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Abstract

BACKGROUND:

It is essential in the management of ovarian cancers to identify the patients who will benefit from primary complete cytoreductive surgery and those who will rather benefit from neoadjuvant chemotherapy.

OBJECTIVE:

To evaluate the predictive value of preoperative inflammatory markers, i.e. platelet to lymphocyte ratio (PLR), neutrophil to lymphocyte ratio (NLR), monocyte to lymphocyte ratio (MLR), red cell distribution width (RDW), and serum CA125 level for surgical outcome in epithelial ovarian cancer.

METHODS:

A retrospective study was carried out in Sanglah Hospital, Denpasar, Bali. A total of 54 patients with epithelial ovarian cancer who underwent primary exploratory laparotomy from January 2018 to November 2019 was recruited. Data about clinical characteristics, preoperative inflammatory markers, serum CA125 level, and surgical outcome (optimal vs. suboptimal) was collected from the medical records. Predictive value of the markers were evaluated using ROC curve to determine their accuracy (area under the curve, sensitivity, specificity, positive and negative predictive value).

RESULTS:

Mean age, parity, and tumor size did not differ between the study groups ( $p>$ 0.05). The group with suboptimal outcome had significantly higher PLR, NLR, MLR, and RDW value ( $p<$ 0.05). Using the ROC curve, a cut off value was determined for each predictor, i.e. PLR: 196.50, NLR: 3.34, MLR: 0.24, RDW: 13.19, CA125: 300.85. AUC for each predictor were as follows: PLR 0.718 (95% CI: 0.578–0.859), NLR 0.676 (95% CI: 0.529–0.823), MLR 0.700 (95% CI: 0.560–0.839), RDW 0.712 (95% CI: 0.572–0.852), CA125 0.593 (95% CI: 0.436–0.750). Sensitivity, specificity, and accuracy for predicting suboptimal outcome were as follows: PLR (74.2%, 69.6%, 72.2%), NLR (64.5%, 60.9%, 62.9%), MLR (74.2%, 59.1%, 66.7%), RDW (74.2%, 60.9%, 68.5%), CA125 (54.8%, 60.9%, 57.4%). We have some limitations such as small numbers of sample, we generalized whole kinds of ovarian cancer, and this study does not describe follow-up features.

CONCLUSION:

Preoperative serum inflammatory markers (PLR, MLR, and RDW) may serve as useful markers to predict the surgical outcome with fair accuracy in patients with epithelial ovarian cancer.

Keywords

Epithelial ovarian cancer inflammatory markers predictive value surgical outcome

1. Introduction

Ovarian cancer still has the highest mortality rate among malignant tumors of the female reproductive system [4]. Its mortality rate among Indonesian women even ranks the highest in Asia (6.1 per 100.000 population) [20]. One of Indonesian survey organization, Riskesdas 2007, reported that around 20% (927 people from 4,647 cancer respondence) diagnosed as ovarian or cervical cancer and 795 people of them in reproductive age (20–59 years-old) with low socioeconomic background [7]. From 1989–1992, there was 1,726 cases of gynecological cancer in Dr. Cipto Mangunkusumo, Jakarta, Indonesia and 13.6% was ovarian cancer. Patients proned to visit medical healthcare when stage II-IV ovarian cancer. It was reported that ovarian cancer costed 22.6% death from 327 death of gynecological cancer. Data from 1996–2001, Dr. Cipto Mangunkusumo Hospital obtained 214 patients with ovarian cancer. The 5-year survival rate on stage I-IV about 76.39%, 66.67%, 24.64%, and 8.13%, respectively, with median survival approximately 1,294 days [22]. Another study from Prof Dr. RD Kandou Manado, Indonesia included 95 patients with ovarian cancer from 2013–2015 showed the majority cases were women aged $\geqslant$ 51 years, BMI 18.5–24.9, and multipara ( $P\geqslant$ 1) [9].

