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Review Special Issues

Transforming lung cancer care: Synergizing artificial intelligence and clinical expertise for precision diagnosis and treatment

  • Received: 29 July 2023 Revised: 12 September 2023 Accepted: 18 September 2023 Published: 22 September 2023
  • Lung cancer is a predominant cause of global cancer-related mortality, highlighting the urgent need for enhanced diagnostic and therapeutic modalities. With the integration of artificial intelligence (AI) into clinical practice, a new horizon in lung cancer care has emerged, characterized by precision in both diagnosis and treatment. This review delves into AI's transformative role in this domain. We elucidate AI's significant contributions to imaging, pathology, and genomic diagnostics, underscoring its potential to revolutionize early detection and accurate categorization of the disease. Shifting the focus to treatment, we spotlight AI's synergistic role in tailoring patient-centric therapies, predicting therapeutic outcomes, and propelling drug research and development. By harnessing the combined prowess of AI and clinical expertise, there's potential for a seismic shift in the lung cancer care paradigm, promising more precise, individualized interventions, and ultimately, improved survival rates for patients.

    Citation: Meiling Sun, Changlei Cui. Transforming lung cancer care: Synergizing artificial intelligence and clinical expertise for precision diagnosis and treatment[J]. AIMS Bioengineering, 2023, 10(3): 331-361. doi: 10.3934/bioeng.2023020

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  • Lung cancer is a predominant cause of global cancer-related mortality, highlighting the urgent need for enhanced diagnostic and therapeutic modalities. With the integration of artificial intelligence (AI) into clinical practice, a new horizon in lung cancer care has emerged, characterized by precision in both diagnosis and treatment. This review delves into AI's transformative role in this domain. We elucidate AI's significant contributions to imaging, pathology, and genomic diagnostics, underscoring its potential to revolutionize early detection and accurate categorization of the disease. Shifting the focus to treatment, we spotlight AI's synergistic role in tailoring patient-centric therapies, predicting therapeutic outcomes, and propelling drug research and development. By harnessing the combined prowess of AI and clinical expertise, there's potential for a seismic shift in the lung cancer care paradigm, promising more precise, individualized interventions, and ultimately, improved survival rates for patients.



    Population migration is a common phenomenon. With the migration of population, infectious diseases can easily spread from one area to another, so it is meaningful to consider population migration when studying the spread of infectious diseases [1,2,3,4,5,6,7].

    Wang and Mulone [2] established an SIS infectious disease model with standard incidence based on two patches. It is proved that the basic reproduction number is the threshold of the uniform persistence and disappearance of the disease. The dispersal rate of the population will make the infectious disease persist or disappear in all patches. There will be no the phenomena that infectious diseases persists in one patch but disappears in the other.

    Sun et al. [3] put forward an SIS epidemic model with media effect in a two patches setting. Under the assumption that the migration matrix is irreducible, it is proved that if the basic reproduction number is greater than 1 then the system persists and solutions converge to an endemic equilibrium and that if the basic reproduction number is less than 1 then solutions tend to an equilibrium without disease.

    Gao et al. [5,6] studied an SIS multi-patch model with variable transmission coefficients. Their results show that the basic reproduction number R0 is a threshold parameter of the disease dynamics.

    All the patch models referenced above assume that the migration matrix is irreducible. The studies in which the migration matrix is reducible are few. Therefore, based on the case of two patches, we consider that the individuals can only migrate from one patch to the other. In this case, the migration matrix is reducible. It can characterize the phenomenon that individuals migrate in one direction between two regions, such as, from the rural patch to the urban one [8] and from a small community hospital to a large teaching hospital [4].

    It is well known that the incidence rate plays an important role in the modeling of infectious disease. Considering the saturation phenomenon for numerous infected individuals, Capasso and Serio [9] first introduce a nonlinear bounded function g(I) to form the interaction term g(I)S in 1978. It can characterize the behavioral changes of individuals, such as wearing masks or reducing their social activities and direct contact with others with the increase of infectious individuals. After that, the saturation incidence rate has attracted much attention and various nonlinear types of incidence rate are employed. The most commonly used types are Holling type Ⅱ λSI1+αI [10,11,12] and βSI1+αS [13], Monod-Haldane type λSI1+αI2 [14], Beddington-DeAngelis type λSI1+αS+βI [15,16,17] and Crowley-Martin type λSI(1+αS)(1+βI) [18,19].

    In this paper, we consider infectious disease transmission models with saturation incidence rate. The rest of this paper is organized as follows: In Section 2, we establish a two-patch SIS model with saturating contact rate and one-directing population dispersal. We discuss the existence of disease-free equilibrium, boundary equilibrium and endemic equilibrium and prove the global asymptotic stability of the equilibriums in Section 3. In Section 4, we perform simulations to illustrate the results and analyze the effect of the contact rate and population migration on epidemic transmission. Finally, we discuss in Section 5.

    In the two patches, the population is divided into two states: susceptible and infective. Thus we can establish a two-patch SIS model with saturating contact rate and one-directing population dispersal

    {dS1(t)dt=A1d1S1β1S1I11+α1I1mS1+γ1I1,dS2(t)dt=A2d2S2β2S2I21+α2I2+mS1+γ2I2,dI1(t)dt=β1S1I11+α1I1d1I1mI1γ1I1,dI2(t)dt=β2S2I21+α2I2d2I2+mI1γ2I2, (2.1)

    where Si is the number of susceptible population in patch i (i=1,2), Ii is the number of infective population in patch i (i=1,2), Ai is the recruitment into patch i (i=1,2), di is the natural mortality rate, γi is the recovery rate of an infective individual in patch i (i=1,2), m is the migration rate form patch 1 to patch 2. Since the individuals can migrate from the first patch to the second, patch 1 is the source patch and patch 2 is the sink patch. The initial conditions is

    Si(0)>0,  Ii(0)0,  i=1,2,  I1(0)+I2(0)>0. (2.2)

    Denote the population in patch i by Ni. Then Ni=Si+Ii. From system (2.1), the differential equations governing the evolution of N1 and N2 are

    {dN1(t)dt=A1(d1+m)N1,dN2(t)dt=A2d2N2+mN1. (2.3)

    Obviously, system (2.3) has a unique equilibrium (N1,N2)=(A1d1+m,A2d2+mA1d2(d1+m)) which is globally asymptotically stable for (2.3). So (2.1) is equivalent the following system

    {dN1(t)dt=A1(d1+m)N1,dN2(t)dt=A2d2N2+mN1,dI1(t)dt=β1(N1I1)I11+α1I1d1I1mI1γ1I1,dI2(t)dt=β2(N2I2)I21+α2I2d2I2+mI1γ2I2. (2.4)

    Because limtNi(t)Ni (i=1,2), system (2.4) leads to the following limit system

    {dI1(t)dt=β1(N1I1)I11+α1I1d1I1mI1γ1I1,dI2(t)dt=β2(N2I2)I21+α2I2d2I2+mI1γ2I2. (2.5)

    Let Ω={(I1,I2)|0I1N1,0I2N2}. Then Ω is invariant region for system (2.5).

    Define the basic reproduction number in the two patches respectively by R10=β1A1(d1+γ1+m)2=β1d1+γ1+mN1, R20=β2(d2+γ2)N2. The basic reproduction number R10 gives the expected secondary infections in the source patch produced by a primary infected individual in the source patch when the population is supposed to be in the disease-free equilibrium. The basic reproduction number R20 gives the expected secondary infections in the sink patch produced by a primary infected individual in the sink patch when the population is supposed to be in the disease-free equilibrium. Then we have the following theorem.

    Theorem 3.1. For the system (2.5), we have

    (i) The disease-free equilibrium E0:=(0,0) always exists;

    (ii) The boundary equilibrium E1:=(0,β2N2d2γ2(d2+γ2)α2+β2) exists if R20>1;

    (iii) There is a unique epidemic equilibrium E if R10>1.

    Proof. (ⅰ) can be easily proved.

    Let

    β1(N1I1)I11+α1I1d1I1mI1γ1I1=0, (3.1)
    β2(N2I2)I21+α2I2d2I2+mI1γ2I2=0. (3.2)

    From Eq (3.1), we can have I1=0 always satisfies Eq (3.1). When I1=0, from Eq (3.2), we have

    I2=β2N2d2γ2(d2+γ2)α2+β2.

    If R20>1, then I2=β2N2d2γ2(d2+γ2)α2+β2>0. So The boundary equilibrium E1:=(0,β2N2d2γ2(d2+γ2)α2+β2) exists if R20>1. The conclusion (ⅱ) is proved.

    If R10>1, Eq (3.1) has a positive solution I1=β1N1(d1+m+γ1)(d1+m+γ1)α1+β1. Solve Eq (3.2), we have

    I2=(β2N2d2γ2+mα2I1)±(β2N2d2γ2+mα2I1)2+4[(d2+γ2)α2+β2]mI12[(d2+γ2)α2+β2]. (3.3)

    Substituting I1 into Eq (3.3), we have

    I2=(β2N2d2γ2+mα2I1)±(β2N2d2γ2+mα2I1)2+4[(d2+γ2)α2+β2]mI12[(d2+γ2)α2+β2].

    Since I20 is meaning only, we take

    I2=(β2N2d2γ2+mα2I1)+(β2N2d2γ2+mα2I1)2+4[(d2+γ2)α2+β2]mI12[(d2+γ2)α2+β2].

    So if R10>1, there is a unique epidemic equilibrium E=(I1,I2), where I1=β1N1(d1+m+γ1)(d1+m+γ1)α1+β1 and I2=(β2N2d2γ2+mα2I1)+(β2N2d2γ2+mα2I1)2+4[(d2+γ2)α2+β2]mI12[(d2+γ2)α2+β2]. The conclusion (ⅲ) is proved.

    This completes the proof of the theorem.

    From the above analysis, we have the following theorem.

    Theorem 3.2. For the system (2.5), we have

    (i) If R10<1 and R20<1, there is the disease-free equilibrium E0 only;

    (ii) If R10<1 and R20>1, there are the disease-free equilibrium E0 and the boundary equilibrium E1;

    (iii) If R10>1 and R20<1, there are the disease-free equilibrium E0 and the epidemic equilibrium E;

    (iv) If R10>1 and R20>1, there are the disease-free equilibrium E0, the boundary equilibrium E1 and the epidemic equilibrium E.

    Remark 3.1. Define the basic reproduction number R0 of the system (2.5) by the spectral radius of the next generation matrix [20], we have

    R0=ρ(β1d1+γ1+mN10mβ2N2(d2+γ2)(d1+γ1+m)β2(d2+γ2)N2),

    where ρ(A) denotes the spectral radius of a matrix A. So from the above analysis, we know that R0=max{R10,R20}.

    The next, we shall discuss the local stability of the disease-free equilibrium firstly. Then we discuss the global asymptotical stability.

    Theorem 3.3. For the system (2.5), we have

    (i) If R10<1 and R20<1, the disease-free equilibrium E0 is locally asymptotically stable;

    (ii) If R10>1 or R20>1, the disease-free equilibrium E0 is unstable.

    Proof. The linearized system of (2.5) at the equilibrium E0 is

    {dI1(t)dt=(β1N1d1mγ1)I1,dI2(t)dt=(β2N2d2γ2)I2+mI1. (3.4)

    The associated characteristic equation of the linearized system of (3.4) at the equilibrium E0 is

    F(λ)=|λ(β1N1(d1+m+γ1))0mλ(β2N2d2γ2)|=0 (3.5)

    It is easy to see that the two eigenvalues of characteristic Eq (3.5) are

    λ1=β1N1(d1+m+γ1)=(R101)(d1+m+γ1)

    and

    λ2=β2N2d2γ2=(R201)(d2+γ2).

    So, when R10<1 and R20<1, the disease-free equilibrium E0 is locally asymptotically stable; However, if R10>1 or R20>1, the disease-free equilibrium E0 is unstable.

    Remark 3.2. From Theorem 3.3, we know that for the system (2.5), if R0<1 the disease-free equilibrium E0 is locally asymptotically stable; if R0>1, the disease-free equilibrium E0 is unstable.

    Theorem 3.4. For the system (2.5), if R10<1 and R20<1, the disease-free equilibrium E0 is globally asymptotically stable.

    Proof. Since Ii1+αiIiIi for i=1,2, from system (2.5), we can obtain that

    {dI1(t)dt(β1N1d1mγ1)I1,dI2(t)dt(β2N2d2γ2)I2+mI1. (3.6)

    Define an auxiliary linear system using the right hand side of (3.6) as follows

    {dI1(t)dt=(β1N1d1mγ1)I1,dI2(t)dt=(β2N2d2γ2)I2+mI1.

    It can be rewritten as

    (I1I2)=(β1N1d1mγ10mβ2N2d2γ2)(I1I2). (3.7)

    if R10<1 and R20<1, we can solve (3.7) and know that limtI1(t)=0 and limtI2(t)=0. By the comparison principle [21], we can conclude that when R10<1 and R20<1, all non-negative solutions of (2.5) satisfy limtIi(t)=0 for i=1,2. So the disease-free equilibrium E0 is globally asymptotically stable.

    In this subsection, we will discuss the local stability of the boundary equilibrium firstly. Then discuss the global asymptotical stability.

    Theorem 3.5. For the system (2.5), if R10<1 and R20>1, the boundary equilibrium E1 is globally asymptotically stable.

    Proof. The Jacobian matrix at the boundary equilibrium E1 of system (2.5) is

    J=(β1N1d1mγ10m(d2+γ2)(1R20)(β2α2+α22)((d2+γ2)(R201)(d2+γ2)α2+β2)2(1+α2(d2+γ2)(R201)(d2+γ2)α2+β2)2).

    The two eigenvalues of the Jacobian matrix are

    λ1=β1N1(d1+m+γ1)=(R011)(d1+m+γ1)

    and

    λ2=(d2+γ2)(1R02)(β2α2+α22)((d2+γ2)(R21)(d2+γ2)α2+β2)2(1+α2(d2+γ2)(R21)(d2+γ2)α2+β2)2.

    So, when R10<1 and R20>1, λ1<0 and λ2<0. That is the boundary equilibrium E1 is locally asymptotically stable.

    For every (I1(0),I2(0))Ω, assume the solution of the system (2.5) with initial value (I1(0),I2(0)) is (I1(t),I2(t)). Since

    dI1(t)dt=(β1+(d1+γ1)α1+mα1)(β1N1d1mγ1β1+(d1+γ1)α1+mα1I1)I11+α1I1,

    if R10<1, dI1(t)dt<0, then I1(t) is positive and decreasing and limtI1(t)=0. So for sufficiently small positive number ϵ1, there exists a T, such that I1(T)=ϵ1 and I1(t)<ϵ1 when t>T.

    The following, we prove that for any ϵ>0, there exists a T>T such that |I2(T)β2N2d2γ2β2+(d2+γ2)α2|<ϵ. And because E1=(0,β2N2d2γ2β2+(d2+γ2)α2) is locally asymptotically stable, we have E1 is globally asymptotically stable.

    Since

    dI2(t)dt=(β2+(d2+γ2)α2)(β2N2d2γ2β2+(d2+γ2)α2I2)I21+α2I2+mI1,

    if I2(T)<β2N2d2γ2β2+(d2+γ2)α2, then dI2(t)dt>0 for t>T. So I2(t) is increasing and there exists T1 such that |I2(T1)β2N2d2γ2β2+(d2+γ2)α2|<ϵ;

    if I2(T)>β2N2d2γ2β2+(d2+γ2)α2, there are two cases:

    ⅰ) I2(t) is decreasing for t>T. In this case, there exists T2>T, such that |I2(T2)β2N2d2γ2β2+(d2+γ2)α2|<ϵ;

    ⅱ) There exists T1>T, such that dI2(T1)dt>0. That is

    (β2+(d2+γ2)α2)(β2N2d2γ2β2+(d2+γ2)α2I2(T1))I2(T1)1+α2I2(T1)+mI1(T1)>0.

    Since I1(t)<ϵ1 for t>T, we have

    (β2+(d2+γ2)α2)(β2N2d2γ2β2+(d2+γ2)α2I2(T1))I2(T1)1+α2I2(T1)+mϵ1>0.

    Since I2(T)>β2N2d2γ2β2+(d2+γ2)α2, we have

    (β2+(d2+γ2)α2)(β2N2d2γ2β2+(d2+γ2)α2I2(T1))α2+β2+(d2+γ2)α2β2N2d2γ2+mϵ1>0.

    So

    I2(T1)β2N2d2γ2β2+(d2+γ2)α2<α2+β2+(d2+γ2)α2β2N2d2γ2(d2+γ2)α2+β2mϵ1.

    If only ϵ1<(d2+γ2)α2+β2(α2+β2+(d2+γ2)α2β2N2d2γ2)mϵ, then |I2(T2)β2N2d2γ2β2+(d2+γ2)α2|<ϵ. It is completed.

    In this subsection, we will discuss the local stability of the epidemic equilibrium firstly and then discuss the global asymptotical stability.

    Theorem 3.6. For the system (2.5), if R10>1, the epidemic equilibrium E is locally asymptotically stable.

    Proof. The Jacobian matrix at the epidemic equilibrium E of system (2.5) is

    J=(β1I11+α1I1+β1(N1I1)(1+α1I1)2d1mγ10mβ2N22β2I22β2α2I22(d2+γ2)(1+α2I2)2(1+α2I2)2)=((d1+m+γ1)1R10(β1α1d1+m+γ1+α21)I21(1+α1I1)20mβ2N22β2I22β2α2I22(d2+γ2)(1+α2I2)2(1+α2I2)2).

    The two eigenvalues of the Jacobian matrix are

    λ1=(d1+m+γ1)1R10(β1α1d1+m+γ1+α21)I21(1+α1I1)2

    and

    λ2=β2N22β2I22β2α2I22(d2+γ2)(1+α2I2)2(1+α2I2)2.

    It is easy to see that if R10>1, λ1<0. The next, we need only prove the second eigenvalue λ2<0 if R10>1. Since (1+α2I2)2>0, we need only prove β2N22β2I22β2α2I22(d2+γ2)(1+α2I2)2<0. Let

    G(I2)=β2N22β2I22β2α2I22(d2+γ2)(1+α2I2)2.

    Since I2(β2N2d2γ2+mα1I1)(d2+γ2)α2+β2, so

    G(I2)=β2N22β2I22β2α2I22(d2+γ2)(1+α2I2)2β2N2(β2+(d2+γ2)α2)(β2N2d2γ2+mα1I1)(d2+γ2)α2+β2d2γ2=mα1I1<0.

    This completes the proof.

    Theorem 3.7. For the systeml (2.5), if R10>1, the epidemic equilibrium E is globally asymptotically stable.

    Proof. Since E is stable when R10>1, we need only prove E is globally attractive.

    Consider the equation

    dI1(t)dt=I1(β1(N1I1)1+α1I1d1mγ1).

    Let

    f1(I1)=β1(N1I1)1+α1I1d1mγ1.

    Then f1(I1)=β11N1α1(1+α1I1)2<0. So f1(I1) is a monotonic decreasing function for all I1>0. Furthermore, f1(0)>0, f1(N1)<0 and f1(I1)=0 when R10>1. That means if I1(0,I1), f1(I1)>0 and dI1(t)dt>0; if I1(I1,N1), f1(I1)<0 and dI1(t)dt<0. Hence limtI1(t)=I1. By Eq (3.3), limtI2(t)=I2. Thus E is globally asymptotically stable.

    The results about the existence and stability of equilibria are summarized in Table 1.

    Table 1.  Existence and stability of equilibria.
    Conditions E0 E1 E
    R10<1 and R20<1 Yes (GAS) No No
    R10<1 and R20>1 Yes (Unstable) Yes (GAS) No
    R10>1 and R20<1 Yes (Unstable) No Yes (GAS)
    R10>1 and R20>1 Yes (Unstable) Yes (Unstable) Yes (GAS)

     | Show Table
    DownLoad: CSV

    Remark 3.3. From Theorems 3.5 and 3.7, we know that for the system (2.5), if R0>1, the infectious disease is uniformly persistent. However, the infectious disease is not always uniformly persistent in every patch. If R0>1, but R10<1, the disease is uniformly persistent in the sink patch, but is extinct in the source patch. If R10>1, the disease is always uniformly persistent in every patch. This is a different conclusion resulted by the reducible migration matrix.

    Remark 3.4. From Theorem 3.5, in the case that R10<1 and R20>1, the infection does not persist in the source patch but is able to persist in the sink patch. So, in the early stage of the spread of infectious disease, the sink patch should assess the reproduction number R20 reasonably and take control measures timely to prevent the epidemic.

    In this section, we carry on numerical simulations to verify the theoretical conclusions, reveal the influence of the migration rate form patch 1 to patch 2 on the basic reproduction number, the transmission scale and transmission speed, and discuss the influence of the parameters α1 and α2 that measure the inhibitory effect on the basic reproduction number, the transmission scale and transmission speed.

    To numerically illustrate the theoretical results, we need to choose some parameter values (see Table 2).

    Table 2.  Description and values of parameters.
    Parameter Description Value
    A1 the recruitment rate of the population in patch 1 0.018 (Figures 1 and 2)
    0.03 (Figure 3)
    A2 the recruitment rate of the population in patch 2 0.0005 (Figure 1)
    0.004 (Figures 2 and 3)
    β1 the transmission rate in patch 1 0.00001
    β2 the transmission rate in patch 2 0.00005
    α1 the parameter that measure the inhibitory effect in patch 1 0.02
    α2 the parameter that measure the inhibitory effect in patch 1 0.02
    d1 the death rate in patch 1 0.0003
    d2 the death rate in patch 2 0.0003
    γ1 the death rate in patch 1 0.0001
    γ2 the death rate in patch 2 0.0001
    m the migration rate form the patch 1 to the patch 2 0.00005

     | Show Table
    DownLoad: CSV

    We verify the theoretical conclusions firstly. Denote the density of the infective individuals in patch 1 by i1(t)=I1(t)N1. Denote the density of the infective individuals in patch 2 by i2(t)=I2(t)N2. Figure 1 shows the evolution of the density of infective individuals in the two patches when R10=0.8889 and R20=0.7500. As predicted by the analytic calculation, the infectious disease in the two patches will disappear eventually. Figure 2 shows the evolution of the density of infective individuals in the two patches when R10=0.8889 and R20=1.8750. We can see the infectious disease will be endemic in patch 2 and the infectious disease in patch 1 will disappear eventually. Figure 3 shows the evolution of the density of infective individuals in the two patches when R10=1.4815 and R20=2.2917. We can see the infectious disease will be endemic in the two patches. And we can see the infectious disease will be endemic in the two patches if R10>1 from the subfigures (c) and (d) of Figure 4.

    Figure 1.  When R10=0.8889 and R20=0.7500, the evolution of the density of the infective individuals in the two patches.
    Figure 2.  When R10=0.8889 and R20=1.8750, the evolution of the density of the infective individuals in the two patches.
    Figure 3.  When R10=1.4815 and R20=2.2917, the evolution of the density of the infective individuals in the two patches.
    Figure 4.  Phase portraits for (a) R10<1 and R20<1; (b) R10<1 and R20>1; (c) R10>1 and R20<1; (d) R10>1 and R20>1.

    Second, we reveal the influence of the migration rate m on the transmission in Figure 5. With the increasing of m, the density of infective individuals in patch 1 i1 is decreasing, however the density of infective individuals in patch 2 i2 is increasing.

    Figure 5.  When m is increasing, (a) the density of infective individuals in patch 1 i1 is decreasing; (b) the density of infective individuals in patch 2 i2 is increasing.

    Third, we reveal the parameters α1 and α2 on the transmission scale and transmission speed. We can see that when α1 is increasing, the density of infective individuals in patch 1 i1 is decreasing from Figure 6 and When α2 is increasing, the density of infective individuals in patch 2 i1 is decreasing from Figure 7.

