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Clinical Article
Application of neurite orientation dispersion and density imaging to predict IDH genotype of adult diffuse glioma
ZHANG Chi  WU Qiong  HE Jinlong  XIE Shenghui  WANG Peng  WANG Shaoyu  ZHANG Huapeng  GAO Yang 

Cite this article as: ZHANG C, WU Q, HE J L, et al. Application of neurite orientation dispersion and density imaging to predict IDH genotype of adult diffuse glioma[J]. Chin J Magn Reson Imaging, 2024, 15(4): 38-44. DOI:10.12015/issn.1674-8034.2024.04.007.


[Abstract] Objective To evaluate the differences of tumors with different isocitrate dehydrogenase (IDH) gene states by using the quantitative parameters of neurite orientation dispersion and density imaging (NOODI) and to explore the clinical application value of NOODI in genotyping of adult-type diffuse glioma (ADG).Materials and Methods Fifty-one patients with adult diffuse glioma (IDH mutant 21 cases, IDH wild type 30 cases) were collected. Routine scanning and multi-shell diffusion scanning were performed before operation, and four quantitative parameter maps were obtained after image preprocessing and post-processing. All patients underwent conventional scan and multi-shell diffusion scan before operation. Four quantitative parametric maps were obtained after image preprocessing and post-processing. The 3D-Slicer was used to delineate the regions of interest in tumor solid areas, peritumoral edema areas, and contralateral normal white matter areas, compared the differences between groups with different IDH status. FeAture Explorer (FAE) software was used to construct the prediction model, and multiple pipeline combinations were considered during the development of the prediction model, including 1 data dimensionality reduction method (Pearson's correlation coefficient), 4 feature selection methods (multivariate analysis of variance, Kruskal Wallis test, recursive feature elimination and Relief algorithm) and 4 linear classifiers (support vector machine, linear discriminant analysis, logistic regression and logistic regression with LASSO regularization term), with a total of 16 pipelines. The comparisons between models were using the receiver operating characteristic (ROC) curve. The final model was evaluated by integrated discrimination improvement (IDI), net reclssification improvement (NRI) index and leave-one-out cross-validation.Results The age of IDH wild-type group was higher than that of IDH mutant group (P=0.001). Only the orientation dispersion index (ODI) of IDH wild-type group was lower than that of IDH mutant group (P=0.019), and there was no difference among other parameter groups. The contralateral white matter area and edema area had the highest and lowest intra-cellular volume fraction (ICVF) values, respectively (P<0.05). The comprehensive model constructed by linear discriminant analysis has the highest diagnostic efficiency, and the area under ROC curve is 0.835 (95% CI: 0.703-0.941). It is higher than the age of single use [0.773 (95% CI: 0.624-0.894)] and ODI [0.695 (95% CI: 0.538-0.845)]. The IDI and NRI prove that the final model has the highest diagnostic performance (all P<0.001). Decision curve analysis and calibration curve prove the clinical net benefit of the final model and the distribution closest to the real data (Brier score is 0.163).Conclusions The quantitative parameters of NODDI can be used to describe the microenvironment differences of ADG. The final model can effectively predict the different IDH gene states of ADG, and the diagnostic efficiency of the composite model is better than that of the single model.
[Keywords] glioma;genotyping;isocitrate dehydrogenase;neurite directional dispersion and density imaging;magnetic resonance imaging;biomark

ZHANG Chi1   WU Qiong1   HE Jinlong1   XIE Shenghui1   WANG Peng1   WANG Shaoyu2   ZHANG Huapeng2   GAO Yang1*  

1 Department of Imaging Diagnosis, Affiliated Hospital of Inner Mongolia Medical University, Hohhot 010059, China

2 Siemens Medical System Co., Ltd., Shanghai 200126, China

Corresponding author: GAO Y, E-mail: 1390903990@qq.com

Conflicts of interest   None.