The prognosis is poor for advance stage, in which the five year survival rate is only 25% [17]. The major prognostic factor that determines the survival of patients with ovarian cancer is the residual tumor after primary cytoreductive surgery [8]. One meta-analysis suggested that for each 10% increase in the proportion of patients undergoing complete cytoreduction to no gross residual disease (R0) was associated with a significant and independent 2.3-month increase in cohort median survival compared to a 1.8-month increase in cohort median survival for optimal cytoreduction (residual disease $\leqslant$ 1 cm) [5]. Hence, the goal of treatment in ovarian cancer is to achieve R0, or, for the very least, the optimal cytoreduction

The ability to identify a subset of patients who are most likely to achieve optimal cytoreduction/R0 is of paramount importance. Poor candidate of surgery who are less likely to achieve that ideal target may have delayed chemotherapy after unnecessary laparotomy and their prognosis can be adversely affected. If optimal cytoreduction/R0 is difficult to attain at primary cytoreduction, the use of neoadjuvant chemotherapy followed by interval debulking to allow for R0 is recommended [12]. Currently, there are no general consensus on the preoperative method for establishment of cytoreducibility. Several studies have suggested the use of preoperative imaging modalities (laparoscopy, CT, PET scan) or serum biomarkers (CA 125, HE-4) but those modalities are sometimes expensive and inaccessible for low-resource setting that commonly present in developed country such as Indonesia. Identification of low-cost biological indicators that can help to improve prediction of the surgical outcome will be crucial.

Cancer is a pro-inflammatory state. On the other hand, it was suggested that inflammatory reaction and immune status have played a pivotal role in tumorigenesis and cancer progression, i.e. they generate a tumor-related inflammatory microenvironment which exerted an important role in the pathogenesis and development of tumors [6, 16, 10]. Inflammation often manifests as neutrophilia, thrombocytosis and relative lymphopenia in the peripheral blood [11]. Several inflammatory markers which are simply obtained from peripheral blood count such as absolute platelet count (PLT), platelet to lymphocyte ratio (PLR), monocyte to lymphocyte ratio (MLR), neutrophil to lymphocyte ratio (NLR), and the red cell distribution width (RDW) are gaining much attention for its role in predicting cancer progression and survival. In a study by Qin et al. [18], it was reported that the RDW, absolute neutrophil count (N), NLR, PLR, and CA-125 level were significantly higher in the ovarian cancer than benign ovarian tumor. Thus, serum inflammatory markers obtained from peripheral blood count may serve as potential predictors for surgical outcome in patients with epithelial ovarian cancer undergoing primary cytoreductive surgery.

This study is aimed to evaluate the predictive value of preoperative inflammatory markers obtained from the peripheral blood count (PLR, NLR, MLR, and RDW) and serum CA125 level for tumor resectability in epithelial ovarian cancer. These parameters are convenient, inexpensive, and simple to measure for the low resource setting.

2. Methods

2.1 Design

This is a retrospective study that was carried out in Gynecologic Oncology Division, Obstetric and Gynecology Department, Sanglah Hospital/Faculty of Medicine Udayana University, Bali, Indonesia, during January 2018 to November 2019. This study is approved by the Ethical Committee Faculty of Medicine Udayana University (No. 1921/UN14.2.2.VII.14/LT/2020).

Table 1
Characteristics of study population

Characteristics	Total sample N(%) 54 (100)	No residual tumor or R0 N(%) 23 (42.5)	With residual tumor or R1 N(%) 31 (57.4)	$P$ value
*Age (mean)	47.6 $\pm$ 9.7	47.5 $\pm$ 9.0	47.7 $\pm$ 10.4	0.923
Parity (mean)	1.5 $\pm$ 1.4	1.5 $\pm$ 1.4	1.5 $\pm$ 1.4	0.956
Tumor size (mean)	19.7 $\pm$ 9.2	19.6 $\pm$ 10.2	19.8 $\pm$ 8.6	0.769
Stage (%)
I	8 (14.8)	8 (34.8)	0 (0.0)	0.000
II	13 (24.1)	9 (39.1)	4 (12.9)
III	30 (55.6)	6 (26.1)	24 (77.4)
IV	3 (5.6)	0 (0.0)	3 (9.7)
Histology (%)
Serous	20 (37.0)	7 (30.4)	13 (41.9)	0.349
Mucinous	10 (18.5)	6 (26.1)	4 (12.9)
Endometrioid	12 (22.2)	5 (21.7)	7 (22.6)
Clear cell	8 (14.8)	2 (8.7)	6 (19.4)
Mixed epithelial	4 (7.4)	3 (13.0)	1 (3.2)

*Normal distribution.

