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Review Topical Sections

Non-small cell lung cancer: epidemiology, screening, diagnosis, and treatment

  • Received: 16 January 2022 Revised: 20 May 2022 Accepted: 22 May 2022 Published: 26 May 2022
  • Lung cancer is the most common cancer worldwide and is also one of the leading causes of cancer related deaths. The 5-year survival rate depends largely on stage at diagnosis. Multiple large randomized controlled trials have demonstrated the clinical utility of lung cancer screening. Low dose computed tomography is recommended for lung cancer screening in several guidelines. Lung cancer is often diagnosed at more advanced stages owing to the nonspecific symptoms with which it presents. Diagnosis relies primarily upon imaging and biopsy. Diagnosis and staging should be performed simultaneously, if possible, by performing a biopsy at the site that will confer the highest stage. Sufficient material should be obtained to allow for molecular testing. Liquid biopsy may have a complementary role in detecting actionable mutations in non-small cell lung cancer. Treatment is predicated upon stage and may involve surgery, targeted chemotherapy, radiation therapy, immunotherapy, or some combination thereof.

    Citation: Anuj A. Shukla, Shreya Podder, Sana R. Chaudry, Bryan S. Benn, Jonathan S. Kurman. Non-small cell lung cancer: epidemiology, screening, diagnosis, and treatment[J]. AIMS Medical Science, 2022, 9(2): 348-361. doi: 10.3934/medsci.2022016

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  • Lung cancer is the most common cancer worldwide and is also one of the leading causes of cancer related deaths. The 5-year survival rate depends largely on stage at diagnosis. Multiple large randomized controlled trials have demonstrated the clinical utility of lung cancer screening. Low dose computed tomography is recommended for lung cancer screening in several guidelines. Lung cancer is often diagnosed at more advanced stages owing to the nonspecific symptoms with which it presents. Diagnosis relies primarily upon imaging and biopsy. Diagnosis and staging should be performed simultaneously, if possible, by performing a biopsy at the site that will confer the highest stage. Sufficient material should be obtained to allow for molecular testing. Liquid biopsy may have a complementary role in detecting actionable mutations in non-small cell lung cancer. Treatment is predicated upon stage and may involve surgery, targeted chemotherapy, radiation therapy, immunotherapy, or some combination thereof.


    Abbreviations

    CISNET:

    Cancer intervention and surveillance modeling network; 

    CT:

    Computed tomography; 

    ctDNA:

    Circulating tumor DNA; 

    EBUS:

    Endobronchial ultrasound; 

    EBUS-NA:

    Endobronchial ultrasound-guided needle aspiration; 

    EGFR:

    Epidermal growth factor receptor; 

    ESTS:

    European Society of Thoracic Surgeons; 

    EUS-NA:

    Endoscopic ultrasound-guided needle aspiration; 

    IASLC:

    International Association for the Study of Lung Cancer; 

    LDCT:

    Low-dose computed tomography; 

    NCCN:

    National Comprehensive Cancer Network; 

    NELSON:

    Dutch-Belgian Randomized Lung Cancer Screening trial; 

    NILE:

    Non-invasive versus invasive evaluation; 

    NLST:

    National Lung Cancer Screen Trial; 

    NSCLC:

    Non-small cell lung cancer; 

    PD-L1:

    Programmed cell death ligand; 

    PET:

    Positron emission tomography; 

    RCTs:

    Randomized controlled trials; 

    RT:

    Radiation therapy; 

    SEER:

    Surveillance, Epidemiology and End Results; 

    TBNA:

    Transbronchial needle aspiration; 

    TKI:

    Tyrosine kinase inhibitor; 

    TNM:

    Tumor-node-metastasis; 

    USPTF:

    United States Preventative Service Task Force; 

    WHO:

    World Health Organization

    For a convex function σ:IRR on I with ν1,ν2I and ν1<ν2, the Hermite-Hadamard inequality is defined by [1]:

    σ(ν1+ν22)1ν2ν1ν2ν1σ(η)dησ(ν1)+σ(ν2)2. (1.1)

    The Hermite-Hadamard integral inequality (1.1) is one of the most famous and commonly used inequalities. The recently published papers [2,3,4] are focused on extending and generalizing the convexity and Hermite-Hadamard inequality.

