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WO2019037999A1 - Procédé et système permettant de simuler une tumeur humaine - Google Patents

Procédé et système permettant de simuler une tumeur humaine Download PDF

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Publication number
WO2019037999A1
WO2019037999A1 PCT/EP2018/069936 EP2018069936W WO2019037999A1 WO 2019037999 A1 WO2019037999 A1 WO 2019037999A1 EP 2018069936 W EP2018069936 W EP 2018069936W WO 2019037999 A1 WO2019037999 A1 WO 2019037999A1
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WO
WIPO (PCT)
Prior art keywords
tumor
status
cell
tumor status
therapy
Prior art date
Application number
PCT/EP2018/069936
Other languages
German (de)
English (en)
Inventor
Christoph Morhard
Sebastian Richter
Original Assignee
Prokando Gmbh
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Prokando Gmbh filed Critical Prokando Gmbh
Publication of WO2019037999A1 publication Critical patent/WO2019037999A1/fr

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Classifications

    • 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
    • G16H50/50—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders
    • 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

Definitions

  • the invention relates to a method and a system for simulating a human tumor.
  • Malignant human tumors are complex cell structures consisting, for example, of different sub-clones of a primary tumor. These sub-clones respond
  • Patient-specific tumor can be considered as a black box, because the internal state is not or only partially observed.
  • a large number of therapeutic options for the treatment of a malignant tumor are available.
  • Simulation of a human tumor is to be created, which simulates a patient-specific development of a tumor.
  • the problem underlying the invention is achieved by a
  • a method for simulating a human tumor is proposed. This involves detecting a number of tumor status, the
  • Simulation function is performed, and selecting one of the tumor status, wherein selecting the one
  • Tumor status is performed from the number of tumor statuses by means of a first selection function trained by supervised learning.
  • a system for simulating a human tumor is proposed. This involves detecting a number of tumor status, the
  • Simulation function is performed, and selecting one of the tumor status, wherein selecting the one
  • Tumor status is performed from the number of tumor statuses by means of a first selection function trained by supervised learning.
  • the number of tumor statuses is at least one time prior to at least one determined tumor status, depending on the
  • the tumor status will depend on the number of tumor status
  • the process or system are the essential
  • a therapy proposal is determined, wherein the determination of the
  • Therapy proposal is performed by means of a second selection function, which is trained by reinforcing learning.
  • Treatment goal depending on the at least one partially lying in the past and the tumor therapy measure, and determined depending on the selected tumor status. This gives the second selection function the information to
  • FIG. 2 is a schematic view of a human tumor; Figures 7, 8, 9, 10, 11, 12, 13 and 14 each a
  • FIG. 15 is a schematic sequence diagram; and FIG. 16 shows by way of example a representation of
  • Figure la shows a schematic block diagram.
  • a block 102 determines a number of tumor statuses Sl (t), S2 (t), SN (t) as a function of at least one temporally previously determined tumor status S (t-1), depending on the
  • a block 104 selects one
  • FIG. 1b shows, in addition to FIG. 1a, a schematic block diagram with a block 106, which has a
  • a treatment goal in a malignant tumor disease includes, for example: complete recovery of the patient, low treatment side effects and / or long-term survival of the patient
  • a therapy method relates to a single possible therapy action such as a radiological treatment or a surgical treatment or a drug treatment but not a combination of the aforementioned possible treatments.
  • a therapy proposal concerns a proposal that includes one or more therapeutic methods and that
  • a therapeutic measure relates to a therapy actually performed on the patient, such as
  • a radiological treatment for example, a surgical treatment, a drug treatment or a combination of the aforementioned treatments.
  • a patient history includes information about the patient's age, whether or not the patient was a smoker, information about whether the patient is a city dweller or not. Consequently, the patient history includes data about
  • Diagnostic data D (t) at a time t include, for example, the size and / or shape of the
  • Patients existing tumor This can, for example, via a computed tomography recording - too machine - be determined. Alternatively or
  • a tumor sample is taken from the patient at the beginning of the treatment and a cell culture
  • the tumor cells of the cell culture thus grow parallel to the patient.
  • Time t thus include, for example, a size of the cell culture and / or genetic data of the
  • an analogous therapy measure is applied to the cell culture.
  • FIG. 2 shows a schematic block diagram with a system 200 for carrying out the methods listed in this description.
  • the system 200 includes a
  • central processing unit 202 such as a number of individual systems comprising data center with increased computing capacity and a remote control terminal 204 such as a commercial personal computer.
  • the central processing unit 202 such as a number of individual systems comprising data center with increased computing capacity and a remote control terminal 204 such as a commercial personal computer.
  • the computing unit 202 comprises in a schematic form a first data memory 202M, a first processor unit 202P and a first network interface 2021
  • Operator terminal 204 includes a second
  • Network interface 2041 a second data store 204M, a second processor unit 204P.
  • the operator terminal 204 includes an input interface 204T such as a keyboard and / or a keyboard
  • Input interface 204T and output interface 204S are collectively referred to as user interface I.
  • FIG. 3 shows by way of example a malignant human tumor in the form of a tumor status S in a schematic manner
  • the tumor status S comprises a number of type A cells Ca, a number of type B cells Cb, and a number of types C cells.
