دورية أكاديمية
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Radiomic-Based MRI for Classification of Solitary Brain Metastases Subtypes From Primary Lymphoma of the Central Nervous System.

التفاصيل البيبلوغرافية
العنوان: Radiomic-Based MRI for Classification of Solitary Brain Metastases Subtypes From Primary Lymphoma of the Central Nervous System.
المؤلفون: Zhao LM; Department of Radiology, Xiangya Hospital, Central South University, Changsha, China.; National Engineering Research Center of Personalized Diagnostic and Therapeutic Technology, Xiangya Hospital, Central South University, Changsha, China., Hu R; Department of Radiology, Xiangya Hospital, Central South University, Changsha, China.; National Engineering Research Center of Personalized Diagnostic and Therapeutic Technology, Xiangya Hospital, Central South University, Changsha, China., Xie FF; Department of Radiology, Xiangya Hospital, Central South University, Changsha, China.; National Engineering Research Center of Personalized Diagnostic and Therapeutic Technology, Xiangya Hospital, Central South University, Changsha, China., Clay Kargilis D; Department of Radiology and Radiological Sciences, Johns Hopkins Hospital, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA., Imami M; Department of Radiology and Radiological Sciences, Johns Hopkins Hospital, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA., Yang S; Department of Radiology, Xiangya Hospital, Central South University, Changsha, China.; National Engineering Research Center of Personalized Diagnostic and Therapeutic Technology, Xiangya Hospital, Central South University, Changsha, China., Guo JQ; Department of Radiology, Xiangya Hospital, Central South University, Changsha, China.; National Engineering Research Center of Personalized Diagnostic and Therapeutic Technology, Xiangya Hospital, Central South University, Changsha, China., Jiao X; Department of Radiology, Xiangya Hospital, Central South University, Changsha, China.; National Engineering Research Center of Personalized Diagnostic and Therapeutic Technology, Xiangya Hospital, Central South University, Changsha, China., Chen RT; Department of Radiology, Xiangya Hospital, Central South University, Changsha, China.; National Engineering Research Center of Personalized Diagnostic and Therapeutic Technology, Xiangya Hospital, Central South University, Changsha, China., Wei-Hua L; Department of Radiology, Xiangya Hospital, Central South University, Changsha, China.; National Engineering Research Center of Personalized Diagnostic and Therapeutic Technology, Xiangya Hospital, Central South University, Changsha, China., Li L; Department of Radiology, Xiangya Hospital, Central South University, Changsha, China.; National Engineering Research Center of Personalized Diagnostic and Therapeutic Technology, Xiangya Hospital, Central South University, Changsha, China.
المصدر: Journal of magnetic resonance imaging : JMRI [J Magn Reson Imaging] 2023 Jan; Vol. 57 (1), pp. 227-235. Date of Electronic Publication: 2022 Jun 02.
نوع المنشور: Journal Article; Research Support, Non-U.S. Gov't
اللغة: English
بيانات الدورية: Publisher: Wiley-Liss Country of Publication: United States NLM ID: 9105850 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1522-2586 (Electronic) Linking ISSN: 10531807 NLM ISO Abbreviation: J Magn Reson Imaging Subsets: MEDLINE
أسماء مطبوعة: Publication: <2005-> : Hoboken , N.J. : Wiley-Liss
Original Publication: Chicago, IL : Society for Magnetic Resonance Imaging, c1991-
مواضيع طبية MeSH: Brain Neoplasms*/pathology , Lymphoma*/diagnostic imaging , Lymphoma*/pathology, Humans ; Retrospective Studies ; Magnetic Resonance Imaging/methods ; Central Nervous System/pathology
مستخلص: Background: Differential diagnosis of brain metastases subtype and primary central nervous system lymphoma (PCNSL) is necessary for treatment decisions. The application of machine learning facilitates the classification of brain tumors, but prior investigations into primary lymphoma and brain metastases subtype classification have been limited.
Purpose: To develop a machine-learning model to classify PCNSL, brain metastases with primary lung and non-lung origin.
Study Type: Retrospective.
Population: A total of 211 subjects with pathologically confirmed PCNSL or brain metastases (training cohort 168 and testing cohort 43).
Field Strength/sequence: A 3.0 T axial contrast-enhanced T1-weighted spin-echo inversion recovery sequence (T1WI-CE), axial T2-weighted fluid-attenuation inversion recovery sequence (T2FLAIR) ASSESSMENT: Several machine-learning models (support vector machine, random forest, and K-nearest neighbors) were built with least absolute shrinkage and selection operator (LASSO) using features from T1WI-CE, T2FLAIR, and clinical. The model with the highest performance in the training cohort was selected to differentiate lesions in the testing cohort. Then, three radiologists conducted a two-round classification (with and without model reference) using images and clinical information from testing cohorts.