    The situation of the fractional calculus (integrals and derivatives) has won vast popularity and significance throughout the previous five decades or so, due generally to its demonstrated applications in numerous seemingly numerous and great fields of science and engineering [5,6,7].

    Now, we recall the definitions of Riemann-Liouville fractional integrals.

    Definition 1.1 ([5,6,7]). Let σL1[ν1,ν2]. The Riemann-Liouville integrals Jϑν1+σ and Jϑν2σ of order ϑ>0 with ν10 are defined by

    Jϑν1+σ(x)=1Γ(ϑ)xν1(xη)ϑ1σ(η)dη,   ν1<x (1.2)

    and

    Jϑν2σ(x)=1Γ(ϑ)ν2x(ηx)ϑ1σ(η)dη,  x<ν2, (1.3)

    respectively. Here Γ(ϑ) is the well-known Gamma function and J0ν1+σ(x)=J0ν2σ(x)=σ(x).

    With a huge application of fractional integration and Hermite-Hadamard inequality, many researchers in the field of fractional calculus extended their research to the Hermite-Hadamard inequality, including fractional integration rather than ordinary integration; for example see [8,9,10,11,12,13,14,15,16,17,18,19,20,21].

    In this paper, we consider the integral inequality of Hermite-Hadamard-Mercer type that relies on the Hermite-Hadamard and Jensen-Mercer inequalities. For this purpose, we recall the Jensen-Mercer inequality: Let 0<x1x2xn and μ=(μ1,μ2,,μn) nonnegative weights such that ni=1μi=1. Then, the Jensen inequality [22,23] is as follows, for a convex function σ on the interval [ν1,ν2], we have

    σ(ni=1μixi)ni=1μiσ(xi), (1.4)

    where for all xi[ν1,ν2] and μi[0,1], (i=¯1,n).

    Theorem 1.1 ([2,23]). If σ is convex function on [ν1,ν2], then

    σ(ν1+ν2ni=1μixi)σ(ν1)+σ(ν2)ni=1μiσ(xi), (1.5)

    for each xi[ν1,ν2] and μi[0,1], (i=¯1,n) with ni=1μi=1. For some results related with Jensen-Mercer inequality, see [24,25,26].

    In view of above indices, we establish new integral inequalities of Hermite-Hadamard-Mercer type for convex functions via the Riemann-Liouville fractional integrals in the current project. Particularly, we see that our results can cover the previous researches.

    Theorem 2.1. For a convex function σ:[ν1,ν2]RR with x,y[ν1,ν2], we have:

    σ(ν1+ν2x+y2)2ϑ1Γ(ϑ+1)(yx)ϑ[Jϑ(ν1+ν2y)+σ(ν1+ν2x+y2)+Jϑ(ν1+ν2x)σ(ν1+ν2x+y2)]σ(ν1)+σ(ν2)σ(x)+σ(y)2. (2.1)

    Proof. By using the convexity of σ, we have

    σ(ν1+ν2u+v2)12[σ(ν1+ν2u)+σ(ν1+ν2v)], (2.2)

    and above with u=1η2x+1+η2y, v=1+η2x+1η2y, where x,y[ν1,ν2] and η[0,1], leads to

    σ(ν1+ν2x+y2)12[σ(ν1+ν2(1η2x+1+η2y))+σ(ν1+ν2(1+η2x+1η2y))]. (2.3)

    Multiplying both sides of (2.3) by ηϑ1 and then integrating with respect to η over [0,1], we get

    1ϑσ(ν1+ν2x+y2)12[10ηϑ1σ(ν1+ν2(1η2x+1+η2y))dη+10ηϑ1σ(ν1+ν2(1+η2x+1η2y))dη]=12[2ϑ(yx)ϑν1+ν2x+y2ν1+ν2y((ν1+ν2x+y2)w)ϑ1σ(w)dw+2ϑ(yx)ϑν1+ν2xν1+ν2x+y2(w(ν1+ν2x+y2))ϑ1σ(w)dw]=2ϑ1Γ(ϑ)(yx)ϑ[Jϑ(ν1+ν2y)+σ(ν1+ν2x+y2)+Jϑ(ν1+ν2x)σ(ν1+ν2x+y2)],

    and thus the proof of first inequality in (2.1) is completed.

