope between two points on a curve S3 in the time node t, and acquiring acceleration ACa; establishing a two-dimensional dynamic curve Sn2 of a speed Vb-time node t, and measuring and calculating the slope between two points on a curve S4 in the time node t to obtain an acceleration ACb, wherein the acceleration= (the end speed V2 of the time node-the initial speed V1 of the time node)/the time node t;
then, a two-dimensional dynamic curve Sc1 of the acceleration ACa-time node t is established, and a two-dimensional dynamic curve Sc2 of the acceleration ACb-time node t is established;
s2-12: the specific procedure for curve comparison is as follows:
b1: establishing a curve comparison model, and obtaining a consistency evaluation coefficient by comparing two curves:
b2-1: the abscissa of the marked curve No. 1 is time ti, the ordinate is yi, the abscissa of the marked curve No. 2 is time tj, and the ordinate is yj;
b2-2: the abscissa of the two curves are overlapped, and the deviation degree of the ordinate of the two curves is compared, so that the time ti=tj is calculated, and a formula is establishedObtaining an evaluation coefficient PG of the deviation degree;
when the distance difference between the two curves is larger, the deviation degree of the curves is higher, and the evaluation coefficient PG of the deviation degree is larger;
b3: substituting the two-dimensional dynamic curve Sn1 and the curve Sn2 of the speed into a curve comparison model to obtain an evaluation coefficient PG1 of the speed deviation;
substituting the two-dimensional dynamic curve Sc1 and the curve Sc2 of the acceleration into a curve comparison model to obtain an evaluation coefficient PG2 of the acceleration deviation;
b4: an evaluation coefficient PG1 of the deviation degree of the integrated speed and an evaluation coefficient PG2 of the deviation degree of the acceleration are established by establishing a formulaObtaining a stability coefficient WD of positioning;
wherein α1 and α2 are weight factors of the evaluation coefficients PG1 and PG2, respectively, and α1 and α2 are both greater than 0, when the evaluation coefficients PG1 and PG2 are lower, the smaller the deviation degree of the speed and the deviation degree of the acceleration are explained, the better the consistency of positioning is indicated, and the higher the stability coefficient WD of positioning is;
the higher the stability factor WD is, the better the consistency of the positioning is, wherein, by setting a threshold value of consistency for the stability factor WD of the positioning, the consistency of the positioning is further evaluated, for example, a primary threshold value YZ1 and a secondary threshold value YZ2 are set:
when the stability factor WD is lower than the first-order threshold YZ1, it indicates that the consistency of positioning is poor; when the stability coefficient WD is positioned between the first-level threshold YZ1 and the second-level threshold YZ2, the consistency of positioning is good; when the stability coefficient WD exceeds the second-level threshold YZ2, the consistency of the positioning is excellent;
corresponding signal prompt can be carried out by evaluating the consistency of positioning, so that the abnormal reasons of the model consistency are processed;
s2-2: analyzing the distance error and the target position error of the preset path A and the actual path B through a positioning accuracy analysis module, and evaluating the positioning accuracy;
the specific treatment process is as follows:
c1: the initial position is taken as a base point O, a preset path A of the positioning needle is marked as a three-dimensional dynamic curve S1, an actual path B of the positioning needle is marked as a three-dimensional dynamic curve S2, and the three-dimensional dynamic curve S1 and the three-dimensional dynamic curve S2 are directly compared;
c2: presetting a time node t1, and respectively dispersing a curve S1 and a curve S2 into n1 position points;
any point on the marker curve S1 is P (Xa, ya, za), and the target position of the marker curve S1 is W1 (X1, Y1, Z1);
any point on the marker curve S2 is Q (Xb, yb, zb), and the target position of the marker curve S2 is W2 (X2, Y2, Z2);
and C3: establishing a formula to obtain a locating fine coefficient JX:
when the larger the difference between the distances of Q (Xb, yb, zb) and P (Xa, ya, za) is, the larger the difference between W1 (X1, Y1, Z1) and W2 (X2, Y2, Z2) is, the worse the accuracy of the positioning is explained, so that the finer coefficient JX of the positioning is lower;
