J Cancer 2019; 10(9):2047-2056. doi:10.7150/jca.29410 This issue Cite

Research Paper

Preoperative anemia as a prognostic factor in patients with lung cancer: a systematic review and meta-analysis of epidemiological studies

Yang Liu, Yun-Peng Bai, Zi-Fang Zhou, Chang-Rui Jiang, Zhe Xu, Xiao-Xi Fan Corresponding address

Department of Thoracic Surgery, the First Affiliated Hospital of China Medical University, Shenyang, China.

Liu Y, Bai YP, Zhou ZF, Jiang CR, Xu Z, Fan XX. Preoperative anemia as a prognostic factor in patients with lung cancer: a systematic review and meta-analysis of epidemiological studies. J Cancer 2019; 10(9):2047-2056. doi:10.7150/jca.29410. https://www.jcancer.org/v10p2047.htm
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The evidence of current epidemiological studies investigating the relationship between preoperative anemia and progression of lung cancer (LC) patients remains controversial. The PubMed, EMBASE, and Web of Science databases were comprehensively searched by two independent authors to identify related epidemiological studies from inception through January 31, 2019. Similarly, two researchers separately extracted data and any differences were resolved by discussion. Summarized hazard ratios (HRs) and 95% confidence intervals (CIs) were summarized with inverse variance weighted random effects meta-analysis. Heterogeneity among studies was assessed with the I² statistic. Twenty-two studies were included in this meta-analysis. As compared with LC patients without anemia, those with pre-operative anemia were at a 1.6-fold greater risk of death (summarized HR = 1.58; 95% CI = 1.44-1.75), with moderate heterogeneity (I2 = 53.1%). Funnel plot and statistical analyses showed no evidence of publication bias. Associations between pre-operative anemia and OS were broadly consistent across numerous subgroups analyses stratified by the study design, geographic location, number of cases, tumor, node, and metastasis (TNM) stage, histology, quality, and adjustment for potential confounders (age, sex, body mass index, TNM stage, histology, performance status, surgery, blood transfusion, and systemic inflammatory response markers). Similar patterns were observed in the sensitivity analyses. The results of meta-regression analysis suggested no evidence of significant heterogeneity between subgroups. In conclusion, pre-operative anemia was associated with poorer overall survival among LC patients.

Keywords: pre-operative anemia, overall survival, lung cancer (LC) patients, meta-analysis


Lung cancer (LC) is the leading cause of cancer-related death worldwide, accounting for approximately 1.59 million deaths in 2012 [1]. Non-small cell lung cancer (NSCLC) accounts for approximately 85% of all LCs and almost 70% of patients present with locally advanced or metastatic disease at the time of diagnosis [2, 3]. Despite advances in treatment, the 5-year overall survival (OS) rate remains at less than 20% [4]. Established predictors of OS among LC patients include age at diagnosis, sex, tumor stage, histologic type, and certain genetic mutations [4]. However, monitoring of these factors is either invasive or costly and provides insufficient evidence for validation [5-7]. Therefore, economical and convenient clinical biomarkers for individualized prognosis and prediction of treatment outcomes are still urgently required for LC patients.

Hemoglobin (Hb) is a biochemical biomarker commonly assessed during clinical examinations [8]. Notably, anemia and low Hb levels are quite common in patients with malignant tumors, including LC, and might be multifactorial. Indeed, low Hb levels, particularly in patients with more aggressive tumors, may also be related to complex interactions among the immune system, tumor microenvironment, and cancer cells [9, 10]. Although the number of studies exploring the prognostic role of preoperative anemia and Hb levels has been increasing, the results are inconsistent and often based on small samples. Of note, Caro et al. [11] summarized the evidence of this topic in 2001 and several later reports have indicated that preoperative anemia or low Hb levels are associated with poor survival among LC patients [8, 12-33]; however, contradictory or null reports also exist [34-37].

