<?xml version="1.0" encoding="UTF-8"?><!DOCTYPE article  PUBLIC "-//NLM//DTD Journal Publishing DTD v3.0 20080202//EN" "http://dtd.nlm.nih.gov/publishing/3.0/journalpublishing3.dtd"><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" dtd-version="3.0" xml:lang="en" article-type="research article"><front><journal-meta><journal-id journal-id-type="publisher-id">OJOG</journal-id><journal-title-group><journal-title>Open Journal of Obstetrics and Gynecology</journal-title></journal-title-group><issn pub-type="epub">2160-8792</issn><publisher><publisher-name>Scientific Research Publishing</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.4236/ojog.2024.145059</article-id><article-id pub-id-type="publisher-id">OJOG-133174</article-id><article-categories><subj-group subj-group-type="heading"><subject>Articles</subject></subj-group><subj-group subj-group-type="Discipline-v2"><subject>Medicine&amp;Healthcare</subject></subj-group></article-categories><title-group><article-title>
 
 
  Sarcopenia and Anemia Are Predictors of Poor Prognostic in Cervical Cancer Patients
 
</article-title></title-group><contrib-group><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Leandro</surname><given-names>Santos de Araujo Resende</given-names></name><xref ref-type="aff" rid="aff1"><sup>1</sup></xref></contrib><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Francine</surname><given-names>Vilela de Amorim</given-names></name><xref ref-type="aff" rid="aff2"><sup>2</sup></xref></contrib><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Miguel</surname><given-names>Soares Concei&amp;#231;&amp;#227;o</given-names></name><xref ref-type="aff" rid="aff2"><sup>2</sup></xref></contrib><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Rodrigo</surname><given-names>Menezes Jales</given-names></name><xref ref-type="aff" rid="aff3"><sup>3</sup></xref></contrib><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Patrick</surname><given-names>Nunes Pereira</given-names></name><xref ref-type="aff" rid="aff3"><sup>3</sup></xref></contrib><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Luis</surname><given-names>Ot&amp;#225;vio Sarian</given-names></name><xref ref-type="aff" rid="aff3"><sup>3</sup></xref></contrib><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Glauco</surname><given-names>Baiocchi</given-names></name><xref ref-type="aff" rid="aff4"><sup>4</sup></xref></contrib><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Sophie</surname><given-names>Derchain</given-names></name><xref ref-type="aff" rid="aff3"><sup>3</sup></xref></contrib><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Agnaldo</surname><given-names>Lopes da Silva Filho</given-names></name><xref ref-type="aff" rid="aff1"><sup>1</sup></xref></contrib></contrib-group><aff id="aff3"><addr-line>Department of Obstetrics and Gynecology, School of Medical Science, State University of Campinas (UNICAMP), Campinas, Brazil</addr-line></aff><aff id="aff2"><addr-line>Department of Health Sciences, University of Sao Francisco, S&amp;amp;#227;o Paulo, Brazil</addr-line></aff><aff id="aff1"><addr-line>Department of Gynecology and Obstetrics, Botucatu Medical School, S&amp;amp;#227;o Paulo State University, UNESP, S&amp;amp;#227;o Paulo, Brazil</addr-line></aff><aff id="aff4"><addr-line>Department of Gynecologic Oncology, AC Camargo Cancer Center, Sao Paulo, Brazil</addr-line></aff><pub-date pub-type="epub"><day>08</day><month>05</month><year>2024</year></pub-date><volume>14</volume><issue>05</issue><fpage>693</fpage><lpage>704</lpage><history><date date-type="received"><day>16,</day>	<month>April</month>	<year>2024</year></date><date date-type="rev-recd"><day>14,</day>	<month>May</month>	<year>2024</year>	</date><date date-type="accepted"><day>17,</day>	<month>May</month>	<year>2024</year></date></history><permissions><copyright-statement>&#169; Copyright  2014 by authors and Scientific Research Publishing Inc. </copyright-statement><copyright-year>2014</copyright-year><license><license-p>This work is licensed under the Creative Commons Attribution International License (CC BY). http://creativecommons.org/licenses/by/4.0/</license-p></license></permissions><abstract><p>
 
 
  &lt;b&gt;Objective:&lt;/b&gt; Evaluate pretreatment sarcopenia and anemia as prognostic factors in women undergoing treatment for cervical cancer (CC) with concurrent chemoradiotherapy (CCRT). &lt;b&gt;Methods: &lt;/b&gt;151 women with CC were analysed in this cohort study.&lt;b&gt; &lt;/b&gt;Pretreatment computed tomography (CT) images were analysed to assess skeletal muscle index (SMI). Hazard ratios (HR) and multivariate Cox proportional HR were used to analyse association between low SMI, age, body mass index (BMI), haemoglobin levels, histological type, and International Federation of Gynaecology and Obstetrics (FIGO) stage with PFS and OS. &lt;b&gt;Results: &lt;/b&gt;A total of 151 patients were included, 53 (35.1%) presented pretreatment sarcopenia; 51 (34%) stage I/II and 100 (66%) stage III/IV. Among those patients in advanced stage (III/IV) 37 (70%) (p = 0.28) were sarcopenic at the beginning of treatment. Sarcopenia was associated with worse progression-free survival (PFS) and overall survival (OS) in our cohort [HR 0.97 (p = 0.01)] [HR 0.73 (p = 0.001)], as well as anemia [HR 0.73 (p = 0.001)] [HR 0.78 (p = 0.001)]. Linear regression models indicated that despite showing no association with age, neutrophil or platelet counts, sarcopenia was associated with pretreatment anemia levels (p = 0.01). After a multivariate analysis, only haemoglobin (anemia) and complete CCRT remained associated with PFS and OS. Sarcopenia and anemia were associated with worse PFS and OS in FIGO stage I/II. &lt;b&gt;Conclusion:&lt;/b&gt; Pretreatment sarcopenia was significantly associated with low haemoglobin levels. Anemia and incomplete CCRT were independently associated with poor prognosis in women with CC. Pretreatment sarcopenia, as low SMI, was a predictor of poor prognostic in early stages of CC.