    On the other hand, we have by using the Jensen-Mercer inequality:

    σ(ν1+ν2(1η2x+1+η2y))σ(ν1)+σ(ν2)(1η2σ(x)+1+η2σ(y)) (2.4)
    σ(ν1+ν2(1+η2x+1η2y))σ(ν1)+σ(ν2)(1+η2σ(x)+1η2σ(y)). (2.5)

    Adding inequalities (2.4) and (2.5) to get

    σ(ν1+ν2(1η2x+1+η2y))+σ(ν1+ν2(1+η2x+1η2y))2[σ(ν1)+σ(ν2)][σ(x)+σ(y)]. (2.6)

    Multiplying both sides of (2.6) by ηϑ1 and then integrating with respect to η over [0,1] to obtain

    10ηϑ1σ(ν1+ν2(1η2x+1+η2y))dη+10ηϑ1σ(ν1+ν2(1+η2x+1η2y))dη2ϑ[σ(ν1)+σ(ν2)]1ϑ[σ(x)+σ(y)].

    By making use of change of variables and then multiplying by ϑ2, we get the second inequality in (2.1).

    Remark 2.1. If we choose ϑ=1, x=ν1 and y=ν2 in Theorem 2.1, then the inequality (2.1) reduces to (1.1).

    Corollary 2.1. Theorem 2.1 with

    ϑ=1 becomes [24, Theorem 2.1].

    x=ν1 and y=ν2 becomes:

    σ(ν1+ν22)2ϑ1Γ(ϑ+1)(ν2ν1)ϑ[Jϑν1+σ(ν1+ν22)+Jϑν2σ(ν1+ν22)]σ(ν1)+σ(ν2)2,

    which is obtained by Mohammed and Brevik in [10].

    The following Lemma linked with the left inequality of (2.1) is useful to obtain our next results.

    Lemma 2.1. Let σ:[ν1,ν2]RR be a differentiable function on (ν1,ν2) and σL[ν1,ν2] with ν1ν2 and x,y[ν1,ν2]. Then, we have:

    2ϑ1Γ(ϑ+1)(yx)ϑ[Jϑ(ν1+ν2y)+σ(ν1+ν2x+y2)+Jϑ(ν1+ν2x)σ(ν1+ν2x+y2)]σ(ν1+ν2x+y2)=(yx)410ηϑ[σ(ν1+ν2(1η2x+1+η2y))σ(ν1+ν2(1+η2x+1η2y))]dη. (2.7)

    Proof. From right hand side of (2.7), we set

    ϖ1ϖ2:=10ηϑ[σ(ν1+ν2(1η2x+1+η2y))σ(ν1+ν2(1+η2x+1η2y))]dη=10ηϑσ(ν1+ν2(1η2x+1+η2y))dη10ηϑσ(ν1+ν2(1+η2x+1η2y))dη. (2.8)

    By integrating by parts with w=ν1+ν2(1η2x+1+η2y), we can deduce:

    ϖ1=2(yx)σ(ν1+ν2y)+2ϑ(yx)10ηϑ1σ(ν1+ν2(1η2x+1+η2y))dη=2(yx)σ(ν1+ν2y)+2ϑ+1ϑ(yx)ϑ+1ν1+ν2x+y2ν1+ν2yσ((ν1+ν2x+y2)w)ϑ1σ(w)dw=2(yx)σ(ν1+ν2y)+2ϑ+1Γ(ϑ+1)(yx)ϑ+1Jϑ(ν1+ν2y)+σ(ν1+ν2x+y2).

    Similarly, we can deduce:

    ϖ2=2yxσ(ν1+ν2x)2ϑ+1Γ(ϑ+1)(yx)ϑ+1Jϑ(ν1+ν2x)σ(ν1+ν2x+y2).