  • benign tumors can also be used depending on the patient and
  • FIG. 4 shows a schematic block diagram of a first model.
  • a simulation function SIM determines the tumor status Sl (t), S2 (t), SN (t).
  • the simulation function SIM simulates several times the growth of a tumor, for example, based on a past tumor status Sl (t-1) at time t-1.
  • several tumor statuses may also be available on the basis of which the simulation function SIM determines a number of different tumor statuses.
  • the simulation function SIM determines the tumor status Sl (t), S2 (t), SN (t).
  • the simulation function SIM simulates several times the growth of a tumor, for example, based on a past tumor status Sl (t-1) at time t-1.
  • several tumor statuses may also be available on the basis of which the simulation function SIM determines a number of different tumor statuses.
  • the simulation function SIM determines the tumor status Sl (t), S2 (t), SN (t).
  • Rule-based determines a number of tumor states, with the rules defining a respective one
  • Presently available diagnostic data D (t) which relate to the actual tumor of a patient, are stored in a patient-specific memory area M3
  • Patient history H relates to the condition of the patient before the occurrence of the tumor disease and is stored in a patient-specific memory area M4. Based on the tumor status Sl (t-l), derived from a database DB biological parameters P and at least one lagging ago and the tumor concerned
  • T (tl) is a number of tumor status Sl (t), S2 (t) at time t and a number of tumor status Sl (t + 1), S2 (t + 1), S3 (t + 1 ) and S4 (t + 1) at the time t + 1 determined. Assuming the time t as the present and the time t + 1 as the future, starting from a number of current estimated tumor statuses Sl (t), S2 (t), a number of future estimated tumor status Sl (t +1) to S4 (t + 1) closed.
  • the tumor statuses Sl (t) to S4 (t + 1) are called tree
  • This forest in the form of simulation data SD is patient-individual and is stored in the patient ⁇ individual memory area Ml.
  • a first selection function SEL1 is based on machine learning and, for example, supervised learning
  • the first selection function SELl provides each
  • the first selection function SELl chooses one - from the point of view of the first one
  • Selection function SEL1 also the tumor status S (t + 1) from the number of determined tumor status Sl (t + 1) to S4 (t + 1), which comes closest to a state of the actual tumor in the future.
  • Development process V the selection function SELl the simulation data SD, the patient history H and the diagnostic data D (t) are provided.
  • the most likely course of development V of the simulated tumor will help a treating physician to decide on the next Treatment of the patient brought to the knowledge.
  • the physician can initiate a self-selected therapeutic measure for the patient.
  • a comparison of the current diagnosis data D (t) with the most probable tumor status Sl (t) can be brought to the attention of the attending physician.
  • the size of the actual tumor according to the diagnostic data D (t) with the size of the simulated tumor according to the tumor status Sl (t) are displayed. This display can be done, for example, by a figurative representation or by an estimate error determined from the comparison in the form of a number. Reference is made to the exemplary representation in FIG. 16.
  • diagnostic data D (t) of the respective patient In one embodiment of the first selection function SEL1, diagnostic data D (t) of the respective patient
  • tumor status Sl (t) to S4 (t + 1) is not selected, in which this particular mutation is not
  • FIG. 5 shows a schematic block diagram of a second model.
  • Treatment goal Z (t) can not in
  • a second selection function SEL2 is based on machine learning and is trained, for example, by reinforcing learning.
  • the second selection function SEL2 determines the therapy proposal R (t), which includes one or a number of therapy methods Ma, Mb, Mc, to Mx.
  • Therapy methods Ma to Mx are stored in another database DB2.
  • the second selection function SEL2 selects from the further database the therapy methods Ma to Mx, which depend on the previous ones
  • the selection functions SEL1 and SEL2 are, according to this description, for example, machine-trained neural networks.
  • FIG. 6 shows a schematic block diagram of a third model.
  • the third model of FIG. 6 does not include a first selection function SEL1. The possible
  • Development course V of the simulation function SIM is selected, for example, at random.
  • the second selection function SEL2 selects from the other
  • FIG. 7 shows a schematic flow diagram for
  • a cell is to be understood as a data structure which is a biological structure
  • Cancer cell and its environment such as
  • an initial cell is determined as a function of stochastic mutation probabilities and as a function of the patient's history and added to a list.
  • the next cell of the list is selected for editing.
  • predetermined initialization parameters such as the predetermined tumor size have already been reached. If so, at step 708, the predetermined initialization parameters such as the predetermined tumor size have already been reached.
  • step 900 which is described in greater detail with respect to FIG. 9.
  • Figure 8 shows a schematic flow diagram of
  • Simulation function SIM for existing tumor status in the form of a cell list in a step 802, the cell list with a number of cells
  • step 804 the next cell is selected from the list.
  • step 806 checks whether all cells from the list have already been processed in this simulation step. If this is the case, then in step 808 the determined list is stored as a further tumor status. If this is not the case, it is changed to step 900, which is described in more detail in FIG. 9.
  • Figure 9 shows a schematic flow diagram for
  • a step 904 it is probabilistically determined whether a first cell
  • Zellfitness determined and as a result, whether the first cell dies. For this purpose, first an individual cell fitness is determined and depending on the
  • step 908 first mutation parameters are applied to the first cell when the first cell is not dead.
  • a mutation parameter is applied to the first cell when the first cell is not dead.
  • a step 912 it is probabilistically determined if the first cell splits if the first cell is not dead.
  • a second cell is determined in dependence on the state of the first cell and added to the list
