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Open Access 01-12-2023 | Glioblastoma | Research

Radiomic texture analysis based on neurite orientation dispersion and density imaging to differentiate glioblastoma from solitary brain metastasis

Authors: Jie Bai, Mengyang He, Eryuan Gao, Guang Yang, Hongxi Yang, Jie Dong, Xiaoyue Ma, Yufei Gao, Huiting Zhang, Xu Yan, Yong Zhang, Jingliang Cheng, Guohua Zhao

Published in: BMC Cancer | Issue 1/2023

Abstract

Background

We created discriminative models of different regions of interest (ROIs) using radiomic texture features of neurite orientation dispersion and density imaging (NODDI) and evaluated the feasibility of each model in differentiating glioblastoma multiforme (GBM) from solitary brain metastasis (SBM).

Methods

We conducted a retrospective study of 204 patients with GBM (n = 146) or SBM (n = 58). Radiomic texture features were extracted from five ROIs based on three metric maps (intracellular volume fraction, orientation dispersion index, and isotropic volume fraction of NODDI), including necrosis, solid tumors, peritumoral edema, tumor bulk volume (TBV), and abnormal bulk volume. Four feature selection methods and eight classifiers were used for the radiomic texture feature selection and model construction. Receiver operating characteristic (ROC) curve analysis was used to evaluate the diagnostic performance of the models. Routine magnetic resonance imaging (MRI) radiomic texture feature models generated in the same manner were used for the horizontal comparison.

Results

NODDI-radiomic texture analysis based on TBV subregions exhibited the highest accuracy (although nonsignificant) in differentiating GBM from SBM, with area under the ROC curve (AUC) values of 0.918 and 0.882 in the training and test datasets, respectively, compared to necrosis (AUC_training:0.845, AUC_test:0.714), solid tumor (AUC_training:0.852, AUC_test:0.821), peritumoral edema (AUC_training:0.817, AUC_test:0.762), and ABV (AUC_training:0.834, AUC_test:0.779). The performance of the five ROI radiomic texture models in routine MRI was inferior to that of the NODDI-radiomic texture model.

Conclusion

Preoperative NODDI-radiomic texture analysis based on TBV subregions shows great potential for distinguishing GBM from SBM.

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Fordham AJ, Hacherl CC, Patel N, et al. Differentiating glioblastomas from solitary brain metastases: an update on the current literature of advanced imaging modalities. Cancers. 2021;13(12):2960. https://doi.org/10.3390/cancers13122960CrossRefPubMedPubMedCentral

Boire A, Brastianos PK, Garzia L, Valiente M. Brain metastasis. Nat Rev Cancer. 2020;20(1):4–11. https://doi.org/10.1038/s41568-019-0220-yCrossRefPubMed

Bette S, Huber T, Wiestler B, et al. Analysis of fractional anisotropy facilitates differentiation of glioblastoma and brain metastases in a clinical setting. Eur J Radiol. 2016;85(12):2182–7. https://doi.org/10.1016/j.ejrad.2016.10.002CrossRefPubMed

Weller M, van den Bent M, Hopkins K, et al. EANO guideline for the diagnosis and treatment of anaplastic gliomas and glioblastoma. Lancet Oncol. 2014;15(9):e395–403. https://doi.org/10.1016/S1470-2045(14)70011-7CrossRefPubMed

Dong F, Li Q, Jiang B, et al. Differentiation of supratentorial single brain metastasis and glioblastoma by using peri-enhancing oedema region-derived radiomic features and multiple classifiers. Eur Radiol. 2020;30(5):3015–22. https://doi.org/10.1007/s00330-019-06460-wCrossRefPubMed

Lah TT, Novak M, Breznik B. Brain malignancies: Glioblastoma and brain metastases. Semin Cancer Biol. 2020;60:262–73. https://doi.org/10.1016/j.semcancer.2019.10.010CrossRefPubMed

Artzi M, Bressler I, Ben Bashat D. Differentiation between glioblastoma, brain metastasis and subtypes using radiomics analysis. J Magn Reson Imaging: JMRI. 2019;50(2):519–28. https://doi.org/10.1002/jmri.26643CrossRefPubMed

Huang Y, Huang S, Liu Z. Multi-task learning-based feature selection and classification models for glioblastoma and solitary brain metastases. Front Oncol. 2022;12:1000471. https://doi.org/10.3389/fonc.2022.1000471CrossRefPubMedPubMedCentral

Lai PH, Chung HW, Chang HC, et al. Susceptibility-weighted imaging provides complementary value to diffusion-weighted imaging in the differentiation between pyogenic brain abscesses, necrotic glioblastomas, and necrotic metastatic brain tumors. Eur J Radiol. 2019;117:56–61. https://doi.org/10.1016/j.ejrad.2019.05.021CrossRefPubMed

10.

Romano A, Moltoni G, Guarnera A, et al. Single brain metastasis versus glioblastoma multiforme: a VOI-based multiparametric analysis for differential diagnosis. Radiol Med. 2022;127(5):490–7. https://doi.org/10.1007/s11547-022-01480-xCrossRefPubMedPubMedCentral

11.

Zhao J, Li JB, Wang JY, et al. Quantitative analysis of neurite orientation dispersion and density imaging in grading gliomas and detecting IDH-1 gene mutation status. Neuroimage Clin. 2018;19:174–81. https://doi.org/10.1016/j.nicl.2018.04.011CrossRefPubMedPubMedCentral

12.

Zhang H, Schneider T, Wheeler-Kingshott CA, Alexander DC. NODDI: practical in vivo neurite orientation dispersion and density imaging of the human brain. NeuroImage. 2012;61(4):1000–16. https://doi.org/10.1016/j.neuroimage.2012.03.072CrossRefPubMed

13.

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. Front Neurosci. 2021;22(15):783361. https://doi.org/10.3389/fnins.2021.783361CrossRef

14.

Gao E, Gao A, Kit Kung W, et al. Histogram analysis based on diffusion kurtosis imaging: differentiating glioblastoma multiforme from single brain metastasis and comparing the diagnostic performance of two region of interest placements. Eur J Radiol. 2022;147:110104. https://doi.org/10.1016/j.ejrad.2021.110104CrossRefPubMed

15.

Qi J, Wang P, Zhao G et al. (2022) 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 Magn Reson Imaging. https://doi.org/10.1002/jmri.28419

16.

Castellano G, Bonilha L, Li LM, Cendes F. Texture analysis of medical images. Clin Radiol. 2004;59:1061–9. https://doi.org/10.1016/j.crad.2004.07.008CrossRefPubMed

17.

Ruiz-España S, Ortiz-Ramón R, Pérez-Ramírez Ú, et al. MRI texture-based radiomics analysis for the identification of altered functional networks in alcoholic patients and animal models. Comput Med Imaging Graph. 2023;104:102187. https://doi.org/10.1016/j.compmedimag.2023.102187CrossRefPubMed

18.

Mo X, Chen W, Chen S, et al. MRI texture-based machine learning models for the evaluation of renal function on different segmentations: a proof-of-concept study. Insights into Imaging. 2023;14(1):28. https://doi.org/10.1186/s13244-023-01370-4CrossRefPubMedPubMedCentral

19.

Wang X, Dai Y, Lin H, et al. Shape and texture analyses based on conventional MRI for the preoperative prediction of the aggressiveness of pituitary adenomas. Eur Radiol. 2023;33(5):3312–21. https://doi.org/10.1007/s00330-023-09412-7CrossRefPubMed

20.

Li J, Fu S, Gong Z, et al. MRI-based texture analysis of infrapatellar fat pad to predict knee osteoarthritis incidence. Radiology. 2022;304:611–21. https://doi.org/10.1148/radiol.212009CrossRefPubMed

21.

Cui Z, Ren G, Cai R, et al. MRI-based texture analysis for differentiate between pediatric posterior fossa ependymoma type A and B. Eur J Radiol. 2022;152:110288. https://doi.org/10.1016/j.ejrad.2022.110288CrossRefPubMed

22.

Isensee F, Jaeger PF, Kohl SAA, Petersen J, Maier-Hein KH. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat Methods. 2021;18(2):203–11. https://doi.org/10.1038/s41592-020-01008-zCrossRefPubMed

23.

Song Y, Zhang J, Zhang YD, et al. FeAture Explorer (FAE): a tool for developing and comparing radiomics models. PLoS ONE. 2020;15(8):e0237587. https://doi.org/10.1371/journal.pone.0237587CrossRefPubMedPubMedCentral

24.

Ortiz-Ramón R, Ruiz-España S, Mollá-Olmos E, Moratal D. Glioblastomas and brain metastases differentiation following an MRI texture analysis-based radiomics. Approach Phys Med. 2020;76:44–54. https://doi.org/10.1016/j.ejmp.2020.06.016CrossRefPubMed

25.

Han Y, Zhang L, Niu S, et al. Differentiation between glioblastoma multiforme and metastasis from the lungs and other sites using combined clinical/routine MRI radiomics. Front cell Dev Biology. 2021;9:710461. https://doi.org/10.3389/fcell.2021.710461CrossRef

26.

Suh CH, Kim HS, Jung SC, Kim SJ. Diffusion-weighted imaging and diffusion tensor imaging for differentiating high-grade glioma from solitary brain metastasis: a systematic review and meta-analysis. AJNR Am J Neuroradiol. 2018;39(7):1208–14. https://doi.org/10.3174/ajnr.A5650CrossRefPubMedPubMedCentral

27.

Skogen K, Schulz A, Helseth E, et al. Texture analysis on diffusion tensor imaging: discriminating glioblastoma from single brain metastasis. Acta Radiol. 2019;60(3):356–66. https://doi.org/10.1177/0284185118780889CrossRefPubMed

28.

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. BMC Med Imaging. 2020;20(1):124. https://doi.org/10.1186/s12880-020-00524-wCrossRefPubMedPubMedCentral

29.

Kadota Y, Hirai T, Azuma M, et al. Differentiation between glioblastoma and solitary brain metastasis using neurite orientation dispersion and density imaging. J Neuroradiol. 2020;47(3):197–202. https://doi.org/10.1016/j.neurad.2018.10.005CrossRefPubMed

30.

Demircioğlu A. Evaluation of the dependence of radiomic features on the machine learning model. Insights into Imaging. 2022;13(1):28. https://doi.org/10.1186/s13244-022-01170-2CrossRefPubMedPubMedCentral

31.

Louis DN, Perry A, Wesseling P, et al. The 2021 WHO classification of tumors of the central nervous system: a summary. Neuro Oncol. 2021;23(8):1231–51. https://doi.org/10.1093/neuonc/noab106CrossRefPubMedPubMedCentral

32.

Liu C, Zhang M, Yan X, et al. Single-cell dissection of cellular and molecular features underlying human cervical squamous cell carcinoma initiation and progression. Sci Adv. 2023;9(4):eadd8977. https://doi.org/10.1126/sciadv.add8977CrossRefPubMedPubMedCentral

33.

Liu C, Wang P, Sun Y, et al. Neoadjuvant chemoradiotherapy changes the landscape of soluble immune checkpoint molecules in patients with locally advanced rectal cancer. Front Oncol. 2022;12:756811. https://doi.org/10.3389/fonc.2022.756811CrossRefPubMedPubMedCentral

Title: Radiomic texture analysis based on neurite orientation dispersion and density imaging to differentiate glioblastoma from solitary brain metastasis
Authors: Jie Bai
Mengyang He
Eryuan Gao
Guang Yang
Hongxi Yang
Jie Dong
Xiaoyue Ma
Yufei Gao
Huiting Zhang
Xu Yan
Yong Zhang
Jingliang Cheng
Guohua Zhao
Publication date: 01-12-2023
Publisher: BioMed Central
Keywords: Glioblastoma
Glioblastoma
Glioblastoma
Metastasis
Magnetic Resonance Imaging
Magnetic Resonance Imaging
Edema
Solid Tumor
Published in: BMC Cancer / Issue 1/2023
Electronic ISSN: 1471-2407
DOI: https://doi.org/10.1186/s12885-023-11718-0

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