Statistical Tests: Five-fold cross-validation was used for model evaluation and calibration. Model performance was assessed based on sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC).
Results: Twenty-five image features were selected by LASSO analysis. Random forest classifier was selected for its highest performance on the training set with an AUC of 0.73. After calibration, this model achieved an accuracy of 0.70 on the testing set. Accuracies of all three radiologists improved under model reference (0.49 vs. 0.70, 0.60 vs. 0.77, 0.58 vs. 0.72, respectively).
Data Conclusion: The random forest model based on conventional MRI and clinical data can diagnose PCNSL and brain metastases subtypes (lung and non-lung origin). Model classification can help foster the diagnostic accuracy of specialists and streamline prognostication workflow.
Evidence Level: 4 TECHNICAL EFFICACY: Stage 2.
(© 2022 International Society for Magnetic Resonance in Medicine.)
التعليقات: Comment in: J Magn Reson Imaging. 2023 Jan;57(1):236-237. (PMID: 35657016)
References: Ostrom QT, Cioffi G, Gittleman H, et al. CBTRUS statistical report: Primary brain and other central nervous system tumors diagnosed in the United States in 2012-2016. Neuro-Oncology 2019;21:v1-v100.
Hoang-Xuan K, Bessell E, Bromberg J, et al. Diagnosis and treatment of primary CNS lymphoma in immunocompetent patients: Guidelines from the European Association for Neuro-Oncology. Lancet Oncol 2015;16(7):e322-e332.
Le Rhun E, Guckenberger M, Smits M, et al. EANO-ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up of patients with brain metastasis from solid tumours. Ann Oncol 2021;32(11):1332-1347.
Baraniskin A, Deckert M, Schulte-Altedorneburg G, Schlegel U, Schroers R. Current strategies in the diagnosis of diffuse large B-cell lymphoma of the central nervous system. Br J Haematol 2012;156(4):421-432.
Kang D, Park JE, Kim Y-H, et al. Diffusion radiomics as a diagnostic model for atypical manifestation of primary central nervous system lymphoma: Development and multicenter external validation. Neuro Oncol 2018;20(9):1251-1261.
Artzi M, Bressler I, Ben BD. Differentiation between glioblastoma, brain metastasis and subtypes using radiomics analysis. J Magn Reson Imaging 2019;50(2):519-528.
Deckert M, Brunn A, Montesinos-Rongen M, Terreni MR, Ponzoni M. Primary lymphoma of the central nervous system-a diagnostic challenge. Hematol Oncol 2014;32(2):57-67.
Boire A, Brastianos PK, Garzia L, Valiente M. Brain metastasis. Nat Rev Cancer 2020;20(1):4-11.
Deangelis LM. Brain tumors. N Engl J Med 2001;344(2):114-123.
Kwok HM, Li KY, Chan RLS, et al. Different facets of intracranial central nervous system lymphoma and its imaging mimics. J Clin Imaging Sci 2022;12:4.
Goyal P, Kumar Y, Gupta N, et al. Usefulness of enhancement-perfusion mismatch in differentiation of CNS lymphomas from other enhancing malignant tumors of the brain. Quant Imaging Med Surg 2017;7(5):511-519.
Lu S, Gao Q, Yu J, et al. Utility of dynamic contrast-enhanced magnetic resonance imaging for differentiating glioblastoma, primary central nervous system lymphoma and brain metastatic tumor. Eur J Radiol 2016;85(10):1722-1727.
Shim WH, Kim HS, Choi C-G, Kim SJ. Comparison of apparent diffusion coefficient and intravoxel incoherent motion for differentiating among glioblastoma, metastasis, and lymphoma focusing on diffusion-related parameter. PLOS One 2015;10(7):e0134761.
Mora P, Majós C, Castañer S, et al. 1H-MRS is useful to reinforce the suspicion of primary central nervous system lymphoma prior to surgery. Eur Radiol 2014;24(11):2895-2905.
Shrot S, Salhov M, Dvorski N, Konen E, Averbuch A, Hoffmann C. Application of MR morphologic, diffusion tensor, and perfusion imaging in the classification of brain tumors using machine learning scheme. Neuroradiology 2019;61(7):757-765.
Bette S, Wiestler B, Delbridge C, et al. Discrimination of different brain metastases and primary CNS lymphomas using morphologic criteria and diffusion tensor imaging. RöFo 2016;188(12):1134-1143.
Xi YB, Kang XW, Wang N, et al. Differentiation of primary central nervous system lymphoma from high-grade glioma and brain metastasis using arterial spin labeling and dynamic contrast-enhanced magnetic resonance imaging. Eur J Radiol 2019;112:59-64.
Priya S, Liu Y, Ward C, et al. Machine learning based differentiation of glioblastoma from brain metastasis using MRI derived radiomics. Sci Rep 2021;11(1):10478.
Zacharaki EI, Wang S, Chawla S, et al. Classification of brain tumor type and grade using MRI texture and shape in a machine learning scheme. Magn Reson Med 2009;62(6):1609-1618.
Park JE, Kim HS, Lee J, et al. Deep-learned time-signal intensity pattern analysis using an autoencoder captures magnetic resonance perfusion heterogeneity for brain tumor differentiation. Sci Rep 2020;10(1):21485.
Priya S, Liu Y, Ward C, et al. Radiomic based machine learning performance for a three class problem in neuro-oncology: Time to test the waters? Cancer 2021;13(11):2568.
Polyzoidis KS, Miliaras G, Pavlidis N. Brain metastasis of unknown primary: A diagnostic and therapeutic dilemma. Cancer Treat Rev 2005;31(4):247-255.
Berghoff AS, Schur S, Füreder LM, et al. Descriptive statistical analysis of a real life cohort of 2419 patients with brain metastases of solid cancers. ESMO Open 2016;1(2):e000024.
Soffietti R, Abacioglu U, Baumert B, et al. Diagnosis and treatment of brain metastases from solid tumors: Guidelines from the European Association of Neuro-Oncology (EANO). Neuro Oncol 2017;19(2):162-174.
Ortiz-Ramón R, Larroza A, Ruiz-España S, Arana E, Moratal D. Classifying brain metastases by their primary site of origin using a radiomics approach based on texture analysis: A feasibility study. Eur Radiol 2018;28(11):4514-4523.
Kniep HC, Madesta F, Schneider T, et al. Radiomics of brain MRI: Utility in prediction of metastatic tumor type. Radiology 2019;290(2):479-487.
Schiff D. Single brain metastasis. Curr Treat Options Neurol 2001;3(1):89-99.
van Griethuysen JJM, Fedorov A, Parmar C, et al. Computational radiomics system to decode the radiographic phenotype. Cancer Res 2017;77(21):e104-e107.
Pedregosa F, Varoquaux G, Gramfort A, et al. Scikit-learn: Machine learning in python. J Mach Learn Res 2011;12:2825-2830.
Chandra MA, Bedi SS. Survey on SVM and their application in imageclassification. Int J Inf Technol 2021;13(5):1-11.
Ramteke R, Khachane M. Automatic medical image classification and abnormality detection using K-nearest neighbour. Int J Adv Comput Res 2012;2:190-196.
Sheykhmousa M, Mahdianpari M, Ghanbari H, Mohammadimanesh F, Ghamisi P, Homayouni S. Support vector machine versus random forest for remote sensing image classification: A meta-analysis and systematic review. IEEE J Sel Top Appl Earth Obs Remote Sens 2020;13:6308-6325.
Bühring U, Herrlinger U, Krings T, Thiex R, Weller M, Küker W. MRI features of primary central nervous system lymphomas at presentation. Neurology 2001;57(3):393-396.
Achrol AS, Rennert RC, Anders C, et al. Brain metastases. Nat Rev Dis Primers 2019;5:1.
Yeh RH, Yu JC, Chu CH, et al. Distinct MR imaging features of triple-negative breast cancer with brain metastasis. J Neuroimaging 2015;25(3):474-481.
Xi IL, Zhao Y, Wang R, et al. Deep learning to distinguish benign from malignant renal lesions based on routine MR imaging. Clin Cancer Res 2020;26:1944-1952.
Wang T, Gong J, Li Q, et al. A combined radiomics and clinical variables model for prediction of malignancy in T2 hyperintense uterine mesenchymal tumors on MRI. Eur Radiol 2021;31(8):6125-6135.
Jiao Z, Choi JW, Halsey K, et al. Prognostication of patients with COVID-19 using artificial intelligence based on chest x-rays and clinical data: A retrospective study. Lancet Digital Health 2021;3(5):e286-e294.
Fox BD, Cheung VJ, Patel AJ, Suki D, Rao G. Epidemiology of metastatic brain tumors. Neurosurg Clin N Am 2011;22(1):1-6.
فهرسة مساهمة: Keywords: brain metastases; differential diagnosis; lymphoma; machine learning; magnetic resonance imaging
تواريخ الأحداث: Date Created: 20220602 Date Completed: 20221214 Latest Revision: 20230203
رمز التحديث: 20230203
DOI: 10.1002/jmri.28276
PMID: 35652509
قاعدة البيانات: MEDLINE
الوصف
تدمد:1522-2586
DOI:10.1002/jmri.28276