  • step 916 second mutation parameters are applied to the first cell and / or the second cell when the first cell has split.
  • a change of the genome of the first and / or the second cell is effected, which is caused by a cell division.
  • the foregoing mutation parameters are determined, for example, as a function of the biological parameters P and as a function of the therapeutic measure T (t-1).
  • FIG. 10 shows a schematic flowchart which represents the procedure for generating a therapy proposal.
  • a step 1002 at time t
  • present medical history of the patient comprising the patient history and the previously used
  • the diagnostic data is processed, for example, a tumor size and / or a tumor shape becomes
  • step 1004 may also be replaced by a manual entry of the physician.
  • step 1006 the
  • Simulation function SIM performed from the previous figures to a number of different tumor status too determine.
  • the first selection function SEL1 from the previous figures is executed to select the tumor status that best suits the
  • the second selection function SEL2 from the previous figures is executed to select the therapy proposal that best suits the specific patient.
  • Step 1012 visualizes the selected tumor status and the selected therapy proposal.
  • Step 1014 checks whether new diagnostic data is available at a subsequent time. If so, step 1002 is entered. If this is not the case, then a change is made to a step 1200, which is described below in FIG. Alternatively, instead of step 1200, step 1100 is performed
  • FIG. 11 shows a schematic flow diagram for
  • a therapy proposal is manually provided.
  • the simulation function SIM from one of the previous figures is performed and a number of tumor statuses are determined.
  • one of the previously determined tumor status is selected by means of the first selection function SEL1.
  • the selected tumor status that was determined for the manually prescribed therapy proposal is visualized.
  • one Step 1110 checks if there is another one
  • step 1102 is entered. If this is not the case, then a change is made to step 1112 and the method is ended.
  • FIG. 12 shows a schematic flow diagram for
  • the simulation function SIM executed is determined as a number of tumor statuses as a function of a previously determined tumor status.
  • a step 1212 by means of the second selection function SEL2, at least one of the
  • the determined tumor status selected a therapy proposal is updated with the
  • step 1216 it is checked if another pass is required. If this is the case, then the step 1210 is changed. If this is not the case, then a change is made to step 1218 and the process is ended.
  • FIG. 13 shows a schematic flow chart for the training of the first selection function SEL1 through supervised learning.
  • a step 1304 an actual
  • Course of a tumor disease comprising an actual first tumor status and an actual second tumor status SR (t) from a disease database.
  • a number of simulated tumor status is obtained from the first actual tumor status determined.
  • one of the determined simulated tumor status S (t) is selected by means of the first selection function SEL1.
  • the selection function SEL1 is trained by the estimation error.
  • FIG. 14 shows a schematic flow chart for the training of the second selection function SEL2
  • a third tumor status S (t) is determined.
  • a therapy proposal R (t) is determined as a function of the third tumor status S (t).
  • a fourth tumor status S (t + 1) is determined as a function of the third tumor status S (t) and in dependence on the selected therapy proposal R (t).
  • a reward value is determined based on a reward function in response to the fourth tumor status S (t + 1).
  • the second selection function SEL2 becomes dependent on the determined reward value
  • FIG. 15 shows a schematic sequence diagram.
  • a physician A examines a patient P and uses the acquired information to determine patient history H as well
  • Diagnostic data D (t) is provided by the physician via the
  • Input user interface I and are thus the simulation function SIM and the two selection functions SEL1 and SEL2 available.
  • the patient history H and diagnostic data D (t) are used to initialize the
  • Transfer simulation function SIM After initialization, the simulation function requires only the therapeutic measures T and the diagnostic data D (t) performed on the patient P.
  • the simulation function SIM is executed and determines tumor status Sn.
  • the tumor statuses Sn are used by the first selection function SEL1 in a step 1504 to provide one of the delivered tumor statuses Sn as the selected tumor status Ss of the second selection function.
  • Selection function SEL2 determines in step 1506
  • User interface I determines a visualization VIS of the tumor status Ss (t) and the determined mediated therapy proposal R (t), which is made available to the doctor A.
  • the physician A can now decide which therapy measure T (t) he selects P for the patient.
  • the therapy measure T (t) may coincide with the therapy proposal R (t) or not.
  • the doctor A transmits the applied
  • the central processing unit 202 determines the number of tumor statuses Sl (t), S2 (t), SN (t) by means of the
  • the central processing unit 202 transmits the ascertained tumor statuses Sl (t), S2 (t), SN (t) to the operating terminal 204.
  • the remotely located operator terminal 204 selects one of the simulated tumor statuses Sl (t), S2 (t) , SN (t) by means of the first selection function SEL1.
  • the remotely located operator terminal 204 determines the therapy proposal R (t) by means of the second
  • Operator terminal 204 displays the determined tumor status S (t) and the determined therapy proposal R (t) by means of the user interface I.
  • the central processing unit 202 is designed to train the first and / or the second selection function SEL1, SEL2. After completion of the respective training, the central processing unit 202 transmits the trained first and / or second selection function SEL1; SEL2 to the operator terminal 204.
  • the operator terminal 204 replaces a previously used first and / or second selection function SEL1; SEL2 by the transmitted first and / or second selection function SEL1; SEL2.
  • Embodiments 200, 1520, 1530 may be distributed differently to the central processing unit 202 and the operator terminal 204.
  • FIG. 16 shows by way of example a representation of

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Abstract

L'invention concerne un procédé de simulation d'une tumeur humaine, comprenant les étapes suivantes : détermination (102) d'un certain nombre d'états tumoraux ; et sélection (104) d'un de ces états tumoraux.
PCT/EP2018/069936 2017-08-23 2018-07-23 Procédé et système permettant de simuler une tumeur humaine WO2019037999A1 (fr)

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DE102017119278.6 2017-08-23
DE102017119278.6A DE102017119278A1 (de) 2017-08-23 2017-08-23 Verfahren und System zur Simulation eines menschlichen Tumors

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Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20030144798A1 (en) * 2002-01-07 2003-07-31 The Regents Of The University Of California Computational model, method, and system for kinetically-tailoring multi-drug chemotherapy for individuals

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20030144798A1 (en) * 2002-01-07 2003-07-31 The Regents Of The University Of California Computational model, method, and system for kinetically-tailoring multi-drug chemotherapy for individuals

Non-Patent Citations (3)

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Title
CHALLAPALLI NIHARIKA ET AL: "Modelling drug response and resistance in cancer: Opportunities and challenges", 2016 IEEE 55TH CONFERENCE ON DECISION AND CONTROL (CDC), IEEE, 12 December 2016 (2016-12-12), pages 2488 - 2493, XP033030647, DOI: 10.1109/CDC.2016.7798635 *
DIRK DRASDO ET AL: "A single-cell-based model of tumor growth in vitro: monolayers and spheroids; A single-cell-based model of tumor growth in vitro: monolayers and spheroids", PHYSICAL BIOLOGY, INSTITUTE OF PHYSICS PUBLISHING, BRISTOL, GB, vol. 2, no. 3, 1 September 2005 (2005-09-01), pages 133 - 147, XP020093381, ISSN: 1478-3975, DOI: 10.1088/1478-3975/2/3/001 *
SUN SHUHAO ET AL: "A new mathematical model for progression of colorectal cancer", 2013 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE, IEEE, 18 December 2013 (2013-12-18), pages 560 - 565, XP032562233, DOI: 10.1109/BIBM.2013.6732558 *

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