Nomograms combining computed tomography-based body composition changes with clinical prognostic factors to predict survival in locally advanced cervical cancer patients
Restricted accessResearch articleFirst published online March 27, 2024
Nomograms combining computed tomography-based body composition changes with clinical prognostic factors to predict survival in locally advanced cervical cancer patients
To explore the value of body composition changes (BCC) measured by quantitative computed tomography (QCT) for evaluating the survival of patients with locally advanced cervical cancer (LACC) underwent concurrent chemoradiotherapy (CCRT), nomograms combined BCC with clinical prognostic factors (CPF) were constructed to predict overall survival (OS) and progression-free survival (PFS).
METHODS:
Eighty-eight patients with LACC were retrospectively selected. All patients underwent QCT scans before and after CCRT, bone mineral density (BMD), subcutaneous fat area (SFA), visceral fat area (VFA), total fat area (TFA), paravertebral muscle area (PMA) were measured from two sets of computed tomography (CT) images, and change rates of these were calculated.
RESULTS:
Multivariate Cox regression analysis showed ΔBMD, ΔSFA, SCC-Ag, LNM were independent factors for OS (HR = 3.560, 5.870, 2.702, 2.499, respectively, all P < 0.05); ΔPMA, SCC-Ag, LNM were independent factors for PFS (HR = 2.915, 4.291, 2.902, respectively, all P < 0.05). Prognostic models of BCC combined with CPF had the highest predictive performance, and the area under the curve (AUC) for OS and PFS were 0.837, 0.846, respectively. The concordance index (C-index) of nomograms for OS and PFS were 0.834, 0.799, respectively. Calibration curves showed good agreement between the nomograms’ predictive and actual OS and PFS, decision curve analysis (DCA) showed good clinical benefit of nomograms.
CONCLUSION:
CT-based body composition changes and CPF (SCC-Ag, LNM) were associated with survival in patients with LACC. The prognostic nomograms combined BCC with CPF were able to predict the OS and PFS in patients with LACC reliably.
SalehM., VirarkarM., JavadiS.et al. Cervical Cancer: 2018 Revised International Federation of Gynecology and Obstetrics Staging System and the Role of Imaging, AJR Am J Roentgenol214(5) (2020), 1182–1195. doi: 10.2214/AJR.19.21819.
4.
KohW.J., Abu-RustumN.R., BeanS.et al. Cervical Cancer, Version 3.2019, NCCN Clinical Practice Guidelines in Oncology, J Natl Compr Canc Netw17(1) (2019), 64–84. doi: 10.6004/jnccn.2019.0001.
5.
FangC., ZhangP., YuA.et al. Different prognosis of stage IIIB cervical cancer patients with lower third of vaginal invasion and those without, Gynecol Oncol162(1) (2021), 50–55. doi: 10.1016/j.ygyno.2021.04.005.
6.
MarnitzS., TsunodaA.T., MartusP.et al. Surgical versus clinical staging prior to primary chemoradiation in patients with cervical cancer FIGO stages IIB-IVA: oncologic results of a prospective randomized international multicenter (Uterus-11) intergroup study, Int J Gynecol Cancer30(12) (2020), 1855–1861. doi: 10.1136/ijgc-2020-001973.
7.
ChenF., ChenL., ZhangY.et al. Survival Comparison Between Squamous Cell Carcinoma and Adenocarcinoma for Radiotherapy-Treated Patients with Stage IIB-IVA Cervical Cancer, Front Oncol (2022). doi: 10.3389/fonc.2022.895122.
8.
ZhengX., GuoW., DongJ.et al. Prediction of early response to concurrent chemoradiotherapy in cervical cancer: Value of multi-parameter MRI combined with clinical prognostic factors, Magn Reson Imaging10(72) (2020), 159–166. doi: 10.1016/j.mri.2020.06.014.
9.
XuF., LiY., FanL.et al. Preoperative SCC-Ag and thrombocytosis as predictive markers for pelvic lymphatic metastasis of squamous cervical cancer in early FIGO stage, J Cancer9(9) (2018), 1660–1666. doi: 10.7150/jca.24049.