therefore, when the fine coefficient JX is higher, the accuracy of positioning is better, wherein, by setting a threshold value of accuracy for the fine coefficient JX of positioning, the accuracy of positioning is further evaluated, and the specific process is the same as the above-mentioned evaluation process of consistency, and details are not repeated herein, and corresponding signal prompt can be performed by evaluating the accuracy of positioning, so that the abnormal cause of the model accuracy is processed;
s2-3: analyzing the diffusion path of the drug molecules by a drug diffusion analysis module: dividing a target area into N1 grids, collecting the concentration of drug molecules of each grid for analysis, and evaluating the release performance of the drug;
the specific treatment process is as follows:
d1: dividing the target area into N1 grids, collecting the concentration of drug molecules in each grid for analysis,
d2: firstly, marking the concentration of the drug molecules in any grid as Ci, setting the threshold value of the concentration of the drug molecules in the grid as Ch, and then carrying out contrast analysis:
when the concentration of the drug molecules in the grid exceeds a threshold value, judging that the concentration of the drug molecules in the grid reaches a qualified standard, marking the grid as a qualified grid, and measuring and calculating the occupation ratio phi of the qualified grid;
d3: averaging the drug molecule concentrations in N1 grids to obtain average drug minute concentration C0;
d4: establishing a formulaObtaining a medicine diffusion coefficient KS;
wherein, mu is a diffusion factor, mu is larger than 0, the diffusion factor is a diffusion coefficient for converting the concentration of a drug molecule into a drug, when the concentration difference between grids is smaller, the more uniform administration in the grids is indicated, and when the duty ratio of qualified grids is synchronously improved, the better the release performance of the drug is further indicated, the higher the drug diffusion coefficient KS is;
when the drug diffusion coefficient KS is larger, the better the drug release performance is indicated, the threshold value is set for the drug diffusion coefficient KS, and then the drug release performance is evaluated, and the specific process is the same as the above-mentioned evaluation process, and is not repeated here, the corresponding signal prompt can be carried out by evaluating the drug release performance, so that the abnormal reasons of the drug release performance in the model are processed;
s3: the positioning efficiency of the targeted drug delivery positioning model is obtained through comprehensively analyzing the consistency and the accuracy of positioning and the release performance of the drug, the positioning efficiency is fitted into an early warning mathematical model, and the early warning mathematical model is analyzed to generate a corresponding early warning signal;
the specific process of comprehensively analyzing the consistency, the accuracy and the release performance of the medicine is as follows:
e1: acquiring a positioning efficiency index Zdw of the targeted drug delivery positioning model;
e1-1: firstly, obtaining a positioned stability coefficient WD, a fine coefficient JX and a medicine diffusion coefficient KS;
e1-2: establishing the formula Zdw =wd e1 +JX e2 +KS e3 Acquiring a positioning efficiency index Zdw of the model;
wherein e1, e2 and e3 are respectively the weight indexes of a stability coefficient WD, a fine coefficient JX and a drug diffusion coefficient KS, and e1, e2 and e3 are all larger than 0, and the weight coefficients are used for carrying out normalization processing on data;
setting a threshold value for the positioning efficiency index Zdw of the model to further evaluate the positioning efficiency of the model, and performing corresponding signal prompt by evaluating the positioning efficiency of the model so as to realize real-time monitoring and management;
e2: fitting the positioning efficiency into an early warning mathematical model:
e2-1: the time node t2 is preset, a dynamic graph of the positioning efficiency index Zdw-the time node t2 is established, and the growth rate K of the graph is obtained, wherein the specific process is as follows:
e2-11: selecting any point of the curve as d0, determining the coordinate value d0 (xd 0, yd 0) of the point, selecting the adjacent point d1 of the point, determining the coordinate value d1 (xd 1, yd 1) of the point, calculating the average growth rate Kp of the curve between the two points,
e2-12: repeating the calculation through two continuous points to obtain N2 average growth rates, and averaging the average growth rate Kp to obtain the growth rate K of the curve:
e2-2: by combining the positioning efficiency index Zdw with the curve growth rate K, an early warning index Zyj of the positioning efficiency is established, and the formula of an early warning index Zyj of the preset positioning efficiency is as follows: zyj =τk Zdw;
wherein, τ is the time value from the current time node to the predicted time node, and τ is greater than 0, when the growth rate of the curve is lower and the positioning efficiency index Zdw is lower, it indicates that the positioning efficiency is gradually worse and the early warning index Zyj is lower over time, so that corresponding early warning is required;
e3: analyzing the early warning mathematical model to generate corresponding early warning signals:
e3-1: setting the preset interval of the early warning index Zyj of the positioning efficiency as (H1, H2);
e3-2: comparing and analyzing the early warning index Zyj with a preset interval (H1, H2):
when the early warning index Zyj is more than or equal to H2, generating a first-level early warning signal;
when the early warning index Zyj is located in a preset interval, generating a secondary early warning signal;
when the early warning index Zyj is smaller than or equal to H1, generating a three-level early warning signal;
e3-3: marking the generated primary early warning signals, secondary early warning signals and tertiary early warning signals as early warning signal groups, and sending the early warning signal groups to an early warning correction unit;
s4: the early warning correction unit is used for receiving the early warning signal and performing corresponding positioning correction operation, and early warning is performed on the effectiveness of the positioning needle in tumor targeted drug delivery treatment so as to realize stable and accurate drug delivery positioning effect;
when a first-level early warning signal is received, a green indicator lamp of a visual background terminal is controlled to flash, the positioning prediction efficiency of a targeting drug delivery positioning model is judged to be excellent, a text 'positioning prediction efficiency excellent' is edited for display, and the model is not processed;
when a secondary early warning signal is received, a yellow indicator lamp of a visual background terminal is controlled to flash, the positioning prediction efficiency of a targeting drug delivery positioning model is judged to be good, a text 'good positioning prediction efficiency' is edited for display, and algorithm parameters of a preset path of the model are adjusted by a special person, so that a positioning needle approaches to an actual path;
when three-level early warning signals are received, a red indicator lamp of a visual background terminal is controlled to flash, the positioning prediction efficiency of a targeting drug delivery positioning model is judged to be poor, and a text 'poor positioning prediction efficiency' is edited to display, so that a special person is arranged to update a software system in the model, and hardware equipment is repaired at the same time, so that the corrected positioning system can perform stable and accurate positioning control function on a positioning needle;
in summary, the invention establishes the targeting drug delivery positioning model through the model design unit, generates the preset path of the positioning needle, collects the actual path of the positioning needle, carries out comparative analysis through the comprehensive analysis unit, evaluates the consistency and the accuracy of positioning and the release performance of drugs, further comprehensively analyzes the positioning efficiency of the targeting drug delivery positioning model, carries out early warning and positioning correction on the efficiency of the positioning needle in tumor targeting drug delivery treatment through predicting the positioning efficiency of the positioning needle, and can realize stable and accurate drug delivery positioning effect by using the positioning needle controlled by the positioning system.
The interval and the threshold are set for the convenience of comparison, and the size of the threshold depends on the number of sample data and the number of cardinalities set for each group of sample data by a person skilled in the art; as long as the proportional relation between the parameter and the quantized value is not affected.
The formulas are all formulas with dimensions removed and numerical calculation, the formulas are formulas with a large amount of data collected for software simulation to obtain the latest real situation, and preset parameters in the formulas are set by a person skilled in the art according to the actual situation;
the foregoing is only a preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art, who is within the scope of the present invention, should make equivalent substitutions or modifications according to the technical scheme of the present invention and the inventive concept thereof, and should be covered by the scope of the present invention.
