Introduction
Brain metastasis develops in one-third of the patients
with adult cancer. It causes significant morbidity and
mortality on the patients.[]
The primary cancer region is known in the majority
of brain metastasis, while the primary region is
not known in 5-10% of the patients. Most of the brain
metastases arise from lung (40-50%), breast (15-25%),
melanoma (5-20%) and kidney (5-10%).[]
Treatment options are surgical resection, radiotherapy
(whole-brain radiotherapy or stereotactic radiosurgery)
and systemic drugs. These treatments are applied
according to the tumor location, number and size
of the tumor in the brain, extracranial disease status,
patient"s performance status, histological and molecular
characteristics of the tumor.[]
Several prognostic factors have been identified for
predicting the outcome of the treatments, which included
the number of brain metastases, the extracranial
tumor status and the Karnofsky performance
scale. However, these factors were insufficient to predict
survival.[]
Cancer cachexia is characterized by systemic inflammation,
negative protein and energy balance, loss of lean
tissue mass.[] Sarcopenia is defined as the mass and
function of the skeletal muscle and has a core role in cancer-
related cachexia.[] It is associated with functional
impairment and disability and can be used as an objective,
measurable frailty parameter. Recent studies with
various cancers have shown a decrease in long-term results
with sarcopenia.[,] The majority of these observations
were based on measurements of skeletal muscle
index at the level of the third lumbar spine obtained by
computed tomography (CT) scans. In one study, there
was a positive relationship between the psoas major
muscle cross-sectional area and temporal muscle thickness
(TMT).[] Therefore, the measurement of craniofacial
muscles concerning sarcopenia in patients with
brain metastasis appears to be a suitable option.
Previous studies have shown the relationship between
temporal muscle thickness measurement and
survival in lung, breast and malignant melanoma patients
with brain metastasis.[,]
Recently, it has been reported that the immunological
status, including the state of inflammation and
nutritional status, is important for survival in cancer.
Routine immune and inflammatory cells, such as neutrophils,
monocytes and lymphocytes in the systemic
circulation, may contribute to tumor cell invasion and
metastasis.[] Different inflammation-based scores
have been proposed and are considered useful in this
respect.[] Previous studies showed neutrophil-lymphocyte
ratio (NLR) and platelet-lymphocyte ratio
(PLR) as an indicator of inflammation; prognostic nutritional
index (PNI) was also studied in patients with
lung cancer for nutritional status.[,] Systemic inflammation
response index (SIRI), another marker of
inflammation, has been shown to be associated with
prognosis in other cancers [,] but has not previously
been studied in lung cancer.
In this study, we aimed to show the prognostic impact
of temporal muscle thickness (TMT) measured in
brain MR images and inflammatory (NLR, PLR, SIRI)
and nutritional parameter (PNI) on survival in lung
cancer patients with brain metastasis.
Methods
Patients and Study Design
A total of 94 cancer patients diagnosed with brain
metastasis lung cancer were evaluated at the Gaziantep
University Faculty of Medicine between 2011-2018,
retrospectively. This study was approved by the ethics
committee of Gaziantep University. The inclusion criteria
were as follows: available MR images examination
of the brain, absence of craniectomy defect and muscle
edema in the temporal muscle region, accessible laboratory
results, confirmed pathologically as the small
cell lung carcinoma (SCLC) and non-small cell lung
carcinoma (NSCLC). In SCLC, patients who had brain
metastasis at the time of diagnosis and who had not received
radiotherapy before were included in this study.
Informed consent was obtained from all individual
participants before brain MR imaging. Routine blood
sampling, such as the counts of neutrophils, lymphocytes,
monocytes, platelets and albumin, was tested
within 10 days of brain MR imaging before the use of
steroid therapy. Systemic inflammation and nutrition
parameters were calculated according to the following
formula:
NLR: Peripheral neutrophil count (per mm3)/lymphocyte
count ratio (per mm3)
PLR: Peripheral platelet count (per mm3)/lymphocyte
count ratio (per mm3)
SIRI: Peripheral neutrophil count (per mm3)x monocyte
count (per mm3)/lymphocyte count (per mm3)
PNI: [10×serum albumin (g/dL)]+[0.005×total
lymphocyte count (per mm3)]
Analyses of the Temporal Muscle Thickness
Temporal muscle thickness (TMT) measurement at
diagnosis of brain metastasis was measured on the
contrast-enhanced axial T1- weighted MR images.
Measurement was made perpendicular to the long axis
of the temporal muscle at the level of the orbital roof.
On axial T1- weighted MR images, left and right TMT
were measured by an experienced radiologist for all
patients. The radiologist was blinded to the results of
patients" clinical characteristics. The parameters of axial
T1- weighted MR images were a TR of 160 ms, a TE
of 2 ms, a FOV of 210 mm, a slice thickness of 5 mm, assessa
gap of 1 mm. Examples of TMT assessment on MR
images was shown in Figure 1.
Fig 1: Temporal muscle thickness measurement on a 52
years- old-male patient with non-small cell lung
cancer. Contrast-enhanced axial T1 weighted MR
image shows temporal muscle thickness measurement
bilaterally.
Statistical Analysis
Median TMT was assessed by summing the measurements
from each side and then dividing by two, which
resulted in one variable? the median TMT per patient.
TMT was grouped according to the median per group.
Overall survival (OS) was defined as from the diagnosis
of brain metastasis to death or date of the last follow-up.
The survival durations were presented as weeks. The Kaplan-
Meier was applied to retrieve survival curves. The
log-rank test was used to investigate differences between
groups. TMT was entered as a scale variable in a Cox
regression model to investigate the association with survival
times. The strength of the association between two
scale variables was calculated using a Spearman correlation
coefficient. A two-tailed p-value of<0.05 was considered
statistically significant. Statistical analyses were
performed using the Statistical Package for the Social
Sciences, Version 22.0 (SPSS, Chicago, Illinois).
Results
The patients" characteristics are shown in Table 1.
Overall, 94 patients were available for further analysis, assembled from two groups that consisted of 71 patients
with brain metastases from NSCLC and 23 patients
with brain metastases from SCLC. The median
age was 60 (range: 39-78); 87.2% of the patients were
male. In the NSCLC group, the majority of patients (50;
70.4%) had adenocarcinoma, while 21 (29.5%) patients
had squamous cell carcinoma. The median TMT on the
right side was 4.43 mm (range 1.9-9.5), and 4.36 mm
(range 1.9-8.4) on the left side, resulting in a median
of 4.38 mm (range 1.9-8.7) in the NSCLC group. The
median TMT on the right side was 4.22 mm (range
2.4-7.7), and 3.98 mm (range 2.2-6.2) on the left side,
resulting in a median of 3.90 mm (range 2.4-6.7) in the
SCLC group. According to median TMT 4.32, no difference
was observed between the groups of metastasis
at the time of diagnosis (p=0.778), brain metastasis at
the time of diagnosis (p=0.148) and the number of additional
metastatic regions (p=0.548) (Chi-square test).
Survival analysis was performed according to median
TMT 4.32 value and Table 2 showed survival values for
TMT and inflammatory-nutritional parameters. The
OS analysis of all lung cancer patients was 13 weeks, according
to subgroups of TMT, 12 weeks for TMT <4.32
and 15weeks for ≥4.32. There was no difference in OS
between the values of <4.32 and ≥4.32 in the NSCLC
group (15 weeks). In the SCLC group, the OS was 19
weeks for ≥4.32, six weeks for <4.32 (p=0.016) (Fig. 2).
Table 1: An overview of the patients' characteristics
Table 2: Survival values for the temporal muscle thickness
and inflammatory-nutritional parameters
Fig 2: The Overall survival analysis of small cell lung
cancer patients between the median temporal
muscle thickness values of <4.32 and ≥4.32.
The cut-off point for NLR from inflammatory parameters
was 3.80. Survival was 33 weeks for NLR
<3.80, while six weeks for ≥3.80 (p=0.002); 16 weeks
for PLR <188, 10 weeks for ≥188 (p=0.125). The cut-off
point for SIRI was 2.81. Median OS analysis was seven
weeks for SIRI <2.81 and 29 weeks for ≥2.81 (p=0.008).
According to the PNI parameter for nutritional assessment, survival was 33 weeks for ≥42.7 and <42.7 for
six weeks (p=0.001). Median OS in the SCLC group
was six weeks for TMT <4.32 and 19 weeks for ≥4.32
(p=0.016). OS was 19 weeks for NLR <3.80, five weeks
for ≥3.80 (p=0.001); 18 weeks for PLR <188, five weeks
for ≥188 (p=0.106); nine weeks for SIRI <2.81, 12 weeks
for ≥2.81 (p=0.405); 22 weeks for PNI ≥42.7, four
weeks for <42.7 (p=0.000). According to the Spearman
correlation analysis, there was no correlation between
median TMT and NLR, PLR, SIRI and PNI.
A survival analysis using a Cox regression model
was performed with baseline TMT diameters to predict
survival time in general lung cancer patients, SCLC and
NSCLC group. In general group, according to cut-off
4.32, TMT was statistically significantly associated with
survival prognosis with an HR of 0.812 (95% CI 0.66-
0.99; p=0.040). Markedly, the risk of death increased by
18.8% with every millimeter reduction in TMT. However,
this survival difference was not detected in SCLC
and NSCLC subgroups (p=0.059; p=0.124).
In the multivariate cox analysis, including TMT,
NLR, PLR, SIRI and PNI parameters, only PNI showed
a statistically significant association with survival prognosis
(HR 0.483; 95% CI 0.28-0.82; p=0.008).
Discussion
According to our experience in the daily population,
an increase in the incidence of brain metastases is observed
upon the prolongation of the survival of cancer
patients and the progression of imaging techniques.
BM is a heterogeneous group with the varied response
to treatment and survival. Therefore, it is important
to consider the various factors affecting the
prognosis of patients with BM before therapeutic decision
making.
In this study, the effects of both the measurement of
TMT and inflammatory and nutritional parameters on
survival in brain metastatic lung adenocarcinoma were
investigated.
Multiple factors play a role in the etiology of cancerassociated
cachexia, which are malnutrition, catabolic
metabolism, chronic inflammation, paraneoplastic effects,
anti-neoplastic therapies, psychological reasons
(such as anxiety disorder and depression) and other
factors.
Sarcopenia is a loss of muscle and is used as an indicator
of cachexia. In previous publications, craniofacial skeletal muscles have been shown to be beneficial in
predicting muscle mass loss, in addition to abdominal
skeletal muscles. In Leitner et al."s study, a high correlation
was found between TMT and lumbar skeletal
muscle measurements.[] In the study of Swartz et al.,
paravertebral muscles and sternocleidomastoid muscles
in the third cervical vertebra region were measured in
patients with head and neck cancer.[] These results
were correlated with muscle measurements in the third
lumbar vertebra region. Kilgour et al. showed a loss of
muscle mass with the cross-sectional measurement of
neck muscles in the second cervical vertebrae in elderly
patients.[] Lisiecki et al."s study, TMT was measured
in patients with mandibular fractures due to trauma.
[] As a result of the study, the duration of stay in the
hospital, intensive care unit and the ventilator was reported
longer in patients with low TMT. In a study with
children diagnosed with non-syndromic craniosynostosis,
a negative correlation was found between temporal
muscle volume and length of hospital stay.[]
In our study, there was a significant relationship between
TMT and survival. The overall survival of patients
with brain metastatic lung cancer was 13 weeks. In these
patients, the risk of death increased by 18.8% with every
millimeter reduction in TMT. The patients were classified
into two main groups. Unfortunately, there was
no difference in survival according to median TMT in
SCLC and NSCLC group. This situation was thought to
be due to the small number of patients in groups.
In the study of Further et al., median TMT was calculated
as 5.9 mm in patients with NSCLC and survival
difference was shown.[] In the study of Leitner et al.,
the mean TMT value was 6.1 mm.[] In our study, the
median TMT value of our patients was lower than the
literature. This situation suggested that sarcopenia was
advanced in our patients or there was a possibility of
social difference.
In our study, the TMT value was not affected by age
and gender. Further et al. reported a negative correlation
between age and TMT.[] They emphasized that
physical condition was more important than the patient"s
age.
It was thought that the prognosis might be worse in
patients with metastatic phase at the time of diagnosis,
brain metastasis at the time of diagnosis or additional
metastatic region other than brain metastasis. Chisquare
analysis performed with this thought; however,
there was no correlation between TMT and these clinical
indicators (p=0.778; p=0.148; p=0.548).
Inflammation is recognized as one of the hallmarks
of cancer.[] Cancer-related inflammation plays an important role in the development and progression of
cancer. Immune and inflammatory cells are considered
to be essential components of the tumor microenvironment.[]
The actual mechanisms underlying how the NLR,
PLR and SIRI affect prognosis in patients with cancer
are unclear. High NLR represents increased neutrophil
count and/or decreased lymphocyte count; similarly,
high PLR indicates increased platelet and/or decreased
lymphocyte count. In preclinical studies, neutrophils
have been shown to increase cancer cell metastasis, invasion
and proliferation, but also help to escape the immune
system and thus promote tumor activity. Platelets
can collect granulocytes and facilitate the early formation
of metastatic niches. Platelet-derived growth factors
and direct contact of the tumor cell with platelet
can activate tumor pathways in malignant cells and facilitate
the formation of metastases. Lymphocytes have
strong effects that can inhibit tumor cell migration and
growth and stimulate cytotoxic cell death. Monocytes
can induce tumor cells or immune cells by producing
chemokines, such as vascular endothelial growth
factor, tumor necrosis factor-alpha and monocyte
chemoattractant protein-1, to promote tumor formation,
angiogenesis and distant metastasis.[] Yu et al.
conducted a meta-analysis of a large sample with 7219
patients and demonstrated that a high NLR predicted
poor OS (HR: 1.46; 95% CI: 1.30?1.64).[] Liu et al.
found that a high PLR was a significant independent
prognostic factor for patients with NSCLC (HR: 2.025;
95% CI: 1.405?2.919; p<0.001).[] Dong et al. reported
that NLR was an independent prognostic factor
for poor OS (HR: 1.954; 95% CI: 1.172?3.257; p=0.01),
but not the PLR.[] Mitsuya et al. reported a survival
benefit in patients with low NLR levels who had been
operated for brain metastasis (14 months versus five
months, p=0.01).[] Doi et al. reported that low NLR
levels and positive programmed death-1 (PD-1) values
in patients with brain metastasis might be associated
with a strong immune response to tumor.[]
Lung cancer is a heterogeneous group and has different
subtypes. Therefore, Igor et al. investigated that
NLR and PLR parameters may differ between subtypes
in lung cancer patients.[] As a result of their study,
there was no difference between these subtypes concerning
NLR and PLR parameters. Similar results were
found in the SCLC and NSCLC groups for NLR and
PLR in our study (for NLR p=0.001, p=0.002; for PLR
p=0.106, p=0.125).
The SIRI parameter has been studied in various cancers
and can be used as a prognostic marker.[,,] According to the literature review, SIRI has not been
studied previously in lung cancer.
In our study, low SIRI was associated with improved
survival in the analysis of all lung cancer patients (HR:
1.70; 95% CI: 1.07-2.71; p=0.023). In subgroup analysis,
the survival of SIRI was statistically significant
in NSCLC and no difference was observed in SCLC
(p=0.008; p=0.405). In this case, the small number
of patients in the SCLC group was thought to be the
causative factor.
PNI is a parameter that shows the immune and nutritional
status calculated by the combination of serum
albumin and lymphocyte values. It predicts the prognosis
of patients with cancer independent of origin.
[] It has not been studied previously in patients with
brain metastasis. In our study, high PNI values were
associated with increased survival in all lung cancer
patients (HR: 2.59; 95% CI: 1.61-4.15; p<0.001). In
subgroup analysis, survival in both SCLC and NSCLC
was statistically significant (p<0.001, p=0.001).
In patients with brain metastases, temporal muscle
measurements were performed to evaluate sarcopenia
more quickly and easily. In one study, TMT measurement
was shown to last 30 seconds.[] Similarly, our
measurements were made in a short time. Abdominal
imaging was not performed during the diagnosis of
brain metastasis in the majority of patients. Therefore,
abdominal muscular thickness measurement was not
used for the evaluation of sarcopenia. Because of this
reason, TMT measurement is useful for the evaluation of
sarcopenia in patients with brain metastasis. The disadvantage
of TMT measurement is that it is affected by oral
and dental diseases and rheumatic diseases (e.g. temporal
arteritis) in the differential diagnosis of headache in
elderly patients. Therefore, both sides of the muscle were
measured and analyzed according to the median value.
Patients who had steroid use and infection because
of the effect of steroid use on hematological parameters
and hypoalbuminemia as a negative acute-phase reaction
during infection were not included in this study.
There were restrictive aspects of our study. First,
the number of patients in this study was low, and the
number of patients in the subgroups was not evenly
distributed; the second limitation was that this was a
retrospective study.
The TMT value of our patients was lower than the
other studies. The reason for this was the inclusion of
more complicated cases in our study because our center
was the tertiary center. It is thought that the importance
of TMT and inflammatory-nutritional status
assessment will be better understood in the future by studies that are prospectively designed and included a
larger number of patients.
Conclusion
In this study, the findings suggest the use of TMT and
inflammatory-nutritional parameters for the evaluation
of sarcopenia were practical and rapid methods
in lung cancer patients with brain metastasis. In this
group of patients with a short duration of survival, the
prognosis of patients can be predicted and their exposure
to toxic treatment may be prevented by the evaluation
of sarcopenia.
Peer-review: Externally peer-reviewed.
Conflict of Interest: The authors declare that they have no
conflict of interest.
Ethics Committee Approval: The protocol and informed
consent documentation were reviewed and approved by the
Independent Ethics Committee of the University and agreed
with the ethical principles of the Declaration of Helsinki.
Financial Support: Financial and material support was not
recevied.
Authorship contributions: Concept - H.Y.Ç., H.Ç.E.; Design
- H.Y.Ç., H.Ç.E.; Supervision - H.Y.Ç., H.Ç.E.; Funding
- H.Y.Ç.; Materials - H.Y.Ç., H.Ç.E.; Data collection and/or
processing - H.Y.Ç., H.Ç.E.; Data analysis and/or interpretation
- H.Y.Ç., H.Ç.E.; Literature search - H.Y.Ç.; Writing
- H.Y.Ç.; Critical review - H.Y.Ç., H.Ç.E.
References
Arvold ND, Lee EQ, Mehta MP, Margolin K, Alexander
BM, Lin NU, et al. Updates in the management of
brain metastases. Neuro Oncol 2016;18(8):1043-65.
Ekici K, Temelli O, Dikilitas M, Halil Dursun I, Bozdag
Kaplan N, Kekilli E. Survival and prognostic factors in
patients with brain metastasis: Single center experience.
J BUON 2016;21(4):958-63.
Kondziolka D, Parry PV, Lunsford LD, Kano H,
Flickinger JC, Rakfal S, et al. The accuracy of predicting
survival in individual patients with cancer. J Neurosurg
2014;120(1):24-30.
Fearon K, Strasser F, Anker SD, Bosaeus I, Bruera E,
Fainsinger RL, et al. Definition and classification of
cancer cachexia: an international consensus. Lancet
Oncol 2011;12(5):489-95.
Prado CM, Lieffers JR, McCargar LJ, Reiman T,
Sawyer MB, Martin L, et al. Prevalence and clinical
implications of sarcopenic obesity in patients with
solid tumours of the respiratory and gastrointestinal tracts: a population-based study. Lancet Oncol
2008;9(7):629-35.
Tamandl D, Paireder M, Asari R, Baltzer PA, Schoppmann
SF, Ba-Ssalamah A. Markers of sarcopenia quantified
by computed tomography predict adverse longterm
outcome in patients with resected oesophageal
or gastro-oesophageal junction cancer. Eur Radiol
2016;26(5):1359-67.
Ranganathan K, Terjimanian M, Lisiecki J, Rinkinen J,
Mukkamala A, Brownley C, et al. Temporalis muscle
morphomics: the psoas of the craniofacial skeleton. J
Surg Res 2014;186(1):246-52.
Furtner J, Berghoff AS, Albtoush OM, Woitek R,
Asenbaum U, Prayer D, et al. Survival prediction
using temporal muscle thickness measurements
on cranial magnetic resonance images in patients
with newly diagnosed brain metastases. Eur Radiol
2017;27(8):3167-73.
Furtner J, Berghoff AS, Schöpf V, Reumann R, Pascher
B, Woitek R, et al. Temporal muscle thickness is an independent
prognostic marker in melanoma patients
with newly diagnosed brain metastases. J Neurooncol
2018;140(1):173-8.
Diakos CI, Charles KA, McMillan DC, Clarke SJ.
Cancer related inflammation and treatment effectiveness.
Lancet Oncol 2014;15(11):e493-503.
Chen J, Fang A, Chen M, Tuoheti Y, Zhou Z, Xu L, et
al. A novel inflammation?based nomogram system to
predict survival of patients with hepatocellular carcinoma.
Cancer Med 2018;7(10):5027-35.
Sun H, Hu P, Du J, Wang X. Predictive value of inflammatory
indexes on the chemotherapeutic response in
patients with unresectable lung cancer: A retrospective
study. Oncol Lett 2018;15(3):4017-25.
Shimizu K, Okita R, Saisho S, Yukawa T, Maeda A, Nojima
Y, et al. Prognostic nutritional index before adjuvant
chemotherapy predicts chemotherapy compliance
and survival among patients with non-small-cell
lung cancer. Ther Clin Risk Manag 2015;11:1555-61.
Xu L, Yu S, Zhuang L, Wang P, Shen Y, Lin J, et al. Systemic
inflammation response index (SIRI) predicts
prognosis in hepatocellular carcinoma patients. Oncotarget
2017;8(21):34954-60.
Qi Q, Zhuang L, Shen Y, Geng Y, Yu S, Chen H, et al. A
novel systemic inflammation response index (SIRI) for
predicting the survival of patients with pancreatic cancer
after chemotherapy. Cancer 2016;122(14):2158-67.
Leitner J, Pelster S, Schöpf V, Berghoff AS, Woitek
R, Asenbaum U, et al. High correlation of temporal
muscle thickness with lumbar skeletal muscle crosssectional
area in patients with brain metastases. PLoS
One 2018;13(11):e0207849.
Swartz JE, Pothen AJ, Wegner I, Smid EJ, Swart KM,
de Bree R, et al. Feasibility of using head and neck CT imaging to assess skeletal muscle mass in head and
neck cancer patients. Oral Oncol 2016;62:28-33.