ChoiM.H., KimK.A., HwangS.S.et al. CT-quantified muscle and fat change in patients after surgery or endoscopic resection for early gastric cancer and its impact on long-term outcomes, Medicine (Baltimore)97(52) (2018), e13878. doi: 10.1097/MD.0000000000013878.
12.
YingP., JinW., WuX.et al. Association between CT-Quantified Body Composition and Recurrence, Survival in Nonmetastasis Colorectal Cancer Patients Underwent Regular Chemotherapy after Surgery, Biomed Res Int (2021). doi: 10.1155/2021/6657566.
13.
WuN., SuX., SongH.et al. A Multi-Institutional Retrospective Analysis of Oncologic Outcomes for Patients With Locally Advanced Cervical Cancer Undergoing Platinum-Based Adjuvant Chemotherapy After Concurrent Chemoradiotherapy, Cancer Control (2021). doi: 10.1177/1073274821989307.
14.
LiuY., GuoD., NiuZ.et al. Prediction of the Risk of Laparoscopy-Assisted Gastrectomy by Comparing Visceral Fat Area and Body Mass Index, Gastroenterol Res Pract (2018). doi: 10.1155/2018/1359626.
15.
BatesD.D.B., PickhardtP.J., CT-Derived Body Composition Assessment as a Prognostic Tool in Oncologic Patients: From Opportunistic Research to Artificial Intelligence-Based Clinical Implementation, AJR Am J Roentgenol219(4) (2022), 671–680. doi: 10.2214/AJR.22.27749.
16.
GolderA.M., SinL.K.E., AlaniF.et al. The relationship between the mode of presentation, CT-derived body composition, systemic inflammatory grade and survival in colon cancer, J Cachexia Sarcopenia Muscle13(6) (2022), 2863–2874. doi: 10.1002/jcsm.13097.
17.
KiyotokiT., NakamuraK., HaragaJ.et al. Sarcopenia Is an Important Prognostic Factor in Patients With Cervical Cancer Undergoing Concurrent Chemoradiotherapy, Int J Gynecol Cancer28(1) (2018), 168–175. doi: 10.1097/IGC.0000000000001127.
18.
LeeJ., LinJ.B., WuM.H.et al. Muscle radiodensity loss during cancer therapy is predictive for poor survival in advanced endometrial cancer, J Cachexia Sarcopenia Muscle10(4) (2019), 814–826. doi: 10.1002/jcsm.12440.
19.
CampR.L., Dolled-FilhartM., RimmD.L., X-tile: a new bio-informatics tool for biomarker assessment and outcome-based cut-point optimization, Clin Cancer Res10(21) (2004), 7252–9. doi: 10.1158/1078-0432.CCR-04-0713.
20.
BaracosV.E., ArribasL., Sarcopenic obesity: hidden muscle wasting and its impact for survival and complications of cancer therapy, Ann Oncol29(suppl_2) (2018), ii1–ii9. doi: 10.1093/annonc/mdx810.
21.
SiddiquiJ.A., PothurajuR., JainM.et al. Advances in cancer cachexia: Intersection between affected organs, mediators, and pharmacological interventions, Biochim Biophys Acta Rev Cancer1873(2) (2020), 188359. doi: 10.1016/j.bbcan.2020.188359.
22.
TroschelA.S., TroschelF.M., BestT.D.et al. Computed Tomography-based Body Composition Analysis and Its Role in Lung Cancer Care, J Thorac Imaging35(2) (2020), 91–100. doi: 10.1097/RTI.0000000000000428.
23.
SánchezM., Castro-EguiluzD., Luvián-MoralesJ.et al. Deterioration of nutritional status of patients with locallyadvanced cervical cancer during treatment with concomitantchemoradiotherapy, J Hum Nutr Diet32(4) (2019), 480–491. doi: 10.1111/jhn.12649.
24.
LeeJ., LinJ.B., WuM.H.et al. Muscle Loss after Chemoradiotherapy as a Biomarker of Distant Failures in Locally Advanced Cervical Cancer, Cancers (Basel)12(3) (2020), 595–10.3390/cancers12030595.
25.
HagensE.R.C., FeenstraM.L., van EgmondM.A.et al. Influence of body composition and muscle strength on outcomes after multimodal oesophageal cancer treatment, J Cachexia Sarcopenia Muscle11(3) (2020), 756–767. doi: 10.1002/jcsm.12540.