 
</p></abstract><kwd-group><kwd>Cervical Cancer</kwd><kwd> Sarcopenia</kwd><kwd> Anemia</kwd><kwd> Chemoradiotherapy</kwd></kwd-group></article-meta></front><body><sec id="s1"><title>1. Introduction</title><p>Cervical cancer (CC) is the fourth most frequently diagnosed cancer among women and responsible for approximately 342,000 deaths in 2020. It is one of the leading causes of cancer-related deaths in women worldwide, mostly in low-income countries [<xref ref-type="bibr" rid="scirp.133174-ref1">1</xref>] . The main prognostic factor in women with CC is the stage of disease at diagnosis according to FIGO [<xref ref-type="bibr" rid="scirp.133174-ref2">2</xref>] .</p><p>Locally advanced tumours (i.e., FIGO stages IB3-IVA) are preferred treated with concurrent chemoradiotherapy (CCRT) [<xref ref-type="bibr" rid="scirp.133174-ref3">3</xref>] . Some systemic conditions, such as sarcopenia (reduced skeletal muscle index) and anemia (low haemoglobin levels) are also related to a worse prognosis [<xref ref-type="bibr" rid="scirp.133174-ref4">4</xref>] [<xref ref-type="bibr" rid="scirp.133174-ref5">5</xref>] [<xref ref-type="bibr" rid="scirp.133174-ref6">6</xref>] [<xref ref-type="bibr" rid="scirp.133174-ref7">7</xref>] [<xref ref-type="bibr" rid="scirp.133174-ref8">8</xref>] .</p><p>Furthermore, a significant skeletal muscle decrease has emerged as another critical prognostic factor in CC patients. Current data suggests SMI is an independent predictor of clinical outcomes among cancer patients, such as worse PFS and OS [<xref ref-type="bibr" rid="scirp.133174-ref9">9</xref>] - [<xref ref-type="bibr" rid="scirp.133174-ref15">15</xref>] . Sarcopenia is characterized as a progressive and generalized syndrome of loss of skeletal muscle mass, which can result in worsening quality of life and even death. According to The European Working Group on Sarcopenia in Older People (EWGSOP), this condition may be related to low muscle mass and low muscle function [<xref ref-type="bibr" rid="scirp.133174-ref16">16</xref>] .</p><p>The gold standard for assessing skeletal muscle index is a CT scan at the level of the third lumbar vertebra (L3) [<xref ref-type="bibr" rid="scirp.133174-ref16">16</xref>] [<xref ref-type="bibr" rid="scirp.133174-ref17">17</xref>] . Considering previous results investigating patients with CC and other types of cancer, we hypothesized that pre-treatment low SMI would be a predictor of patient outcomes.</p></sec><sec id="s2"><title>2. Methods</title><sec id="s2_1"><title>2.1. Participants</title><p>Retrospective cohort with 151 women with locally advanced invasive cervical carcinoma treated with CCRT between 2015-2019, and followed up until 2022. Proposed standardized treatment was performed with external beam RT (50 Gy in 25 fractions over 5 weeks) concurrent weekly cisplatin chemotherapy (40 mg/m<sup>2</sup> per week). Exclusion criteria were patients previously diagnosed with other cancers who had undergone chemotherapy or radiotherapy, as well as pregnant women.</p></sec><sec id="s2_2"><title>2.2. Diagnosis and Staging</title><p>Clinical and pathological data were obtained from the medical records. The staging was performed according to the classification established by FIGO [<xref ref-type="bibr" rid="scirp.133174-ref2">2</xref>] , assessed by clinical examination, chest radiograph, pelvic and abdominal ultrasounds, and planning pelvic CT scans before the start of treatment so that this does not represent a bias in the result of the muscle mass index after treatment.</p></sec><sec id="s2_3"><title>2.3. Response to Treatment, Follow-Up, and Adverse Effects</title><p>The response to treatment was evaluated according to the WHO criteria—the Response Evaluation Criteria in Solid Tumors (RECIST) [<xref ref-type="bibr" rid="scirp.133174-ref18">18</xref>] . Clinical examination, total abdominal and chest CT, as well as MRI, were used during and after treatment to classify the response.</p><p>Haematological toxicity was evaluated before each course and graded by the Common Terminology Criteria for Adverse Events (CTCAE) version 5.0 [<xref ref-type="bibr" rid="scirp.133174-ref19">19</xref>] .</p></sec><sec id="s2_4"><title>2.4. CT Image Analysis</title><p>Pretreatment planning CT images were analysed to assess SMI within measurements of psoas and paraspinal muscles, bilaterally. For each muscle, two measurements were taken to determine the muscle mass index. Each assessment was carried out multiple times, at different times, by different researchers. Muscle tissue was delineated on an axial image at L3 level using Arya<sup>&#174;</sup> software and a cutoff point of &lt;38.9 cm<sup>2</sup>/m<sup>2</sup> for identifying patients with sarcopenia due to low muscle indices [<xref ref-type="bibr" rid="scirp.133174-ref20">20</xref>] [<xref ref-type="bibr" rid="scirp.133174-ref21">21</xref>] .</p></sec><sec id="s2_5"><title>2.5. Statistical Analysis</title><p>Categorical data were presented in relative and absolute frequencies. The association between pretreatment low SMI and age, BMI, anemia, FIGO stage, adverse effects, treatment response, and site of progression was assessed using χ<sup>2</sup> tests and expressed in terms of odds ratios (OR) with 95% confidence intervals. A p-value &lt; 0.05 was considered significant.</p></sec></sec><sec id="s3"><title>3. Results</title><p><xref ref-type="table" rid="table1">Table 1</xref> describes the patient’s socio-demographics, tumour characteristics, and</p><table-wrap id="table1" ><label><xref ref-type="table" rid="table1">Table 1</xref></label><caption><title> Describes the patient’s socio-demographics, tumour characteristics, and association with pretreatment SMI</title></caption><table><tbody><thead><tr><th align="center" valign="middle"  rowspan="2"  >Variable</th><th align="center" valign="middle" >Overall N = 151 (100%)</th><th align="center" valign="middle" >Normal SMI N = 98 (64.9%)</th><th align="center" valign="middle" >Low SMI N = 53 (35.1%)</th><th align="center" valign="middle"  rowspan="2"  >p</th></tr></thead><tr><td align="center" valign="middle"  colspan="3"  >n (%)</td></tr><tr><td align="center" valign="middle" >Menopausal Status</td><td align="center" valign="middle" ></td><td align="center" valign="middle" ></td><td align="center" valign="middle" ></td><td align="center" valign="middle" >0.37</td></tr><tr><td align="center" valign="middle" >Post-menopausal</td><td align="center" valign="middle" >63 (41.7)</td><td align="center" valign="middle" >44 (44.9)</td><td align="center" valign="middle" >19 (35.8)</td><td align="center" valign="middle" ></td></tr><tr><td align="center" valign="middle" >Pre-menopausal</td><td align="center" valign="middle" >88 (58.3)</td><td align="center" valign="middle" >54 (55.1)</td><td align="center" valign="middle" >34 (64.2)</td><td