:: Volume 21, Issue 2 (4-2023) ::
Int J Radiat Res 2023, 21(2): 275-280 Back to browse issues page
Non-invasive radiomics nomogram model for determining the low and high-grade glioma base on MRI images
S. Bijari , A. Jahanbakhshi , P. Abdolmaleki
Department of Biophysics, Faculty of Biological Sciences, Tarbiat Modares University, Tehran, Iran , parviz@modares.ac.ir
Abstract:   (627 Views)
Background: Glioma is the most common type of tumor in the nervous system. Glioma grading remains challenging despite advancements in diagnostic and treatment systems. Preoperative classification is essential to determining optimal treatment and prognosis for gliomas. This study aimed to use magnetic resonance imaging (MRI) to develop accurate nomogram models for glioma grading. Materials and Methods: Eighty-three patients who had undergone a glioma biopsy from June 2017 to November 2021 were retrospectively collected. Two multiparametric MRIs were acquired: T2-weighted and T1-weighted gadolinium contrast-enhanced of 83 glioma patients from one medical institution. Using the open-source python package PyRadiomics, 107 radiomics features were identified for each sequence MRI. We analyzed the probabilities of low-grade gliomas (LGG) and high-grade gliomas (HGG) using logistic regression and the least absolute shrinkage and selection operator regression (LASSO). We identified seven features affecting LGG and HGG differentiated using the lasso algorithm. Next, logistic regression analysis was performed to build a classification model, and five features were obtained. Nomograms were created to predict the incidence of HGG and LLG. To evaluate the prediction performance of the models, receiver operating characteristic (ROC) curves were plotted, and the area under the curve (AUC), sensitivity, specificity, and accuracy were calculated. Results: For multivariate logistic regression models, according to the best-selected features based on MRI images and clinical data, five parameters were independent predictors of LGG from HGG (P<0.001). The highest prediction performance in terms of AUC, sensitivity, specificity, and accuracy was 0.97, 89.19%, 91.11%, and 90.24%, respectively. Conclusion: The radiomics nomogram models created from quantitative images and clinical data performed well in differentiating LGG from HGG.
Keywords: Glioma, low-grade glioma (LGG), high-grade glioma (HGG), radiomics, nomogram.
Full-Text [PDF 634 kb]   (456 Downloads)    
Type of Study: Original Research | Subject: Radiation Biology
References
1. Wang Z, Xiao X, He K, Wu D, Pang P, Wu T (2022) A study of MRI-based machine-learning methods for glioma grading. Int J Radiat Res, 20(1): 115-20. [DOI:10.52547/ijrr.20.1.18]
2. Niu L, Feng WH, Duan CF, Liu YC, Liu JH, Liu XJ (2020) The value of enhanced mr radiomics in estimating the IDH1 genotype in high-grade gliomas. Biomed Res Int, 2020. [DOI:10.1155/2020/4630218] [PMID] []
3. Kazerooni AF, Bagley SJ, Akbari H, Saxena S, Bagheri S, Guo J, et al. (2021) Applications of radiomics and radiogenomics in high-grade gliomas in the era of precision medicine. Cancers (Basel), Dec 1;13(23). [DOI:10.3390/cancers13235921] [PMID] []
4. Patel D, Vankawala F, Bhatt BA (2019) Survey on identification of glioblastoma multiforme and low-grade glioma brain tumor type. In: 2019 International Conference on Communication and Signal Processing (ICCSP). IEEE, 335-9. [DOI:10.1109/ICCSP.2019.8698100]
5. Qi Y, Wu S, Tao L, Shi Y, Yang W, Zhou L, et al. (2021) Development of nomograms for predicting lymph node metastasis and distant metastasis in newly diagnosed T1-2 non-small cell lung cancer: A population-based analysis. Front Oncol, 8: 11. [DOI:10.3389/fonc.2021.683282] [PMID] []
6. Chen C, Ou X, Wang J, Guo W, Ma X (2019) Radiomics-based machine learning in differentiation between glioblastoma and metastatic brain tumors. Front Oncol, 9: 806. [DOI:10.3389/fonc.2019.00806] [PMID] []
7. Tateishi M, Nakaura T, Kitajima M, Uetani H, et al. (2020) An initial experience of machine learning based on multi-sequence texture parameters in magnetic resonance imaging to differentiate glioblastoma from brain metastases. J Neurol Sci, 410: 116514. [DOI:10.1016/j.jns.2019.116514] [PMID]
8. Wu J, Liang F, Wei R, Lai S, Lv X, Luo S, et al. (2021) A multiparametric MR-based RadioFusionOmics Model with robust capabilities of differentiating glioblastoma multiforme from solitary brain metastasis. Cancers (Basel), 13(22): 5793. [DOI:10.3390/cancers13225793] [PMID] []
9. van Gómez O, Herraiz JL, Udías JM, Haug A, et al. (2022) Analysis of cross-combinations of feature selection and machine-learning classification methods based on [18F]F-FDG PET/CT radiomic features for metabolic response prediction of metastatic breast cancer lesions. Cancers (Basel), 14(12). [DOI:10.3390/cancers14122922] [PMID] []
10. Duan L, Shan W, Guo L, Bo G (2022) Correlation in high resolution computed tomography signs with pathological subtype and differentiation degree of lung adenocarcinoma. Int J Radiat Res, 20(3): 679-85.
11. Chen S, Wang C, Gu Y, Ruan R, Yu J, Wang S (2022) Prediction of microvascular invasion and its M2 classification in hepatocellular carcinoma based on nomogram analyses. Front Oncol, 14: 11. https://doi.org/10.3389/fonc.2021.774800 [DOI:10.3389/fonc.2022.888008] [PMID] []
12. Li Y, Wu ZQ, Xu Q, Goyal H, Xu HG (2021) Development and validation of novel nomograms using serum tumor markers for the prediction of preoperative histologic grades in gastroenteropancreatic neuroendocrine tumors. Front Oncol, 24: 11. [DOI:10.3389/fonc.2021.681149] [PMID] []
13. Bijari S, Jahanbakhshi A, Hajishafiezahramini P, Abdolmaleki P (2022) Differentiating glioblastoma multiforme from brain metastases using multidimensional radiomics features derived from MRI and multiple machine learning models. Biomed Res Int, 2022: 1-10. [DOI:10.1155/2022/2016006] [PMID] []
14. Abrigo JM, Fountain DM, Provenzale JM, et al. (2018) Magnetic resonance perfusion for differentiating low‐grade from high‐grade gliomas at first presentation. Cochrane Database of Systematic Reviews, 2018:(1)CD011551. [DOI:10.1002/14651858.CD011551.pub2] [PMID] []
15. Pasquini L, Jenabi M, Yildirim O, Silveira P, et al. (2022) Brain functional connectivity in low-and high-grade gliomas: Differences in network dynamics associated with tumor grade and location. Cancers (Basel), 14(14):3327. [DOI:10.3390/cancers14143327] [PMID] []
16. Quazi S (2022) Artificial intelligence and machine learning in precision and genomic medicine. Medical Oncology, 39(8): 1-18. [DOI:10.1007/s12032-022-01711-1] [PMID] []
17. Bhatele KR and Bhadauria SS (2022) Machine learning application in Glioma classification: review and comparison analysis. Archives of Computational Methods in Engineering, 29: 247-274. [DOI:10.1007/s11831-021-09572-z]
18. Tibshirani R (2011) Regression shrinkage and selection via the lasso: a retrospective Journal of the Royal Statistical Society Series B, 73(3): 273-282. [DOI:10.1111/j.1467-9868.2011.00771.x]
19. Chougule T, Gupta RK, Saini J, Agrawal S, et al. (2022) Radiomics signature for temporal evolution and recurrence patterns of glioblastoma using multimodal magnetic resonance imaging. NMR Biomed, 35(3): e4647. [DOI:10.1002/nbm.4647] [PMID]
20. Zhang Z and Kattan MW (2017) Drawing nomograms with R: Applications to categorical outcome and survival data. Ann Transl Med, 5(10). [DOI:10.21037/atm.2017.04.01] [PMID] []
21. Shin MS and Lee JY (2022) Building a nomogram for metabolic syndrome using logistic regression with a complex sample-a study with 39,991,680 cases. Healthcare (Switzerland), 10(2). [DOI:10.3390/healthcare10020372] [PMID] []
22. Winter A, Kneib T, Wasylow C, Reinhardt L, et al. (2017) Updated nomogram incorporating percentage of positive cores to predict probability of lymph node invasion in prostate cancer patients undergoing sentinel lymph node dissection. J Cancer, 8(14). [DOI:10.7150/jca.20409] [PMID] []
23. Bathla G, Priya S, Liu Y, Ward C, et al. (2021)Radiomics-based differentiation between glioblastoma and primary central nervous system lymphoma: A comparison of diagnostic performance across different MRI sequences and machine learning techniques. Eur Radiol, 31(11): 8703-13. [DOI:10.1007/s00330-021-07845-6] [PMID]
24. Soni N, Priya S, Bathla G (2019) Texture analysis in cerebral gliomas: a review of the literature. American Journal of Neuroradiology, 40(6): 928-34. [DOI:10.3174/ajnr.A6075] [PMID] []
25. Vabalas A, Gowen E, Poliakoff E, Casson AJ (2019) Machine learning algorithm validation with a limited sample size. PLoS One, 14(11): e0224365. [DOI:10.1371/journal.pone.0224365] [PMID] []
26. Kadota Y, Hirai T, Azuma M, Hattori Y, et al. (2020) Differentiation between glioblastoma and solitary brain metastasis using neurite orientation dispersion and density imaging. Journal of Neuroradiology, 47(3): 197-202. [DOI:10.1016/j.neurad.2018.10.005] [PMID]
27. Damon A, Clifton W, Valero-Moreno F, Quinones-Hinojosa A (2020) Cost-effective method for 3-dimensional printing dynamic multiobject and patient-specific brain tumor models. World Neurosurg, 140: 173-9. [DOI:10.1016/j.wneu.2020.04.184] [PMID]
28. Mishra SK and Deepthi VH (2021) Brain image classification by the combination of different wavelet transforms and support vector machine classification. J Ambient Intell Humaniz Comput, 12(6): 6741-9. [DOI:10.1007/s12652-020-02299-y]
29. Yu J, Shi Z, Lian Y, Li Z, Liu T, Gao Y, et al. (2017) Noninvasive IDH1 mutation estimation based on a quantitative radiomics approach for grade II glioma. Eur Radiol, 27(8): 3509-22. [DOI:10.1007/s00330-016-4653-3] [PMID]
30. Bangalore Yogananda CG, Shah BR, Vejdani-Jahromi M, et al. (2020) A novel fully automated MRI-based deep-learning method for classification of IDH mutation status in brain gliomas. Neuro Oncol, 22(3): 402-11. [DOI:10.1093/neuonc/noz199] [PMID] []
31. Lohmann P, Kocher M, Ruge MI, Visser-Vandewalle V, et al. (2020) PET/MRI radiomics in patients with brain metastases. Front Neurol, 11: 1. [DOI:10.3389/fneur.2020.00001] [PMID] []
32. Kurc T, Bakas S, Ren X, Bagari A, Momeni A, Huang Y, et al. (2020S) egmentation and classification in digital pathology for glioma research: challenges and deep learning approaches. Front Neurosci, 14: 27. [DOI:10.3389/fnins.2020.00027] [PMID] []
33. Shaikh F, Dupont-Roettger D, Dehmeshki J, et al. (2020) The role of imaging biomarkers derived from advanced imaging and radiomics in the management of brain tumors. Front Oncol, 1807. [DOI:10.3389/fonc.2020.559946] [PMID] []
34. Ortiz-Ramón R, Ruiz-España S, Mollá-Olmos E, Moratal D (2020) Glioblastomas and brain metastases differentiation following an MRI texture analysis-based radiomics approach. Physica Medica, 76:44-54. [DOI:10.1016/j.ejmp.2020.06.016] [PMID]

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Volume 21, Issue 2 (4-2023) Back to browse issues page