Received  2023-07-24
Accepted  2024-03-04
DOI: 10.12015/issn.1674-8034.2024.04.007
Cite this article as: ZHANG C, WU Q, HE J L, et al. Application of neurite orientation dispersion and density imaging to predict IDH genotype of adult diffuse glioma[J]. Chin J Magn Reson Imaging, 2024, 15(4): 38-44. DOI:10.12015/issn.1674-8034.2024.04.007.

[1]
MORGAN L L. The epidemiology of glioma in adults: a "state of the science" review[J]. Neuro Oncol, 2015, 17(4): 623-624. DOI: 10.1093/neuonc/nou358.
[2]
LOUIS D N, PERRY A, WESSELING P, et al. The 2021 WHO Classification of Tumors of the Central Nervous System: a summary[J]. Neuro Oncol, 2021, 23(8): 1231-1251. DOI: 10.1093/neuonc/noab106.
[3]
GUSYATINER O, HEGI M E. Glioma epigenetics: From subclassification to novel treatment options[J]. Semin Cancer Biol, 2018, 51: 50-58. DOI: 10.1016/j.semcancer.2017.11.010.
[4]
LE RHUN E, PREUSSER M, ROTH P, et al. Molecular targeted therapy of glioblastoma[J/OL]. Cancer Treat Rev, 2019, 80: 101896 [2023-07-24]. https://pubmed.ncbi.nlm.nih.gov/31541850/. DOI: 10.1016/j.ctrv.2019.101896.
[5]
ŚLEDZIŃSKA P, BEBYN M G, FURTAK J, et al. Prognostic and predictive biomarkers in gliomas[J/OL]. Int J Mol Sci, 2021, 22(19): 10373 [2023-07-24]. https://pubmed.ncbi.nlm.nih.gov/34638714/. DOI: 10.3390/ijms221910373.
[6]
ZHANG H, SCHNEIDER T, WHEELER-KINGSHOTT C A, et al. NODDI: practical in vivo neurite orientation dispersion and density imaging of the human brain[J]. Neuroimage, 2012, 61(4): 1000-1016. DOI: 10.1016/j.neuroimage.2012.03.072.
[7]
WEI Z H, WANG H. Research progress of magnetic resonance neurite orientation dispersion and density imaging in Alzheimer's disease[J]. Chin J Magn Reson Imaging, 2021, 12(4): 103-105. DOI: 10.12015/issn.1674-8034.2021.04.026.
[8]
MOODY J F, DEAN D C, 3rd, KECSKEMETI S R, et al. Associations between diffusion MRI microstructure and cerebrospinal fluid markers of Alzheimer's disease pathology and neurodegeneration along the Alzheimer's disease continuum[J/OL]. Alzheimers Dement (Amst), 2022, 14(1): e12381 [2023-07-24]. https://pubmed.ncbi.nlm.nih.gov/36479018/. DOI: 10.1002/dad2.12381.
[9]
LIU W X, LU P, ZHANG X B, et al. Clinical application of magnetic resonance neurite dispersion and density imaging in putamen lesions of Parkinson's disease[J]. Chin J Magn Reson Imaging, 2020, 11(8): 610-614. DOI: 10.12015/issn.1674-8034.2020.08.003.
[10]
WOOD H. NODDI reveals brain microstructural changes in multiple sclerosis[J/OL]. Nat Rev Neurol, 2022, 18(1): 1 [2023-07-24]. https://pubmed.ncbi.nlm.nih.gov/34880470/. DOI: 10.1038/s41582-021-00600-x.
[11]
BY S, XU J, BOX B A, et al. Application and evaluation of NODDI in the cervical spinal cord of multiple sclerosis patients[J]. Neuroimage Clin, 2017, 15: 333-342. DOI: 10.1016/j.nicl.2017.05.010.
[12]
