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Clinical value of a nomogram model based on ADC values within 1 cm around the tumor for predicting the postoperative progression of glioma
CHENG Mengyu  YANG Zhe  FAN Jiawei  LI Wenfei  WANG Wenxi  WANG Zhanqiu 

Cite this article as: CHENG M Y, YANG Z, FAN J W, et al. Clinical value of a nomogram model based on ADC values within 1 cm around the tumor for predicting the postoperative progression of glioma[J]. Chin J Magn Reson Imaging, 2023, 14(1): 136-142, 150. DOI:10.12015/issn.1674-8034.2023.01.024.


[Abstract] Objective To investigate the clinical value of nomogram model based on the apparent diffusion coefficient (ADC) within 1 cm around the tumor for predicting the postoperative progression of glioma.Materials and Methods Clinical data of glioma patients underwent surgery retrospectively retrieved from First Hospital of Qinhuangdao were obtained. Mean apparent diffusion coefficient (mADC) was collected and measured by Picture Archiving and Communication Systems (PACS). Kaplan-Meier survival curve was performed with optimal mADC threshold determined by X-tile. Cox regression analysis was used to screen independent risk factors, then a nomogram was developed to predict the progression of postoperative glioma patients. The receiver operating characteristic (ROC) curve was drawn to evaluate the prediction accuracy of the model, and the decision curve analysis (DCA) was carried out to assess the clinical value of the nomogram.Results Univariate and multivariate Cox regression analysis showed that the peritumoral mADC values, the degree of peritumoral enhancement, age and the degree of tumor resection were independent risk factors for predicting the postoperative progression of glioma (all P＜0.05). The ROC curve of the nomogram predicting 1 and 2 years postoperative progression was 0.79 and 0.76. The calibration curve showed that there was a good consistency between the observed values and the predicted values in the model. The curve showed that the nomogram model had good clinical application value.Conclusions The nomogram model established for the first time based on mADC value within 1 cm around the tumor can predict the postoperative condition of glioma patients intuitively and comprehensively. It can provide a relatively accurate prediction tool for neurosurgeons to individualized evaluation of survival and prognosis and formulated treatment plans for patients.
[Keywords] glioma；peritumoral edema；peritumoral enhancement；postoperative progression；magnetic resonance imaging；diffusion-weighted imaging；apparent diffusion coefficient；nomogram

CHENG Mengyu1   YANG Zhe2   FAN Jiawei1   LI Wenfei3   WANG Wenxi1   WANG Zhanqiu3*  

1 Department of Radiation Medicine, Hebei Medical University, Shijiazhuang 050000, China

2 Hebei North University, Zhangjiakou 075000, China

3 Department of Radiology, Qinhuangdao First Hospital Affiliated to Hebei Medical University, Qinhuangdao 066000, China

Corresponding author: Wang ZQ, E-mail: wangzhanqiu2007@sina.com

Conflicts of interest   None.

ACKNOWLEDGMENTS Qinhuangdao Science and Technology Plan Project (No. 201805A078).
Received  2022-07-12
Accepted  2022-11-08
DOI: 10.12015/issn.1674-8034.2023.01.024
Cite this article as: CHENG M Y, YANG Z, FAN J W, et al. Clinical value of a nomogram model based on ADC values within 1 cm around the tumor for predicting the postoperative progression of glioma[J]. Chin J Magn Reson Imaging, 2023, 14(1): 136-142, 150. DOI:10.12015/issn.1674-8034.2023.01.024.

[1]
OSTROM Q T, GITTLEMAN H, FULOP J, et al. CBTRUS Statistical Report: Primary Brain and Central Nervous System Tumors Diagnosed in the United States in 2008-2012[J]. Neuro Oncol, 2015, 17(Suppl 4): iv1-iv62. DOI: 10.1093/neuonc/nov189.
[2]
WESSELING P, CAPPER D. WHO 2016 Classification of gliomas[J]. Neuropathol Appl Neurobiol, 2018, 44(2): 139-150. DOI: 10.1111/nan.12432.
[3]
ZHONG Q Y, FAN E X, FENG G Y, et al. A gene expression-based study on immune cell subtypes and glioma prognosis[J/OL]. BMC Cancer, 2019, 19(1): 1116 [2022-07-11]. https://pubmed.ncbi.nlm.nih.gov/31729963/. DOI: 10.1186/s12885-019-6324-7.
[4]
SONG Z Z, SUN X L, LI H O, et al. Value of MRI radiomics of glioma and peritumoral edema in evaluating tumor recurrence[J]. J Shandong Univ (Health Sci), 2021, 59(11): 53-60. DOI: 10.6040/j.issn.1671-7554.0.2021.0997.
[5]
LIU J L, HAN H N, XU Y, et al. A comparison of the multimodal magnetic resonance imaging features of brain metastases vs. high-grade gliomas[J/OL]. Am J Transl Res, 2021, 13(4): 3543-3548 [2022-07-11]. https://pubmed.ncbi.nlm.nih.gov/34017534/.
[6]
SUN X, PANG P, LOU L, et al. Radiomic prediction models for the level of Ki-67 and p53 in glioma[J/OL]. J Int Med Res, 2020, 48(5): 300060520914466 [2022-10-11]. https://www.ncbi.nlm.nih.gov/pubmed/32431205. DOI: 10.1177/0300060520914466.
[7]
KOH D M, COLLINS D J. Diffusion-weighted MRI in the body: applications and challenges in oncology[J]. AJR Am J Roentgenol, 2007, 188(6): 1622-1635. DOI: 10.2214/AJR.06.1403.
[8]
YUAN Y, CHEN X L, LI Z L, et al. The application of apparent diffusion coefficients derived from intratumoral and peritumoral zones for assessing pathologic prognostic factors in rectal cancer[J]. Eur Radiol, 2022, 32(8): 5106-5118. DOI: 10.1007/s00330-022-08717-3.
[9]
HU G C, ZHANG Q Q, MAO N, et al. Research progress of preoperative prediction of microvascular invasion of hepatocellular carcinoma based on magnetic resonance imaging[J]. Chin J Magn Reson Imaging, 2022, 13(2): 159-162. DOI: 10.12015/issn.1674-8034.2022.02.040.
[10]
WANG X Y, LIU Y. Feasibility Study of Evaluating the Histological Grade and Prognosis of Infiltrating Ductal Carcinoma of Breast with MRI ADC Value[J]. Chin J CT & MRI, 2020, 18(8): 56-59. DOI: 10.3969/j.issn.1672-5131.2020.08.019.
[11]
KUANG J, SHI G F, LI R X, et al. Application of ADC imaging-based different radiomics models in predicting the efficacy of neoadjuvant chemoradiotherapy for locally advanced rectal cancer[J]. Oncoradiology, 2020, 29(5): 467-75. DOI: 10.19732/j.cnki.2096-6210.2020.05.008.
[12]
LU R L, XU Z F, GAO M Y, et al. Study of the relationship of ADC value, SUVmax value and p prognostic factors in patients with invasive ductal carcinoma of breast[J]. Radiol Practice, 2017, 32(7): 710-714. DOI: 10.13609/j.cnki.1000-0313.2017.07.010.
[13]
GENG L, SUN Y, ZHAO Y, et al. Application of ADC values and rADC values in the diagnosis and differentiation of primary central ner-vous system lymphoma and its correlation with Ki-67[J]. The J Pract Med, 2021, 37(19): 2530-2534. DOI: 10.3969/j.issn.1006-5725.2021.19.019.
[14]
MARDOR Y, ROTH Y, OCHERSHVILLI A, et al. Pretreatment prediction of brain tumors' response to radiation therapy using high b-value diffusion-weighted MRI[J]. Neoplasia, 2004, 6(2): 136-142. DOI: 10.1593/neo.03349.
[15]
LIU J, LI Q, TANG L, et al. Correlations of Mean and Mimimum Apparent Diffusion Coefficient Values With the Clinicopathological Features in Rectal Cancer[J]. Acad Radiol, 2021, 28(Suppl 1): S105-S111. DOI: 10.1016/j.acra.2020.10.018.
[16]
ZHAO H, BAI Y, WANG M Y, et al. Progress of multimodality magnetic resonance imaging in genotyping and prognostic evaluation of gliomas[J]. Chin J Magn Reson Imaging, 2021, 12(9): 98-102. DOI: 10.12015/issn.1674-8034.2021.09.025.