Claims (8)
1. A tumor targeted drug delivery treatment positioning system, which is characterized in that: comprises a model design unit, a comprehensive analysis unit and an early warning correction unit, which are connected by signals,
the comprehensive analysis unit comprises a dynamic path analysis module, a positioning accuracy analysis module and a medicine diffusion analysis module, wherein the dynamic path analysis module, the positioning accuracy analysis module and the medicine diffusion analysis module are in signal connection;
the model design unit is used for establishing a targeting drug delivery positioning model, generating and outputting a preset path A of the positioning needle to the comprehensive analysis unit;
the comprehensive analysis unit acquires a preset path A of the positioning needle, acquires an actual path B of the positioning needle through a medical imaging technology, and further analyzes, generates and sends an early warning signal to the early warning correction unit:
the dynamic path analysis module is used for analyzing dynamic changes of the preset path A and the actual path B, curve pretreatment is carried out first, curve comparison is carried out, the stability coefficient of positioning is comprehensively obtained, and the consistency of positioning is evaluated;
analyzing the distance error and the target position error of the preset path A and the actual path B through a positioning accuracy analysis module, and evaluating the positioning accuracy;
and analyzing the diffusion path of the drug molecules by a drug diffusion analysis module: dividing a target area into N1 grids, collecting the concentration of drug molecules of each grid for analysis, and evaluating the release performance of the drug;
the positioning efficiency of the targeted drug delivery positioning model is obtained through comprehensively analyzing the consistency and the accuracy of positioning and the release performance of the drug, the positioning efficiency is fitted into an early warning mathematical model, and the early warning mathematical model is analyzed to generate a corresponding early warning signal;
the early warning correction unit is used for receiving the early warning signal and carrying out corresponding positioning correction operation, and early warning is carried out on the effectiveness of the positioning needle in tumor targeted drug delivery treatment so as to realize stable and accurate drug delivery positioning effect.
2. A tumor targeted drug delivery therapy positioning system according to claim 1, wherein: the specific process of curve preprocessing by the dynamic path analysis module is as follows:
a1: marking a preset path A of the positioning needle as a three-dimensional dynamic curve S1, marking an actual path B of the positioning needle as a three-dimensional dynamic curve S2, and establishing a curve preprocessing model:
a1-1: inputting a three-dimensional dynamic curve Sm, and marking any point on the curve Sm as M (Xm, ym, zm);
presetting a time node t0, dispersing a curve Sm into n0 position points, performing distance measurement and calculation on two position points in adjacent time to obtain a distance value Lm of the two adjacent points, obtaining a speed vm=the distance value Lm/the time node t0 in the time node, and establishing and outputting a two-dimensional dynamic curve Sn of the speed Vm-the time node t 0;
wherein, the distance measurement formula of two position points is: presetting two adjacent points as (Xi, yi, zi) and (Xj, yj, zj), and then the distance L:
a1-2: then, the slope between two points on the curve Sn in the time node t0 is measured, the acceleration ACm is obtained, and a two-dimensional dynamic curve Sc of the acceleration ACm-time node t0 is established and output;
a2: substituting the three-dimensional dynamic curve S1 and the three-dimensional dynamic curve S2 into a curve preprocessing model respectively, and outputting a corresponding two-dimensional dynamic curve Sn and a corresponding two-dimensional dynamic curve Sc:
for the three-dimensional dynamic curve S1, a two-dimensional dynamic curve Sn1 of a speed Va-time node t0 and a two-dimensional dynamic curve Sc1 of an acceleration ACa-time node t0 are obtained;
for the three-dimensional dynamic curve S2, a two-dimensional dynamic curve Sn2 of the velocity Vb-time node t0 and a two-dimensional dynamic curve Sc2 of the acceleration ACb-time node t0 are obtained.
3. A tumor targeted drug delivery therapy positioning system according to claim 2, wherein: the specific process of curve comparison by the dynamic path analysis module is as follows:
b1: establishing a curve comparison model, and obtaining a consistency evaluation coefficient by comparing two curves:
b2-1: the abscissa of the marked curve No. 1 is time ti, the ordinate is yi, the abscissa of the marked curve No. 2 is time tj, and the ordinate is yj;
b2-2: the abscissa of the two curves are overlapped, and the deviation degree of the ordinate of the two curves is compared, so that the time ti=tj is calculated, and a formula is establishedObtaining an evaluation coefficient PG of the deviation degree;
b3: substituting the two-dimensional dynamic curve Sn1 and the curve Sn2 of the speed into a curve comparison model to obtain an evaluation coefficient PG1 of the speed deviation;
substituting the two-dimensional dynamic curve Sc1 and the curve Sc2 of the acceleration into a curve comparison model to obtain an evaluation coefficient PG2 of the acceleration deviation;
b4: an evaluation coefficient PG1 of the deviation degree of the integrated speed and an evaluation coefficient PG2 of the deviation degree of the acceleration are established by establishing a formulaObtaining a stability coefficient WD of positioning;
wherein α1 and α2 are weight factors of the evaluation coefficients PG1 and PG2, respectively, and α1 and α2 are both greater than 0.
4. A tumor targeted drug delivery therapy positioning system according to claim 3, wherein: the specific processing procedure of the positioning accuracy analysis module is as follows:
c1: the initial position is taken as a base point O, a preset path A of the positioning needle is marked as a three-dimensional dynamic curve S1, an actual path B of the positioning needle is marked as a three-dimensional dynamic curve S2, and the three-dimensional dynamic curve S1 and the three-dimensional dynamic curve S2 are directly compared;
c2: presetting a time node t1, and respectively dispersing a curve S1 and a curve S2 into n1 position points;
any point on the marker curve S1 is P (Xa, ya, za), and the target position of the marker curve S1 is W1 (X1, Y1, Z1);
any point on the marker curve S2 is Q (Xb, yb, zb), and the target position of the marker curve S2 is W2 (X2, Y2, Z2);
and C3: establishing a formula to obtain a locating fine coefficient JX:
5. the tumor targeted drug delivery treatment positioning system according to claim 4, wherein: the specific processing procedure of the drug diffusion analysis module is as follows:
d1: dividing the target area into N1 grids, collecting the concentration of drug molecules in each grid for analysis,
d2: firstly, marking the concentration of the drug molecules in any grid as Ci, setting the threshold value of the concentration of the drug molecules in the grid as Ch, and then carrying out contrast analysis:
when the concentration of the drug molecules in the grid exceeds a threshold value, judging that the concentration of the drug molecules in the grid reaches a qualified standard, marking the grid as a qualified grid, and measuring and calculating the occupation ratio phi of the qualified grid;
d3: averaging the drug molecule concentrations in N1 grids to obtain average drug minute concentration C0;
d4: establishing a formulaObtaining a medicine diffusion coefficient KS;
wherein μ is a diffusion factor, and μ is greater than 0.
6. A tumor targeted drug delivery therapy positioning system according to claim 5, wherein: the specific process of comprehensively analyzing the consistency, the accuracy and the release performance of the medicine is as follows:
e1: acquiring a positioning efficiency index Zdw of the targeted drug delivery positioning model;
e1-1: firstly, obtaining a positioned stability coefficient WD, a fine coefficient JX and a medicine diffusion coefficient KS;
e1-2: establishing the formula Zdw =wd e1 +JX e2 +KS e3 Acquiring a positioning efficiency index Zdw of the model;
wherein e1, e2 and e3 are respectively the weight indexes of the stability coefficient WD, the fine coefficient JX and the medicine diffusion coefficient KS, and e1, e2 and e3 are all larger than 0;
e2: fitting the positioning efficiency into an early warning mathematical model:
e2-1: the time node t2 is preset, a dynamic graph of the positioning efficiency index Zdw-the time node t2 is established, and the growth rate K of the graph is obtained, wherein the specific process is as follows:
e2-11: selecting any point of the curve as d0, determining the coordinate value d0 (xd 0, yd 0) of the point, selecting the adjacent point d1 of the point, determining the coordinate value d1 (xd 1, yd 1) of the point, calculating the average growth rate Kp of the curve between the two points,
e2-12: repeating the calculation through two continuous points to obtain N2 average growth rates, and averaging the average growth rate Kp to obtain the growth rate K of the curve:
e2-2: by combining the positioning efficiency index Zdw with the curve growth rate K, an early warning index Zyj of the positioning efficiency is established, and the formula of an early warning index Zyj of the preset positioning efficiency is as follows: zyj =τk Zdw;
where τ is the time value from the current time node to the predicted time node, and τ is greater than 0.