26.
GigicB., NattenmüllerJ., SchneiderM.et al. The Role of CT-Quantified Body Composition on Longitudinal Health-Related Quality of Life in Colorectal Cancer Patients: The Colocare Study, Nutrients12(5) (2020), 1247–10.3390/nu12051247.
27.
ZhangL., RuanX., JuR.et al. Lumbar bone mineral density measured by quantitative computed tomography (QCT): association with abdominal adipose tissue in different menopausal periods of Chinese women, Gynecol Endocrinol37(3) (2021), 264–268. doi: 10.1080/09513590.2020.1836147.
28.
RierH.N., JagerA., SleijferS.et al. Changes in body composition and muscle attenuation during taxane-based chemotherapy in patients with metastatic breast cancer, Breast Cancer Res Treat68(1) (2018), 95–105. doi: 10.1007/s10549-017-4574-0.
29.
WangX., ZhangC., CaoF.et al. Nomogram of Combining CT-Based Body Composition Analyses and Prognostic Inflammation Score: Prediction of Survival in Advanced Epithelial Ovarian Cancer Patients, Acad Radiol29(9) (2022), 1394–1403. doi: 10.1016/j.acra.2021.11.011.
30.
LeeJ., ChangC.L., LinJ.B.et al. Skeletal Muscle Loss Is an Imaging Biomarker of Outcome after Definitive Chemoradiotherapy for Locally Advanced Cervical Cancer, Clin Cancer Res24(20) (2018), 5028–5036. doi: 10.1158/1078-0432.
31.
Kazemi-BajestaniS.M., MazurakV.C., BaracosV., Computed tomography-defined muscle and fat wasting are associated with cancer clinical outcomes, Semin Cell Dev Biol (54) (2016), 2–10. doi: 10.1016/j.semcdb.2015.09.001.
32.
KimJ.M., ParkJ.H., JeongS.H.et al. Relationship between low body mass index and morbidity after gastrectomy for gastric cancer, Ann Surg Treat Res90(4) (2016), 207–12. doi: 10.4174/astr.2016.90.4.207.
33.
KaneH., LynchL., Innate immune control of adipose tissue homeostasis, Trends Immunol40(9) (2019), 857–872. doi: 10.1016/j.it.2019.07.006.
34.
OzekiY., MasakiT., YoshidaY.et al. Relationships between computed tomography-assessed density, abdominal fat volume, and glucose metabolism after sleeve gastrectomy in Japanese patients with obesity, Endocr J66(7) (2019), 605–613. doi: 10.1507/endocrj.EJ18-0543.
35.
ShiJ., BaoG., HongJ.et al. Deciphering CT texture features of human visceral fat to evaluate metabolic disorders and surgery-induced weight loss effects, EBioMedicine (2021). doi: 10.1016/j.ebiom.2021.103471.
36.
CamusV., LanicH., KrautJ.et al. Prognostic impact of fat tissue loss and cachexia assessed by computed tomography scan in elderly patients with diffuse large B-cell lymphoma treated with immunochemotherapy, Eur J Haematol93(1) (2014), 9–18. doi: 10.1111/ejh.12285.
37.
ZhengX., CaoF., QianL.et al. Body Composition Changes in Hepatocellular Carcinoma: Prediction of Survival to Transcatheter Arterial Chemoembolization in Combination With Clinical Prognostic Factors, Cancer Control (2021). doi: 10.1177/10732748211038445.
38.
GoulartA., MalheiroN., RiosH.et al. Influence of Visceral Fat in the Outcomes of Colorectal Cancer, Dig Surg36(1) (2019), 33–40. doi: 10.1159/000486143.
39.
HuangT., ChenZ., ShenL.et al. Associations of Cognitive Function with BMI, Body Fat Mass and Visceral Fat in Young Adulthood, Medicina (Kaunas)55(6) (2019), 221–10.3390/medicina55060221.
40.
ZhangY., LiuL., ZhangK.et al. Nomograms Combining Clinical and Imaging Parameters to Predict Recurrence and Disease-free Survival After Concurrent Chemoradiotherapy in Patients With Locally Advanced Cervical Cancer, Acad Radiol30(3) (2023), 499–508. doi: 10.1016/j.acra.2022.08.002.