align="center" valign="middle" ></td></tr><tr><td align="center" valign="middle" >Smoker</td><td align="center" valign="middle" ></td><td align="center" valign="middle" ></td><td align="center" valign="middle" ></td><td align="center" valign="middle" >0.54</td></tr><tr><td align="center" valign="middle" >No</td><td align="center" valign="middle" >135 (89.4)</td><td align="center" valign="middle" >86 (87.8)</td><td align="center" valign="middle" >49 (92.5)</td><td align="center" valign="middle" ></td></tr><tr><td align="center" valign="middle" >Yes</td><td align="center" valign="middle" >16 (10.6)</td><td align="center" valign="middle" >12 (12.2)</td><td align="center" valign="middle" >4 (7.5)</td><td align="center" valign="middle" ></td></tr><tr><td align="center" valign="middle" >Previous Radical Hysterectomy</td><td align="center" valign="middle" ></td><td align="center" valign="middle" ></td><td align="center" valign="middle" ></td><td align="center" valign="middle" >1.0</td></tr><tr><td align="center" valign="middle" >No</td><td align="center" valign="middle" >146 (96.8)</td><td align="center" valign="middle" >95 (96.9)</td><td align="center" valign="middle" >51 (96.2)</td><td align="center" valign="middle" ></td></tr><tr><td align="center" valign="middle" >Yes</td><td align="center" valign="middle" >5 (3.2)</td><td align="center" valign="middle" >3 (3.1)</td><td align="center" valign="middle" >2 (3.8)</td><td align="center" valign="middle" ></td></tr><tr><td align="center" valign="middle" >Histological Type</td><td align="center" valign="middle" ></td><td align="center" valign="middle" ></td><td align="center" valign="middle" ></td><td align="center" valign="middle" >0.21</td></tr><tr><td align="center" valign="middle" >Squamous</td><td align="center" valign="middle" >126 (83.4)</td><td align="center" valign="middle" >85 (86.7)</td><td align="center" valign="middle" >41 (77.4)</td><td align="center" valign="middle" ></td></tr><tr><td align="center" valign="middle" >Adenocarcinoma/others</td><td align="center" valign="middle" >25 (16.6)</td><td align="center" valign="middle" >13 (13.3)</td><td align="center" valign="middle" >12 (22.6)</td><td align="center" valign="middle" ></td></tr><tr><td align="center" valign="middle" >FIGO stage</td><td align="center" valign="middle" ></td><td align="center" valign="middle" ></td><td align="center" valign="middle" ></td><td align="center" valign="middle" >0.28</td></tr><tr><td align="center" valign="middle" >I</td><td align="center" valign="middle" >11 (7.3)</td><td align="center" valign="middle" >10 (10.2)</td><td align="center" valign="middle" >1 (1.9)</td><td align="center" valign="middle" ></td></tr><tr><td align="center" valign="middle" >II</td><td align="center" valign="middle" >40 (26.5)</td><td align="center" valign="middle" >25 (25.5)</td><td align="center" valign="middle" >15 (28.3)</td><td align="center" valign="middle" ></td></tr><tr><td align="center" valign="middle" >III</td><td align="center" valign="middle" >81 (53.7)</td><td align="center" valign="middle" >50 (51)</td><td align="center" valign="middle" >31 (58.5)</td><td align="center" valign="middle" ></td></tr><tr><td align="center" valign="middle" >IV</td><td align="center" valign="middle" >19 (12.5)</td><td align="center" valign="middle" >13 (13.3)</td><td align="center" valign="middle" >6 (11.3)</td><td align="center" valign="middle" ></td></tr><tr><td align="center" valign="middle" >Parametrial Infiltration</td><td align="center" valign="middle" ></td><td align="center" valign="middle" ></td><td align="center" valign="middle" ></td><td align="center" valign="middle" >0.15</td></tr><tr><td align="center" valign="middle" >Infiltered</td><td align="center" valign="middle" >121(80.2)</td><td align="center" valign="middle" >75 (76.5)</td><td align="center" valign="middle" >46 (86.8)</td><td align="center" valign="middle" ></td></tr><tr><td align="center" valign="middle" >Not Infiltered</td><td align="center" valign="middle" >30 (19.8)</td><td align="center" valign="middle" >23 (23.5)</td><td align="center" valign="middle" >7 (13.2)</td><td align="center" valign="middle" ></td></tr><tr><td align="center" valign="middle" >Nodal Status</td><td align="center" valign="middle" ></td><td align="center" valign="middle" ></td><td align="center" valign="middle" ></td><td align="center" valign="middle" >0.16</td></tr><tr><td align="center" valign="middle" >Negative</td><td align="center" valign="middle" >13 (22)</td><td align="center" valign="middle" >11 (28.9)</td><td align="center" valign="middle" >2 (9.5)</td><td align="center" valign="middle" ></td></tr><tr><td align="center" valign="middle" >Positive</td><td align="center" valign="middle" >46 (78)</td><td align="center" valign="middle" >27 (71.1)</td><td align="center" valign="middle" >19 (90.5)</td><td align="center" valign="middle" ></td></tr><tr><td align="center" valign="middle" >Largest Tumour Diameter (cm)</td><td align="center" valign="middle" ></td><td align="center" valign="middle" ></td><td align="center" valign="middle" ></td><td align="center" valign="middle" >0.46</td></tr><tr><td align="center" valign="middle" >≤4</td><td align="center" valign="middle" >63 (49.2)</td><td align="center" valign="middle" >39 (47)</td><td align="center" valign="middle" >24 (53.3)</td><td align="center" valign="middle" ></td></tr><tr><td align="center" valign="middle" >˂4≤6</td><td align="center" valign="middle" >34 (26.6)</td><td align="center" valign="middle" >25 (30.1)</td><td align="center" valign="middle" >9 (20.0)</td><td align="center" valign="middle" ></td></tr><tr><td align="center" valign="middle" >˃6</td><td align="center" valign="middle" >31 (24.2)</td><td align="center" valign="middle" >19 (22.9)</td><td align="center" valign="middle" >12 (26.7)</td><td align="center" valign="middle" ></td></tr><tr><td align="center" valign="middle" >Pretreatment Creatinine ˃ 1.09</td><td align="center" valign="middle" ></td><td align="center" valign="middle" ></td><td align="center" valign="middle" ></td><td align="center" valign="middle" >0.17</td></tr><tr><td align="center" valign="middle" >No</td><td align="center" valign="middle" >126 (86.9)</td><td align="center" valign="middle" >84 (90.3)</td><td align="center" valign="middle" >42 (80.8)</td><td align="center" valign="middle" ></td></tr><tr><td align="center" valign="middle" >Yes</td><td align="center" valign="middle" >19 (13.1)</td><td align="center" valign="middle" >9 (9.7)</td><td align="center" valign="middle" >10 (19.2)</td><td align="center" valign="middle" ></td></tr></tbody></table></table-wrap><p>SMI = skeletal muscle index.</p><p>association with sarcopenia.</p><p>Survival outcomes</p><p>In the univariate analysis, SMI, BMI, and FIGO stage were significantly associated with PFS. However, after adjustment in a multivariate Cox proportional model, only pretreatment haemoglobin and incomplete CCTR remained as significantly associated with PFS (<xref ref-type="table" rid="table2">Table 2</xref>).</p><p>Similarly, as evidenced by the results of the univariate analysis, SMI, BMI, pretreatment neutrophil count, haemoglobin levels, CCRT, and FIGO stage were significantly associated with OS (<xref ref-type="table" rid="table3">Table 3</xref>). However, after a multivariate analysis, only haemoglobin levels and CCRT remained associated with OS.</p><table-wrap id="table2" ><label><xref ref-type="table" rid="table2">Table 2</xref></label><caption><title> Cox proportional hazard ratio of progression free survival analysis considering key clinical features</title></caption><table><tbody><thead><tr><th align="center" valign="middle"  rowspan="3"  >Variable</th><th align="center" valign="middle"  colspan="6"  >Progression free survival</th></tr></thead><tr><td align="center" valign="middle"  colspan="3"  >Univariate</td><td align="center" valign="middle"  colspan="3"  >Multivariate</td></tr><tr><td align="center" valign="middle" >HR</td><td align="center" valign="middle" >(95% CI)</td><td align="center" valign="middle" >p</td><td align="center" valign="middle" >HR</td><td align="center" valign="middle" >(95% CI)</td><td align="center" valign="middle" >p<sub>adjusted</sub></td></tr><tr><td align="center" valign="middle" >Skeletal mass index (cm<sup>2</sup>/m<sup>2</sup>)</td><td align="center" valign="middle" >0.97</td><td align="center" valign="middle" >(0.94 to 0.99)</td><td align="center" valign="middle" >0.01</td><td align="center" valign="middle" >0.99</td><td align="center" valign="middle" >(0.96 to 1.03)</td><td align="center" valign="middle" >0.93</td></tr><tr><td align="center" valign="middle" >Age (Years)</td><td align="center" valign="middle" >1.00</td><td align="center" valign="middle" >(0.98 to 1.02)</td><td align="center" valign="middle" >0.68</td><td align="center" valign="middle" >1.00</td><td align="center" valign="middle" >(0.98 to 1.02)</td><td align="center" valign="middle" >0.88</td></tr><tr><td align="center" valign="middle" >BMI (Kg/m<sup>2</sup>)</td><td align="center" valign="middle" >0.96</td><td align="center" valign="middle" >(0.92 to 0.99)</td><td align="center" valign="middle" >&lt;0.001</td><td align="center" valign="middle" >0.98</td><td align="center" valign="middle" >(0.94 to 1.02)</td><td align="center" valign="middle" >0.58</td></tr><tr><td align="center" valign="middle" >Pretreatment Neutrophil count (mm<sup>3</sup>)</td><td align="center" valign="middle" >1.09</td><td align="center" valign="middle" >(1.03 to 1.14)</td><td align="center" valign="middle" >&lt;0.001</td><td align="center" valign="middle" >1.01</td><td align="center" valign="middle" >(0.93 to 1.10)</td><td align="center" valign="middle" >0.64</td></tr><tr><td align="center" valign="middle" >Pretreatment Platelet count (mm<sup>3</sup>)</td><td align="center" valign="middle" >1.00</td><td align="center" valign="middle" >(1.00 to 1.00)</td><td align="center" valign="middle" >0.7</td><td align="center" valign="middle" >1.00</td><td align="center" valign="middle" >(0.99 to 1.00)</td><td align="center" valign="middle" >0.40</td></tr><tr><td align="center" valign="middle" >Pretreatment Haemoglobin (g/dL)</td><td align="center" valign="middle" >0.73</td><td align="center" valign="middle" >(0.66 to 0.81)</td><td align="center" valign="middle" >&lt;0.001</td><td align="center" valign="middle" >0.73</td><td align="center" valign="middle" >(0.64 to 0.84)</td><td align="center" valign="middle" >&lt;0.001</td></tr><tr><td align="center" valign="middle" >Pretreatment creatinine (mg/dL)</td><td align="center" valign="middle" >1.80</td><td align="center" valign="middle" >(0.95 to 3.43)</td><td align="center" valign="middle" >0.07</td><td align="center" valign="middle" >0.85</td><td align="center" valign="middle" >(0.40 to 1.83)</td><td align="center" valign="middle" >0.69</td></tr><tr><td align="center" valign="middle" >Histological type</td><td align="center" valign="middle" >0.73</td><td align="center" valign="middle" >(0.36 to 1.46)</td><td align="center" valign="middle" >0.37</td><td align="center" valign="middle" >1.12</td><td align="center" valign="middle" >(0.53 to 2.35)</td><td align="center" valign="middle" >0.75</td></tr><tr><td align="center" valign="middle" >Incomplete CCTR</td><td align="center" valign="middle" >1.95</td><td align="center" valign="middle" >(1.23 to 3.11)</td><td align="center" valign="middle" >&lt;0.001</td><td align="center" valign="middle" >2.84</td><td align="center" valign="middle" >(1.64 to 4.91)</td><td align="center" valign="middle" >˂0.001</td></tr><tr><td align="center" valign="middle" >Advanced Stage</td><td align="center" valign="middle" >1.99</td><td align="center" valign="middle" >(1.16 to 3.43)</td><td align="center" valign="middle" >0.01</td><td align="center" valign="middle" >1.09</td><td align="center" valign="middle" >(0.57 to 2.07)</td><td align="center" valign="middle" >0.79</td></tr></tbody></table></table-wrap><p>Cox proportional hazard ratio: HR (95% confidence interval). Adjusted by skeletal mass index (SMI), age, body mass index (BMI), neutrophil, platelet, haemoglobin, Pretreatment creatinine (normal vs &gt; 1.09), histological type (squamous vs adenocarcinoma/others), Complete vs incomplete concurrent chemoradiotherapy (CCTR); Stage: I, II, III or IV.</p><table-wrap id="table3" ><label><xref ref-type="table" rid="table3">Table 3</xref></label><caption><title> Cox proportional hazard ratio of overall survival analysis considering key clinical features</title></caption><table><tbody><thead><tr><th align="center" valign="middle"  rowspan="3"  >Variable</th><th align="center" valign="middle"  colspan="6"  >Overall survival</th></tr></thead><tr><td align="center" valign="middle"  colspan="3"  >Univariate</td><td align="center" valign="middle"  colspan="3"  >Multivariate</td></tr><tr><td align="center" valign="middle" >HR</td><td align="center" valign="middle" >(95% CI)</td><td align="center" valign="middle" >p</td><td align="center" valign="middle" >HR</td><td align="center" valign="middle" >(95% CI)</td><td align="center" valign="middle" >p<sub>adjusted</sub></td></tr><tr><td align="center" valign="middle" >Skeletal mass index (cm<sup>2</sup>/m<sup>2</sup>)</td><td align="center" valign="middle" >0.97</td><td align="center" valign="middle" >(0.94 to 0.99)</td><td align="center" valign="middle" >0.01</td><td align="center" valign="middle" >0.99</td><td align="center" valign="middle" >(0.95 to 1.02)</td><td align="center" valign="middle" >0.57</td></tr><tr><td align="center" valign="middle" >Age (Years)</td><td align="center" valign="middle" >1.01</td><td align="center" valign="middle" >(0.99 to 1.02)</td><td align="center" valign="middle" >0.36</td><td align="center" valign="middle" >1.01</td><td align="center" valign="middle" >(0.99 to 1.03)</td><td align="center" valign="middle" >0.20</td></tr><tr><td align="center" valign="middle" >BMI (Kg/m<sup>2</sup>)</td><td align="center" valign="middle" >0.95</td><td align="center" valign="middle" >(0.91 to 0.98)</td><td align="center" valign="middle" >0.01</td><td align="center" valign="middle" >0.97</td><td align="center" valign="middle" >(0.93 to 1.01)</td><td align="center" valign="middle" >0.29</td></tr><tr><td align="center" valign="middle" >Pretreatment Neutrophil count (mm<sup>3</sup>)</td><td align="center" valign="middle" >1.07</td><td align="center" valign="middle" >(1.01 to 1.13)</td><td align="center" valign="middle" >0.01</td><td align="center" valign="middle" >1.02</td><td align="center" valign="middle" >(0.94 to 1.12)</td><td align="center" valign="middle" >0.51</td></tr><tr><td align="center" valign="middle" >Pretreatment Platelet count (mm<sup>3</sup>)</td><td align="center" valign="middle" >1.00</td><td align="center" valign="middle" >(1.00 to 1.00)</td><td align="center" valign="middle" >0.7</td><td align="center" valign="middle" >0.99</td><td align="center" valign="middle" >(0.99 to 1.00)</td><td align="center" valign="middle" >0.89</td></tr><tr><td align="center" valign="middle" >High pretreatment Haemoglobin (g/dL)</td><td align="center" valign="middle" >0.78</td><td align="center" valign="middle" >(0.69 to 0.86)</td><td align="center" valign="middle" >&lt;0.001</td><td align="center" valign="middle" >0.79</td><td align="center" valign="middle" >(0.69 to 0.91)</td><td align="center" valign="middle" >&lt;0.001</td></tr><tr><td align="center" valign="middle" >Normal pretreatment creatinine (mg/dL)</td><td align="center" valign="middle" >1.2</td><td align="center" valign="middle" >(0.59 to 2.41)</td><td align="center" valign="middle" >0.61</td><td align="center" valign="middle" >0.59</td><td align="center" valign="middle" >(0.25 to 1.39)</td><td align="center" valign="middle" >0.23</td></tr><tr><td align="center" valign="middle" >Histological type</td><td align="center" valign="middle" >0.57</td><td align="center" valign="middle" >(0.27 to 1.18)</td><td align="center" valign="middle" >0.13</td><td align="center" valign="middle" >1.20</td><td align="center" valign="middle" >(0.27 to 1.38)</td><td align="center" valign="middle" >0.24</td></tr><tr><td align="center" valign="middle" >Incomplete CCTR</td><td align="center" valign="middle" >2.07</td><td align="center" valign="middle" >(1.28 to 3.31)</td><td align="center" valign="middle" >&lt;0.001</td><td align="center" valign="middle" >2.69</td><td align="center" valign="middle" >(1.55 to 4.67)</td><td align="center" valign="middle" >&lt;0.001</td></tr><tr><td align="center" valign="middle" >Advanced Stage</td><td align="center" valign="middle" >1.71</td><td align="center" valign="middle" >(0.99 to 2.92)</td><td align="center" valign="middle" >0.05</td><td align="center" valign="middle" >1.20</td><td align="center" valign="middle" >(0.63 to 2.28)</td><td align="center" valign="middle" >0.57</td></tr></tbody></table></table-wrap><p>Cox proportional hazard ratio: HR (95% confidence interval). Adjusted by skeletal muscle index (SMI), age, body mass index (BMI), neutrophil, platelet, haemoglobin, pretreatment creatinine (normal vs &gt; 1.09), histological type (squamous vs adenocarcinoma/others), Complete or incomplete concurrent chemoradiotherapy (CCTR); Stage: I, II, III or IV.</p></sec><sec id="s4"><title>4. Discussion</title><sec id="s4_1"><title>4.1. Summary of Main Results</title><p>This study aimed to evaluate sarcopenia and anemia are prognostic factors in women diagnosed with CC who underwent CCRT. Accordingly, the main results of this investigation showed that pretreatment sarcopenia (low SMI) was associated with low haemoglobin levels. Although sarcopenia appears as a worse prognostic factor in the univariate analysis, only haemoglobin levels (anemia) and complete CCRT remained independently associated with progression-free survival and overall survival. Sarcopenia was significantly associated with worse PFS and OS in FIGO stage III/IV. Patients with normal SMI at stages I and II presented higher PFS and OS.</p></sec><sec id="s4_2"><title>4.2. Results in the Context of Published Literature</title><p>According to Wassie et al. [<xref ref-type="bibr" rid="scirp.133174-ref22">22</xref>] (2021) anemia affects about 1 in 2 women with CC and is significantly associated with worse survival. In women with CC, pretreatment anemia is a result of diverse factors: the aggressiveness of advanced stage, bleeding, renal failure. And even in women without bleeding and renal failure, the chronic inflammatory status associated with advanced stage and proinflammatory cytokines by immune and cancer cells can lead to anemia. In these cases, biological and hematologic features resemble those described in anemia associated with chronic inflammatory disease. Anemia leads to worse performance status, quality of life, cognitive function, energy-activity capacity and increase of fatigue, lethargy, dyspnea, anorexia with a consequent difficulty to adhere to full treatment [<xref ref-type="bibr" rid="scirp.133174-ref9">9</xref>] . A low haemoglobin (Hb) is related to tumour hypoxia which interferes with the effectiveness of both chemotherapy and radiotherapy. In addition to reducing the sensitivity of the tumor to treatment, anemia promotes tissue acidosis, production of ROS, immunodepression, and alterations in tumor cells apoptosis [<xref ref-type="bibr" rid="scirp.133174-ref23">23</xref>] . We previously demonstrated that a low level of haemoglobin during treatment (&lt; 10 mg/dL) was significantly associated with a poorer DFS and OS even in women who completed CCRT [<xref ref-type="bibr" rid="scirp.133174-ref24">24</xref>] . Since anemia is associated with shorter survival times for patients with CC [<xref ref-type="bibr" rid="scirp.133174-ref4">4</xref>] , it is suggested to treat anemia before and during cancer treatment. Previous studies showed that anemia, among other factors, have been pointed out as an essential biomarker for low muscle mass in the general population. Tseng et al. [<xref ref-type="bibr" rid="scirp.133174-ref7">7</xref>] found that haemoglobin levels were independently associated with sarcopenia, and that older adults with anemia had a higher risk of muscle weakness. In the present study, patients with anemia presented a higher rate of pretreatment sarcopenia.</p><p>Few studies have assessed pretreatment sarcopenia as a prognostic factor in women with CC, and the results are controversial. Matsuoka et al. [<xref ref-type="bibr" rid="scirp.133174-ref25">25</xref>] investigated pretreatment SMI patients undergoing CCRT: their study included 236 patients (155 CCRT and 81 radiotherapy alone) and used two indexes to evaluate pre and post-treatment muscle indexes: psoas muscle and skeletal muscle index. Thus, results showed that both indexes were not prognostic factors of outcome in CC. However, it must be emphasized that the results were not separated by treatment and that muscle mass was evaluated as a prognostic factor regardless of FIGO stage. Still, Yoshikawa et al. [<xref ref-type="bibr" rid="scirp.133174-ref26">26</xref>] showed that pretreatment low skeletal muscle mass, assessed through psoas muscle index, was a prognostic factor for patients with visceral metastases in CC, even with a lower number of participants (28 patients undergoing CCRT and 12 undergoing other treatments). Finally, Han et al. [<xref ref-type="bibr" rid="scirp.133174-ref27">27</xref>] , in a group of women with early stage CC (IB1-IIA) found a significantly worse PFS and OS when low muscle mass was assessed volumetrically, although this difference was not observed when evaluated by area of skeletal muscle and adipose tissue at the third lumbar vertebral. Aichi et al. [<xref ref-type="bibr" rid="scirp.133174-ref28">28</xref>] concluded that a low pretreatment SMI is an independent prognostic factor for OS in patients diagnosed with stage III cervical cancer and treated with concurrent chemotherapy. In their study, the optimal cutoff value for predicting 5-year survival was 35.6 cm<sup>2</sup>/m<sup>2</sup>, defined based on data derived from 24 patients with a low SMI and 68 patients without a low SMI. A low SMI was significantly associated with shorter OS, with no significant difference in PFS. In their study, a low SMI remained as an independent OS-defining prognostic factor after multivariate analysis [<xref ref-type="bibr" rid="scirp.133174-ref28">28</xref>] .</p></sec><sec id="s4_3"><title>4.3. Strengths and Weaknesses</title><p>In our series, pretreatment normal SMI was not associated with more chances of survival without progression, after multiple analyses. Recently, a systematic review and meta-analysis pointed out adverse effects of pretreatment sarcopenia in OS and PFS in CC patients [<xref ref-type="bibr" rid="scirp.133174-ref15">15</xref>] . Contrary, Yoshikawa et al. [<xref ref-type="bibr" rid="scirp.133174-ref26">26</xref>] found that metastatic CC patients with a higher psoas muscle index (PMI) had a significantly better OS when treated by CCRT than those with a lower PMI, even after multivariate analysis. One explanation for this phenomenon is that when patients with sarcopenia are diagnosed in an advanced stage, they probably enter a vicious cycle cause their conditions already represent a progressive systemic chronic inflammation, worsening skeletal muscle loss from the beginning of treatment, and, consequently, the prognostic. Recently, a systematic review and meta-analysis pointed out adverse effects of pretreatment sarcopenia in OS and PFS in CC patients [<xref ref-type="bibr" rid="scirp.133174-ref15">15</xref>] . Even though we did not find a relationship between the SMI measured in the pretreatment with FIGO stages, we found that patients at the advanced stage and low SMI had lower PFS and OS.</p><p>Despite CCRT being the standard of care for patients with locoregional cervical cancer [<xref ref-type="bibr" rid="scirp.133174-ref29">29</xref>] , it is already clear that not completing the planned CCRT is a factor associated with worse PFS and OS [<xref ref-type="bibr" rid="scirp.133174-ref30">30</xref>] . In our sample, women who received incomplete treatment were associated with lower PFS and OS. Disparities in the receipt of standard of care are due to many factors such as treatment toxicities, comorbidities, hospital facilities, socioeconomic disparities, age, race and ethnicity. For example, Uppal et al. [<xref ref-type="bibr" rid="scirp.133174-ref30">30</xref>] evaluated the disparities in guideline-based care in locally advanced CC using the National Cancer Database (women diagnosed between 2004 and 2012) and the final cohort consisted of 16,195 patients. The rate of complete guideline-based care varied between 58.4% for non-Hispanic white and 51.5% for Hispanic women.</p><p>Our sample had a limited number of women treated for cervical cancer, but it has a similar distribution to other articles and, in addition, our institution where the patients underwent treatment is a national reference for this type of cancer. As a result, the distribution of patients is heterogeneous, as it receives patients from all states in the country.</p><p>A strength of our work is the assessment of sarcopenia through direct and objective analysis of the SMI of the lumbar area from the CT scan used for treatment planning. Other studies analyze sarcopenia by measuring the SMI using software, which is not always available, and thus reduces the chance of reproducibility in other centers.</p></sec><sec id="s4_4"><title>4.4. Implications for Practice and Future Research</title><p>Our study has limitations; we investigated 151 patients with different FIGO stages of CC under a single treatment type. Although this fact brings up robustness regarding the association between pretreatment sarcopenia and the prognosis of CC patients, the sample is still heterogeneous, which can induce divergent results. In the meta-analysis conducted by Sutton et al. [<xref ref-type="bibr" rid="scirp.133174-ref15">15</xref>] , psoas-based sarcopenia measurements showed negative effects on survival outcomes. However, psoas atrophy at L3 only represents &lt; 10% of the total SMA. Thus, while the assessment of psoas-based sarcopenia may have some prognostic suitability, further investigation is required. We suggest including a volumetric calculation of low muscle mass under the same treatment protocol in future studies to confirm our results. Also, skeletal muscle density could be analysed as a predictor of outcomes. Future studies are challenged to investigate strategies such as protein intake and exercise to prevent low muscle mass before and during treatment.</p></sec></sec><sec id="s5"><title>5. Conclusion</title><p>In summary, our data showed pretreatment low SMI was associated with low haemoglobin levels. Although low SMI appears as a worse prognostic factor in the univariate analysis, only haemoglobin level and complete CCRT remained independently associated with PFS and OS. Patients with normal SMI at FIGO stage I and II presented higher PFS and OS.</p></sec><sec id="s6"><title>Conflicts of Interest</title><p>The authors declare no conflicts of interest regarding the publication of this paper.</p></sec><sec id="s7"><title>Cite this paper</title><p>Resende, L.S.A., de Amorim, F.V., Concei&#231;&#227;o, M.S., Jales, R.M., Pereira, P.N., Sarian, L.O., Baiocchi, G., Derchain, S. and da Silva Filho, A.L. (2024) Sarcopenia and Anemia Are Predictors of Poor Prognostic in Cervical Cancer Patients. Open Journal of Obstetrics and Gynecology, 14, 693-704. https://doi.org/10.4236/ojog.2024.145059</p></sec></body><back><ref-list><title>References</title><ref id="scirp.133174-ref1"><label>1</label><mixed-citation publication-type="other" xlink:type="simple">Sung, H., Ferlay, J., Siegel, R.L., Laversanne, M., Soerjomataram, I., Jemal, A., &lt;i&gt;et al&lt;/i&gt;. (2021) Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. &lt;i&gt;CA&lt;/i&gt;:&lt;i&gt; A Cancer Journal for Clinicians&lt;/i&gt;, 71, 209-249. &lt;br&gt;https://doi.org/10.3322/caac.21660</mixed-citation></ref><ref id="scirp.133174-ref2"><label>2</label><mixed-citation publication-type="other" xlink:type="simple">Bhatla, N., Aoki, D., Sharma, D.N. and Sankaranarayanan, R. (2021) Cancer of the Cervix Uteri: 2021 Update. &lt;i&gt;International Journal of Gynecology &amp; Obstetrics&lt;/i&gt;, 155, 28-44. &lt;br&gt;https://doi.org/10.1002/ijgo.13865</mixed-citation></ref><ref id="scirp.133174-ref3"><label>3</label><mixed-citation publication-type="other" xlink:type="simple">Mohamud, A., H&amp;#248;gdall, C. and Schnack, T. (2022) Prognostic Value of the 2018 FIGO Staging System for Cervical Cancer. &lt;i&gt;Gynecologic Oncology&lt;/i&gt;, 165, 506-513.&lt;br&gt;https://doi.org/10.1016/j.ygyno.2022.02.017</mixed-citation></ref><ref id="scirp.133174-ref4"><label>4</label><mixed-citation publication-type="other" xlink:type="simple">Caro, J.J., Salas, M., Ward, A. and Goss, G. (2001) Anemia as an Independent Prognostic Factor for Survival in Patients with Cancer: A Systemic, Quantitative Review. &lt;i&gt;Cancer&lt;/i&gt;, 91, 2214-2221.&lt;br&gt;https://doi.org/10.1002/1097-0142(20010615)91:12&lt;2214::AID-CNCR1251&gt;3.0.CO;2-P</mixed-citation></ref><ref id="scirp.133174-ref5"><label>5</label><mixed-citation publication-type="other" xlink:type="simple">Varlotto, J. and Stevenson, M.A. (2005) Anemia, Tumor Hypoxemia, and the Cancer Patient. &lt;i&gt;International Journal of Radiation Oncology&lt;/i&gt;,&lt;i&gt; Biology&lt;/i&gt;,&lt;i&gt; Physics&lt;/i&gt;, 63, 25-36. &lt;br&gt;https://doi.org/10.1016/j.ijrobp.2005.04.049</mixed-citation></ref><ref id="scirp.133174-ref6"><label>6</label><mixed-citation publication-type="other" xlink:type="simple">Wang, H., Chen, W.-M., Zhou, Y.-H., Shi, J.-P., Huang, Y.-Q. and Wang, W.-J. (2020) Combined PLT and NE to Predict the Prognosis of Patients with Locally Advanced Cervical Cancer. &lt;i&gt;Scientific Reports&lt;/i&gt;, 10, Article No. 11210.&lt;br&gt;https://doi.org/10.1038/s41598-020-66387-x</mixed-citation></ref><ref id="scirp.133174-ref7"><label>7</label><mixed-citation publication-type="other" xlink:type="simple">Tseng, S.-H., Lee, W.-J., Peng, L.-N., Lin, M.-H. and Chen, L.-K. (2021) Associations between Hemoglobin Levels and Sarcopenia and Its Components: Results from the I-Lan Longitudinal Study. &lt;i&gt;Experimental Gerontology&lt;/i&gt;, 150, Article 111379.&lt;br&gt;https://doi.org/10.1016/j.exger.2021.111379</mixed-citation></ref><ref id="scirp.133174-ref8"><label>8</label><mixed-citation publication-type="other" xlink:type="simple">Maccio, A., Sanna, E., Neri, M., Oppi, S. and Madeddu, C. (2021) Cachexia as Evidence of the Mechanisms of Resistance and Tolerance during the Evolution of Cancer Disease. &lt;i&gt;International Journal of Molecular Sciences&lt;/i&gt;, 22, Article 2890.&lt;br&gt;https://doi.org/10.3390/ijms22062890</mixed-citation></ref><ref id="scirp.133174-ref9"><label>9</label><mixed-citation publication-type="other" xlink:type="simple">Madeddu, C., Mantovani, G., Gramignano, G., Astara, G. and Macci&amp;#242;, A. (2015) Muscle Wasting as Main Evidence of Energy Impairment in Cancer Cachexia: Future Therapeutic Approaches. &lt;i&gt;Future Oncology&lt;/i&gt;, 11, 2697-2710.&lt;br&gt;https://doi.org/10.2217/fon.15.195</mixed-citation></ref><ref id="scirp.133174-ref10"><label>10</label><mixed-citation publication-type="other" xlink:type="simple">Lee, J., Chang, C.-L., Lin, J.-B., Wu, M.-H., Sun, F.-J., Jan, Y.-T., &lt;i&gt;et al&lt;/i&gt;. (2018) Skeletal Muscle Loss Is an Imaging Biomarker of Outcome after Definitive Chemoradiotherapy for Locally Advanced Cervical Cancer. &lt;i&gt;Clinical Cancer Research&lt;/i&gt;, 24, 5028-5036. &lt;br&gt;https://doi.org/10.1158/1078-0432.CCR-18-0788</mixed-citation></ref><ref id="scirp.133174-ref11"><label>11</label><mixed-citation publication-type="other" xlink:type="simple">Silva de Paula, N., de Aguiar Bruno, K., Azevedo Aredes, M. and Villa&amp;#231;a Chaves, G. (2018) Sarcopenia and Skeletal Muscle Quality as Predictors of Postoperative Complication and Early Mortality in Gynecologic Cancer. &lt;i&gt;International Journal of G&lt;/i&gt;&lt;i&gt;y&lt;/i&gt;&lt;i&gt;necologic Cancer&lt;/i&gt;, 28, 412-420. &lt;br&gt;https://doi.org/10.1097/IGC.0000000000001157</mixed-citation></ref><ref id="scirp.133174-ref12"><label>12</label><mixed-citation publication-type="other" xlink:type="simple">Pamoukdjian, F., Bouillet, T., L&amp;#233;vy, V., Soussan, M., Zelek, L. and Paillaud, E. (2018) Prevalence and Predictive Value of Pre-Therapeutic Sarcopenia in Cancer Patients: A Systematic Review. &lt;i&gt;Clinical Nutrition&lt;/i&gt;, 37, 1101-1113.&lt;br&gt;https://doi.org/10.1016/j.clnu.2017.07.010</mixed-citation></ref><ref id="scirp.133174-ref13"><label>13</label><mixed-citation publication-type="other" xlink:type="simple">Rinninella, E., Fagotti, A., Cintoni, M., Raoul, P., Scaletta, G., Scambia, G., &lt;i&gt;et al&lt;/i&gt;. (2020) Skeletal Muscle Mass as a Prognostic Indicator of Outcomes in Ovarian Cancer: A Systematic Review and Meta-Analysis. &lt;i&gt;International Journal of Gynec&lt;/i&gt;&lt;i&gt;o&lt;/i&gt;&lt;i&gt;logic Cancer&lt;/i&gt;, 30, 654-663. &lt;br&gt;https://doi.org/10.1136/ijgc-2020-001215</mixed-citation></ref><ref id="scirp.133174-ref14"><label>14</label><mixed-citation publication-type="other" xlink:type="simple">Lee, J., Lin, J.-B., Chen, T.-C., Jan, Y.-T., Sun, F.-J., Chen, Y.-J., &lt;i&gt;et al&lt;/i&gt;. (2022) Progressive Skeletal Muscle Loss after Surgery and Adjuvant Radiotherapy Impact Survival Outcomes in Patients with Early Stage Cervical Cancer. &lt;i&gt;Frontiers in Nutrition&lt;/i&gt;, 8, Article 773506. &lt;br&gt;https://doi.org/10.3389/fnut.2021.773506</mixed-citation></ref><ref id="scirp.133174-ref15"><label>15</label><mixed-citation publication-type="other" xlink:type="simple">Sutton, E.H., Plyta, M., Fragkos, K. and Di Caro, S. (2022) Pre-Treatment Sarcopenic Assessments as a Prognostic Factor for Gynaecology Cancer Outcomes: Systematic Review and Meta-Analysis. &lt;i&gt;European Journal of Clinical Nutrition&lt;/i&gt;, 76, 1513-1527. &lt;br&gt;https://doi.org/10.1038/s41430-022-01085-7</mixed-citation></ref><ref id="scirp.133174-ref16"><label>16</label><mixed-citation publication-type="other" xlink:type="simple">Cruz-Jentoft, A.J., Baeyens, J.P., Bauer, J.M., Boirie, Y., Cederholm, T., Landi, F., &lt;i&gt;et al&lt;/i&gt;. (2010) Sarcopenia: European Consensus on Definition and Diagnosis: Report of the European Working Group on Sarcopenia in Older People. &lt;i&gt;Age&lt;/i&gt;&lt;i&gt; and&lt;/i&gt;&lt;i&gt; Ageing&lt;/i&gt;, 39, 412-423. &lt;br&gt;https://doi.org/10.1093/ageing/afq034</mixed-citation></ref><ref id="scirp.133174-ref17"><label>17</label><mixed-citation publication-type="other" xlink:type="simple">Vangelov, B., Bauer, J., Kotevski, D. and Smee, R.I. (2022) The Use of Alternate Vertebral Levels to L3 in Computed Tomography Scans for Skeletal Muscle Mass Evaluation and Sarcopenia Assessment in Patients with Cancer: A Systematic Review. &lt;i&gt;British Journal of Nutrition&lt;/i&gt;, 127, 722-735.&lt;br&gt;https://doi.org/10.1017/S0007114521001446</mixed-citation></ref><ref id="scirp.133174-ref18"><label>18</label><mixed-citation publication-type="other" xlink:type="simple">Eisenhauer, E.A., Therasse, P., Bogaerts, J., Schwartz, L.H., Sargent, D., Ford, R., &lt;i&gt;et al&lt;/i&gt;. (2009) New Response Evaluation Criteria in Solid Tumours: Revised RECIST Guideline (Version 1.1). &lt;i&gt;European Journal of Cancer&lt;/i&gt;, 45, 228-247.&lt;br&gt;https://doi.org/10.1016/j.ejca.2008.10.026</mixed-citation></ref><ref id="scirp.133174-ref19"><label>19</label><mixed-citation publication-type="other" xlink:type="simple">National Cancer Institute (U.S.) (2017) Common Terminology Criteria for Adverse Events: (CTCAE) Version 5. U.S. Department Of Health and Human Services, Washington, DC.</mixed-citation></ref><ref id="scirp.133174-ref20"><label>20</label><mixed-citation publication-type="other" xlink:type="simple">Avrutin, E., Moisey, L.L., Zhang, R., Khattab, J., Todd, E., Premji, T., &lt;i&gt;et al&lt;/i&gt;. (2018) Clinically Practical Approach for Screening of Low Muscularity Using Electronic Linear Measures on Computed Tomography Images in Critically Ill Patients. &lt;i&gt;Jou&lt;/i&gt;&lt;i&gt;r&lt;/i&gt;&lt;i&gt;nal of Parenteral and Enteral Nutrition&lt;/i&gt;, 42, 885-891. &lt;br&gt;https://doi.org/10.1002/jpen.1019</mixed-citation></ref><ref id="scirp.133174-ref21"><label>21</label><mixed-citation publication-type="other" xlink:type="simple">Mourtzakis, M., Prado, C.M.M., Lieffers, J.R., Reiman, T., McCargar, L.J. and Baracos, V.E. (2008) A Practical and Precise Approach to Quantification of Body Composition in Cancer Patients Using Computed Tomography Images Acquired during Routine Care. &lt;i&gt;Applied Physiology&lt;/i&gt;,&lt;i&gt; Nutrition&lt;/i&gt;,&lt;i&gt; and Metabolism&lt;/i&gt;, 33, 997-1006.&lt;br&gt;https://doi.org/10.1139/H08-075</mixed-citation></ref><ref id="scirp.133174-ref22"><label>22</label><mixed-citation publication-type="other" xlink:type="simple">Wassie, M., Aemro, A. and Fentie, B. (2021) Prevalence and Associated Factors of Baseline Anemia among Cervical Cancer Patients in Tikur Anbesa Specialized Hospital, Ethiopia. &lt;i&gt;BMC Women&lt;/i&gt;&amp;#8217;&lt;i&gt;s Health&lt;/i&gt;, 21, Article No. 36.&lt;br&gt;https://doi.org/10.1186/s12905-021-01185-9</mixed-citation></ref><ref id="scirp.133174-ref23"><label>23</label><mixed-citation publication-type="other" xlink:type="simple">Xia, Y., Jiang, L. and Zhong, T. (2018) The Role of HIF-1&amp;#945; in Chemo-/Radioresistant Tumors. &lt;i&gt;OncoTargets and Therapy&lt;/i&gt;, 11, 3003-3011.&lt;br&gt;https://doi.org/10.2147/OTT.S158206</mixed-citation></ref><ref id="scirp.133174-ref24"><label>24</label><mixed-citation publication-type="other" xlink:type="simple">Zuliani, A.C., Esteves, S.C., Teixeira, L.C., Teixeira, J.C., De Souza, G.A. and Sarian, L.O. (2014) Concomitant Cisplatin Plus Radiotherapy and High-Dose-Rate Brachytherapy versus Radiotherapy Alone for Stage IIIB Epidermoid Cervical Cancer: A Randomized Controlled Trial. &lt;i&gt;Journal of Clinical Oncology&lt;/i&gt;, 32, 542-547.&lt;br&gt;https://doi.org/10.1200/JCO.2013.50.1205</mixed-citation></ref><ref id="scirp.133174-ref25"><label>25</label><mixed-citation publication-type="other" xlink:type="simple">Matsuoka, H., Nakamura, K., Matsubara, Y., Ida, N., Nishida, T., Ogawa, C., &lt;i&gt;et al&lt;/i&gt;. (2019) Sarcopenia Is Not a Prognostic Factor of Outcome in Patients with Cervical Cancer Undergoing Concurrent Chemoradiotherapy or Radiotherapy. &lt;i&gt;Anticancer Research&lt;/i&gt;, 39, 933-939. &lt;br&gt;https://doi.org/10.21873/anticanres.13196</mixed-citation></ref><ref id="scirp.133174-ref26"><label>26</label><mixed-citation publication-type="other" xlink:type="simple">Yoshikawa, N., Shirakawa, A., Yoshida, K., Tamauchi, S., Suzuki, S., Kikkawa, F., &lt;i&gt;et al&lt;/i&gt;. (2020) Sarcopenia as a Predictor of Survival among Patients with Organ Metastatic Cervical Cancer. &lt;i&gt;Nutrition in Clinical Practice&lt;/i&gt;, 35, 1041-1046.&lt;br&gt;https://doi.org/10.1002/ncp.10482</mixed-citation></ref><ref id="scirp.133174-ref27"><label>27</label><mixed-citation publication-type="other" xlink:type="simple">Han, Q., Kim, S.I., Yoon, S.H., Kim, T.M., Kang, H.C., Kim, H.J., &lt;i&gt;et al&lt;/i&gt;. (2021) Impact of Computed Tomography-Based, Artificial Intelligence-Driven Volumetric Sarcopenia on Survival Outcomes in Early Cervical Cancer. &lt;i&gt;Frontiers in Oncology&lt;/i&gt;, 11, Article 741071. &lt;br&gt;https://doi.org/10.3389/fonc.2021.741071</mixed-citation></ref><ref id="scirp.133174-ref28"><label>28</label><mixed-citation publication-type="other" xlink:type="simple">Aichi, M., Hasegawa, S., Kurita, Y., Shinoda, S., Kato, S., Mizushima, T., &lt;i&gt;et al&lt;/i&gt;. (2023) Low Skeletal Muscle Mass Predicts Poor Prognosis for Patients with Stage III Cervical Cancer on Concurrent Chemoradiotherapy. &lt;i&gt;Nutrition&lt;/i&gt;, 109, Article 111966.&lt;br&gt;https://doi.org/10.1016/j.nut.2022.111966</mixed-citation></ref><ref id="scirp.133174-ref29"><label>29</label><mixed-citation publication-type="other" xlink:type="simple">Petrelli, F., De Stefani, A., Raspagliesi, F., Lorusso, D. and Barni, S. (2014) Radiotherapy with Concurrent Cisplatin-Based Doublet or Weekly Cisplatin for Cervical Cancer: A Systematic Review and Meta-Analysis. &lt;i&gt;Gynecologic Oncology&lt;/i&gt;, 134, 166-171. &lt;br&gt;https://doi.org/10.1016/j.ygyno.2014.04.049</mixed-citation></ref><ref id="scirp.133174-ref30"><label>30</label><mixed-citation publication-type="other" xlink:type="simple">Uppal, S., Chapman, C., Spencer, R.J., Jolly, S., Maturen, K., Rauh-Hain, J.A., &lt;i&gt;et al&lt;/i&gt;. (2017) Association of Hospital Volume with Racial and Ethnic Disparities in Locally Advanced Cervical Cancer Treatment. &lt;i&gt;Obstetrics &amp; Gynecology&lt;/i&gt;, 129, 295-304.&lt;br&gt;https://doi.org/10.1097/AOG.0000000000001819</mixed-citation></ref></ref-list></back></article>