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

BMC Cancer
Jie Bai1, Mengyang He2, Eryuan Gao1, Guang Yang3, Hongxi Yang3, Jie Dong4, Xiaoli Ma1, Yufei Gao2, Huiting Zhang5, Yujin Xu5, Yong Zhang1, Jingliang Cheng1, Guohua Zhao1
1Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
2School of Cyber Science and Engineering, Zhengzhou University, Zhengzhou, China
3Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China
4School of Information Engineering, North China University of Water Resources and Electric Power, Zhengzhou, China
5MR Research Collaboration, Siemens Healthineers, Wuhan, China

Tóm tắt

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 (AUCtraining:0.845, AUCtest:0.714), solid tumor (AUCtraining:0.852, AUCtest:0.821), peritumoral edema (AUCtraining:0.817, AUCtest:0.762), and ABV (AUCtraining:0.834, AUCtest: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.

Từ khóa


Tài liệu tham khảo

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/cancers13122960

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-y

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.2016.10.002

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)70011-7

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-w

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.2019.10.010

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.26643

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.2022.1000471

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.2019.05.021

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-x

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.2018.04.011

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.2012.03.072

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.2021.783361

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.2021.110104

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

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.2004.07.008

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.2023.102187

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/s13244-023-01370-4

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/s00330-023-09412-7

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.212009

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.2022.110288

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-z

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.0237587

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.2020.06.016

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.2021.710461

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.A5650

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/10.1177/0284185118780889

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-w

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.2018.10.005

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/s13244-022-01170-2

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/noab106

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.add8977

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.2022.756811

Thông tin
Thông tin xuất bản

BMC Cancer

Thông tin tác giả