[17]
KETTUNEN T, OKUMA H, AUVINEN P, et al. Peritumoral ADC values in breast cancer: region of interest selection, associations with hyaluronan intensity, and prognostic significance[J]. Eur Radiol, 2020, 30(1): 38-46. DOI: 10.1007/s00330-019-06361-y.
[18]
OKUMA H, SUDAH M, KETTUNEN T, et al. Peritumor to tumor apparent diffusion coefficient ratio is associated with biologically more aggressive breast cancer features and correla6tes with the prognostication tools[J/OL]. PLoS One, 2020, 15(6): e0235278 [2022-07-18]. https://www.ncbi.nlm.nih.gov/pubmed/32584887. DOI: 10.1371/journal.pone.0235278.
[19]
HOU G D, ZHENG Y, WEI D, et al. Establishment and internal validation of the prognostic nomogram for patients with micropapillary bladder cancer[J]. J Clin Urology (China), 2019, 34(10): 759-763, 769. DOI: 10.13201/j.jssn.1001-1420.2019.10.001.
[20]
OU Y L, ZHANG L, ZHANG X, et al. Nomogram prediction of overall survival for cervical cancer[J]. Chin J Cancer Trev Treat, 2015, 22(16): 1303-1307. DOI: 10.16073/j.cnki.cjcpt.2015.16.013.
[21]
LI Z, CAI X W, YAN P, et al. Establishment and application of a nomogram model for prognostic risk prediction in patients with epithelial ovarian cancer[J]. Chin J Obstet Gynecol, 2022, 57(3): 190-197. DOI: 10.3760/cma.j.cn112141-20220110-00017.
[22]
Medical Administration and Hospital Administration Bureau of the National Health Commission. Guidelines for the diagnosis and treatment of glioma (2018 edition)[J]. Chin J Neurosurg, 2019, 35(3): 217-239. DOI: 10.3760/cma.j.issn.1001-2346.2019.03.001.
[23]
ELLINGSON B M, SAMPSON J, ACHROL A S, et al. Modified RANO, Immunotherapy RANO, and Standard RANO Response to Convection-Enhanced Delivery of IL4R-Targeted Immunotoxin MDNA55 in Recurrent Glioblastoma[J]. Clin Cancer Res, 2021, 27(14): 3916-3925. DOI: 10.1158/1078-0432.CCR-21-0446.
[24]
WEN P Y, MACDONALD D R, REARDON D A, et al. Updated response assessment criteria for high-grade gliomas: response assessment in neuro-oncology working group[J]. J Clin Oncol, 2010, 28(11): 1963-1972. DOI: 10.1200/JCO.2009.26.3541.
[25]
LI W M. Expert consensus on MRI examination technology[J]. Chin J Radiol, 2016, 50(10): 724-739. DOI: 10.3760/cma.j.issn.1005-1201.2016.10.002.
[26]
CHADDAD A, DESROSIERS C, ABDULKARIM B, et al. Predicting the Gene Status and Survival Outcome of Lower Grade Glioma Patients With Multimodal MRI Features[J]. IEEE Access, 2019, 7: 75976-75984. DOI: 10.1109/access.2019.2920396.
[27]
MISTRY A M, KELLY P D, GALLANT J N, et al. Comparative Analysis of Subventricular Zone Glioblastoma Contact and Ventricular Entry During Resection in Predicting Dissemination, Hydrocephalus, and Survival[J/OL]. Neurosurgery, 2019, 85(5): E924-E932 [2022-09-28]. https://www.ncbi.nlm.nih.gov/pubmed/31058968. DOI: 10.1093/neuros/nyz144.
[28]
WAKS A G, WINER E P. Breast Cancer Treatment: A Review[J]. JAMA, 2019, 321(3): 288-300. DOI: 10.1001/jama.2018.19323.
[29]
FABIAN C, HAN M, BJERKVIG R, et al. Novel facets of glioma invasion[J]. Int Rev Cell Mol Biol, 2021, 360: 33-64. DOI: 10.1016/bs.ircmb.2020.08.001.
[30]
GAO Y J, ZHOU N N, LI N, et al. A preliminary study of 11C-methionine PET/CT in diagnosis of recurrence or residue after glioma surgery[J]. J Pract Oncol, 2021, 36(2): 154-159. DOI: 10.13267/j.cnki.syzlzz.2021.032.
[31]
SHAW T B, JEFFREE R L, THOMAS P, et al. Diagnostic performance of 18F-fluorodeoxyglucose positron emission tomography in the evaluation of glioma[J]. J Med Imaging Radiat Oncol, 2019, 63(5): 650-656. DOI: 10.1111/1754-9485.12929.
[32]
LIU B, SHEN H, HUANG H X, et al. Analysis of related factors of brain glioma recurrence[J]. Zhejiang J Traumat Surg, 2010, 15(2): 139-141.
[33]
SCHOENEGGER K, OBERNDORFER S, WUSCHITZ B, et al. Peritumoral edema on MRI at initial diagnosis: an independent prognostic factor for glioblastoma?[J]. Eur J Neurol, 2009, 16(7): 874-878. DOI: 10.1111/j.1468-1331.2009.02613.x.
[34]
ZHANG X, ZHANG W, MAO XG, et al. Malignant Intracranial High Grade Glioma and Current Treatment Strategy[J]. Curr Cancer Drug Targets, 2019, 19(2): 101-108. DOI: 10.2174/1568009618666180530090922.
[35]
ZHOU Q, KE X A, XUE C Q, et al. Nomogram for predicting early recurrence in patients with high-grade gliomas[J/OL]. World Neurosurg, 2022, 164: e619-e628 [2022-07-11]. https://pubmed.ncbi.nlm.nih.gov/35589036/. DOI: 10.1016/j.wneu.2022.05.039.
[36]
LIANG H T, MIZUMOTO M, ISHIKAWA E, et al. Peritumoral edema status of glioblastoma identifies patients reaching long-term disease control with specific progression patterns after tumor resection and high-dose proton boost[J]. J Cancer Res Clin Oncol, 2021, 147(12): 3503-3516. DOI: 10.1007/s00432-021-03765-6.
[37]
KIM B R, CHOI S H, YUN T J, et al. MR Imaging Analysis of Non-Measurable Enhancing Lesions Newly Appearing after Concomitant Chemoradiotherapy in Glioblastoma Patients for Prognosis Prediction[J/OL]. PLoS One, 2016, 11(11): e0166096 [2022-10-07]. https://www.ncbi.nlm.nih.gov/pubmed/27835666. DOI: 10.1371/journal.pone.0166096.
[38]
GITTLEMAN H, SLOAN A E, BARNHOLTZ-SLOAN J S. An independently validated survival nomogram for lower-grade glioma[J]. Neuro Oncol, 2020, 22(5): 665-674. DOI: 10.1093/neuonc/noz191.
[39]
TOH C H, WEI K C, CHANG C N, et al. Differentiation of primary central nervous system lymphomas and glioblastomas: comparisons of diagnostic performance of dynamic susceptibility contrast-enhanced perfusion MR imaging without and with contrast-leakage correction[J]. AJNR Am J Neuroradiol, 2013, 34(6): 1145-1149. DOI: 10.3174/ajnr.A3383.
[40]
YAMASHITA K, YOSHIURA T, HIWATASHI A, et al. Differentiating primary CNS lymphoma from glioblastoma multiforme: assessment using arterial spin labeling, diffusion-weighted imaging, and 18F-fluorodeoxyglucose positron emission tomography[J]. Neuroradiology, 2013, 55(2): 135-143. DOI: 10.1007/s00234-012-1089-6.
[41]
HUANG G Y, ZHANG X, MING Y, et al. Analysis of postoperative survival and prognostic factors in 95 patients with malignant glioma[J]. Chongqing Medicine, 2016, 45(5): 664-666. DOI: 10.3969/J.ISSN.1671-8348.2016.05.028.95.
[42]
LI F, ZHANG Y, WANG N, et al. Evaluation of the Prognosis of Neuroglioma Based on Dynamic Magnetic Resonance Enhancement[J]. World Neurosurg, 2020, 138: 663-671. DOI: 10.1016/j.wneu.2020.01.087.
[43]
DAS P, PURI T, JHA P, et al. A clinicopathological and molecular analysis of glioblastoma multiforme with long-term survival[J]. J Clin Neurosci, 2011, 18(1): 66-70. DOI: 10.1016/j.jocn.2010.04.050.
[44]
QIU Y N, ZHANG J, FAN H, et al. Related Factors Analysis on the Time of Gliomas Recurrence and Its Prognostics[J]. Medicine and Philosophy, 2018, 39(8): 47-49. DOI: 10.12014/j.issn.1002-0772.2018.08b.14.
[45]
TAN A C, ASHLEY D M, LOPEZ G Y, et al. Management of glioblastoma: State of the art and future directions[J]. CA Cancer J Clin, 2020, 70(4): 299-312. DOI: 10.3322/caac.21613.
[46]
WEN P Y, WELLER M, LEE E Q, et al. Glioblastoma in adults: a Society for Neuro-Oncology (SNO) and European Society of Neuro-Oncology (EANO) consensus review on current management and future directions[J]. Neuro Oncol, 2020, 22(8): 1073-1113. DOI: 10.1093/neuonc/noaa106.
[47]
ZHANG K X, JI X L, YUAN T, et al. Preliminary Study of MRI Recurrence Pattern of High-Grade Glioma and Its Effect on Survival[J]. J Clin Radiol, 2021, 40(3): 601-606. DOI: 10.13437/j.cnki.jcr.2021.03.041.

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