    Figure 6.  When α1 is increasing, the density of infective individuals in patch 1 i1 is decreasing.
    Figure 7.  When α2 is increasing, the density of infective individuals in patch 2 i1 is decreasing.

    Many scholars have studied infectious disease transmission with population migration [1,2,3,4,5,6,7], assuming that the migration matrix is irreducible, and found that the propagation dynamics of infectious diseases is determined by the basic reproduction number of the system. When the basic reproduction number is less than 1, the infectious disease eventually becomes extinct; when the basic reproduction number is larger than 1, the infectious disease is epidemic eventually. Since the migration matrix is irreducible, all patches are a connected whole. In all patches, infectious diseases are either extinct or epidemic. That is there is not the phenomenon that infectious diseases are extinct in some patches but epidemic in the others.

    Because the studies about the spread of infectious diseases with reducible migration matrix are rare, in this paper, we proposed a two-patch SIS model with saturating contact rate and one-directing population dispersal, discussed the global asymptotic stability of the disease-free equilibrium, the boundary equilibrium and the endemic equilibrium respectively, and revealed the influence of saturating contact rate and migration rate on basic reproduction number and the transmission scale. We have the following main conclusions:

    1) If R10>1 then the system tends to a global endemic equilibrium in which infected individuals are present in both patches provided initially there were infected individuals in the source patch; If R10<1 and R20>1 then the system converges to an equilibrium with infected individuals only in the sink patch; If R10<1 and R20<1 then the system converges to the disease-free equilibrium.

    2) When migration rate is increasing, the density of infective individuals in the source patch is decreasing; but the density of infective individuals in the sink patch is increasing;

    3) With the increasing of the parameter αi (i=1,2) in saturating contact rate, the density of infective individuals in patch i (i=1,2) is decreasing.

    The similar conclusions can be obtained for the two patch SI model

    {dS1(t)dt=A1d1S1β1S1I11+α1I1mS1,dS2(t)dt=A2d2S2β2S2I21+α2I2+mS1,dI1(t)dt=β1S1I11+α1I1d1I1mI1,dI2(t)dt=β2S2I21+α2I2d2I2+mI1.

    We can generalize the current model in many aspects to increase realism. For instance, the infection rate can be given by β(I)SI. We can give the properties on function β(I) such that β(I) is decreasing and tends to 0 when I tends to infinity. The mortality rates of the susceptible and infected individuals are the same in the current model. In fact, the disease-induced death rate can not be neglected sometimes. So the disease-induced death rate can be considered. It is also significant to consider heterogeneous number of contacts for each individual on complex network. There are many paper on this topic [22,23]. One can investigate the multi-patch epidemic model with reducible migration matrix.

    This work is supported by the National Natural Sciences Foundation of China (Nos.12001501, 12071445, 11571324, 61603351), Shanxi Province Science Foundation for Youths (201901D211216), the Fund for Shanxi '1331KIRT'.

    All authors declare no conflicts of interest in this paper.


    Acknowledgments



    All sources of funding of the study must be disclosed.

    Conflict of interest



    The authors declare there is no conflict of interest.

    [1] Xia C, Dong X, Li H, et al. (2022) Cancer statistics in China and United States, 2022: profiles, trends, and determinants. Chin Med J 135: 584-590. https://doi.org/10.1097/CM9.0000000000002108
    [2] Kanwal M, Ding XJ, Cao Y (2017) Familial risk for lung cancer. Oncol Lett 13: 535-542. https://doi.org/10.3892/ol.2016.5518
    [3] Boloker G, Wang C, Zhang J (2018) Updated statistics of lung and bronchus cancer in United States. J Thorac Dis 10: 1158. https://doi.org/10.21037/jtd.2018.03.15
    [4] Siegel RL, Miller KD, Jemal A (2018) Cancer statistics, 2018. CA Cancer J Clin 68: 7-30. https://doi.org/10.3322/caac.21442
    [5] Planchard D, Popat ST, Kerr K, et al. (2018) Metastatic non-small cell lung cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann Oncol 29: iv192-iv237. https://doi.org/10.3322/caac.21442
    [6] Wadowska K, Bil-Lula I, Trembecki Ł, et al. (2020) Genetic markers in lung cancer diagnosis: a review. Int J Mol Sci 21: 4569. https://doi.org/10.3390/ijms21134569
    [7] Pennell NA, Arcila ME, Gandara DR, et al. (2019) Biomarker testing for patients with advanced non–small cell lung cancer: real-world issues and tough choices. Am Soc Clin Oncol Educ Book 39: 531-542. https://doi.org/10.1200/EDBK_237863
    [8] Khanna P, Blais N, Gaudreau PO, et al. (2017) Immunotherapy comes of age in lung cancer. Clin Lung Cancer 18: 13-22. https://doi.org/10.1016/j.cllc.2016.06.006
    [9] Hansen RN, Zhang Y, Seal B, et al. (2020) Long-term survival trends in patients with unresectable stage iii non-small cell lung cancer receiving chemotherapy and radiation therapy: a seer cancer registry analysis. BMC cancer 20: 1-6. https://doi.org/10.1186/s12885-020-06734-3
    [10] Bradley JD, Hu C, Komaki RR, et al. (2020) Long-term results of nrg oncology rtog 0617: standard-versus high-dose chemoradiotherapy with or without cetuximab for unresectable stage iii non–small-cell lung cancer. J Clin Oncol 38: 706. https://doi.org/10.1200/JCO.19.01162
    [11] Yoon SM, Shaikh T, Hallman M (2017) Therapeutic management options for stage iii non-small cell lung cancer. World J Clin Oncol 8: 1-20. https://doi.org/10.5306/wjco.v8.i1.1
    [12] Wang Y, Liu Z, Xu J, et al. (2022) Heterogeneous network representation learning approach for ethereum identity identification. IEEE Trans Comput Social Syst 10: 890. https://10.1109/TCSS.2022.3164719
    [13] Shi Y, Li L, Yang J, et al. (2023) Center-based transfer feature learning with classifier adaptation for surface defect recognition. Mech Syst Signal Process 188: 110001. https://doi.org/10.1016/j.ymssp.2022.110001
    [14] Shi Y, Li H, Fu X, et al. (2023) Self-powered difunctional sensors based on sliding contact-electrification and tribovoltaic effects for pneumatic monitoring and controlling. Nano Energy 110: 108339. https://doi.org/10.1016/j.nanoen.2023.108339
    [15] Tian C, Xu Z, Wang L, et al. (2023) Arc fault detection using artificial intelligence: challenges and benefits. Math Biosci Eng 20: 12404-12432. https://10.3934/mbe.2023552
    [16] Liu Z, Yang D, Wang Y, et al. (2023) Egnn: Graph structure learning based on evolutionary computation helps more in graph neural networks. Appl Soft Comput 135: 110040. https://doi.org/10.1016/j.asoc.2023.110040
    [17] Wang S, Yang DM, Rong R, et al. (2019) Artificial intelligence in lung cancer pathology image analysis. Cancers 11: 1673. https://doi.org/10.3390/cancers11111673
    [18] Asuntha A, Srinivasan A (2020) Deep learning for lung cancer detection and classification. Multimed Tools Appl 79: 7731-7762. https://doi.org/10.1007/s11042-019-08394-3
    [19] Riquelme D, Akhloufi MA (2020) Deep learning for lung cancer nodules detection and classification in ct scans. Ai 1: 28-67. https://doi.org/10.3390/ai1010003
    [20] Chiu HY, Chao HS, Chen YM (2022) Application of artificial intelligence in lung cancer. Cancers 14: 1370. https://doi.org/10.3390/cancers14061370
    [21] Dlamini Z, Francies FZ, Hull R, et al. (2020) Artificial intelligence (ai) and big data in cancer and precision oncology. Comput Struct Biotechnol J 18: 2300-2311. https://doi.org/10.1016/j.csbj.2020.08.019
    [22] Mann M, Kumar C, Zeng WF, et al. (2021) Artificial intelligence for proteomics and biomarker discovery. Cell Syst 12: 759-770. https://doi.org/10.1016/j.cels.2021.06.006
    [23] Subramanian M, Wojtusciszyn A, Favre L, et al. (2020) Precision medicine in the era of artificial intelligence: implications in chronic disease management. J Transl Med 18: 1-12. https://doi.org/10.1186/s12967-020-02658-5
    [24] Schork NJ (2019) Artificial intelligence and personalized medicine. Precis Med Cancer Ther 178: 265-283. https://doi.org/10.1007/978-3-030-16391-4_11
    [25] Magrabi F, Ammenwerth E, McNair JB, et al. (2019) Artificial intelligence in clinical decision support: challenges for evaluating AI and practical implications. Yearb Med Inform 28: 128-134. https://doi.org/10.1055/s-0039-1677903
    [26] Kim MS, Park HY, Kho BG, et al. (2020) Artificial intelligence and lung cancer treatment decision: agreement with recommendation of multidisciplinary tumor board. Transl Lung Cancer Res 9: 507. https://doi.org/10.21037/tlcr.2020.04.11
    [27] Giordano C, Brennan M, Mohamed B, et al. (2021) Accessing artificial intelligence for clinical decision-making. Frontiers Digit Health 3: 645232. https://doi.org/10.3389/fdgth.2021.645232
    [28] Khanagar SB, Al-Ehaideb A, Vishwanathaiah S, et al. (2021) Scope and performance of artificial intelligence technology in orthodontic diagnosis, treatment planning, and clinical decision-making-a systematic review. J Dent Sci 16: 482-492. https://doi.org/10.1016/j.jds.2020.05.022
    [29] Zhao J, Lv Y (2023) Output-feedback robust tracking control of uncertain systems via adaptive learning. Int J Control Autom Syst 21: 1108-1118. https://doi.org/10.1007/s12555-021-0882-6
    [30] Qi W, Su H (2022) A cybertwin based multimodal network for ecg patterns monitoring using deep learning. IEEE Trans Industr Inform 18: 6663-6670. https://doi.org/10.1109/TII.2022.3159583
    [31] Su H, Qi W, Chen J, et al. (2022) Fuzzy approximation-based task-space control of robot manipulators with remote center of motion constraint. IEEE Trans Fuzzy Syst 30: 1564-1573. https://doi.org/10.1109/TFUZZ.2022.3157075
    [32] Kadir T, Gleeson F (2018) Lung cancer prediction using machine learning and advanced imaging techniques. Transl Lung Cancer Res 7: 304. https://doi.org/10.21037/tlcr.2018.05.15
    [33] Tuncal K, Sekeroglu B, Ozkan C (2020) Lung cancer incidence prediction using machine learning algorithms. J Adv Inform Technol Vol 11: 91-96. https://doi.org/10.12720/jait.11.2.91-96
    [34] Tu SJ, Wang CW, Pan KT, et al. (2018) Localized thin-section CT with radiomics feature extraction and machine learning to classify early-detected pulmonary nodules from lung cancer screening. Phys Med Biol 63: 065005. https://doi.org/10.1088/1361-6560/aaafab
    [35] Li Y, Lu L, Xiao M, et al. (2018) CT slice thickness and convolution kernel affect performance of a radiomic model for predicting EGFR status in non-small cell lung cancer: a preliminary study. Sci Rep 8: 17913. https://doi.org/10.1038/s41598-018-36421-0
    [36] McBee MP, Awan OA, Colucci AT, et al. (2018) Deep learning in radiology. Acad Radiol 25: 1472-1480. https://doi.org/10.1016/j.acra.2018.02.018
    [37] Yasaka K, Abe O (2018) Deep learning and artificial intelligence in radiology: current applications and future directions. PLoS Med 15: e1002707. https://doi.org/10.1371/journal.pmed.1002707
    [38] Hua KL, Hsu CH, Hidayati SC, et al. (2015) Computer-aided classification of lung nodules on computed tomography images via deep learning technique. Onco Targets Ther 8: 2015-2022. https://doi.org/10.2147/OTT.S80733
    [39] Cengil E, Cinar A (2018) A deep learning based approach to lung cancer identification. 2018 International conference on artificial intelligence and data processing (IDAP) 2018: 1-5. https://doi.org/10.1109/IDAP.2018.8620723
    [40] Coudray N, Ocampo PS, Sakellaropoulos T, et al. (2018) Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning. Nat Med 24: 1559-1567. https://doi.org/10.1038/s41591-018-0177-5
    [41] Thawani R, McLane M, Beig N, et al. (2018) Radiomics and radiogenomics in lung cancer: a review for the clinician. Lung cancer 115: 34-41. https://doi.org/10.1016/j.lungcan.2017.10.015
    [42] Su H, Qi W, Schmirander Y, et al. (2022) A human activity-aware shared control solution for medical human–robot interaction. Assem Autom 42: 388-394. https://doi.org/10.1108/AA-12-2021-0174
    [43] Su H, Qi W, Hu Y, et al. (2020) An incremental learning framework for human-like redundancy optimization of anthropomorphic manipulators. IEEE Trans Industr Inform 18: 1864-1872. https://10.1109/TII.2020.3036693
    [44] Nair JKR, Saeed UA, McDougall CC, et al. (2021) Radiogenomic models using machine learning techniques to predict EGFR mutations in non-small cell lung cancer. Can Assoc Radiol J 72: 109-119. https://doi.org/10.1177/0846537119899526
    [45] Singal G, Miller PG, Agarwala V, et al. (2019) Association of patient characteristics and tumor genomics with clinical outcomes among patients with non–small cell lung cancer using a clinicogenomic database. Jama 321: 1391-1399. https://doi.org/10.1001/jama.2019.3241
    [46] Huang S, Yang J, Shen N, et al. (2023) Artificial intelligence in lung cancer diagnosis and prognosis: Current application and future perspective. Semin Cancer Biol 89: 30-37. https://doi.org/10.1016/j.semcancer.2023.01.006
    [47] Petousis P, Winter A, Speier W, et al. (2019) Using sequential decision making to improve lung cancer screening performance. Ieee Access 7: 119403-119419. https://doi.org/10.1109/ACCESS.2019.2935763
    [48] Tortora M, Cordelli E, Sicilia R, et al. (2021) Deep reinforcement learning for fractionated radiotherapy in non-small cell lung carcinoma. Artif Intell Med 119: 102137. https://doi.org/10.1016/j.artmed.2021.102137
    [49] Pei Q, Luo Y, Chen Y, et al. (2022) Artificial intelligence in clinical applications for lung cancer: diagnosis, treatment and prognosis. Clin Chem Lab Med 60: 1974-1983. https://doi.org/10.1515/cclm-2022-0291
    [50] Wang M, Herbst RS, Boshoff C (2021) Toward personalized treatment approaches for non-small-cell lung cancer. Nat Med 27: 1345-1356. https://doi.org/10.1038/s41591-021-01450-2
    [51] Wang L (2022) Deep learning techniques to diagnose lung cancer. Cancers 14: 5569. https://doi.org/10.3390/cancers14225569
    [52] Bi WL, Hosny A, Schabath MB, et al. (2019) Artificial intelligence in cancer imaging: clinical challenges and applications. CA Cancer J Clin 69: 127-157. https://doi.org/10.3322/caac.21552
    [53] Abid MMN, Zia T, Ghafoor M, et al. (2021) Multi-view convolutional recurrent neural networks for lung cancer nodule identification. Neurocomputing 453: 299-311. https://doi.org/10.1016/j.neucom.2020.06.144
    [54] Gu Y, Lu X, Yang L, et al. (2018) Automatic lung nodule detection using a 3D deep convolutional neural network combined with a multi-scale prediction strategy in chest CTs. Comput Biol Med 103: 220-231. https://doi.org/10.1016/j.compbiomed.2018.10.011
    [55] Setio AAA, Ciompi F, Litjens G, et al. (2016) Pulmonary nodule detection in CT images: false positive reduction using multi-view convolutional networks. IEEE Trans Med Imaging 35: 1160-1169. https://doi.org/10.1109/TMI.2016.2536809
    [56] Xie H, Yang D, Sun N, et al. (2019) Automated pulmonary nodule detection in CT images using deep convolutional neural networks. Pattern Recognit 85: 109-119. https://doi.org/10.1016/j.patcog.2018.07.031
    [57] Pezeshk A, Hamidian S, Petrick N, et al. (2018) 3-D convolutional neural networks for automatic detection of pulmonary nodules in chest CT. IEEE J Biomed Health Inform 23: 2080-2090. https://doi.org/10.1109/JBHI.2018.2879449
    [58] Toğaçar M, Ergen B, Cömert Z (2020) Detection of lung cancer on chest CT images using minimum redundancy maximum relevance feature selection method with convolutional neural networks. Biocybern Biomed Eng 40: 23-39. https://doi.org/10.1016/j.bbe.2019.11.004
    [59] Ardila D, Kiraly AP, Bharadwaj S, et al. (2019) End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography. Nat Med 25: 954-961. https://doi.org/10.1038/s41591-019-0447-x
    [60] Teramoto A, Yamada A, Kiriyama Y, et al. (2019) Automated classification of benign and malignant cells from lung cytological images using deep convolutional neural network. Inform Med Unlocked 16: 100205. https://doi.org/10.1016/j.imu.2019.100205
    [61] Onishi Y, Teramoto A, Tsujimoto M, et al. (2019) Automated pulmonary nodule classification in computed tomography images using a deep convolutional neural network trained by generative adversarial networks. Biomed Res Int 2019: 6051939. https://doi.org/10.1155/2019/6051939
    [62] Bharati S, Podder P, Mondal MRH (2020) Hybrid deep learning for detecting lung diseases from X-ray images. Inform Med Unlocked 20: 100391. https://doi.org/10.1016/j.imu.2020.100391
    [63] Ke Q, Zhang J, Wei W, et al. (2019) A neuro-heuristic approach for recognition of lung diseases from X-ray images. Expert Syst Appl 126: 218-232. https://doi.org/10.1016/j.eswa.2019.01.060
    [64] Gordienko Y, Gang P, Hui J, et al. (2019) Deep learning with lung segmentation and bone shadow exclusion techniques for chest X-ray analysis of lung cancer. Advances in Computer Science for Engineering and Education 2019: 638-647. https://doi.org/10.48550/arXiv.1712.07632
    [65] Ausawalaithong W, Thirach A, Marukatat S, et al. (2018) Automatic lung cancer prediction from chest X-ray images using the deep learning approach. 2018 11th biomedical engineering international conference (BMEiCON) 2018: 1-5. https://doi.org/10.1109/BMEiCON.2018.8609997
    [66] Philip B, Jain A, Wojtowicz M, et al. (2023) Current investigative modalities for detecting and staging lung cancers: a comprehensive summary. Indian J Thorac Cardiovasc Surg 39: 42-52. https://doi.org/10.1007/s12055-022-01430-2
    [67] Bhandary A, Prabhu GA, Rajinikanth V, et al. (2020) Deep-learning framework to detect lung abnormality–A study with chest X-Ray and lung CT scan images. Pattern Recogn Lett 129: 271-278. https://doi.org/10.1016/j.patrec.2019.11.013
    [68] Li X, Shen L, Xie X, et al. (2020) Multi-resolution convolutional networks for chest X-ray radiograph based lung nodule detection. Artif Intell Med 103: 101744. https://doi.org/10.1016/j.artmed.2019.101744
    [69] Sim AJ, Kaza E, Singer L, et al. (2020) A review of the role of mri in diagnosis and treatment of early stage lung cancer. Clin Transl Radiat Oncol 24: 16-22. https://doi.org/10.1016/j.ctro.2020.06.002
    [70] Rustam Z, Hartini S, Pratama RY, et al. (2020) Analysis of architecture combining convolutional neural network (cnn) and kernel k-means clustering for lung cancer diagnosis. Int J Adv Sci Eng Inf Technol 10: 1200-1206. https://doi.org/10.18517/ijaseit.10.3.12113
    [71] Isaksson LJ, Raimondi S, Botta F, et al. (2020) Effects of MRI image normalization techniques in prostate cancer radiomics. Phys Med 71: 7-13. https://doi.org/10.1016/j.ejmp.2020.02.007
    [72] Rahman MM, Sazzad TMS, Ferdaus FS (2021) Automated detection of lung cancer using MRI images. 2021 3rd International Conference on Sustainable Technologies for Industry 4.0 (STI) 2021: 1-5. https://doi.org/10.1109/STI53101.2021.9732603
    [73] Wahengbam M, Sriram M (2023) MRI Lung Tumor Segmentation and Classification Using Neural Networks. International Conference on Communication, Electronics and Digital Technology 2023: 605-616. https://doi.org/10.1007/978-981-99-1699-3_42
    [74] Baxi V, Edwards R, Montalto M, et al. (2022) Digital pathology and artificial intelligence in translational medicine and clinical practice. Mod Pathol 35: 23-32. https://doi.org/10.1038/s41379-021-00919-2
    [75] Acs B, Rantalainen M, Hartman J (2020) Artificial intelligence as the next step towards precision pathology. J Intern Med 288: 62-81. https://doi.org/10.1111/joim.13030
    [76] Garg S, Garg S (2020) Prediction of lung and colon cancer through analysis of histopathological images by utilizing Pre-trained CNN models with visualization of class activation and saliency maps. Proceedings of the 2020 3rd Artificial Intelligence and Cloud Computing Conference 2020: 38-45. https://doi.org/10.1145/3442536.3442543
    [77] Wang S, Chen A, Yang L, et al. (2018) Comprehensive analysis of lung cancer pathology images to discover tumor shape and boundary features that predict survival outcome. Sci Rep 8: 10393. https://doi.org/10.1038/s41598-018-27707-4
    [78] Šarić M, Russo M, Stella M, et al. (2019) CNN-based method for lung cancer detection in whole slide histopathology images. 2019 4th International Conference on Smart and Sustainable Technologies (SpliTech) 2019: 1-4. https://doi.org/10.23919/SpliTech.2019.8783041
    [79] Sha L, Osinski BL, Ho IY, et al. (2019) Multi-field-of-view deep learning model predicts nonsmall cell lung cancer programmed death-ligand 1 status from whole-slide hematoxylin and eosin images. J Pathol Inform 10: 24. https://doi.org/10.4103/jpi.jpi_24_19
    [80] Gertych A, Swiderska-Chadaj Z, Ma Z, et al. (2019) Convolutional neural networks can accurately distinguish four histologic growth patterns of lung adenocarcinoma in digital slides. Sci Rep 9: 1483. https://doi.org/10.1038/s41598-018-37638-9
    [81] Yu KH, Wang F, Berry GJ, et al. (2020) Classifying non-small cell lung cancer types and transcriptomic subtypes using convolutional neural networks. J Am Med Inform Assoc 27: 757-769. https://doi.org/10.1093/jamia/ocz230
    [82] Tiwari A, Trivedi R, Lin SY (2022) Tumor microenvironment: barrier or opportunity towards effective cancer therapy. J Biomed Sci 29: 1-27. https://doi.org/10.1186/s12929-022-00866-3
    [83] Saltz J, Gupta R, Hou L, et al. (2018) Spatial organization and molecular correlation of tumor-infiltrating lymphocytes using deep learning on pathology images. Cell Rep 23: 181-193. https://doi.org/10.1016/j.celrep.2018.03.086
    [84] Yi F, Yang L, Wang S, et al. (2018) Microvessel prediction in H&E Stained Pathology Images using fully convolutional neural networks. BMC bioinformatics 19: 1-9. https://doi.org/10.1186/s12859-018-2055-z
    [85] Wang S, Wang T, Yang L, et al. (2019) ConvPath: A software tool for lung adenocarcinoma digital pathological image analysis aided by a convolutional neural network. EBioMedicine 50: 103-110. https://doi.org/10.1016/j.ebiom.2019.10.033
    [86] Rączkowski Ł, Paśnik I, Kukiełka M, et al. (2022) Deep learning-based tumor microenvironment segmentation is predictive of tumor mutations and patient survival in non-small-cell lung cancer. BMC cancer 22: 1001. https://doi.org/10.1186/s12885-022-10081-w
    [87] Nooreldeen R, Bach H (2021) Current and future development in lung cancer diagnosis. Int J Mol Sci 22: 8661. https://doi.org/10.3390/ijms22168661
    [88] Wang S, Zimmermann S, Parikh K, et al. (2019) Current diagnosis and management of small-cell lung cancer. Mayo Clin Proc 94: 1599-1622. https://doi.org/10.1016/j.mayocp.2019.01.034
    [89] Li B, Zhu L, Lu C, et al. (2021) circNDUFB2 inhibits non-small cell lung cancer progression via destabilizing IGF2BPs and activating anti-tumor immunity. Nat Commun 12: 295. https://doi.org/10.1038/s41467-020-20527-z
    [90] Xu Y, Wang Q, Xie J, et al. (2021) The predictive value of clinical and molecular characteristics or immunotherapy in non-small cell lung cancer: a meta-analysis of randomized controlled trials. Front Oncol 11: 732214. https://doi.org/10.3389/fonc.2021.732214
    [91] Xiao Y, Wu J, Lin Z, et al. (2018) A deep learning-based multi-model ensemble method for cancer prediction. Comput Methods Programs Biomed 153: 1-9. https://doi.org/10.1016/j.cmpb.2017.09.005
    [92] Seijo LM, Peled N, Ajona D, et al. (2019) Biomarkers in lung cancer screening: achievements, promises, and challenges. J Thorac Oncol 14: 343-357. https://doi.org/10.1016/j.jtho.2018.11.023
    [93] Yuan F, Lu L, Zou Q (2020) Analysis of gene expression profiles of lung cancer subtypes with machine learning algorithms. Biochim Biophys Acta-Mol Basis Dis 1866: 165822. https://doi.org/10.1016/j.bbadis.2020.165822
    [94] Matsubara T, Ochiai T, Hayashida M, et al. (2019) Convolutional neural network approach to lung cancer classification integrating protein interaction network and gene expression profiles. J Bioinf Comput Biol 17: 1940007. https://doi.org/10.1142/S0219720019400079
    [95] Wiesweg M, Mairinger F, Reis H, et al. (2020) Machine learning reveals a PD-L1–independent prediction of response to immunotherapy of non-small cell lung cancer by gene expression context. Eur J Cancer 140: 76-85. https://doi.org/10.1016/j.ejca.2020.09.015
    [96] Khalifa NEM, Taha MHN, Ali DE, et al. (2020) Artificial intelligence technique for gene expression by tumor RNA-Seq data: a novel optimized deep learning approach. IEEE Access 8: 22874-22883. https://doi.org/10.1109/ACCESS.2020.2970210
    [97] Wang W, Ding M, Duan X, et al. (2019) Diagnostic value of plasma microRNAs for lung cancer using support vector machine model. J Cancer 10: 5090. https://doi.org/10.7150/jca.30528
    [98] Selvanambi R, Natarajan J, Karuppiah M, et al. (2020) Lung cancer prediction using higher-order recurrent neural network based on glowworm swarm optimization. Neural Comput Appl 32: 4373-4386. https://doi.org/10.1007/s00521-018-3824-3
    [99] Banaganapalli B, Mallah B, Alghamdi KS, et al. (2022) Integrative weighted molecular network construction from transcriptomics and genome wide association data to identify shared genetic biomarkers for COPD and lung cancer. Plos one 17: e0274629. https://doi.org/10.1371/journal.pone.0274629
    [100] Tanaka I, Furukawa T, Morise M (2021) The current issues and future perspective of artificial intelligence for developing new treatment strategy in non-small cell lung cancer: Harmonization of molecular cancer biology and artificial intelligence. Cancer Cell Int 21: 1-14. https://doi.org/10.1186/s12935-021-02165-7
    [101] Choi Y, Qu J, Wu S, et al. (2020) Improving lung cancer risk stratification leveraging whole transcriptome RNA sequencing and machine learning across multiple cohorts. BMC Med Genomics 13: 1-15. https://doi.org/10.1186/s12920-020-00782-1
    [102] Khan A, Lee B (2021) Gene transformer: Transformers for the gene expression-based classification of lung cancer subtypes. arXiv preprint arXiv: 2108.11833. https://doi.org/10.48550/arXiv.2108.1183
    [103] Oka M, Xu L, Suzuki T, et al. (2021) Aberrant splicing isoforms detected by full-length transcriptome sequencing as transcripts of potential neoantigens in non-small cell lung cancer. Genome Biol 22: 1-30. https://doi.org/10.1186/s13059-020-02240-8
    [104] Martínez-Ruiz C, Black JRM, Puttick C, et al. (2023) Genomic–transcriptomic evolution in lung cancer and metastasis. Nature : 1-10. https://doi.org/10.1038/s41586-023-05706-4
    [105] Hofman P, Heeke S, Alix-Panabières C, et al. (2019) Liquid biopsy in the era of immuno-oncology: is it ready for prime-time use for cancer patients?. Ann Oncol 30: 1448-1459. https://doi.org/10.1093/annonc/mdz196
    [106] Ilie M, Benzaquen J, Hofman V, et al. (2017) Immunotherapy in non-small cell lung cancer: biological principles and future opportunities. Curr Mol Med 17: 527-540. https://doi.org/10.2174/1566524018666180222114038
    [107] Pantel K, Alix-Panabières C (2019) Liquid biopsy and minimal residual disease—latest advances and implications for cure. Nat Rev Clin Oncol 16: 409-424. https://doi.org/10.1038/s41571-019-0187-3
    [108] He X, Folkman L, Borgwardt K (2018) Kernelized rank learning for personalized drug recommendation. Bioinformatics 34: 2808-2816. https://doi.org/10.1093/bioinformatics/bty132
    [109] Luo S, Xu J, Jiang Z, et al. (2020) Artificial intelligence-based collaborative filtering method with ensemble learning for personalized lung cancer medicine without genetic sequencing. Pharmacol Res 160: 105037. https://doi.org/10.1016/j.phrs.2020.105037
    [110] Ciccolini J, Benzekry S, Barlesi F (2020) Deciphering the response and resistance to immune-checkpoint inhibitors in lung cancer with artificial intelligence-based analysis: when PIONeeR meets QUANTIC. Br J Cancer 123: 337-338. https://doi.org/10.1038/s41416-020-0918-3
    [111] Mu W, Jiang L, Zhang JY, et al. (2020) Non-invasive decision support for NSCLC treatment using PET/CT radiomics. Nat Commun 11: 5228. https://doi.org/10.1038/s41467-020-19116-x
    [112] Chang L, Wu J, Moustafa N, et al. (2021) AI-driven synthetic biology for non-small cell lung cancer drug effectiveness-cost analysis in intelligent assisted medical systems. IEEE J Biomed Health Inform 26: 5055-5066. https://doi.org/10.1109/JBHI.2021.3133455
    [113] Wang S, Yu H, Gan Y, et al. (2022) Mining whole-lung information by artificial intelligence for predicting EGFR genotype and targeted therapy response in lung cancer: a multicohort study. Lancet Digit Health 4: e309-e319. https://doi.org/10.1016/S2589-7500(22)00024-3
    [114] Khorrami M, Khunger M, Zagouras A, et al. (2019) Combination of peri-and intratumoral radiomic features on baseline CT scans predicts response to chemotherapy in lung adenocarcinoma. Radiol Artif Intell 1: 180012. https://doi.org/10.1148/ryai.2019180012
    [115] Song P, Cui X, Bai L, et al. (2019) Molecular characterization of clinical responses to PD-1/PD-L1 inhibitors in non-small cell lung cancer: Predictive value of multidimensional immunomarker detection for the efficacy of PD-1 inhibitors in Chinese patients. Thorac Cancer 10: 1303-1309. https://doi.org/10.1111/1759-7714.13078
    [116] Yu KH, Berry GJ, Rubin DL, et al. (2017) Association of omics features with histopathology patterns in lung adenocarcinoma. Cell Syst 5: 620-627. https://doi.org/10.1016/j.cels.2017.10.014
    [117] Lee TY, Huang KY, Chuang CH, et al. (2020) Incorporating deep learning and multi-omics autoencoding for analysis of lung adenocarcinoma prognostication. Comput Biol Chem 87: 107277. https://doi.org/10.1016/j.compbiolchem.2020.107277
    [118] She Y, Jin Z, Wu J, et al. (2020) Development and validation of a deep learning model for non–small cell lung cancer survival. JAMA Netw Open 3: e205842-e205842. https://doi.org/10.1001/jamanetworkopen.2020.5842
    [119] Emaminejad N, Qian W, Guan Y, et al. (2015) Fusion of quantitative image and genomic biomarkers to improve prognosis assessment of early stage lung cancer patients. IEEE Trans Biomed Eng 63: 1034-1043. https://doi.org/10.1109/TBME.2015.2477688
    [120] Liu WT, Wang Y, Zhang J, et al. (2018) A novel strategy of integrated microarray analysis identifies CENPA, CDK1 and CDC20 as a cluster of diagnostic biomarkers in lung adenocarcinoma. Cancer Lett 425: 43-53. https://doi.org/10.1016/j.canlet.2018.03.043
    [121] Malik V, Dutta S, Kalakoti Y, et al. (2019) Multi-omics Integration based Predictive Model for Survival Prediction of Lung Adenocarcinaoma. 2019 Grace Hopper Celebration India (GHCI) : 1-5. https://doi.org/10.1109/GHCI47972.2019.9071831
    [122] Wang X, Duan H, Li X, et al. (2020) A prognostic analysis method for non-small cell lung cancer based on the computed tomography radiomics. Phys Med Biol 65: 045006. https://doi.org/10.1088/1361-6560/ab6e51
    [123] Johnson M, Albizri A, Simsek S (2022) Artificial intelligence in healthcare operations to enhance treatment outcomes: a framework to predict lung cancer prognosis. Ann Oper Res 308: 275--305. https://doi.org/10.1007/s10479-020-03872-6
    [124] Ekins S, Puhl AC, Zorn KM, et al. (2019) Exploiting machine learning for end-to-end drug discovery and development. Nat Mater 18: 435-441. https://doi.org/10.1038/s41563-019-0338-z
    [125] Chandak T, Mayginnes JP, Mayes H, et al. (2020) Using machine learning to improve ensemble docking for drug discovery. Proteins 88: 1263-1270. https://publons.com/publon/10.1002/prot.25899
    [126] Houssein EH, Hosney ME, Oliva D, et al. (2020) A novel hybrid Harris hawks optimization and support vector machines for drug design and discovery. Comput Chem Eng 133: 106656. https://doi.org/10.1016/j.compchemeng.2019.106656
    [127] Zhao L, Ciallella HL, Aleksunes LM, et al. (2020) Advancing computer-aided drug discovery (CADD) by big data and data-driven machine learning modeling. Drug Discov Today 25: 1624-1638. https://doi.org/10.1016/j.drudis.2020.07.005
    [128] Zhavoronkov A, Ivanenkov YA, Aliper A, et al. (2019) Deep learning enables rapid identification of potent DDR1 kinase inhibitors. Nat Biotechnol 37: 1038-1040. https://doi.org/10.1038/s41587-019-0224-x
    [129] Bhuvaneshwari S, Sankaranarayanan K (2019) Identification of potential CRAC channel inhibitors: Pharmacophore mapping, 3D-QSAR modelling, and molecular docking approach. SAR QSAR Environ Res 30: 81-108. https://doi.org/10.1080/1062936X.2019.1566172
    [130] He G, Gong B, Li J, et al. (2018) An improved receptor-based pharmacophore generation algorithm guided by atomic chemical characteristics and hybridization types. Front Pharmacol 9: 1463. https://doi.org/10.3389/fphar.2018.01463
    [131] Yang H, Wierzbicki M, Du Bois DR, et al. (2018) X-ray crystallographic structure of a teixobactin derivative reveals amyloid-like assembly. J Am Chem Soc 140: 14028-14032. https://doi.org/10.1021/jacs.8b07709
    [132] Trebeschi S, Drago SG, Birkbak NJ, et al. (2019) Predicting response to cancer immunotherapy using noninvasive radiomic biomarkers. Ann Oncol 30: 998-1004. https://doi.org/10.1093/annonc/mdz108
    [133] Wang Q, Xu J, Li Y, et al. (2018) Identification of a novel protein arginine methyltransferase 5 inhibitor in non-small cell lung cancer by structure-based virtual screening. Front Pharmacol 9: 173. https://doi.org/10.3389/fphar.2018.00173
    [134] Haredi Abdelmonsef A (2019) Computer-aided identification of lung cancer inhibitors through homology modeling and virtual screening. Egypt J Med Hum Genet 20: 1-14. https://doi.org/10.1186/s43042-019-0008-3
    [135] Shaik NA, Al-Kreathy HM, Ajabnoor GM, et al. (2019) Molecular designing, virtual screening and docking study of novel curcumin analogue as mutation (S769L and K846R) selective inhibitor for EGFR. Saudi J Biol Sci 26: 439-448. https://doi.org/10.1016/j.sjbs.2018.05.026
    [136] Udhwani T, Mukherjee S, Sharma K, et al. (2019) Design of PD-L1 inhibitors for lung cancer. Bioinformation 15: 139. https://doi.org/10.6026/97320630015139
    [137] Patel HM, Ahmad I, Pawara R, et al. (2021) In silico search of triple mutant T790M/C797S allosteric inhibitors to conquer acquired resistance problem in non-small cell lung cancer (NSCLC): a combined approach of structure-based virtual screening and molecular dynamics simulation. J Biomol Struct Dyn 39: 1491-1505. https://doi.org/10.1080/07391102.2020.1734092
    [138] Su H, Mariani A, Ovur SE, et al. (2021) Toward teaching by demonstration for robot-assisted minimally invasive surgery. IEEE Trans Autom Sci Eng 18: 484-494. https://doi.org/10.1109/TASE.2020.3045655
    [139] Qi W, Aliverti A (2019) A multimodal wearable system for continuous and real-time breathing pattern monitoring during daily activity. IEEE J Biomed Health Inf 24: 2199-2207. https://doi.org/10.1109/JBHI.2019.2963048
    [140] Khan B, Fatima H, Qureshi A, et al. (2023) Drawbacks of artificial intelligence and their potential solutions in the healthcare sector. Biomed Mater Devices 2023: 1-8. https://doi.org/10.1007/s44174-023-00063-2
    [141] Hanif A, Zhang X, Wood S (2021) A survey on explainable artificial intelligence techniques and challenges. 2021 IEEE 25th international enterprise distributed object computing workshop (EDOCW) 2021: 81-89. https://doi.org/10.1109/EDOCW52865.2021.00036
    [142] Dicuonzo G, Donofrio F, Fusco A, et al. (2023) Healthcare system: Moving forward with artificial intelligence. Technovation 120: 102510. https://doi.org/10.1016/j.technovation.2022.102510
    [143] McLennan S, Fiske A, Tigard D, et al. (2022) Embedded ethics: a proposal for integrating ethics into the development of medical AI. BMC Med Ethics 23: 6. https://doi.org/10.1186/s12910-022-00746-3
    [144] Steffens D, Pocovi NC, Bartyn J, et al. (2023) Feasibility, reliability, and safety of remote five times sit to stand test in patients with gastrointestinal cancer. Cancers 15: 2434. https://doi.org/10.3390/cancers15092434
    [145] Askin S, Burkhalter D, Calado G, et al. (2023) Artificial Intelligence Applied to clinical trials: opportunities and challenges. Health Technol 13: 203-213. https://doi.org/10.1007/s12553-023-00738-2
    [146] Albahri AS, Duhaim AM, Fadhel MA, et al. (2023) A systematic review of trustworthy and explainable artificial intelligence in healthcare: Assessment of quality, bias risk, and data fusion. Inf Fusion 96: 156-191. https://doi.org/10.1016/j.inffus.2023.03.008
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