Share this content in WeChat
Clinical Article
Clinical study of preoperative conventional magnetic resonance imaging to predict the recurrence site of glioma
LI Qian  HU Xiaofei  SHI Yu  WANG Jian 

LI Q, HU X F, SHI Y, et al. Clinical study of preoperative conventional magnetic resonance imaging to predict the recurrence site of glioma[J]. Chin J Magn Reson Imaging, 2023, 14(8): 19-26. DOI:10.12015/issn.1674-8034.2023.08.003.

[Abstract] Objective To predict the recurrence of glioma after surgery through preoperative conventional magnetic resonance imaging signs, so as to help clinicians planning more accurate surgical resection range before surgery.Materials and Methods This study is a retrospective study, involving 123 patients with postoperative recurrence of glioma confirmed by pathology in two centers, all of whom have complete preoperative and postoperative MRI images of recurrence. Two radiologists established a plane rectangular coordinate system with the center of the preoperative and postoperative glioma as the midpoint, thus dividing the tumor into four quadrants, respectively evaluating the MR imaging signs of the four quadrants before surgery and whether the quadrant recurred after surgery, and performing interrater reliability (IRR) analysis on the two radiologists; 18 MRI manifestations of Visually Accessible Rembrandt Images (VASAIR) signs were selected as the predictive index variables. The binary logistic regression is used as a classifier to build the prediction model, and the cross-validation method is used to verify the prediction ability of the model, where the training set∶validation set=3∶1; Select meaningful variables to establish a nomogram, and use concordance index curve and decision curve analysis (DCA) to verify.Results One hundred and twenty three patients were divided into four quadrants, a total of 492 quadrants. They were randomly divided into training set (129 non-recurrent quadrants and 240 recurrent quadrants) and validation set (43 non-recurrent quadrants and 80 recurrent quadrants). There were statistically significant differences in the enhancement quality (P=0.03), unenhanced diameter line (P<0.01), deep white matter invasion (P=0.02), unenhanced area crosses midline (P=0.04), ependymal invasion (P<0.01), the T1WI/fluid-attenuated inversion-recovery (FLAIR) (P=0.02). Further establish logistic regression model. The area under the receiver operating characteristic (ROC) curve in the training set is 0.7642 (P<0.05), and the Kappa value is 0.38. The area under the ROC curve in the validation set data is 0.8493 (P<0.05), and the Kappa value is 0.56.Conclusions Enhancement quality, unenhanced diameter line, deep white matter invasion, unenhanced area crosses midline, ependymal invasion, and T1WI/FLAIR in the VASAIR feature concentration can predict glioma recurrence and recurrence site (quadrant) before surgery, which is helpful for neurosurgeons to make surgical plans.
[Keywords] glioma;magnetic resonance imaging;recurrence;quadrant;predict

LI Qian1, 2   HU Xiaofei3   SHI Yu4   WANG Jian1*  

1 Department of Radiology, the Southwest Hospital of AMU, Chongqing 400037, China

2 Department of Radiology, 958th Army Hospital, Chongqing 400020, China

3 Department of Nuclear Medicine, the Southwest Hospital of AMU, Chongqing 400037, China

4 Department of Pathology, the Southwest Hospital of AMU, Chongqing 400037, China

Corresponding author: Wang J, E-mail:

Conflicts of interest   None.

ACKNOWLEDGMENTS National Natural Science Foundation of China (No. 92059103); Sichuan Provincial Regional Innovation Cooperation Project (No. 2023YFQ0002).
Received  2023-03-22
Accepted  2023-07-27
DOI: 10.12015/issn.1674-8034.2023.08.003
LI Q, HU X F, SHI Y, et al. Clinical study of preoperative conventional magnetic resonance imaging to predict the recurrence site of glioma[J]. Chin J Magn Reson Imaging, 2023, 14(8): 19-26. DOI:10.12015/issn.1674-8034.2023.08.003.

