LinkedIn

 آمار

تعداد نشریات20
تعداد شماره‌ها1,122
تعداد مقالات10,291
تعداد مشاهده مقاله43,332,255
تعداد دریافت فایل اصل مقاله9,606,693
Tavakoli, Hassan, Pirzad Jahromi, Gila, Sedaghat, Abdolrasoul. (1402). Investigating the Ability of Radiomics Features for Diagnosis of the Active Plaque of Multiple Sclerosis Patients. سامانه مدیریت نشریات علمی, 13(5), 421-432. doi: 10.31661/jbpe.v0i0.2302-1597
Hassan Tavakoli; Gila Pirzad Jahromi; Abdolrasoul Sedaghat. "Investigating the Ability of Radiomics Features for Diagnosis of the Active Plaque of Multiple Sclerosis Patients". سامانه مدیریت نشریات علمی, 13, 5, 1402, 421-432. doi: 10.31661/jbpe.v0i0.2302-1597
Tavakoli, Hassan, Pirzad Jahromi, Gila, Sedaghat, Abdolrasoul. (1402). 'Investigating the Ability of Radiomics Features for Diagnosis of the Active Plaque of Multiple Sclerosis Patients', سامانه مدیریت نشریات علمی, 13(5), pp. 421-432. doi: 10.31661/jbpe.v0i0.2302-1597
Tavakoli, Hassan, Pirzad Jahromi, Gila, Sedaghat, Abdolrasoul. Investigating the Ability of Radiomics Features for Diagnosis of the Active Plaque of Multiple Sclerosis Patients. سامانه مدیریت نشریات علمی, 1402; 13(5): 421-432. doi: 10.31661/jbpe.v0i0.2302-1597

Investigating the Ability of Radiomics Features for Diagnosis of the Active Plaque of Multiple Sclerosis Patients

Journal of Biomedical Physics and Engineering
دوره 13، شماره 5، آذر و دی 2023، صفحه 421-432    اصل مقاله (2.21 M)
نوع مقاله: Original Research
شناسه دیجیتال (DOI): 10.31661/jbpe.v0i0.2302-1597
نویسندگان
Hassan Tavakoli* 1، 2، 3؛ Gila Pirzad Jahromi1؛ Abdolrasoul Sedaghat4
1Neuroscience Research Center, Baqiyatallah University of Medical Sciences, Tehran, Iran
2Radiation Injuries Research Center, Baqiyatallah University of Medical Sciences, Tehran, Iran
3Department of Physiology and Biophysics, Baqiyatallah University of Medical Sciences, Tehran, Iran
4Department of Radiology, Karaj Central Medical Imaging Institute, Karaj, Alborz, Iran
چکیده
Background: Multiple sclerosis (MS) is the most common non-traumatic disabling disease.
Objective: The aim of this study is to investigate the ability of radiomics features for diagnosing active plaques in patients with MS from T2 Fluid Attenuated Inversion Recovery (FLAIR) images.
Material and Methods: In this experimental study, images of 82 patients with 122 MS lesions were investigated. Boruta and Relief algorithms were used for feature selection on the train data set (70%). Four different classifier algorithms, including Multi-Layer Perceptron (MLP), Gradient Boosting (GB), Decision Tree (DT), and Extreme Gradient Boosting (XGB) were used as classifiers for modeling. Finally, Performance metrics were obtained on the test data set (30%) with 1000 bootstrap and 95% confidence intervals (95% CIs).
Results: A total of 107 radiomics features were extracted for each lesion, of which 7 and 8 features were selected by the Relief method and Boruta method, respectively. DT classifier had the best performance in the two feature selection algorithms. The best performance on the test data set was related to Boruta-DT with an average accuracy of 0.86, sensitivity of 1.00, specificity of 0.84, and Area Under the Curve (AUC) of 0.92 (95% CI: 0.92-0.92). 
Conclusion: Radiomics features have the potential for diagnosing MS active plaque by T2 FLAIR image features. Additionally, choosing the feature selection and classifier algorithms plays an important role in the diagnosis of active plaque in MS patients. The radiomics-based predictive models predict active lesions accurately and non-invasively.

تازه های تحقیق

Hassan Tavakoli (Google Scholar)

کلیدواژه‌ها
Multiple Sclerosis؛ Machine Learning؛ Magnetic Resonance Imaging؛ Radiomics؛ Flair
سایر فایل های مرتبط با مقاله
مراجع
  1. Kobelt G, Thompson A, Berg J, Gannedahl M, Eriksson J. New insights into the burden and costs of multiple sclerosis in Europe. Mult Scler. 2017;23(8):1123-36. doi: 10.1177/1352458517694432. PubMed PMID: 28273775. PubMed PMCID: PMC5476197.
  2. Browne P, Chandraratna D, Angood C, Tremlett H, Baker C, Taylor BV, Thompson AJ. Atlas of Multiple Sclerosis 2013: A growing global problem with widespread inequity. Neurology. 2014;83(11):1022-4. doi: 10.1212/WNL.0000000000000768. PubMed PMID: 25200713. PubMed PMCID: PMC4162299.
  3. Ascherio A. Environmental factors in multiple sclerosis. Expert Rev Neurother. 2013;13(12 Suppl):3-9. doi: 10.1586/14737175.2013.865866. PubMed PMID: 24289836.
  4. Bakshi R, Thompson AJ, Rocca MA, Pelletier D, Dousset V, Barkhof F, et al. MRI in multiple sclerosis: current status and future prospects. Lancet Neurol. 2008;7(7):615-25. doi: 10.1016/S1474-4422(08)70137-6. PubMed PMID: 18565455. PubMed PMCID: PMC2586926.
  5. Bakshi R, Minagar A, Jaisani Z, Wolinsky JS. Imaging of multiple sclerosis: role in neurotherapeutics. 2005;2(2):277-303. doi: 10.1602/neurorx.2.2.277. PubMed PMID: 15897951. PubMed PMCID: PMC1064992.
  6. Filippi M, Rocca MA. Conventional MRI in multiple sclerosis. J Neuroimaging. 2007;17(Suppl 1):3S-9. doi: 10.1111/j.1552-6569.2007.00129.x. PubMed PMID: 17425727.
  7. Lennon VA, Wingerchuk DM, Kryzer TJ, Pittock SJ, Lucchinetti CF, Fujihara K, et al. A serum autoantibody marker of neuromyelitis optica: distinction from multiple sclerosis. 2004;364(9451):2106-12. doi: 10.1016/S0140-6736(04)17551-X. PubMed PMID: 15589308.
  8. Pretorius PM, Quaghebeur G. The role of MRI in the diagnosis of MS. Clin Radiol. 2003;58(6):434-48. doi: 10.1016/s0009-9260(03)00089-8. PubMed PMID: 12788312.
  9. Gillies RJ, Kinahan PE, Hricak H. Radiomics: Images Are More than Pictures, They Are Data. Radiology. 2016;278(2):563-77. doi: 10.1148/radiol.2015151169. PubMed PMID: 26579733. PubMed PMCID: PMC4734157.
  10. Lambin P, Leijenaar RTH, Deist TM, Peerlings J, De Jong EEC, Van Timmeren J, et al. Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol. 2017;14(12):749-62. doi: 10.1038/nrclinonc.2017.141. PubMed PMID: 28975929.
  11. Larue RTHM, Van Timmeren JE, De Jong EEC, Feliciani G, Leijenaar RTH, Schreurs WMJ, et al. Influence of gray level discretization on radiomic feature stability for different CT scanners, tube currents and slice thicknesses: a comprehensive phantom study. Acta Oncol. 2017;56(11):1544-53. doi: 10.1080/0284186X.2017.1351624. PubMed PMID: 28885084.
  12. Michoux N, Guillet A, Rommel D, Mazzamuto G, Sindic C, Duprez T. Texture Analysis of T2-Weighted MR Images to Assess Acute Inflammation in Brain MS Lesions. PLoS One. 2015;10(12):e0145497. doi: 10.1371/journal.pone.0145497. PubMed PMID: 26693908. PubMed PMCID: PMC4687842.
  13. Zhang Y. MRI texture analysis in multiple sclerosis. Int J Biomed Imaging. 2012;2012:762804. doi: 10.1155/2012/762804. PubMed PMID: 22144983. PubMed PMCID: PMC3227516.
  14. Peng Y, Zheng Y, Tan Z, Liu J, Xiang Y, Liu H, et al. Prediction of unenhanced lesion evolution in multiple sclerosis using radiomics-based models: a machine learning approach. Mult Scler Relat Disord. 2021;53:102989. doi: 10.1016/j.msard.2021.102989. PubMed PMID: 34052741.
  15. Uysal E, Erturk SM, Yildirim H, Seleker F, Basak M. Sensitivity of immediate and delayed gadolinium-enhanced MRI after injection of 0.5 M and 1.0 M gadolinium chelates for detecting multiple sclerosis lesions. Am J Roentgenol. 2007;188(3):697-702. doi: 10.2214/AJR.05.2212. PubMed PMID: 17312056.
  16. Van Griethuysen JJM, Fedorov A, Parmar C, Hosny A, Aucoin N, Narayan V, et al. Computational Radiomics System to Decode the Radiographic Phenotype. Cancer Res. 2017;77(21):e104-7. doi: 10.1158/0008-5472.CAN-17-0339. PubMed PMID: 29092951. PubMed PMCID: PMC5672828.
  17. Kursa MB, Rudnicki WR. Feature selection with the boruta package. J Stat Softw. 2010;36(11):1-13. doi: 10.18637/jss.v036.i11.
  18. Kursa MB, Jankowski A, Rudnicki WR. Boruta–a system for feature selection. Fundamenta Informaticae. 2010;101(4):271-85. doi: 10.3233/FI-2010-288.
  19. Urbanowicz RJ, Meeker M, La Cava W, Olson RS, Moore JH. Relief-based feature selection: Introduction and review. J Biomed Inform. 2018;85:189-203. doi: 10.1016/j.jbi.2018.07.014. PubMed PMID: 30031057. PubMed PMCID: PMC6299836.
  20. Wan S, Liang Y, Zhang Y, Guizani M. Deep multi-layer perceptron classifier for behavior analysis to estimate Parkinson’s disease severity using smartphones. IEEE Access. 2018;6:36825-33. doi: 10.1109/ACCESS.2018.2851382.
  21. Jafarzadeh H, Mahdianpari M, Gill E, Mohammadimanesh F, Homayouni S. Bagging and boosting ensemble classifiers for classification of multispectral, hyperspectral and PolSAR data: a comparative evaluation. Remote Sens. 2021;13(21):4405. doi: 10.3390/rs13214405.
  22. Akkaya B, Çolakoğlu N. Comparison of multi-class classification algorithms on early diagnosis of heart diseases. y-BIS Conference 2019: Recent Advances in Data Science and Business Analytics; Istanbul, Turkey: Mimar Sinan Fine Arts University Publications; 2019. p. 162-72.
  23. Ravanshad A. Gradient Boosting Vs Random Forest. 2018 [Accessed 2019 August 12]. Available from: https://medium.com/@aravanshad/gradient-boosting-versus-random-forest-cfa3fa8f0d80.
  24. Rao H, Shi X, Rodrigue A K, Feng J, Xia Y, Elhoseny M, et al. Feature selection based on artificial bee colony and gradient boosting decision tree. Applied Soft Computing. 2019;74:634-42. doi: 10.1016/j.asoc.2018.10.036.
  25. Kumar N. Advantages of XGBoost algorithm in machine learning. 2019 [Accessed 2019 August 13]. Available from: http://theprofessionalspoint.blogspot.com/2019/03/advantages-of-xgboostalgorithm-in.html.
  26. Nielsen D. Why Does XGBoost Win “Every” Machine Learning Competition? [Dissertation]. Norwegian University of Science and Technology Department of Mathematical Sciences; 2016.