JIANG R, HU X, DENG K, et al. Neurite orientation dispersion and density imaging in evaluation of high-grade glioma-induced corticospinal tract injury[J/OL]. Eur J Radiol, 2021, 140: 109750 [2023-07-24]. https://pubmed.ncbi.nlm.nih.gov/33991969/. DOI: 10.1016/j.ejrad.2021.109750.
[13]
MAO J, ZENG W, ZHANG Q, et al. Differentiation between high-grade gliomas and solitary brain metastases: a comparison of five diffusion-weighted MRI models[J/OL]. BMC Med Imaging, 2020, 20(1): 124 [2023-07-24]. https://pubmed.ncbi.nlm.nih.gov/33228564/. DOI: 10.1016/j.ejrad.2021.109750.
[14]
QI J, WANG P, ZHAO G, et al. Histogram analysis based on neurite orientation dispersion and density MR imaging for differentiation between glioblastoma multiforme and solitary brain metastasis and comparison of the diagnostic performance of two ROI placements[J]. J Magn Reson Imaging, 2023, 57(5): 1464-1474. DOI: 10.1002/jmri.28419.
[15]
GAO L Y, LI Y H, LI L, et al. Multi-parameter diffusion-weighted magnetic resonance imaging for the evaluation of glioma IDH1 genotype and tumor proliferative activity[J]. Radiol Pract, 2023, 38(1): 39-46. DOI: 10.13609/j.cnki.1000-0313.2023.01.008.
[16]
GUO H, LIU J, HU J, et al. Diagnostic performance of gliomas grading and IDH status decoding A comparison between 3D amide proton transfer APT and four diffusion-weighted MRI models[J]. J Magn Reson Imaging, 2022, 56(6): 1834-1844. DOI: 10.1002/jmri.28211.
[17]
ZHAO J, LI J B, WANG J Y, et al. Quantitative analysis of neurite orientation dispersion and density imaging in grading gliomas and detecting IDH-1 gene mutation status[J]. Neuroimage Clin, 2018, 19: 174-181. DOI: 10.1016/j.nicl.2018.04.011.
[18]
GARYFALLIDIS E, BRETT M, AMIRBEKIAN B, et al. Dipy, a library for the analysis of diffusion MRI data[J/OL]. Front Neuroinform, 2014, 8: 8 [2023-07-24]. https://pubmed.ncbi.nlm.nih.gov/24600385/. DOI: 10.3389/fninf.2014.00008.
[19]
SONG Y, ZHANG J, ZHANG Y D, et al. FeAture Explorer (FAE): A tool for developing and comparing radiomics models[J/OL]. PLoS One, 2020, 15(8): e0237587 [2023-07-24]. https://pubmed.ncbi.nlm.nih.gov/32804986/. DOI: 10.1371/journal.pone.0237587.
[20]
ZHANG H, LIU K, BA R, et al. Histological and molecular classifications of pediatric glioma with time-dependent diffusion MRI-based microstructural mapping[J]. Neuro Oncol, 2023, 25(6): 1146-1156. DOI: 10.1093/neuonc/noad003.
[21]
LAWRENCE K E, NABULSI L, SANTHALINGAM V, et al. Age and sex effects on advanced white matter microstructure measures in 15,628 older adults: A UK biobank study[J]. Brain Imaging Behav, 2021, 15(6): 2813-2823. DOI: 10.1007/s11682-021-00548-y.
[22]
YOUSSEF G, MILLER J J. Lower grade gliomas[J/OL]. Curr Neurol Neurosci Rep, 2020, 20(7): 21 [2023-07-24]. https://pubmed.ncbi.nlm.nih.gov/32444979/. DOI: 10.1007/s11910-020-01040-8.
[23]
JIANG J, ZHANG X L, ZHOU J L. Research progress of isocitrate dehydrogenase genotype and imaging in glioma[J]. Chin J Magn Reson Imaging, 2021, 12(5): 103-106. DOI: 10.12015/issn.1674-8034.2021.05.025.
[24]
DING H, HUANG Y, LI Z, et al. Prediction of IDH status through MRI features and enlightened reflection on the delineation of target volume in low-grade gliomas[J/OL]. Technol Cancer Res Treat, 2019, 18: 1533033819877167 [2023-07-24]. https://pubmed.ncbi.nlm.nih.gov/31564237/. DOI: 10.1177/1533033819877167.
[25]
PARK M, KIM J W, AHN S J, et al. Evaluation of brain tumors using NODDI technique: A promising tool[J]. J Neuroradiol, 2020, 47(3): 185-186. DOI: 10.1016/j.neurad.2020.04.001.
[26]
WANG J Y, CHU J P, ZHAO J, et al. A preliminary study of NODDI in the classification of glioma[J]. Radiol Pract, 2018, 33(7): 664-667. DOI: 10.13609/j.cnki.1000-0313.2018.07.002.
[27]
COLLINS V P. Pathology of gliomas and developments in molecular testing[J]. Clin Oncol (R Coll Radiol), 2014, 26(7): 377-384. DOI: 10.1016/j.clon.2014.04.025.
[28]
FIGINI M, RIVA M, GRAHAM M, et al. Prediction of isocitrate dehydrogenase genotype in brain gliomas with MRI: Single-shell versus multishell diffusion models[J]. Radiology, 2018, 289(3): 788-796. DOI: 10.1148/radiol.2018180054.
[29]
LUAN J, WU M, WANG X, et al. The diagnostic value of quantitative analysis of ASL, DSC-MRI and DKI in the grading of cerebral gliomas: a meta-analysis[J/OL]. Radiat Oncol, 2020, 15(1): 204 [2023-07-24]. https://pubmed.ncbi.nlm.nih.gov/32831106/. DOI: 10.1186/s13014-020-01643-y.
[30]
HUANG Z, LU C, LI G, et al. Prediction of lower grade insular glioma molecular pathology using diffusion tensor imaging metric-based histogram parameters[J/OL]. Front Oncol, 2021, 11: 627202 [2023-07-24]. https://pubmed.ncbi.nlm.nih.gov/33777772/. DOI: 10.3389/fonc.2021.627202.
[31]
LUO Z Q, LIU X X, LI J R, et al. Predicting IDH genotyping of WHO grade Ⅱ/Ⅲ diffuse glioma based on histogram analysis of DTI[J]. J Clin Radiol, 2021, 40(5): 870-874. DOI: 10.13437/j.cnki.jcr.2021.05.008.
[32]
XIE Y, LI S, SHEN N, et al. Assessment of isocitrate dehydrogenase 1 genotype and cell proliferation in gliomas using multiple diffusion magnetic resonance imaging[J/OL]. Front Neurosci, 2021, 15: 783361 [2023-07-24]. https://pubmed.ncbi.nlm.nih.gov/34880724/. DOI: 10.3389/fnins.2021.783361.
[33]
GAO A, ZHANG H, YAN X, et al. Whole-tumor histogram analysis of multiple diffusion metrics for glioma genotyping[J]. Radiology, 2022, 302(3): 652-661. DOI: 10.1148/radiol.210820.
[34]
GENÇ B, ASLAN K, ÖZÇAĞLAYAN A, et al. The role of MR diffusion kurtosis and neurite orientation dispersion and density imaging in evaluating gliomas[J]. J Neuroimaging, 2023, 33(4): 644-651. DOI: 10.1111/jon.13113.
[35]
LU P J, BARAKOVIC M, WEIGEL M, et al. GAMER-MRI in multiple sclerosis identifies the diffusion-based microstructural measures that are most sensitive to focal damage: A deep-learning-based analysis and clinico-biological validation[J/OL]. Front Neurosci, 2021, 15: 647535 [2023-07-24]. https://pubmed.ncbi.nlm.nih.gov/33889069/. DOI: 10.3389/fnins.2021.647535.

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