Importantly, to the best of our knowledge, the prognostic value of preoperative anemia in LC patients has not been investigated in any systematic review or meta-analysis since 2001. Therefore, the purpose of this study was to summarize and update the currently available evidence from epidemiological studies regarding the association between pre-operative anemia and OS of LC patients.

Material and Methods

Search strategy

Two authors (YL and X-XF) independently and systematically searched the PubMed (https://www.ncbi.nlm.nih.gov/pubmed/), Embase (https://www.elsevier.com/solutions/embase-biomedical-research), and Web of Science (https://www.webofknowledge.com) databases from inception to the end of January 2019 for relevant epidemiological studies investigating the association between pre-operative anemia and progression of LC using the following search algorithm: “(anemia OR hemoglobin OR hematocrit OR transfusion OR blood cell OR hematology) AND (lung OR pulmonary) AND (cancer OR neoplasm OR carcinoma OR tumor)”. A manual review of references from eligible systematic and narrative reviews was also performed. A meta-analysis was planned, conducted, and reported according to the guidelines of the Meta-Analysis of Observational Studies in Epidemiology group [38].

Study selection and exclusion

The following inclusion criteria were used: (i) observational or experimental study design; (ii) studies that evaluated the association between preoperative anemia status or Hb level and prognostic outcomes of LC patients; (iii) reported at least one of the outcomes of interest (i.e., OS, cancer-specific mortality, disease-free survival, event-free survival, progression-free survival, and recurrence-free survival); and (iv) studies that included the hazard ratio (HR) or relative risk (RR) with a 95% confidence intervals (CI), or reported sufficient data to calculate those risk estimates. The following exclusion criteria were used: (i) reviews without original data, ecological studies, editorials, and case reports; and (ii) studies that reported risk estimates without a 95% CI (e.g., could not be included in the statistical summary). If multiple studies had a duplicate patient cohort, only that with the largest sample size was included for the same outcome. If multiple studies had a duplicate patient cohort as well as the same sample size, only that with the longest follow-up duration was included for the same outcome. However, multiple studies with duplicate patient cohorts but with different outcomes of interest were included for analysis separately for each outcome.

The titles and abstracts of the retrieved articles were checked for relevancy before the full-text article was examined. The relevant data were extracted from the complete articles. Also, the bibliographies of the selected articles were manually reviewed. The titles, abstracts, and full texts of the resulting articles were examined in detail by two independent authors (YL and X-XF) and discrepancies were resolved by consensus.

Data abstraction and quality assessment

The following information was extracted from each included study by a single investigator (YL): first author, publication year, country, study design, number of patients, outcomes characteristics, and study-specific adjusted risk estimates with 95% CIs. For risk estimates, if both univariate and multivariate analyses were provided, data from multivariate analysis were extracted; otherwise, data from univariate analysis were used. The predefined primary outcome was progression-free survival and the secondary outcome was OS. Extracted data were entered into a standardized Excel (Microsoft Corporation, Redmond, WA, USA) file. Subsequently, an independent author (Y-PB) checked the data and all differences were resolved by a third investigator (X-XF). Although five included studies were randomized controlled trials [22, 23, 33, 34, 37], each retrospectively analyzed the prognostic role of pre-operative anemia in the include patients. Therefore, two independent authors (YL and X-XF) assessed the methodological quality of the included studies according to the Newcastle-Ottawa Scale [39].

Statistical analysis

The HR and 95% CI are presented as summaries of the risk estimates of each study as calculated with a random effects model to investigate the association between preoperative anemia status and Hb levels with the progression of LC. For studies [12-14, 18, 22, 25-27] that set patients with anemia as the reference group, the counting method proposed by Hamling et al. [40] was used to recalculate the HR and 95% CI. Heterogeneity across the studies was quantified using the I2 statistic, which indicates significant heterogeneity when I2 > 50% [41]. Also, post hoc subgroup analyses was conducted according to the study design (retrospective vs. prospective), geographic location (Asia, Europe, and North America), tumor, node, and metastasis (TNM) stage (all, advanced stage, and early stage), histology (non-small cell vs. small cell), study quality (low vs. high risk), the median number of LC cases (≥ 300 vs. < 300), and adjustments made for potential confounders (including age at surgery/diagnosis, sex, body mass index, TNM stage, histology, performance status, surgery, blood transfusion, and systemic inflammatory response markers). Heterogeneity between subgroups was evaluated by meta-regression analysis. Small study biases (e.g., publication bias) were assessed by visually inspecting a funnel plot and conducting tests according to Begg et al. [42] and Egger et al. [43] Sensitivity analyses were conducted by removing one study at a time to examine the effect of data from each study on the overall estimate. The sequential exclusion strategy proposed by Patsopoulos et al. [44] was used to determine whether the overall estimates were influenced by the substantial heterogeneity observed. Studies that accounted for the largest share of heterogeneity were sequentially and cumulatively excluded until I2 was < 50%. Then, further examinations were conducted to determine whether the risk estimates were consistent [45, 46]. All statistical analyses were performed using the Stata statistical software package (ver. 12.0; StataCorp LP, College Station, TX, USA).

 Figure 1 

Selection of studies for inclusion in this meta-analysis.

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Search results, study characteristics, and quality assessment

The detailed processes of literature screening, study selection, and study exclusion are summarized in Figure 1. The initial search retrieved 7752 unique reports. After removing duplicates and screening the titles and abstracts, the reviewers judged that 34 articles were potentially eligible for inclusion and thus consequently subjected to full-text review. After exclusion, 23 studies were included in the meta-analysis.

Table 1 presents the main characteristics of the 23 included studies. These studies were published from 1991 to 2018 and included a total of 10,612 LC patients with a range of 99-2351 cases among the individual studies. These 23 reports were designed as retrospective (n = 16) and prospective studies (n = 7). The majority of the included studies were conducted in North America (n = 9), seven in Asia, and six in Europe. More than half of the included studies adjusted for age at diagnosis/surgery (n = 16) and TNM stage (n = 14), while less than half adjusted for performance status (n = 11), sex (n = 10), histology (n = 10), and surgery (n = 8). Fewer studies adjusted for systemic inflammatory response markers (n = 6), blood transfusion (n = 5), body mass index (n = 2), and chemotherapy (n = 3).

 Table 1 

Characteristics of 23 studies included in the meta-analysis

First author, [ref], year, countryStudy designNo. of casesTNM stageHistologyAnemia cut-off (unit)OutcomeAdjustment
Zhang et al. [8], 2018, ChinaRetrospective cohort416I-IVNSCLC<120 (M) ≤110 (F) (g/L)OSAge, sex, TNM stage, PS, Lung lobectomy, chemotherapy, and radiotherapy
Holgersson et al. [34], 2017, Multi-centersRCT99IIIB-IVNSCLC<110 (g/L)OSAge at diagnosis, sex, histology, PS, white blood cell and platelet at baseline and treatment arm
Holgersson et al. [35], 2017, SwedenRCT222IIIA- IIIBNSCLC<110 (g/L)OSAge at diagnosis, sex, histology, PS, white blood cell and platelet, weight loss, and TNM stage
Lee et al. [12], 2017, KoeraRetrospective cohort135IIIB-IVNSCLC<13 (M) <12 (F) (g/dL)OSN/A
Liu et al. [13], 2017, ChinaRetrospective cohort139ED/LDSCLCN/AOSN/A
Cata et al. [14], 2016, USARetrospective cohort861INSCLC<13 (M) <12 (F) (g/dL)RFS/OSAge, body mass index, sex, PS, histology, preoperative neutrophil-to-lymphocyte ratio, blood transfusion, and surgery type
Ng et al. [36], 2012, USAProspective cohort361IA-IBNSCLC<13 (M) <12 (F) (g/dL)DFS/OSAge, sex, blood transfusion, and TNM stage
Wu et al. [16], 2012, ChinaRetrospective cohort200EDSCLCN/AOSN/A
Park et al. [17], 2009, KoeraRetrospective cohort316IIIB-IVNSCLC<11 (g/dL)OSN/A
Chamogeorgakis et al. [18], 2008, USARetrospective cohort214IA-IIBNSCLC≤12 (g/dL)CSM/OSN/A
Panagopoulos et al. [20], 2008, GreeceRetrospective cohort331I-IVNSCLC<12 (g/dL)OSAge, sex, operation severity, operation type, histology, TNM stage postoperative hospital stay, blood transfusion and number of red blood cell units transfused
Tomita et al. [21], 2008, JapanRetrospective cohort240I-IVNSCLCN/AOSAge, sex, histology, TNM stage, and preoperative serum carcinoma embryonic antigen level
Ademuyiwa et al. [22], 2007, USARCT203IIIA- IIIBNSCLC<12 (g/dL)OSAge ,sex, ethnicity, body mass index, PS, FEV 1, smoking, use of positron emission tomography scan in staging and stage
Gauthier et al. [37], 2007, CanadaRCT482IB- IINSCLC<120 (g/L)OSAge, sex, PS, TNM stage, histology, type of surgery, and baseline lactic dehydrogenase
Mandrekar et al. [23], 2006, USA and CanadaRCT1053IIIB-IVNSCLC<13.2 (M) <11.5 (F) (g/dL)OSAge, sex, PS, TNM stage, BMI, white blood cell, and platelet count
Aoe et al. [24], 2005, JapanRetrospective cohort611I-IVLC<13 (M) <12 (F) (g/dL)OSAge, sex, PS, histology, TNM stage, and lactic dehydrogenase
Berardi et al. [25], 2005, ItalyRetrospective cohort439IA-IIIBNSCLC≤10 (g/dL)OSAge, sex, smoking, PS, histology, TNM stage, type of surgery, and transfusions
Yovino et al. [26], 2005, USARetrospective cohort125I-IINSCLC<12 (g/dL)OSAge, sex, histology, TNM stage, p53 status, and type of surgery
Rzyman et al. [27], 2003, PolandRetrospective cohort493I-IVNSCLC≤12 (g/dL)OSAge, sex, amount and type of transfused blood, sedimentation rate, histology, tumor location, type of resection, and TNM stage
Jazieh et al. [28], 2000, USARetrospective cohort454I-IINSCLC≤10 (g/dL)EFS/OSAge, sex, race, TNM stage, histology, and type of surgery
Wigren et al. [31], 1997, FinlandRetrospective cohort502I-IVNSCLC≤125 (g/L)OSTNM stage, symptoms, PS, and tumor size
Takigawa et al. [32], 1996, JapanRetrospective cohort185III-IVNSCLC≤11 (g/dL)OSPS, TNM stage, calcium
Albain et al. [33], 1991, USARCT2351I-IVNSCLC≤11 (g/dL)OSLactic dehydrogenase, calcium metastasis, therapy, year of registration, other therapy groupings, sex, age, smoking, weight loss, and alkaline phosphatase

CSM, cancer-specific mortality; DFS, disease-free survival; ED, extensive stage; EFS, event-free survival; F, female; LC, lung cancer; LD, limited stage; M, male; NSCLC, non-small cell lung cancer; N/A, not available; OS, overall survival; PFS, progression-free survival; PS, performance status; RCT, randomized controlled trial; RFS, recurrence-free survival; SCLC, small cell lung cancer.

 Table 2 

Methodological quality of all included cohort studies

First author (reference), yearRepresentativeness of the exposed cohortSelection of the unexposed cohortAscertainment of exposureOutcome of interest not present at start of studyControl for important factor or additional factor Assessment of outcomeFollow-up long enough for outcomes to occurAdequacy of cohort follow-up§
Zhang et al. [8], 2018*********
Holgersson et al.[34], 2017*******-*
Holgersson et al. [35], 2017*********
Lee et al. [12], 2017****-*-*
Liu et al. [13], 2017****-***
Cata et al. [14], 2016*********
Ng et al. [36], 2012*********
Wu et al. [16], 2012****-***
Park et al. [17], 2009****-***
Chamogeorgakis et al. [18], 2008****-***
Panagopoulos et al. [20], 2008*********
Tomita et al. [21], 2008********
Ademuyiwa et al. [22], 2007********
Gauthier et al. [37], 2007******-*
Mandrekar et al. [23], 2006****-***
Aoe et al. [24], 2005*******-*
Berardi et al. [25], 2005*********
Yovino et al. [26], 2005*********
Rzyman et al. [27], 2003*********
Jazieh et al. [28], 2000*********
Wigren et al. [31], 1997*******-*
Takigawa et al. [32], 1996*********
Albain et al. [33], 1991******-*

A study could be awarded a maximum of one star for each item except for the item “Control for important factor or additional factor.”

† A maximum of two stars could be awarded for this item. Studies that controlled for age at diagnosis/TNM stage received one star, whereas those that controlled for other important confounders (i.e., performance status, blood transfusion, and surgery/chemotherapy) received an additional star.

‡ A cohort study with a median follow-up time >1 year was assigned one star.

§ A cohort study with a follow-up rate >75% was assigned one star.

The quality assessment characteristics of the included studies are shown in Table 2. The major difference among the included studies was the control for an important factor or an additional factor category; 13 of the included studies were assigned two full scores. Six studies [12, 24, 31, 33, 34, 37] had follow-up periods of less than one year or were not mentioned; therefore, these studies were not assigned a score when testing for whether the follow-up duration was sufficiently long for outcomes to occur.

Pre-operative anemia and OS of LC patients

As compared with LC patients without anemia, those with pre-operative anemia were at approximately a 1.58-fold greater risk of death (summarized HR = 1.58; 95% CI = 1.44-1.75) (Figure 2), with moderate heterogeneity (I2 = 53.1%). Funnel plot and statistical analyses showed no evidence of publication bias (Figure 3).

Subgroup and sensitivity analyses

To determine whether the study characteristics and adjustment for potential confounders had any impact on risk estimates, numerous subgroup analyses stratified by these issues were performed. Significant positive findings were observed throughout these subgroup analyses (Table 3). Sensitivity analyses using an alternative statistical model (fixed-effects HR = 1.50, 95% CI = 1.42-1.60) and by excluding five studies [12, 13, 16-18] without adjustment for any potential confounders (summarized HR = 1.54, 95% CI = 1.38-1.72; I2 = 56.4%) showed robust findings. Additionally, the sensitivity analysis showed that the summarized HR ranged from 1.55 (95% CI = 1.41-1.69; I2 = 44.3%; exclusion of Takigawa et al. [32]) to 1.62 (95% CI = 1.48-1.77; I2 = 37.0%; exclusion of Ademuyiwa et al. [22]) (Figure 4). When the studies that contributed the largest amount to heterogeneity until I2 was less than 50% were sequentially excluded, the summarized HR was 1.62 (95% CI = 1.48-1.77; I2 = 37.0%), which was similar to the original estimate.


This systematic review and meta-analysis with a large sample size (comprising 10,612 LC patients) provides support for the hypothesis that pre-operative anemia is associated with increased mortality among LC patients. This finding should improve the attention of both patents and clinicians to better management of pre-operative anemia before further treatment of LC.

 Table 3 

Risk estimate summary of the relationship between pre-operative anemia and OS of LC patients

No. of studiesHR95% CII2 (%)PhPh
Subgroup analyses
Study design0.104
Geographic location0.116
North America91.611.34-1.9465.30.003
Number of cases (median)0.818
≥ 300131.591.44-1.7634.30.108
< 300101.611.31-1.9764.30.003
TNM stage0.415
Advanced stage91.631.34-1.9973.3<0.001
Early stage61.741.33-2.2847.80.088
Non-small cell201.591.42-1.7757.30.001
Small cell21.911.37-2.6600.941
Quality of study0.847
Low risk201.591.42-1.7656.90.001
High risk31.631.28-2.0813.30.316
Adjustment for potential confounders or risk factors
Age at diagnosis/surgery0.125
Body mass index0.119
TNM stage0.892
Performance status0.102
Blood transfusion0.374
SIR markers0.208

BMI, body mass index; CI, confidence interval; HR, hazard ratio; LC, lung cancer; N/A, not available; OS, overall survival; SIR, systemic inflammatory response.

p-value for heterogeneity within each subgroup.

p-value for heterogeneity between subgroups with meta-regression analysis.

 Figure 2 

Forest plot (random effects model) of pre-operative anemia and OS of LC patients. The squares indicate study-specific hazard ratios (size of the square reflects the study-specific statistical weight); the horizontal lines indicate 95% CIs; and the diamond indicates the summary hazard ratio estimate with its 95% CI.

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 Figure 3 

Test for publication bias for OS through Begg's funnel plot. HR, hazard ratio; SE, standard error. The circles alone are real studies. The vertical lines represent the summary effect estimates and the dashed lines represent pseudo-95% CI limits.

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 Figure 4 

Sensitivity plot corresponding to the relationship between pre-operative anemia and OS of LC patients. The circle indicates the study-specific hazard ratio after excluding the present study; the horizontal dotted lines indicate 95% CIs.

J Cancer Image

One previous systematic review reported an overall estimate of the effect of anemia on survival in patients with malignant disease [11]. In 2001, Caro et al. [11] found that the relative risk of death was increased by 19% among anemic patients with lung carcinoma. However, this report did not provide details of the extracted information. Furthermore, several included studies [47-51] failed to a risk estimate in the primary analysis. In comparison, the results of the present study, which were primarily based on the most comprehensive published studies, suggested a relatively stronger relationship between pre-operative anemia and survival of LC patients. Of note, these findings were robust in subgroup analyses, which were not performed by Caro et al. [11] in 2001.

The precise mechanisms underlying the association between pre-operative anemia and survival in LC patients have not been fully elucidated. In general, cancer-related anemia is due to multiple etiologies, including blood loss, functional iron deficiency, erythropoietin deficiency from renal disease, and bone marrow involvement with cancer, as well as other factors [52]. One hypothesis is that tumor hypoxia may stimulate angiogenesis, which is a marker of increased tumor aggressiveness [53]. Tumor cells are known to secrete various soluble molecules, including interleukin-6 and tumor necrosis factor-α [8]. These molecules can decrease Hb concentrations by changing the hematopoietic environment [54, 55], suppressing erythropoiesis and erythropoietin [56], and impairing the erythropoietin response of erythroid progenitor cells [57]. Additionally, in patients with bone metastasis, bone marrow involvement may lead to bone morrow failure, which may then cause low Hb levels [58] and subsequently lead to hypoxia, which could induce genomic changes and enhance the development of malignancy [59]. Hypoxia may also boost tumor angiogenesis and accelerate metastasis [60]. Furthermore, hypoxia may enhance tumor cell resistance to chemotherapy and radiotherapy through the development of multi-drug resistance [61].

The strengths of this study include the systematic and rigorous approach to the identification of epidemiological studies investigating the impact of pre-operative anemia on survival of LC patients. Furthermore, to the best of our knowledge, the present study included the most published studies and had the largest sample size, which allowed for numerous preplanned subgroup analyses to explore differences in the risks for mortality.

This study also had some limitations that should be addressed. First, the majority of the included studies were retrospective, which might have had potential selection and recall biases, thereby diminishing the general credibility of these findings. However, no significant difference was found in the subgroup analysis stratified by study design. Furthermore, most of included studies were scored as low risk after quality assessment. The only three studies with high risk might be attributed to the adjustment for limited confounders as well as short follow-up periods. Second, the cut-off value and unit of pre-operative anemia varied. For example, six of the included studies defined pre-operative anemia according to patient sex. However, three studies only reported pre-operative anemia as low or normal. Furthermore, five studies used g/L as the unit of anemia. In contrast, 15 studies used g/dL as the unit of anemia. Third, since the majority of the included studies were observational, the association between pre-operative anemia and prognosis of LC may have resulted from unmeasured or residual confounding by other factors. Pre-operative anemia in LC patients may be associated with age at diagnosis, body mass index, TNM stage, histology, performance status, and blood transfusion, which possibly could confound the aforementioned associations. For example, several previous studies have mentioned that blood transfusion might affect adversely the survival of LC patients, especially for stage I non-small cell LC patients [20]. Although the findings were robust in studies that adjusted for these potential confounders, not all were fully adjusted. Notably, five studies provided the risk estimates on the basis of a univariate model, but without adjustment for any confounders. Interestingly, the results of the sensitivity analysis were robust when excluding these five studies. Furthermore, the results of meta-regression analyses found no evidence that these findings differed significantly between studies adjusted for these confounders or not. Fourth, since a limited number of studies reported secondary outcomes (i.e., progression-free survival, recurrence-free survival, or event-free survival), the main focus of the present study was the association between pre-operative anemia and OS of LC patients. Further studies are warranted to provide more information on this issue.

The findings of the present study not only indicate a significant association between pre-operative anemia and an increased risk of LC mortality, but also emphasize the role of systematic reviews to examine focused clinical questions. Pre-operative anemia might be a useful indicator of the prognosis of LC patients. Clinicians should fully consider the status and severity of pre-operative anemia when deciding on an appropriate individualized LC management regime.

Author Contributions

Study concepts: X-XF. Study design: YL, Y-PB, and X-XF. Data acquisition: YL, Y-PB, and X-XF. Quality control of data and algorithms: Z-FZ and C-RJ. Data analysis and interpretation: YL and X-XF. Statistical analysis: YL and X-XF. Manuscript preparation: YL, Y-PB, Z-FZ, C-RJ, ZX, and X-XF. Manuscript editing: X-XF. Manuscript review: YL, Y-PB, Z-FZ, C-RJ, ZX, and X-XF.

Competing Interests

The authors have declared that no competing interest exists.


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Author contact

Corresponding address Corresponding author: Xiao-Xi Fan, M.D. Department of Thoracic Surgery, The First Affiliated Hospital of China Medical University Address: No. 155, Nanjing Bei Street, Shenyang, Liaoning 110001, P. R. China. Phone: +86-024-83283170 E-mail: fanxxcom

Received 2018-8-22
Accepted 2019-4-23
Published 2019-5-12

Citation styles

Liu, Y., Bai, Y.P., Zhou, Z.F., Jiang, C.R., Xu, Z., Fan, X.X. (2019). Preoperative anemia as a prognostic factor in patients with lung cancer: a systematic review and meta-analysis of epidemiological studies. Journal of Cancer, 10(9), 2047-2056. https://doi.org/10.7150/jca.29410.

Liu, Y.; Bai, Y.P.; Zhou, Z.F.; Jiang, C.R.; Xu, Z.; Fan, X.X. Preoperative anemia as a prognostic factor in patients with lung cancer: a systematic review and meta-analysis of epidemiological studies. J. Cancer 2019, 10 (9), 2047-2056. DOI: 10.7150/jca.29410.

Liu Y, Bai YP, Zhou ZF, Jiang CR, Xu Z, Fan XX. Preoperative anemia as a prognostic factor in patients with lung cancer: a systematic review and meta-analysis of epidemiological studies. J Cancer 2019; 10(9):2047-2056. doi:10.7150/jca.29410. https://www.jcancer.org/v10p2047.htm

Liu Y, Bai YP, Zhou ZF, Jiang CR, Xu Z, Fan XX. 2019. Preoperative anemia as a prognostic factor in patients with lung cancer: a systematic review and meta-analysis of epidemiological studies. J Cancer. 10(9):2047-2056.

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