7. A tumor targeted drug delivery therapy positioning system according to claim 6, wherein: the specific process of analyzing the early warning mathematical model to generate the corresponding early warning signal is as follows:
setting the preset interval of the early warning index Zyj of the positioning efficiency as (H1, H2);
comparing and analyzing the early warning index Zyj with a preset interval (H1, H2):
when the early warning index Zyj is more than or equal to H2, generating a first-level early warning signal; when the early warning index Zyj is located in a preset interval, generating a secondary early warning signal; when the early warning index Zyj is smaller than or equal to H1, generating a three-level early warning signal;
and marking the generated primary early warning signals, secondary early warning signals and tertiary early warning signals as early warning signal groups, and sending the early warning signal groups to an early warning correction unit.
8. A tumor targeted drug delivery treatment positioning needle assembly, which is characterized in that: a positioning needle assembly as in any of the preceding claims 1-7 for tumor targeted drug delivery therapy positioning systems.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311322902.2A CN117352046B (en) | 2023-10-13 | 2023-10-13 | Tumor targeting drug delivery treatment positioning needle system and assembly thereof |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311322902.2A CN117352046B (en) | 2023-10-13 | 2023-10-13 | Tumor targeting drug delivery treatment positioning needle system and assembly thereof |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117352046A true CN117352046A (en) | 2024-01-05 |
| CN117352046B CN117352046B (en) | 2024-04-26 |
Family
ID=89366240
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311322902.2A Active CN117352046B (en) | 2023-10-13 | 2023-10-13 | Tumor targeting drug delivery treatment positioning needle system and assembly thereof |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117352046B (en) |
Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101053531A (en) * | 2007-05-17 | 2007-10-17 | 上海交通大学 | Early tumor positioning and tracking method based on multi-mold sensitivity intensifying and imaging fusion |
| US20100142781A1 (en) * | 2005-09-19 | 2010-06-10 | University Of Virginia Patent Foundation | Systems and Method for Adaptive Beamforming for Image Reconstruction and/or Target/Source Localization |
| CN104174027A (en) * | 2014-09-15 | 2014-12-03 | 中国科学院上海硅酸盐研究所 | Tumor vessel-tumor cell membrane-cell nucleus continuous targeted drug delivery system, as well as preparation method and application thereof |
| CN105796177A (en) * | 2010-12-23 | 2016-07-27 | 巴德阿克塞斯系统股份有限公司 | Systems and methods for guiding a medical instrument |
| CN109690689A (en) * | 2016-09-14 | 2019-04-26 | 豪夫迈·罗氏有限公司 | Digital biometric for progressive MS marks |
| CN109767844A (en) * | 2018-12-28 | 2019-05-17 | 郑州大学第一附属医院 | Target tumor intervention therapeutic agent and preparation method thereof and intelligent checking system |
| CN110945362A (en) * | 2018-04-23 | 2020-03-31 | 江成鸿 | Application of CD93 in preparation of umbilical blood detection kit for early warning of infantile hemangioma and treatment medicine |
| CN111514316A (en) * | 2020-04-30 | 2020-08-11 | 复旦大学附属华山医院 | Inflammation targeting and microenvironment responsiveness nano system, preparation method and application |
| CN113066558A (en) * | 2021-03-31 | 2021-07-02 | 南阳市第二人民医院 | Method for targeted inhibition of breast cancer cell infiltration and metastasis |
| CN114247061A (en) * | 2021-12-07 | 2022-03-29 | 苏州雷泰医疗科技有限公司 | Tumor dynamic tracking control method and device and radiotherapy equipment |
| CN115101171A (en) * | 2022-07-05 | 2022-09-23 | 郑州大学第一附属医院 | Patient information analysis management system for hemodialysis |
| CN115282270A (en) * | 2022-10-10 | 2022-11-04 | 修存医药技术开发(天津)有限责任公司 | Novel anti-tumor magnetic targeting system |
| CN115527683A (en) * | 2022-10-08 | 2022-12-27 | 温州医科大学附属第一医院 | Method for predicting targeted therapeutic effect of Lunvatinib of liver cancer patient based on artificial intelligence |
| CN116549018A (en) * | 2023-05-22 | 2023-08-08 | 华润武钢总医院 | Three-dimensional ultrasonic super-resolution method based on nano liquid drops |
-
2023
- 2023-10-13 CN CN202311322902.2A patent/CN117352046B/en active Active
Patent Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20100142781A1 (en) * | 2005-09-19 | 2010-06-10 | University Of Virginia Patent Foundation | Systems and Method for Adaptive Beamforming for Image Reconstruction and/or Target/Source Localization |
| CN101053531A (en) * | 2007-05-17 | 2007-10-17 | 上海交通大学 | Early tumor positioning and tracking method based on multi-mold sensitivity intensifying and imaging fusion |
| CN105796177A (en) * | 2010-12-23 | 2016-07-27 | 巴德阿克塞斯系统股份有限公司 | Systems and methods for guiding a medical instrument |
| CN104174027A (en) * | 2014-09-15 | 2014-12-03 | 中国科学院上海硅酸盐研究所 | Tumor vessel-tumor cell membrane-cell nucleus continuous targeted drug delivery system, as well as preparation method and application thereof |
| CN109690689A (en) * | 2016-09-14 | 2019-04-26 | 豪夫迈·罗氏有限公司 | Digital biometric for progressive MS marks |
| CN110945362A (en) * | 2018-04-23 | 2020-03-31 | 江成鸿 | Application of CD93 in preparation of umbilical blood detection kit for early warning of infantile hemangioma and treatment medicine |
| CN109767844A (en) * | 2018-12-28 | 2019-05-17 | 郑州大学第一附属医院 | Target tumor intervention therapeutic agent and preparation method thereof and intelligent checking system |
| CN111514316A (en) * | 2020-04-30 | 2020-08-11 | 复旦大学附属华山医院 | Inflammation targeting and microenvironment responsiveness nano system, preparation method and application |
| CN113066558A (en) * | 2021-03-31 | 2021-07-02 | 南阳市第二人民医院 | Method for targeted inhibition of breast cancer cell infiltration and metastasis |
| CN114247061A (en) * | 2021-12-07 | 2022-03-29 | 苏州雷泰医疗科技有限公司 | Tumor dynamic tracking control method and device and radiotherapy equipment |
| CN115101171A (en) * | 2022-07-05 | 2022-09-23 | 郑州大学第一附属医院 | Patient information analysis management system for hemodialysis |
| CN115527683A (en) * | 2022-10-08 | 2022-12-27 | 温州医科大学附属第一医院 | Method for predicting targeted therapeutic effect of Lunvatinib of liver cancer patient based on artificial intelligence |
| CN115282270A (en) * | 2022-10-10 | 2022-11-04 | 修存医药技术开发(天津)有限责任公司 | Novel anti-tumor magnetic targeting system |
| CN116549018A (en) * | 2023-05-22 | 2023-08-08 | 华润武钢总医院 | Three-dimensional ultrasonic super-resolution method based on nano liquid drops |
Non-Patent Citations (3)
| Title |
|---|
| AMIRSAEED YAZDANI 等: "Simultaneous Denoising and Localization Network for Photoacoustic Target Localization", 《IEEE TRANSACTIONS ON MEDICAL IMAGING》, vol. 40, no. 9, 31 December 2021 (2021-12-31), pages 2367 - 2379, XP011875587, DOI: 10.1109/TMI.2021.3077187 * |
| 刘伟玲 等: "肿瘤靶向治疗精确靶向定位认知系统的研究", 《生物医学工程研究》, vol. 28, no. 2, 31 December 2009 (2009-12-31), pages 136 - 139 * |
| 李静 等: "PDCA循环在静脉药物配置中心提高抗肿瘤药物配置质量中的应用", 《中华全科医学》, vol. 16, no. 12, 31 December 2018 (2018-12-31), pages 2095 - 2101 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117352046B (en) | 2024-04-26 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Meakin | Diffusion-controlled deposition on fibers and surfaces | |
| EP2692392B1 (en) | Treatment planning system | |
| JP2006175239A (en) | Method and device for calculating radiation dose distribution for radiation treatment system for the purpose of radiation therapy of an animal body | |
| CN105843733B (en) | A kind of method for testing performance and device of big data platform | |
| CN106570859A (en) | Tooth image analysis system | |
| CN104750992A (en) | Method, device and system for simulating particle transport and calculating body dosage in radiotherapy | |
| CN117352046B (en) | Tumor targeting drug delivery treatment positioning needle system and assembly thereof | |
| CN104888364A (en) | Ion beam radiotherapy dosage verification method based on water equivalent coefficients | |
| WO2023119103A1 (en) | Method for analyzing breast signs on basis of orderliness, multiple modalities and symmetry breaking | |
| CN109461188A (en) | A kind of two-dimensional x-ray cephalometry image anatomical features point automatic positioning method | |
| CN110223753A (en) | A kind of Checking prescription distribution method and system based on queuing duration | |
| CN105320808B (en) | Pipeline multiple target layout optimization method based on NSGA algorithms | |
| CN115659284A (en) | Big data fusion platform | |
| CN106970098A (en) | stress analysis device, method and program | |
| CN115040275A (en) | Orthodontic arch wire evaluation method based on space translation sub-coordinate system trigram limit judgment | |
| CN110353852A (en) | The method that computer based measures positional relationship between two teeth | |
| Sun et al. | Combining bootstrap and genetic programming for feature discovery in diesel engine diagnosis | |
| CN116328004B (en) | Cleaning and disinfecting method and system for medicine production equipment | |
| CN116488155A (en) | Electric quantity prediction method and device and computer readable storage medium | |
| US6400841B1 (en) | Method for evaluating three-dimensional rendering systems | |
| CN115024841A (en) | Orthodontic arch wire error evaluation method based on complexity discrimination method | |
| CN109829197A (en) | The propylene polymerization production process optimal soft survey instrument of improved cuckoo optimizing | |
| CN110997065A (en) | Calibration of radiation therapy treatment plan for a system | |
| CN109490176B (en) | FCM analysis data processing method, device and classifying method, device | |
| CN114420252A (en) | Method, device and medium for determining intensity modulated radiotherapy plan evaluation parameter matrix |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| TA01 | Transfer of patent application right | ||
| TA01 | Transfer of patent application right |
Effective date of registration: 20240403 Address after: No. 01, 12th Floor, Rongchuang Building, Intersection of Jiefang Road and Huaihe Road, Longzihu District, Bengbu City, Anhui Province, 233000 Applicant after: Yishu Ronghe (Anhui) Technology Co.,Ltd. Country or region after: China Address before: Room 309, Unit 1, Building 1, No. 128 Guofu Street, Longzihu District, Bengbu City, Anhui Province, 233000 Applicant before: Anhui Yifang Jianshu Information Technology Co.,Ltd. Country or region before: China |
|
| GR01 | Patent grant | ||
| GR01 | Patent grant |