STUPP R, Mason W P, VAN den Bent M J, et al. Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma[J]. N Engl Med, 2005, 352(10): 987-996. DOI: 10.1056/NEJMoa043330.
XU S C, TANG L, LI X Z, et al. Immunotherapy for glioma: Current management and future application[J]. Cancer Lett, 2020, 476: 1-12. DOI: 10.1016/j.canlet.2020.02.002.
HOU Y Z, L Y, L Q, et al. Full-course resection control strategy in glioma surgery using both intraoperative ultrasound and intraoperative MRI[J]. Front Oncol, 2022, 12: 955807. DOI: 10.3389/fonc.2022.955807.
MARIE C M, MIGUEL M R, JANA L, et al. Predicting glioblastoma recurrence from preoperative mr scans using fractional-anisotropy maps with free-water suppression[J]. Cancers, 2020, 12(3): 728. DOI: 10.3390/cancers12030728.
VAN S B T, NIELAND L, CHIOCCA E A, et al. Advances in local therapy for glioblastoma - taking the fight to the tumour[J]. Nat Rev Neurol, 2022, 18(4): 221-236. DOI: 10.1038/s41582-022-00621-0.
TANG S, LIAO J, LONG Y. Comparative assessment of the efficacy of gross total versus subtotal total resection in patients with glioma: A meta-analysis[J]. Int J Surg, 2019, 63: 90-97. DOI: 10.1016/j.ijsu.2019.02.004.
MCGIRT M J, CHAICHANA K L, GATHIN M, et al. Independent association of extent of resection with survival in patients with malignant brain astrocytoma[J]. Neurosurg, 2009, 110(1): 156-162. DOI: 10.3171/2008.4.17536.
LU V M, JUE T R, MCDONALD K L, et al. The Survival Effect of Repeat Surgery at Glioblastoma Recurrence and its Trend: A Systematic Review and Meta-Analysis[J/OL]. World Neurosurg, 2018, 115: 453-459.e3 [2023-03-21]. DOI: 10.1016/j.wneu.2018.04.016.
FLIES C M, VAN L K H, TEN V M, et al. Conventional MRI Criteria to Differentiate Progressive Disease From Treatment-Induced Effects in High-Grade (WHO Grade 3-4) Gliomas[J]. Neurology, 2022, 99: 77-88. DOI: 10.1212/WNL.0000000000200359.
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]. Clin Oncol, 2010, 28(11): 1963-1972. DOI: 10.1200/JCO.2009.26.3541.
Wiki for the VASARI feature set The National Cancer Institute Web site. Available at
SOLTANINEJAD M, YANG G, LAMBROU T, et al. Supervised learning based multimodal MRI brain tumour segmentation using texture features from supervoxels[J]. Comput Methods Programs Biomed, 2018, 157: 69-84. DOI: 10.1016/j.cmpb.2018.01.003.
KHALIFA J, TENSAOUTI F, LOTTERIE J A, et al. Do perfusion and diffusion MRI predict glioblastoma relapse sites following chemoradiation?[J]. Neurooncol, 2016, 130: 181-192. DOI: 10.1007/s11060-016-2232-8.
SCHOBER P, VETTER T R. Logistic Regression in Medical Research[J]. Anesth Analg, 2021, 132: 365-366. DOI: 10.1213/ANE.0000000000005247.
ZABOR E C, REDDY C A, TENDULKAR R D, et al. Logistic Regression in Clinical Studies[J]. Int J Radiat Oncol Biol Phys, 2022, 112: 271-277. DOI: 10.1016/j.ijrobp.2021.08.007.
MICHAEL W B. Cross-Validation Methods[J]. J Math Psychol, 2000, 44(1): 108-132. DOI: 10.1006/jmps.1999.1279.
JASKOWIAK P A, COSTA I G, CAMPELLO R J G B. The area under the ROC curve as a measure of clustering quality[J]. Data Mining and Knowledge Discovery, 2022, 36: 1219-1245. DOI: 10.1007/S10618-022-00829-0.
NORTON E S, WHALEY L A, ULLOA-NAVAS M J, et al. Glioblastoma disrupts the ependymal wall and extracellular matrix structures of the subventricular zone[J]. Fluids Barriers CNS, 2022, 19(1): 58. DOI: 10.1186/s12987-022-00354-8.
SUN T. Prognostic value of imaging features combined with molecular pathology in lower grade glioma [D]. Zhengzhou: Zhengzhou University, 2021. DOI: 10.27466/d.cnki.gzzdu.2021.000336.
CASTET F, ALANYA E, VIDAL N, et al. Contrast-enhancement in suprat entorial low-grade gliomas: a classic prognostic factor in the molecularag[J]. J Neurooncol, 2019, 143(3): 515-523. DOI: 10.1007/s11060-019-03183-2.
HU L S, HAWKINS-DAARUD A, WANG L J, et al. Imaging of intratumoral heterogeneity in high-grade glioma[J]. Cancer Lett, 2020, 477: 97-106. DOI: 10.1016/j.canlet.2020.02.025.
JAIN R, POISSON L M, GUTMAN D, et al. Outcome prediction in patients with glioblastoma by using imaging, clinical, and genomic biomarkers: focus on the nonenhancing component of the tumor[J]. Radiology, 2014, 272: 484-493. DOI: 10.1148/radiol.14131691.
PARSA A T, WACHHORST S, LAMBORN K R, et al. Prognostic significance of intracranial dissemination of glioblastoma multiforme in adults[J]. J Neurosurg, 2005, 102(4): 622-628.
DU X S. Imaging strategies for evaluating glioma grade, prognosis and response to antivascular therapy with MRI quantitative features[D]. Chongqing: the Chinese People's Liberation Army (PLA) Army Military Medical University, 2019. DOI: 10.27001/d.cnki.gtjyu.2019.000144.
WANG X F, LIU X Y, CHEN Y P, et al. Histopathological findings in the peritumoral edema area of human glioma[J]. Histol Histopathol, 2015, 30: 1101-1109. DOI: 10.14670/HH-11-607.
WU C X, LIN G S, LIN Z X, et al. Peritumoral edema on magnetic resonance imaging predicts a poor clinical outcome in malignant glioma[J]. Oncol Lett, 2015, 10: 2769-2776. DOI: 10.3892/ol.2015.3639.
CHANG E L, AKYUREK S, AVALOS T, et al. Evaluation of peritumoral edema in the delineation of radiotherapy clinical target volumes for glioblastoma[J]. Int J Radiat Oncol Biol Phys, 2007, 68: 144-150. DOI: 10.1016/j.ijrobp.2006.12.009.
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: 874-878. DOI: 10.1111/j.1468-1331.2009.02613.x.
MAIER S E, SUN Y, MULKERN R V. Diffusion imaging of brain tumors[J]. NMR Biomed, 2010, 23: 849-864. DOI: 10.1002/nbm.1544.
LUNDEMANN M, MUNCK A P, MUHIC A R P, et al. Feasibility of multi-parametric PET and MRI for prediction of tumour recurrence in patients with glioblastoma[J] .Eur J Nucl Med Mol Imaging, 2019, 46: 603-613. DOI: 10.1007/s00259-018-4180-3.
LI Y, MA Y Q, WU Z J, et al. Advanced Imaging Techniques for Differentiating Pseudoprogression and Tumor Recurrence After Immunotherapy for Glioblastoma[J] .Front Immunol, 2021, 12: 790674. DOI: 10.3389/fimmu.2021.790674.
STADLBAUER A, KINFE T M, EYVPOGLU I, et al. Tissue Hypoxia and Alterations in Microvascular Architecture Predict Glioblastoma Recurrence in Humans[J] .Clin Cancer Res, 2021, 27: 1641-1649. DOI: 10.1158/1078-0432.CCR-20-3580.
LUNDEMANN M, MUNCK A R P, MUHIC A, et al. Feasibility of Multi-Parametric PET and MRI for Prediction of Tumour Recurrence in Patients With Glioblastoma[J]. Eur J Nucl Med Mol Imaging, 2019, 46(3): 603-613. DOI: 10.1007/s00259-018-4180-3.
WANG J, YI X P, FU Y, et al. Preoperative Magnetic Resonance Imaging Radiomics for Predicting Early Recurrence of Glioblastoma[J]. Front Oncol, 2021, 11: 769188. DOI: 10.3389/fonc.2021.769188.
XIAO Y M, EIKENES L, REINERTSEN I, et al. Nonlinear deformation of tractography in ultrasound-guided low-grade gliomas resection[J]. Int J Comput Assist Radiol Surg, 2018, 13: 457-467. DOI: 10.1007/s11548-017-1699-x.
MAURER C R, HILL D L, MARTIN A J, et al. Investigation of intraoperative brain deformation using a 1.5-T interventional MR system: preliminary results[J]. IEEE Trans Med Imaging, 1998, 17: 817-825. DOI: 10.1109/42.736050.
DONG Z P, ZHAO Y, CHEN F, et al. Application of high field intensity MRI combined with fluorescence guided technique in brain glioma resection[J]. Chinese Journal of Contemporary Neurology and Neurosurgery, 2021, 21(11): 988-993.

PREV Application of quantitative magnetic resonance diffusion white matter analysis in the observation of white matter changes in low-grade glioma-associated epilepsy
NEXT Predicting IDH1 gene mutation of gliomas by combining clinical and imaging features with multiple sequence radiomics

Tel & Fax: +8610-67113815    E-mail: