US2021166821A1PendingUtilityA1
Three-dimensional modeling of patient-specific tumors using a lattice of elastic-material points
Est. expiryOct 12, 2038(~12.2 yrs left)· nominal 20-yr term from priority
A61B 5/055G16C 20/80G16C 20/30G01R 33/5608G16H 30/40A61B 2576/00G16H 50/50G06F 3/04815G16H 20/10G16H 15/00
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Claims
Abstract
Three-dimensional modeling of patient-specific tumors. In an embodiment, patient-specific data, representing a tumor microenvironment and one or more metrics, are received. A patient-specific spatial model, representing the tumor microenvironment as a lattice comprising a plurality of elastic material points, is generated from the patient-specific data. Patient-specific drug interaction and metabolism models are also determined. The tumor microenvironment is then simulated, for one or more drug interventions, through a plurality of iterations in which each elastic material point is updated in each iteration based on computations of chemical diffusion, biochemical reactions, metabolism, drug interactions, growth and death, and mechanical forces. A report, comprising a three-dimensional representation of the patient-specific spatial model after one or more iterations, is generated for the drug intervention(s).
Claims
exact text as granted — not AI-modifiedWhat is claimed is:
1 . A method comprising using at least one hardware processor to:
receive patient data for a patient, wherein the patient data represents a tumor microenvironment within the patient and one or more metrics of the patient; generate a patient-specific spatial model, comprising a representation of the tumor microenvironment as a three-dimensional lattice, based on the patient data, wherein the three-dimensional lattice comprises a plurality of elastic-material points representing tissue within the tumor microenvironment; determine a patient-specific drug interaction model based on the one or more metrics within the patient data; for each of one or more drug interventions, simulate the tumor microenvironment by, for each of a plurality of iterations representing time intervals, for each of the plurality of elastic-material points,
modeling chemical diffusion within the tissue represented by the elastic-material point and updating one or more properties of the elastic-material point based on the modeling of chemical diffusion,
modeling one or more biochemical reactions within the tissue represented by the elastic-material point and updating one or more properties of the elastic-material point based on the modeling of the one or more biochemical reactions,
modeling one or more drug interactions with the tissue represented by the elastic-material point using the patient-specific drug interaction model and updating one or more properties of the elastic-material point based on the modeling of the one or more drug interactions,
modeling one or both of growth and death of the tissue represented by the elastic-material point and updating one or more properties of the elastic-material point based on the modeling of one or both of growth and death, and
modeling mechanical forces at the elastic-material point and updating one or more properties of the elastic-material point based on the modeling of mechanical forces; and
output data representing a result of the simulation for at least one of the one or more drug interventions.
2 . The method of claim 1 , wherein simulating the tumor microenvironment further comprises, for two or more of the plurality of iterations, for each of the plurality of elastic-material points:
modeling chemical advection within the tissue represented by the elastic-material point and updating one or more properties of the elastic-material point based on the modeling of chemical advection; modeling regulation within the tissue represented by the elastic-material point and updating one or more properties of the elastic-material point based on the modeling of regulation; and modeling an immune system response within the tissue represented by the elastic-material point and updating one or more properties of the elastic-material point based on the modeling of the immune system response.
3 . The method of claim 2 , wherein modeling an immune system response within the tissue represented by the elastic-material point comprises modeling immune cells as discrete motile agents.
4 . The method of claim 2 , wherein modeling an immune system response within the tissue represented by the elastic-material point comprises modeling immune cells as a scalar field that describes immune cell density.
5 . The method of claim 2 , wherein modeling regulation within the tissue represented by the elastic-material point and updating one or more properties of the elastic-material point based on the modeling of regulation comprises:
receiving a current concentration of each of one or more chemicals at the elastic-material point; receiving a current density and one or more mechanical properties of each of one or more types of tissue at the elastic-material point; and, for each of the one or more types of tissue at the elastic-material point,
determining a regulatory state, and
updating a regulatory state stored for the type of tissue to the determined regulatory state.
6 . The method of claim 5 , wherein each regulatory state is one of a plurality of possible regulatory states, wherein each of the plurality of possible regulatory states is associated with a different one of a plurality of metabolism models, and wherein simulating the tumor microenvironment further comprises, for two or more of the plurality of iterations, for each of the plurality of elastic-material points, modeling a metabolism of the tissue represented by the elastic-material point by, for each of the one or more types of tissue at the elastic-material point, using the metabolism model associated with the regulatory state stored for the type of tissue.
7 . The method of claim 5 , wherein the regulatory state of each of the one or more tissues is represented as a set of values that collectively describe a fraction of the tissue that is in each possible regulatory state.
8 . The method of claim 2 , wherein modeling chemical advection within the tissue represented by the elastic-material point and updating one or more properties of the elastic-material point based on the modeling of chemical advection comprises, for each of one or more chemicals, updating a concentration of the chemical at the elastic-material point (i,j,k) as:
ϕ
(
i
,
j
,
k
)
←
ϕ
(
i
,
j
,
k
)
-
{
[
ϕ
(
i
+
1
,
j
,
k
)
·
u
x
(
i
+
1
,
j
,
k
)
-
ϕ
(
i
-
1
,
j
,
k
)
·
u
x
(
i
-
1
,
j
,
k
)
]
-
[
ϕ
(
i
,
j
+
1
,
k
)
·
u
y
(
i
,
j
+
1
,
k
)
-
ϕ
(
i
,
j
-
1
,
k
)
·
u
y
(
i
,
j
-
1
,
k
)
]
-
[
ϕ
(
i
,
j
,
k
+
1
)
·
u
z
(
i
,
j
,
k
+
1
)
-
ϕ
(
i
,
j
,
k
-
1
)
·
u
z
(
i
,
j
,
k
-
1
)
]
}
·
Δ
t
2
λ
;
wherein φ is a concentration of the chemical at the elastic-material point, λ is a lattice spacing between elastic-material points in the three-dimensional lattice, and u x (i,j,k), u y (i,j,k), and u z (i,j,k) are components of a vector-valued flow field u at the elastic-material point.
9 . The method of claim 1 , wherein each of the plurality of elastic-material points is associated with a plurality of properties, wherein the plurality of properties comprises one or more chemical properties and one or more tissue properties.
10 . The method of claim 9 , wherein the one or more chemical properties comprise a chemical concentration for at least one chemical.
11 . The method of claim 9 , wherein the one or more tissue properties comprise a density of the tissue represented by the elastic-material point.
12 . The method of claim 9 , wherein the one or more tissue properties comprise a permeability of the tissue represented by the elastic-material point.
13 . The method of claim 9 , wherein the one or more tissue properties comprise a regulatory state of the tissue represented by the elastic-material point.
14 . The method of claim 9 , wherein modeling chemical diffusion within the tissue represented by the elastic-material point and updating one or more properties of the elastic-material point based on the modeling of chemical diffusion comprises:
receiving a current concentration, diffusion rate, and boundary value for each of at least one chemical at the elastic-material point; receiving a current density of each of one or more types of tissue at the elastic-material point; computing a new concentration for each of the at least one chemical and a new density of each of the one or more types of tissue, based on the current concentration, diffusion rate, and boundary value for the at least one chemical and the current density of the one or more types of tissue; for each of the at least one chemical, updating the concentration of the chemical at the elastic-material point to the new concentration; and, for each of the one or more types of tissue, updating the density of the type of tissue at the elastic-material point to the new density.
15 . The method of claim 1 , wherein the patient-specific spatial model is a spring model, such that each of the plurality of elastic-material points in the three-dimensional lattice is connected to each adjacent elastic-material point by a spring representation comprising a length value and a stiffness value.
16 . The method of claim 15 , wherein different tissue types are represented at the plurality of elastic-material points by different stiffness values.
17 . The method of claim 15 , wherein updating one or more properties of the elastic-material point based on the modeled mechanical forces comprises updating one or both of the length value and stiffness value of the spring representation between the elastic-material point and each adjacent elastic-material point.
18 . The method of claim 17 , wherein updating one or more properties of the elastic-material point based on the modeled mechanical forces comprises, until the mechanical forces reach an equilibrium:
updating the length value of the spring representation between the elastic-material point and each adjacent elastic-material point based on the mechanical forces; and re-modeling the mechanical forces at the elastic-material point.
19 . The method of claim 15 , wherein each of the plurality of elastic-material points is associated with a plurality of properties, and wherein, for each of the one or more drug interventions, simulating the tumor microenvironment further comprises, at one or more times during the simulation, re-gridding the three-dimensional lattice of the spatial model, such that each of the plurality of elastic-material points maintains its plurality of properties but all of the spring representations between pairs of elastic-material points have equal length values.
20 . The method of claim 1 , further comprising using the at least one hardware processor to simulate the tumor microenvironment without any drug intervention.
21 . The method of claim 1 , wherein the tumor microenvironment comprises a tumor and tissues surrounding the tumor.
22 . The method of claim 1 , wherein the three-dimensional lattice comprises a cubic lattice.
23 . The method of claim 1 , wherein modeling chemical diffusion within the tissue represented by the elastic-material point comprises, for each of one or more chemicals, modeling a chemical reaction as:
ϕ
ext
(
x
,
t
+
Δ
t
)
=
ϕ
ext
(
x
,
t
)
-
k
(
x
,
t
)
Δ
t
;
ϕ
∫
(
x
,
t
+
Δ
t
)
=
ϕ
∫
(
x
,
t
)
-
k
(
x
,
t
)
Δ
t
;
and
k
(
x
,
t
)
=
ρ
(
x
,
t
)
Ek
cat
V
cell
ϕ
ext
(
x
,
t
)
k
m
+
ϕ
ext
(
x
,
t
)
;
wherein φ ext (x,t) is an instantaneous local concentration of an extracellular form of the chemical at simulation time t, φ ƒ (x,t) is an instantaneous local concentration of an intracellular form of the chemical at simulation time t, Δt is the time interval represented by each of the plurality of iterations, k(x,t) represents an instantaneous local uptake rate of the chemical in the tissue at simulation time t, ρ(x,t) represents an instantaneous local cell volume fraction of the tissue at simulation time t, E represents a number of enzymatic transporters on a cell membrane of the tissue, V cell represents a volume of a cell of the tissue, k cat represents an enzyme turnover rate, and k m is a Michaelis constant for the chemical reaction.
24 . The method of claim 1 , wherein modeling one or more drug interactions with the tissue represented by the elastic-material point comprises computing at least one property of drug-related kinetics at the elastic-material point for each of one or more drugs modeled by the patient-specific drug interaction model.
25 . The method of claim 24 , wherein the patient-specific drug interaction model comprises a model of kinetic binding and unbinding rates for at least one drug.
26 . The method of claim 24 , wherein the patient-specific drug interaction model comprises a model of uptake and efflux of at least one drug by one or more types of tissue.
27 . The method of claim 1 , wherein modeling chemical diffusion within the tissue represented by the elastic-material point comprises modeling chemical diffusion using a seven-point stencil finite difference, in which an extracellular concentration of a chemical φ at elastic-material point i is updated as:
ϕ
i
(
t
+
Δ
t
)
=
ϕ
i
(
t
)
+
Δ
t
λ
Σ
j
J
ϕ
j
→
i
(
t
)
;
J
ϕ
j
→
i
(
t
)
=
D
ϕ
j
(
t
)
+
D
ϕ
i
(
t
)
2
ϕ
j
(
t
)
-
ϕ
i
(
t
)
λ
;
wherein λ is a lattice spacing between elastic-material points in the three-dimensional lattice, j indexes over six elastic-material points that neighbor elastic-material point i, L φj→i (t) represents a diffusive flux across a boundary separating elastic-material points j and i, D φi is a diffusion coefficient for the chemical in elastic-material point i, and D φj is a diffusion coefficient for the chemical in elastic-material point j.
28 . The method of claim 1 , wherein simulating the tumor microenvironment further comprises, for two or more of the plurality of iterations, for each of the plurality of elastic-material points, modeling a metabolism of the tissue represented by the elastic-material point and updating one or more properties of the elastic-material point based on the modeling of metabolism by:
receiving one or more properties of each of one or more chemicals at the elastic-material point, wherein the one or more properties comprise a current concentration; modeling a metabolism of each of one or more types of tissue at the elastic-material point; computing a new concentration for each of the one or more chemicals and a growth rate for each of the one or more types of tissue, based on the one or more properties of the one or more chemicals and the modeled metabolism of the one or more types of tissue; for each of the one or more chemicals, updating the concentration of the chemical at the elastic-material point to the new concentration; and, for each of the one or more types of tissue, updating a growth rate for the type of tissue to the new growth rate.
29 . The method of claim 1 , wherein modeling one or more biochemical reactions within the tissue represented by the elastic-material point and updating one or more properties of the elastic-material point based on the modeling of the one or more biochemical reactions comprises:
receiving one or more properties of each of one or more chemicals at the elastic-material point; receiving a density and regulatory state for each of one or more types of tissue at the elastic-material point; determining drug impacts using the patient-specific drug interaction model; and updating concentrations of the one or more chemicals at the elastic-material point based on the one or more properties of the one or more chemicals, the density and regulatory state of the one or more types of tissue, and the drug impacts.
30 . The method of claim 1 , wherein modeling one or more drug interactions with the tissue represented by the elastic-material point using the patient-specific drug interaction model and updating one or more properties of the elastic-material point based on the modeling of the one or more drug interactions comprises:
receiving one or more properties of each of one or more drugs at the elastic-material point; receiving a current density and determining a behavior for each of one or more types of tissue at the elastic-material point; determining drug impacts of the one or more drugs using the patient-specific drug interaction model; and updating densities of the one or more types of tissue at the elastic-material point based on the one or more properties of the one or more drugs, the current density and behavior of the one or more types of tissue, and the drug impacts.
31 . The method of claim 1 , wherein the patient-specific drug interaction model identifies a rate, per simulated time, at which one or more drugs kill one or more types of tissue.
32 . The method of claim 1 , wherein the patient data comprises imaging data of the tumor microenvironment.
33 . The method of claim 32 , wherein the imaging data comprises one or more of magnetic resonance imaging (MRI) data, positron emission topography (PET) data, or computed tomography (CT) data.
34 . A system comprising:
at least one hardware processor; and one or more software modules that are configured to perform the method of claim 1 when executed by the at least one hardware processor.
35 . A non-transitory computer-readable medium having instructions stored therein, wherein the instructions are configured to perform the method of claim 1 when executed by a processor.Join the waitlist — get patent alerts
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