2.2 Participants and recruitment

Patients with ovarian cancer who underwent primary exploratomy laparotomy from January 2018 to November 2019 and had given their written consent to participate in this study were recruited into the study population Patients with non epithelial type of ovarian cancer, those who underwent conservative staging, incomplete data about inflammatory markers, those with systemic diseases (diabetes mellitus, cardiovascular disease, kidney disease, blood disease, acute inflammation, anemia), recent iron therapy, venous thrombosis for period of $>$ 6 months, and recent blood transfusion (within the past 3 months), were excluded from the study. A total of 54 eligible patients were included in the analysis.

2.3 Variables and measurement

Data about the clinical characteristics (i.e., age parity tumor size, FIGO stage, histologic type), preoperative inflammatory markers, and serum CA 125 level were all obtained from the medical records of the patients. We used the 2014 FIGO ovarian cancer staging system to determine the stage of our patients. In our hospital, preoperative venous blood samples (3 mL) were obtained from each patient from their first visit to the Gynecologic Oncology Outpatient Clinic and placed in EDTA-K2 anticoagulation tubes Whole blood cell parameters were determined with a Cell Dyn Ruby ${}^{\@setsize{\scriptsize}{8pt}{\viipt}{\@viipt}\textregistered}$ automated hematology analyzer (Abott, Germany). The absolute neutrophil count (N), absolute monocyte count (M), absolute lymphocyte count (L), total number of platelet count (P), and the red cell distribution width (RDW) were directly obtained by the hematology analyzer. The neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR), platelet-to-lymphocyte ratio (PLR) were calculated as follows: absolute neutrophil count/absolute lymphocyte count, absolute monocyte count/absolute lymphocyte count, and total number of platelets/absolute lymphocyte count, respectively. The RDW ranged from 11.6% to 14.8% in our hospital. The serum cancer antigen 125 (CA 125) concentration was detected with a Roche Cobas ${}^{\@setsize{\scriptsize}{8pt}{\viipt}{\@viipt}\textregistered}$ (Roche Diagnostics GmbH, Sandhofer Strasse Mannheim, Germany).

Table 2
Serum inflammatory markers and CA125 level across the study population

Index	Total ( $n=$ 54)		Optimal ( $n=$ 23)		Suboptimal ( $n=$ 31)		$P$ value
PLT	401.9	$\pm$ 144.3	353.6	$\pm$ 94.6	437.8	$\pm$ 164.6	0.076
RDW	13.9	$\pm$ 1.9	13.3	$\pm$ 1.6	14.5	$\pm$ 1.9	0.008
*PLR	238.7	$\pm$ 113.7	187.6	$\pm$ 77.0	276.5	$\pm$ 122.6	0.004
NLR	4.7	$\pm$ 3.4	3.7	$\pm$ 3.1	5.4	$\pm$ 3.5	0.028
MLR	0.3	$\pm$ 0.2	0.2	$\pm$ 0.1	0.4	$\pm$ 0.2	0.013
CA125	890.1	$\pm$ 1,700.8	653.5	$\pm$ 1029.6	1065.6	$\pm$ 2063.6	0.245

*Normal distribution.

Table 3

Optimized cut off value and AUC for each serum inflammatory markers and CA125

Index	PLT	PLR	NLR	MLR	RDW	CA125
Cut off	365.15	196.50	3.340	0.240	13.19	300.85
AUC	0.642	0.718	0.676	0.700	0.712	0.593
95%CI (Upper Limit)	0.494	0.578	0.529	0.560	0.572	0.436
95%CI (Lower Limit)	0.791	0.859	0.823	0.839	0.852	0.750
$P$ -value	0.076	0.007	0.028	0.013	0.008	0.245

Surgical outcome was classified as optimal or suboptimal and determined using Gynecologic Oncology Group criteria (i.e., optimal cytoreduction: a post-operative surgical residuum of $\leqslant$ 1 cm in largest diameter (R0); suboptimal cytoreduction: a post-operative surgical residuum of $>$ 1 cm in largest diameter (R1)) [5].

2.4 Statistical analysis

Data were analyzed using SPSS version 24.0. Univariate analysis was used to generate frequencies and percentages of categorical variables. Continuous variables were presented as mean $\pm$ SD. Numeric data were tested for normality using Kolmogorov Smirnoff test. Normally distributed continuous variables were analyzed using independent $t$ -test while non-normally distributed continuous variables were analyzed using Mann-Whitney. Cut off value for each predictor were optimized using the receiver operating characteristic (ROC) curve and used to determine the sensitivity, specificity, positive and negative predictive value of each predictor The accuracy of each predictor was generated from the area under the receiver operating characteristic curve (AUC). The value of $p<$ 0.05 was statistically considered to be a significant difference between the groups at a confidence interval of 95%.

3. Results

The characteristics of the study population were summarized in Table 1. Mean age, parity, and tumor size did not differ between the study groups ( $p>$ 0.05).

The group with suboptimal outcome had significantly higher PLR, NLR, MLR, and RDW value ( $p<$ 0.05) (Table 2).

Using the ROC curve (Fig. 1), a cut off value and AUC was optimized for each predictor (Table 3). Sensitivity, specificity, positive and negative predictive value, and accuracy for predicting suboptimal outcome were summarized in Table 4. The association between the inflammatory marker and surgical outcome were summarized in Table 5. The strongest association were observed for PLR, followed by RDW and MLR, consecutively.

Figure 1.

The ROC curves of each inflammatory markers and CA-125.

Table 4

Sensitivity, specificity, positive and negative predictive value, and accuracy for each predictor in predicting suboptimal outcome

Index	PLT	PLR	NLR	MLR	RDW	CA125
Sensitivity	64.5	74.2	64.5	74.2	74.2	54.8
Specificity	60.9	69.6	60.9	59.1	60.9	60.9
PPV	68.9	76.6	68.9	71.8	71.8	65.3
NPV	56.0	66.7	56.0	61.9	63.6	50.0
Accuracy	62.9	72.2	62.9	66.7	68.5	57.4

Table 5

The association between the inflammatory marker and surgical outcome

Index	PLT	PLR	NLR	MLR	RDW	CA125
OR	2.828	6.571	2.828	4.153	4.472	1.889
95% CI (Lower limit)	0.928	1.983	0.928	1.289	1.400	0.631
95% CI (Upper limit)	8.622	21.782	8.622	13.384	14.283	5.653
$P$ -value	0.064	0.001	0.064	0.015	0.010	0.253

4. Discussions

In this study, we found that the level of serum inflammatory markers were significantly higher in the group of patients with suboptimal surgical outcome. Three inflammatory markers, i.e. PLR, MLR, and RDW, can predict the surgical outcome with fair accuracy. A similar result was reported by Ashrafganjoei et al. [2]. They reported that a statistically significant relation was found between surgical outcomes and PLR ( $p<$ 0.001). The sensitivity, specificity, and cut off value for PLR were 0.74, 0.67, and 192, respectively. It almost similar to our result (sensitivity 0.74, specificity 0.69, and cut off value for PLR 196.50). However, they reported better sensitivity for NLR, i.e. 0.74. They also reported better AUC for PLR (0.877, 95% CI: 0.82–0.92) and NLR (0.836, 95% CI: 0.77–0.89), compared to the respective AUC in our study. This may in part due to higher proportion of advance stage in the group of patient with suboptimal outcome in their study, compared to our study. However, our study confirm that PLR is so far the best predictor for surgical outcome in patients with epithelial ovarian cancer.

Platelets are involved in key steps of malignancy progression [3]. The role for platelets has been demonstrated in tumor metastasis, tumor growth and angiogenesis. Bambace and Holmes [3] proposed several roles of platelets in tumorigenesis, including: i) tumor cell-induced platelet aggregation following tumor cell intravasation into the vasculature, thereby the platelets “protect” or “cloak” the circulating tumor cells from physical clearance and immune surveillance, (ii) platelets facilitate tumor cell arrest within the vasculature, endothelial cell retraction and subsequent tissue invasion, (iii) platelets induce endothelial cell proliferation and angiogenesis, and (iv) platelet-tumor and platelet stromal interactions in the tumor microenvironment depend, in part, on platelet activation and platelet protein release, which contribute to the inflammatory response. Furthermore, platelet-related proteins and metabolites that facilitate proteolysis and tissue remodeling also enhance tumor growth and metastasis. Thrombocytosis found in ovarian cancer can be explained through a mechanism in which ovarian cancer produce the inflammatory cytokine interleukin-6 (IL-6) and induce platelet-production regulating hormone thrombopoietin (TPO) in the liver, which in turn, stimulate thrombopoiesis [19].

PLR and NLR are two of the inflammatory response markers, which have been reported to relate with cancer prognosis in various cancers [24, 25]. In a recent meta-analysis by Zhao et al. [28], it was suggested that PLR and NLR could be effective prognostic predictors of ovarian cancer. High PLR adversely affected OS (pooled HR 2.05, 95% CI 1.70–2.48) and PFS (pooled HR 1.85, 95% CI 1.53–2.25) in ovarian cancer. Similarly, high NLR had a poor prognostic impact on OS (pooled HR 1.70, 95% CI 1.35–2.15) and PFS (pooled HR 1.77, 95% CI 1.48–2.12) in ovarian cancer. Another meta-analysis by Zhu et al. [29] also reported similar result. PLR and NLR are simple and easily accessible markers that can be used to predict the prognosis of ovarian cancer patients.

The role of RDW in malignant tumors has also attracted much attention. The RDW is a measure of the range of variation in the size of erythrocyte. The RDW reflects erythrocyte cell volume heterogeneity and is a part of the whole blood cell count. A recent meta-analysis by Wang et al. [26] that involved 49 studies with 19,790 individuals reported that high RDW level adversely affected both OS and DFS/PFS/RFS. Colorectal cancer (CRC) had the strongest relationship with poor OS, followed by hepatic cancer (HCC). Negative OS outcomes were also observed in hematological malignancies [26].

The association between RDW and ovarian cancer has been reported in a recent study by Qin et al. [18]. The RDW was negatively correlated with the hemoglobin concentration and positively correlated with the cancer stage, NLR, PLR, and CA 125 level. The AUC of the RDW was 0.876 (95% confidence interval 0.829–0.923). In our study, RDW had the second best performance in predicting the surgical outcome, the AUC was 0.712 (95% CI: 0.572–0.852) and it had the same sensitivity level as PLR with a cut off value of 13.19%. Wang et al. [26] reported the cut off value of RDW between 13% and 14% was associated with worse HRs for OS and DFS/PFS/RFS than others. A study by Li et al. [15] reported that RDW with cutoff 14.5% had independent prognostic significance for OS in patients with epithelial ovarian cancer. The mechanism on how RDW is increased in ovarian cancer is still unclear. In our study, we did not collect other relevant laboratory data regarding inflammatory factors (e.g., inflammatory mediators, the erythrocyte sedimentation rate and C-reactive protein), erithropoietin, vitamin B6 and B12 concentrations, and serum ferritin concentration due to costly issues. These indicators can help to clarify more about elevated RDW’s mechanism in patients with ovarian cancer. To our knowledge, our study is the first study to report the role of RDW in predicting the surgical outcome in patients with epithelial ovarian cancer. Thus, we propose that RDW is a potential marker of ovarian cancer prognosis.

In a study by Xiang et al. [27], preoperative MLR was reported to be a significant predictor of advanced stages, advanced pathologic grades, and positive lymphatic metastasis in ovarian cancer patients. The optimal cut off value of MLR was reported to be 0.23. In this study, MLR was able to predict the surgical outcome of patients with epithelial ovarian cancer, the AUC was 0.700 (95% CI: 0.560–0.839), sensitivity 74.2%, specificity 59.1%, and optimal cut off value was set to be 0.24.

In this study, the performance of CA 125 in predicting the surgical outcome was poor. With a cut off value of 300.85, the AUC was 0.593 (95% CI: 0.436–0.750), sensitivity was 54.8%, and specificity was 60.9% However, in a recent study by Arab et al. [1], they suggested the best cut-off point for the serum CA125 level to distinguish suboptimal debulking was 420 U/ml with 73.0% (95%CI: 0.5590.862) sensitivity and 78.3% (95% CI 0.684–0.862) specificity. When we adopted this cut off value, the sensitivity and specificity of CA 125 in predicting the suboptimal outcome was 45.2% and 69.6%, respectively. A higher cut off value was proposed in a meta-analysis by Skang et al. A cut-off value of 500 U/ml gave a sensitivity of 68.9% (95% CI 62.0–75.1%) and a specificity of 63.2% (95% CI 53.7–71.7%). However, the optimal cut off value for CA 125 that best correlate with the surgical outcome still need to be determined in further study.

Our study is subjected to several limitations. First, our sample is relatively small. Second, we included patients with epithelial ovarian cancer in all stages. Finally, the follow-up studies should be require to make these results stronger. The patients who are most likely to benefit from optimal cytoreduction are those with advance stage (i.e. FIGO stage III to IV), thus a study selectively involving patients in advance stage is still needed. However, our study is the first study that investigate multiple inflammatory markers in predicting the surgical outcome in patients with epithelial ovarian cancer in Indonesia, from which the data are rarely reported. To date, there is no specific study to explain about hormone-related factors from patients.

5. Conclusion

Preoperative serum inflammatory markers such as PLR, MLR, and RDW may serve as useful markers to predict the surgical outcome with fair accuracy in patients with epithelial ovarian cancer.

References

Arab

Jamdar

Sadat Hosseini

Ghodssi-Ghasemabadi

Farzaneh

Ashrafganjoei

. Model for Prediction of Optimal Debulking of Epithelial Ovarian Cancer. Asian Pac J Cancer Prev. 2018 May 26; 19(5): 1319-1324. doi: 10.22034/APJCP.2018.19.5.1319. PMID: 29802693; PMCID: PMC6031811.

Ashrafganjoei

Mohamadianamiri

Farzaneh

Hosseini

Arab

. Investigating Preoperative Hematologic Markers for Prediction of Ovarian Cancer Surgical Outcome. Asian Pac J Cancer Prev. 2016; 17(3): 1445-8. doi: 10.7314/apjcp.2016.17.3.1445. PMID: 27039787.

Bambace

N.M.

and Holmes

C.E.

, The platelet contribution to cancer progression, J Thromb Haemost 9 (2011), 237–249.

Bray

Ferlay

Soerjomataram

Siegel

R.L.

Torre

L.A.

and Jemal

, Global Cancer Statistics 2018: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries, CA: A Cancer Journal for Clinicians, 2018, doi: 10.3322/caac.21492.

Chang

S.J.

Hodeib

Chang

and Bristow

R.E.

, Survival impact of complete cytoreduction to no gross residual disease for advanced-stage ovarian cancer: a meta-analysis, Gynecol Oncol 130 (2013), 493–8.

Del

P.A.

Allavena

Santoro

et al., Molecular pathways in cancer-related inflammation, Biochem Med (Zagreb) 21 (2011), 264–275.

Dewi

, Sebaran Kanker di Indonesia, Riset Kesehatan Dasar 2007, Indonesian Journal of Cancer 11(1) (2017), 1–8.

Du Bois

Reuss

Pujade-Lauraine

Harter

Ray-Coquard

and Pfisterer

, Role of surgical outcome as prognostic factor in advanced epithelial ovarian cancer: A combined exploratory analysis of 3 prospectively randomized phase 3 multicenter trials: by the AGO-OVAR and the GINECO, Cancer 115(6) (2009), 1234–1244.

Gea

I.T.

Loho

M.F.

and Wagey

F.W.

, Gambaran jenis kanker ovarium di RSUP Prof Dr. RD Kandou Manado periode Januari 2013 – Desember 2015, Jurnal e-Clinic (eCl) 4(2) (2016), 1–5.

10.

Grivennikov

S.I.

Greten

F.R.

and Karin

, Immunity, inflammation, and cancer, Cell 140 (2010), 883–899.

11.

Hanahan

and Weinberg

R.A.

, Hallmarks of cancer: the next generation, Cell 144 (2011), 646–674.

12.

Horowitz

N.S.

Miller

Rungruang

Richard

S.D.

Rodriguez

Bookman

M.A.

et al., Does aggressive surgery improve outcomes? Interaction between preoperative disease burden and complex surgery in patients with advanced-stage ovarian cancer: an analysis of GOG 182. American Society of Clinical Oncology, Journal Clinical Oncology 33 (2015), 1–11. doi: 10.1200/JCO.2014.56.3106.

13.

Kemal

Demirag

Bas

et al., The value of red blood cell distribution width in endometrial cancer, Clin Chem Lab Med 53 (2015), 823–7.

14.

Koma

Onishi

Matsuoka

et al., Increased red blood cell distribution width associates with cancer stage and prognosis in patients with lung cancer, PloS One 8 (2013), e80240.

15.

Hong

Robertson

Wang

and Jiang

, Preoperative red cell distribution width and neutrophil-to-lymphocyte ratio predict survival in patients with epithelial ovarian cancer, Scientific Reports 7(43001) (2017), 1–11.

16.

Mantovani

Allavena

Sica

and Balkwill

, Cancer related inflammation, Nature 454 (2008), 436–444.

17.

Matulonis

U.A.

Sood

A.K.

Fallowfield

Howitt

B.E.

Sehouli

and Karlan

B.Y.

, Ovarian cancer, Nat Rev Dis Primers 2 (2016), 16061.

18.

Qin

Wang

Huang

Tang

and Guo

, The value of red cell distribution width in patients with ovarian cancer, Medicine 96 (2017), 17.

19.

Qiu

Xie

and Lu

, Preoperative plasma fibrinogen, platelet count and prognosis in epithelial ovarian cancer, Journal of Obstetrics and Gynaecology Research 38(4) (2012), 651–7.

20.

Razi

Ghoncheh

Mohammadian-Hafshejani

Aziznejhad

Mohammadian

and Salehiniya

, The incidence and mortality of ovarian cancer and their relationship, Ecancermedicalscience 10 (2016), 628.

21.

Siegel

R.L.

Miller

K.D.

and Jemal

, Cancer statistics, CA: A Cancer Journal for Clinicians 68(1) (2018), 7–30. doi: 10.3322/caac.21442.

22.

Sihombing

and Sirait

A.M.

, Survival Rate of Ovarium Cancer Dr. Cipto Mangunkusumo Hospital, Jakarta, Majalah Kedokteran Indonesia 57(10) (2007), 346–352.

23.

Smirne

Grossi

Pinato

D.J.

et al., Evaluation of the red cell distribution width as a biomarker of early mortality in hepatocellular carcinoma, Dig Liver Dis 47 (2015), 488–94.

24.

Templeton

A.J.

Ace

McNamara

M.G.

Al-Mubarak

Vera-Badillo

F.E.

Hermanns

Seruga

Ocana

Tannock

I.F.

and Amir

, Prognostic role of platelet to lymphocyte ratio in solid tumors: a systematic review and meta-analysis, Cancer Epidemiol Biomark Prev Publ Am Assoc Cancer Res Cospons Am Soc Prev Oncol 23(7) (2014), 1204–1212. doi: 10.1158/1055-9965.epi-14-0146.

25.

Templeton

A.J.

McNamara

M.G.

Seruga

Vera-Badillo

F.E.

Aneja

Ocana

Leibowitz-Amit

Sonpavde

Knox

J.J.

Tran

Tannock

I.F.

and Amir

, Prognostic role of neutrophil-to-lymphocyte ratio in solid tumors: a systematic review and meta-analysis, J Natl Cancer Inst 106(6) (2014), dju124. doi: 10.1093/jnci/dju124.

26.

Wang

P.F.

Song

S.Y.

Guo

Wang

T.J.

Liu

and Yan

C.X.

, Prognostic role of pretreatment red blood cell distribution width in patients with cancer: A meta-analysis of 49 studies, Journal of Cancer 10(18) (2019), 4305–4317. doi: 10.7150/jca.31598.

27.

Xiang

Zhou

Bao

Chen

and Wan

, Preoperative Monocyte-to-Lymphocyte Ratio in Peripheral Blood Predicts Stages, Metastasis, and Histological Grades in Patients with Ovarian Cancer, Translational Oncology 10 (2016), 33–39.

28.

Zhao

Xue

Liu

and Mao

, Prognostic roles of neutrophil to lymphocyte ratio and platelet to lymphocyte ratio in ovarian cancer: a meta-analysis of retrospective studies, Archives of Gynecology and Obstetrics, 2018. doi: 10.1007/s00404-018-4678-8.

29.

Zhu

Zhou

Liu

Zhai

and Sun

, Prognostic value of systemic inflammatory markers in ovarian Cancer: a PRISMAcompliant meta-analysis and systematic review, BMC Cancer 18 (2018), 443.

Predictive value of preoperative inflammatory markers and serum CA 125 level for surgical outcome in Indonesian women with epithelial ovarian cancer

Abstract

BACKGROUND:

OBJECTIVE:

METHODS:

RESULTS:

CONCLUSION:

Keywords

1. Introduction

2. Methods

2.1 Design

Table 1 Characteristics of study population

2.3 Variables and measurement

Table 2 Serum inflammatory markers and CA125 level across the study population

3. Results

5. Conclusion

References

Table 1
Characteristics of study population

Table 2
Serum inflammatory markers and CA125 level across the study population