    By substituting ϖ1 and ϖ2 in (2.8) and then multiplying by (yx)4, we obtain required identity (2.7).

    Corollary 2.2. Lemma 2.1 with

    ϑ=1 becomes:

    1yxν1+ν2xν1+ν2yσ(w)dwσ(ν1+ν2x+y2)=(yx)410η[σ(ν1+ν2(1η2x+1+η2y))σ(ν1+ν2(1+η2x+1η2y))]dη.

    ϑ=1, x=ν1 and y=ν2 becomes:

    1ν2ν1ν2ν1σ(w)dwσ(ν1+ν22)=(ν2ν1)410η[σ(ν1+ν2(1η2ν1+1+η2ν2))σ(ν1+ν2(1+η2ν1+1η2ν2))]dη.

    x=ν1 and y=ν2 becomes:

    2ϑ1Γ(ϑ+1)(ν2ν1)ϑ[Jϑν1+σ(ν1+ν22)+Jϑν2σ(ν1+ν22)]σ(ν1+ν22)=(ν2ν1)410ηϑ[σ(ν1+ν2(1η2ν1+1+η2ν2))σ(ν1+ν2(1+η2ν1+1η2ν2))]dη.

    Theorem 2.2. Let σ:[ν1,ν2]RR be a differentiable function on (ν1,ν2) and |σ| is convex on [ν1,ν2] with ν1ν2 and x,y[ν1,ν2]. Then, we have:

    |2ϑ1Γ(ϑ+1)(yx)ϑ[Jϑ(ν1+ν2y)+σ(ν1+ν2x+y2)+Jϑ(ν1+ν2x)σ(ν1+ν2x+y2)]σ(ν1+ν2x+y2)|(yx)2(1+ϑ)[|σ(ν1)|+|σ(ν2)||σ(x)|+|σ(y)|2]. (2.9)

    Proof. By taking modulus of identity (2.7), we get

    |2ϑ1Γ(ϑ+1)(yx)ϑ[Jϑ(ν1+ν2y)+σ(ν1+ν2x+y2)+Jϑ(ν1+ν2x)σ(ν1+ν2x+y2)]σ(ν1+ν2x+y2)|(yx)4[10ηϑ|σ(ν1+ν2(1η2x+1+η2y))|dη+10ηϑ|σ(ν1+ν2(1+η2x+1η2y))|dη].

    Then, by applying the convexity of |σ| and the Jensen-Mercer inequality on above inequality, we get

    |2ϑ1Γ(ϑ+1)(yx)ϑ[Jϑ(ν1+ν2y)+σ(ν1+ν2x+y2)+Jϑ(ν1+ν2x)σ(ν1+ν2x+y2)]σ(ν1+ν2x+y2)|(yx)4[10ηϑ[|σ(ν1)|+|σ(ν2)|(1+η2|σ(x)|+1η2)|σ(y)|]dη+10ηϑ[|σ(ν1)|+|σ(ν2)|(1η2|σ(x)|+1+η2)|σ(y)|]dη]=(yx)2(1+ϑ)[|σ(ν1)|+|σ(ν2)||σ(x)|+|σ(y)|2],

    which completes the proof of Theorem 2.2.

    Corollary 2.3. Theorem 2.2 with

    ϑ=1 becomes:

    |1yxν1+ν2xν1+ν2yσ(w)dwσ(ν1+ν2x+y2)|(yx)4[|σ(ν1)|+|σ(ν2)||σ(x)|+|σ(y)|2].

    ϑ=1, x=ν1 and y=ν2 becomes [27, Theorem 2.2].

    x=ν1 and y=ν2 becomes:

    |1ν2ν1ν2ν1σ(w)dwσ(ν1+ν22)|(ν2ν1)4[|σ(ν1)|+|σ(ν2)|2].

    Theorem 2.3. Let σ:[ν1,ν2]RR be a differentiable function on (ν1,ν2) and |σ|q,q>1 is convex on [ν1,ν2] with ν1ν2 and x,y[ν1,ν2]. Then, we have:

    |2ϑ1Γ(ϑ+1)(yx)ϑ[Jϑ(ν1+ν2y)+σ(ν1+ν2x+y2)+Jϑ(ν1+ν2x)σ(ν1+ν2x+y2)]σ(ν1+ν2x+y2)|(yx)4pϑp+1[(|σ(ν1)|q+|σ(ν2)|q(|σ(x)|q+3|σ(y)|q4))1q+(|σ(ν1)|q+|σ(ν2)|q(3|σ(x)|q+|σ(y)|q4))1q], (2.10)

    where 1p+1q=1.

    Proof. By taking modulus of identity (2.7) and using Hölder's inequality, we get

    |2ϑ1Γ(ϑ+1)(yx)ϑ[Jϑ(ν1+ν2y)+σ(ν1+ν2x+y2)+Jϑ(ν1+ν2x)σ(ν1+ν2x+y2)]σ(ν1+ν2x+y2)|(yx)4(10ηϑp)1p{(10|σ(ν1+ν2(1η2x+1+η2y))|qdη)1q+(10|σ(ν1+ν2(1+η2x+1η2y))|qdη)1q}.

    Then, by applying the Jensen-Mercer inequality with the convexity of |σ|q, we can deduce

    |2ϑ1Γ(ϑ+1)(yx)ϑ[Jϑ(ν1+ν2y)+σ(ν1+ν2x+y2)+Jϑ(ν1+ν2x)σ(ν1+ν2x+y2)]σ(ν1+ν2x+y2)|(yx)4(10ηϑp)1p{(10|σ(ν1)|q+|σ(ν2)|q(1η2|σ(x)|q+1+η2|σ(y)|q))1q+(10|σ(ν1)|q+|σ(ν2)|q(1+η2|σ(x)|q+1η2|σ(y)|q))1q}=(yx)4pϑp+1[(|σ(ν1)|q+|σ(ν2)|q(|σ(x)|q+3|σ(y)|q4))1q+(|σ(ν1)|q+|σ(ν2)|q(3|σ(x)|q+|σ(y)|q4))1q],

    which completes the proof of Theorem 2.3.

    Corollary 2.4. Theorem 2.3 with

    ϑ=1 becomes:

    |1yxν1+ν2xν1+ν2yσ(w)dwσ(ν1+ν2x+y2)|(yx)4pp+1[(|σ(ν1)|q+|σ(ν2)|q(|σ(x)|q+3|σ(y)|q4))1q+(|σ(ν1)|q+|σ(ν2)|q(3|σ(x)|q+|σ(y)|q4))1q].

    ϑ=1, x=ν1 and y=ν2 becomes:

    |1ν2ν1ν2ν1σ(w)dwσ(ν1+ν22)|(ν2ν1)22p(1p+1)1p[|σ(ν1)|+|σ(ν2)|].

    x=ν1 and y=ν2 becomes:

    |2ϑ1Γ(ϑ+1)(ν2ν1)ϑ[Jϑν1+σ(ν1+ν22)+Jϑν2σ(ν1+ν22)]σ(ν1+ν22)|2ϑ12qν2ν1(1p+1)1p[|σ(ν1)|+|σ(ν2)|].

    Theorem 2.4. Let σ:[ν1,ν2]RR be a differentiable function on (ν1,ν2) and |σ|q,q1 is convex on [ν1,ν2] with ν1ν2 and x,y[ν1,ν2]. Then, we have:

    |2ϑ1Γ(ϑ+1)(yx)ϑ[Jϑ(ν1+ν2y)+σ(ν1+ν2x+y2)+Jϑ(ν1+ν2x)σ(ν1+ν2x+y2)]σ(ν1+ν2x+y2)|(yx)4(ϑ+1)[(|σ(ν1)|q+|σ(ν2)|q(|σ(x)|q+(2ϑ+3)|σ(y)|q2(ϑ+2)))1q+(|σ(ν1)|q+|σ(ν2)|q((2ϑ+3)|σ(x)|q+|σ(y)|q2(ϑ+2)))1q]. (2.11)

    Proof. By taking modulus of identity (2.7) with the well-known power mean inequality, we can deduce

    |2ϑ1Γ(ϑ+1)(yx)ϑ[Jϑ(ν1+ν2y)+σ(ν1+ν2x+y2)+Jϑ(ν1+ν2x)σ(ν1+ν2x+y2)]σ(ν1+ν2x+y2)|(yx)4(10ηϑ)11q{(10ηϑ|σ(ν1+ν2(1η2x+1+η2y))|qdη)1q+(10ηϑ|σ(ν1+ν2(1+η2x+1η2y))|qdη)1q}.

    By applying the Jensen-Mercer inequality with the convexity of |σ|q, we can deduce

    |2ϑ1Γ(ϑ+1)(yx)ϑ[Jϑ(ν1+ν2y)+σ(ν1+ν2x+y2)+Jϑ(ν1+ν2x)σ(ν1+ν2x+y2)]σ(ν1+ν2x+y2)|(yx)4(10ηϑ)11q{(10ηϑ[|σ(ν1)|q+|σ(ν2)|q(1η2|σ(x)|q+1+η2|σ(y)|q)])1q+(10ηϑ[|σ(ν1)|q+|σ(ν2)|q(1+η2|σ(x)|q+1η2|σ(y)|q)])1q}=(yx)4(ϑ+1)[(|σ(ν1)|q+|σ(ν2)|q(|σ(x)|q+(2ϑ+3)|σ(y)|q2(ϑ+2)))1q+(|σ(ν1)|q+|σ(ν2)|q((2ϑ+3)|σ(x)|q+|σ(y)|q2(ϑ+2)))1q],

    which completes the proof of Theorem 2.4.

    Corollary 5. Theorem 2.4 with

    q=1 becomes Theorem 2.2.

    ϑ=1 becomes:

    |1yxν1+ν2xν1+ν2yσ(w)dwσ(ν1+ν2x+y2)|(yx)8[(|σ(ν1)|q+|σ(ν2)|q(|σ(x)|q+5|σ(y)|q6))1q+(|σ(ν1)|q+|σ(ν2)|q(5|σ(x)|q+|σ(y)|q6))1q].

    ϑ=1, x=ν1 and y=ν2 becomes:

    |1ν2ν1ν2ν1σ(w)dwσ(ν1+ν22)|(yx)8[(5|σ(ν1)|q+|σ(ν2)|q6)1q+(|σ(ν1)|q+5|σ(ν2)|q6)1q].

    x=ν1 and y=ν2 becomes:

    |2ϑ1Γ(ϑ+1)(ν2ν1)ϑ[Jϑν1+σ(ν1+ν22)+Jϑν2σ(ν1+ν22)]σ(ν1+ν22)|(ν2ν1)4(ϑ+1)[((2ϑ+3)|σ(ν1)|q+|σ(ν2)|q2(ϑ+2))1q+(|σ(ν1)|q+(2ϑ+3)|σ(ν2)|q2(ϑ+2))1q].

    Here, we consider the following special means:

    ● The arithmetic mean:

    A(ν1,ν2)=ν1+ν22,ν1,ν20.

    ● The harmonic mean:

    H(ν1,ν2)=2ν1ν2ν1+ν2,ν1,ν2>0.

    ● The logarithmic mean:

    L(ν1,ν2)={ν2ν1lnν2lnν1,ifν1ν2,ν1,ifν1=ν2,ν1,ν2>0.

    ● The generalized logarithmic mean:

    Ln(ν1,ν2)={[νn+12νn+11(n+1)(ν2ν1)]1n,ifν1ν2ν1,ifν1=ν2,ν1,ν2>0;nZ{1,0}.

    Proposition 3.1. Let 0<ν1<ν2 and nN, n2. Then, for all x,y[ν1,ν2], we have:

    |Lnn(ν1+ν2y,ν1+ν2x)(2A(ν1,ν2)A(x,y))n|n(yx)4[2A(νn11,νn12)A(xn1,yn1)]. (3.1)

    Proof. By applying Corollary 2.3 (first item) for the convex function σ(x)=xn,x>0, one can obtain the result directly.

    Proposition 3.2. Let 0<ν1<ν2. Then, for all x,y[ν1,ν2], we have:

    |L1(ν1+ν2y,ν1+ν2x)(2A(ν1,ν2)A(x,y))1|(yx)4[2H1(ν21,ν22)H1(x2,y2)]. (3.2)

    Proof. By applying Corollary 2.3 (first item) for the convex function σ(x)=1x,x>0, one can obtain the result directly.

    Proposition 3.3. Let 0<ν1<ν2 and nN, n2. Then, we have:

    |Lnn(ν1,ν2)An(ν1,ν2)|n(ν2ν1)4[A(νn11,νn12)], (3.3)

    and

    |L1(ν1,ν2)A1(ν1,ν2)|(ν2ν1)4H1(ν21,ν22). (3.4)

    Proof. By setting x=ν1 and y=ν2 in results of Proposition 3.1 and Proposition 3.2, one can obtain the Proposition 3.3.

    Proposition 3.4. Let 0<ν1<ν2 and nN, n2. Then, for q>1,1p+1q=1 and for all x,y[ν1,ν2], we have:

    |Lnn(ν1+ν2y,ν1+ν2x)(2A(ν1,ν2)A(x,y))n|n(yx)4pp+1{[2A(νq(n1)1,νq(n1)2)12A(xq(n1),3yq(n1))]1q+[2A(νq(n1)1,νq(n1)2)12A(3xq(n1),yq(n1))]1q}. (3.5)

    Proof. By applying Corollary 2.4 (first item) for convex function σ(x)=xn,x>0, one can obtain the result directly.

    Proposition 3.5. Let 0<ν1<ν2. Then, for q>1,1p+1q=1 and for all x,y[ν1,ν2], we have:

    |L1(ν1+ν2y,ν1+ν2x)(2A(ν1,ν2)A(x,y))1|q2(yx)4pp+1{[H1(ν2q1,ν2q2)34H1(x2q,3y2q)]1q+[H1(ν2q1,ν2q2)34H1(3x2q,y2q)]1q}. (3.6)

    Proof. By applying Corollary 2.4 (first item) for the convex function σ(x)=1x,x>0, one can obtain the result directly.

    Proposition 3.6. Let 0<ν1<ν2 and nN, n2. Then, for q>1 and 1p+1q=1, we have:

    |Lnn(ν1,ν2)An(ν1,ν2)|n(ν2ν1)4pp+1{[2A(νq(n1)1,νq(n1)2)12A(νq(n1)1,3νq(n1)2)]1q+[2A(νq(n1)1,νq(n1)2)12A(3νq(n1)1,νq(n1)2)]1q}, (3.7)

    and

    |L1(ν1,ν2)A1(ν1,ν2)|q2(ν2ν1)4pp+1{[H1(ν2q1,ν2q2)34H1(ν2q1,3ν2q2)]1q+[H1(ν2q1,ν2q2)34H1(3ν2q1,ν2q2)]1q}. (3.8)

    Proof. By setting x=ν1 and y=ν2 in results of Proposition 3.4 and Proposition 3.5, one can obtain the Proposition 3.6.

    As we emphasized in the introduction, integral inequality is the most important field of mathematical analysis and fractional calculus. By using the well-known Jensen-Mercer and power mean inequalities, we have proved new inequalities of Hermite-Hadamard-Mercer type involving Riemann-Liouville fractional operators. In the last section, we have considered some propositions in the context of special functions; these confirm the efficiency of our results.

    We would like to express our special thanks to the editor and referees. Also, the first author would like to thank Prince Sultan University for funding this work through research group Nonlinear Analysis Methods in Applied Mathematics (NAMAM) group number RG-DES-2017-01-17.

    The authors declare no conflict of interest.



    Authors' contributions



    All authors participated in the conception and writing of this manuscript.

    Conflict of interest



    All authors have no conflicts of interest.

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