Kilgour AH, Subedi D, Gray CD, Deary IJ, Lawrie SM,
Wardlaw JM, et al. Design and validation of a novel
method to measure cross-sectional area of neck muscles
included during routine MR brain volume imaging.
PLoS One 2012;7(4):e34444.
Lisiecki J, Zhang P, Wang L, Rinkinen J, De La Rosa
S, Enchakalody B, et al. Morphomic measurement
of the temporalis muscle and zygomatic bone as
novel predictors of hospital-based clinical outcomes
in patients with mandible fracture. J Craniofac Surg
2013;24(5):1577-81.
Rinkinen J, Zhang P, Wang L, Enchakalody B, Terjimanian
M, Holcomb S, et al. Novel temporalis muscle
and fat pad morphomic analyses aids preoperative risk
evaluation and outcome assessment in nonsyndromic
craniosynostosis. J Craniofac Surg 2013;24(1):250-5.
Hanahan D, Weinberg RA. Hallmarks of cancer: the
next generation. Cell 2011;144(5):646-74.
Chen Y, Wang W, Zhang X, Yu X, Xi K, Wen Y, et
al. Prognostic significance of combined preoperative
platelet-to-lymphocyte ratio and lymphocytetomonocyte
ratio in patients undergoing surgery with
stage IB non-small-cell lung cancer. Cancer Manag
Res 2018;10:5411-22.
Yu Y, Qian L, Cui J. Value of neutrophil-to-lymphocyte
ratio for predicting lung cancer prognosis:
a meta-analysis of 7,219 patients. Mol Clin Oncol
2017;7(3):498-506.
Liu H, Wu Y, Wang Z, Yao Y, Chen F, Zhang H, et al.
Pretreatment platelet-to-lymphocyte ratio (PLR) as
a predictor of response to first-line platinum-based chemotherapy and prognosis for patients with non-small
cell lung cancer. J Thorac Dis 2013;5(6):783-9.
Guo D, Li M, Chen D, Jing W, Zhu H, Fu L, et al. Neutrophil-
to-lymphocyte ratio is superior to plateletto-
lymphocyte ratio as a prognostic predictor in
advanced non-small-cell lung cancer treated with
first-line platinum-based chemotherapy. Future Oncol
2019;15:625-35.
Mitsuya K, Nakasu Y, Kurakane T, Hayashi N, Harada
H, Nozaki K. Elevated preoperative neutrophil-tolymphocyte
ratio as a predictor of worse survival after
resection in patients with brain metastasis. J Neurosurg
2017;127(2):433-7.
Doi H, Nakamatsu K, Anami S, Fukuda K, Inada M,
Tatebe H, et al. Neutrophil-to-?Lymphocyte Ratio
Predicts Survival After Whole-brain Radiotherapy in
Non-small Cell Lung Cancer. In Vivo 2019;33(1):195-
201.
Nikolic I, Kukulj S, Samarzija M, Jelec V, Zarak M,
Orehovec B, et al. Neutrophil-to-lymphocyte and
platelet-to-lymphocyte ratio help identify patients
with lung cancer, but do not differentiate between lung
cancer subtypes. Croat Med J 2016;57(3):287-92.
Li S, Lan X, Gao H, Li Z, Chen L, Wang W, et al. Systemic
Inflammation Response Index (SIRI), cancer
stem cells and survival of localised gastric adenocarcinoma
after curative resection. J Cancer Res Clin Oncol.
J Cancer Res Clin Oncol 2017;143(12):2455-68.
Proctor MJ, Morrison DS, Talwar D, Balmer SM,
Fletcher CD, O"Reilly DS, et al. A comparison of inflammation-
based prognostic scores in patients with
cancer. A Glasgow Inflammation Outcome Study. Eur
J Cancer 2011;47(17):2633-41.