  27. Venkateswaran B, Ciaburro G. Neural Networks with R. Packt Publishing; 2017.
  28. Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP. Smote: synthetic minority over sampling technique. J Artif Intell Res. 2002;16:321-57. doi: 10.1613/jair.953.
  29. He H, Garcia EA. Learning from imbalanced data. IEEE Trans Knowl Data Eng. 2009;21(9):1263-84. doi: 10.1109/TKDE.2008.239.
  30. Kabir MF, Ludwig S. Classification of breast cancer risk factors using several resampling approaches. 17th IEEE International Conference on Machine Learning and Applications (ICMLA); Orlando, FL, USA: IEEE; 2018.
  31. Fotouhi S, Asadi S, Kattan MW. A comprehensive data level analysis for cancer diagnosis on imbalanced data. J Biomed Inform. 2019;90:103089. doi: 10.1016/j.jbi.2018.12.003. PubMed PMID: 30611011.
  32. Batuwita R, Palade V. Efficient resampling methods for training support vector machines with imbalanced datasets. The 2010 International Joint Conference on Neural Networks (IJCNN); Barcelona, Spain: IEEE; 2010.
  33. Loyola-González O, Martínez-Trinidad JF, Carrasco-Ochoa JA, García-Borroto M. Study of the impact of resampling methods for contrast pattern based classifiers in imbalanced databases. Neurocomputing. 2016;175:935-47. doi: 10.1016/j.neucom.2015.04.120.
  34. Chawla NV. Data mining for imbalanced datasets: An overview. In: Data mining and knowledge discovery handbook. Springer; 2009. p. 875-86.
  35. Nitesh VC, Nathalie J, Aleksander K. Editorial: special issue on learning from imbalanced data sets. Sigkdd Explorations. 2004;6(1):1-6.
  36. He H, Bai Y, Garcia EA, Li S. ADASYN: Adaptive synthetic sampling approach for imbalanced learning. IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence); Hong Kong: IEEE; 2008.
  37. Han H, Wang WY, Mao BH. Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning. In International Conference on Intelligent Computing; Berlin, Heidelberg: Springer; 2005. p. 878-87.
  38. Xie C, Du R, Ho JW, Pang HH, Chiu KW, Lee EY, Vardhanabhuti V. Effect of machine learning re-sampling techniques for imbalanced datasets in 18F-FDG PET-based radiomics model on prognostication performance in cohorts of head and neck cancer patients. Eur J Nucl Med Mol Imaging. 2020;47(12):2826-35. doi: 10.1007/s00259-020-04756-4. PubMed PMID: 32253486.
  39. Elreedy D, Atiya AF. A comprehensive analysis of synthetic minority oversampling technique (SMOTE) for handling class imbalance. Inf Sci. 2019;505:32-64. doi: 10.1016/j.ins.2019.07.070.
  40. Sotoudeh H, Sarrami AH, Roberson GH, Shafaat O, Sadaatpour Z, Rezaei A, et al. Emerging Applications of Radiomics in Neurological Disorders: A Review. 2021;13(12):e20080. doi: 10.7759/cureus.20080. PubMed PMID: 34987940. PubMed PMCID: PMC8719529.
  41. Yip SS, Aerts HJ. Applications and limitations of radiomics. Phys Med Biol. 2016;61(13):R150-66. doi: 10.1088/0031-9155/61/13/R150. PubMed PMID: 27269645. PubMed PMCID: PMC4927328.
آمار
تعداد مشاهده مقاله: 20,919
تعداد دریافت فایل اصل مقاله: 368


سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب