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AJNR Awards, New Junior Editors, and more. Read the latest AJNR updates

Research ArticleAdult Brain
Open Access

Machine Learning in Differentiating Gliomas from Primary CNS Lymphomas: A Systematic Review, Reporting Quality, and Risk of Bias Assessment

G.I. Cassinelli Petersen, J. Shatalov, T. Verma, W.R. Brim, H. Subramanian, A. Brackett, R.C. Bahar, S. Merkaj, T. Zeevi, L.H. Staib, J. Cui, A. Omuro, R.A. Bronen, A. Malhotra and M.S. Aboian
American Journal of Neuroradiology April 2022, 43 (4) 526-533; DOI: https://doi.org/10.3174/ajnr.A7473
G.I. Cassinelli Petersen
aFrom the Department of Radiology and Biomedical Imaging (G.I.C.P., T.V., H.S., R.C.B., S.M., T.Z., L.H.S., J.C., R.A.B., A.M., M.S.A.)
dUniversitätsmedizin Göttingen (G.I.C.P.), Göttingen, Germany
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J. Shatalov
eUniversity of Richmond (J.S.), Richmond, Virginia
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T. Verma
aFrom the Department of Radiology and Biomedical Imaging (G.I.C.P., T.V., H.S., R.C.B., S.M., T.Z., L.H.S., J.C., R.A.B., A.M., M.S.A.)
fNew York University (T.V.), New York, New York
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W.R. Brim
gWhiting School of Engineering (W.R.B.), Johns Hopkins University, Baltimore, Maryland
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H. Subramanian
aFrom the Department of Radiology and Biomedical Imaging (G.I.C.P., T.V., H.S., R.C.B., S.M., T.Z., L.H.S., J.C., R.A.B., A.M., M.S.A.)
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A. Brackett
bCushing/Whitney Medical Library (A.B.)
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R.C. Bahar
aFrom the Department of Radiology and Biomedical Imaging (G.I.C.P., T.V., H.S., R.C.B., S.M., T.Z., L.H.S., J.C., R.A.B., A.M., M.S.A.)
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S. Merkaj
aFrom the Department of Radiology and Biomedical Imaging (G.I.C.P., T.V., H.S., R.C.B., S.M., T.Z., L.H.S., J.C., R.A.B., A.M., M.S.A.)
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T. Zeevi
aFrom the Department of Radiology and Biomedical Imaging (G.I.C.P., T.V., H.S., R.C.B., S.M., T.Z., L.H.S., J.C., R.A.B., A.M., M.S.A.)
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L.H. Staib
aFrom the Department of Radiology and Biomedical Imaging (G.I.C.P., T.V., H.S., R.C.B., S.M., T.Z., L.H.S., J.C., R.A.B., A.M., M.S.A.)
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J. Cui
aFrom the Department of Radiology and Biomedical Imaging (G.I.C.P., T.V., H.S., R.C.B., S.M., T.Z., L.H.S., J.C., R.A.B., A.M., M.S.A.)
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A. Omuro
cDepartment of Neurology (A.O.), Yale School of Medicine, New Haven, Connecticut
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R.A. Bronen
aFrom the Department of Radiology and Biomedical Imaging (G.I.C.P., T.V., H.S., R.C.B., S.M., T.Z., L.H.S., J.C., R.A.B., A.M., M.S.A.)
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A. Malhotra
aFrom the Department of Radiology and Biomedical Imaging (G.I.C.P., T.V., H.S., R.C.B., S.M., T.Z., L.H.S., J.C., R.A.B., A.M., M.S.A.)
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M.S. Aboian
aFrom the Department of Radiology and Biomedical Imaging (G.I.C.P., T.V., H.S., R.C.B., S.M., T.Z., L.H.S., J.C., R.A.B., A.M., M.S.A.)
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References

  1. 1.↵
    1. Ostrom QT,
    2. Gittleman H,
    3. Liao P, et al
    . CBTRUS Statistical Report: primary brain and other central nervous system tumors diagnosed in the United States in 2010-2014. Neuro Oncol 2017;19:v1–88 doi:10.1093/neuonc/nox158 pmid:29117289
    CrossRefPubMed
  2. 2.↵
    1. Villano JL,
    2. Koshy M,
    3. Shaikh H, et al
    . Age, gender, and racial differences in incidence and survival in primary CNS lymphoma. Br J Cancer 2011;105:1414–18 doi:10.1038/bjc.2011.357 pmid:21915121
    CrossRefPubMedWeb of Science
  3. 3.↵
    1. Tan AC,
    2. Ashley DM,
    3. Lopez GY, et al
    . Management of glioblastoma: state of the art and future directions. CA Cancer J Clin 2020;70:299–312 doi:10.3322/caac.21613 pmid:32478924
    CrossRefPubMed
  4. 4.↵
    1. Hoang-Xuan K,
    2. Bessell E,
    3. Bromberg J, et al
    ; European Association for Neuro-Oncology Task Force on Primary CNS Lymphoma, Diagnosis and treatment of primary CNS lymphoma in immunocompetent patients: guidelines from the European Association for Neuro-Oncology. Lancet Oncol 2015;16:e322–32 doi:10.1016/S1470-2045(15)00076-5 pmid:26149884
    CrossRefPubMed
  5. 5.↵
    1. Batchelor TT
    . Primary central nervous system lymphoma: a curable disease. Hematol Oncol 2019;37(Suppl 1):15–18 doi:10.1002/hon.2598 pmid:31187523
    CrossRefPubMed
  6. 6.↵
    1. Elder JB,
    2. Chen TC
    . Surgical interventions for primary central nervous system lymphoma. Neurosurg Focus 2006;21:E13 doi:10.3171/foc.2006.21.5.14 pmid:17134115
    CrossRefPubMed
  7. 7.↵
    1. Yang H,
    2. Xun Y,
    3. Yang A, et al
    . Advances and challenges in the treatment of primary central nervous system lymphoma. J Cell Physiol 2020;235:9143–65 doi:10.1002/jcp.29790 pmid:32420657
    CrossRefPubMed
  8. 8.↵
    1. Malikova H,
    2. Liscak R,
    3. Latnerova I, et al
    . Complications of MRI-guided stereotactic biopsy of brain lymphoma. Neuro Endocrinol Lett 2014;35:613–18 pmid:25617885
    PubMed
  9. 9.↵
    1. Malone H,
    2. Yang J,
    3. Hershman DL, et al
    . Complications following stereotactic needle biopsy of intracranial tumors. World Neurosurg 2015;84:1084–89 doi:10.1016/j.wneu.2015.05.025 pmid:26008141
    CrossRefPubMed
  10. 10.↵
    1. Weller M,
    2. Martus P,
    3. Roth P, et al
    ; German PCNSL Study Group. Surgery for primary CNS lymphoma? Challenging a paradigm. Neuro Oncol 2012;14:1481–84 doi:10.1093/neuonc/nos159 pmid:22984018
    CrossRefPubMed
  11. 11.↵
    1. Haldorsen IS,
    2. Espeland A,
    3. Larsson EM
    . Central nervous system lymphoma: characteristic findings on traditional and advanced imaging. AJNR Am J Neuroradiol 2011;32:984–92 doi:10.3174/ajnr.A2171 pmid:20616176
    Abstract/FREE Full Text
  12. 12.↵
    1. Yuguang L,
    2. Meng L,
    3. Shugan Z, et al
    . Intracranial tumoural haemorrhage: a report of 58 cases. J Clin Neurosci 2002;9:637–39 doi:10.1054/jocn.2002.1131 pmid:12604273
    CrossRefPubMed
  13. 13.↵
    1. Villanueva-Meyer JE,
    2. Mabray MC,
    3. Cha S
    . Current clinical brain tumor imaging. Neurosurgery 2017;81:397–415 doi:10.1093/neuros/nyx103 pmid:28486641
    CrossRefPubMed
  14. 14.↵
    1. Wang S,
    2. Summers RM
    . Machine learning and radiology. Med Image Anal 2012;16:933–51 doi:10.1016/j.media.2012.02.005 pmid:22465077
    CrossRefPubMedWeb of Science
  15. 15.↵
    1. Nguyen AV,
    2. Blears EE,
    3. Ross E, et al
    . Machine learning applications for the differentiation of primary central nervous system lymphoma from glioblastoma on imaging: a systematic review and meta-analysis. Neurosurg Focus 2018;45:E5 doi:10.3171/2018.8.FOCUS18325 pmid:30453459
    CrossRefPubMed
  16. 16.↵
    1. Whiting P,
    2. Westwood M,
    3. Burke M, et al
    . Systematic reviews of test accuracy should search a range of databases to identify primary studies. J Clin Epidemiol 2008;61:357–64 pmid:18313560
    PubMed
  17. 17.↵
    1. Page MJ,
    2. McKenzie JE,
    3. Bossuyt PM, et al
    . The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 2021;372:n71 doi:10.1136/bmj.n71 pmid:33782057
    FREE Full Text
  18. 18.↵
    1. Collins GS,
    2. Reitsma JB,
    3. Altman DG, et al
    . Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. BMJ 2015;350:g7594 doi:10.1136/bmj.g7594 pmid:25569120
    CrossRefPubMed
  19. 19.↵
    1. Moons KG,
    2. Wolff RF,
    3. Riley RD, et al
    . PROBAST: a tool to assess risk of bias and applicability of prediction model studies: explanation and elaboration. Ann Intern Med 2019;170:W1–33 doi:10.7326/M18-1377 pmid:30596876
    CrossRefPubMed
  20. 20.↵
    1. Altman DG
    . Practical Statistics for Medical Research. Chapman and Hall; 1991
  21. 21.↵
    1. Zhou XH,
    2. Obuchowski NA,
    3. McClish DK
    . Statistical Methods in Diagnostic Medicine. Wiley; 2011
  22. 22.↵
    1. Higgins JP,
    2. Thompson SG,
    3. Deeks JJ, et al
    . Measuring inconsistency in meta-analyses. BMJ 2003;327:557–60 doi:10.1136/bmj.327.7414.557 pmid:12958120
    FREE Full Text
  23. 23.↵
    1. Alcaide-Leon P,
    2. Dufort P,
    3. Geraldo AF, et al
    . Differentiation of enhancing glioma and primary central nervous system lymphoma by texture-based machine learning. AJNR Am J Neuroradiol 2017;38:1145–50 doi:10.3174/ajnr.A5173 pmid:28450433
    Abstract/FREE Full Text
  24. 24.↵
    1. Bao S,
    2. Watanabe Y,
    3. Takahashi H, et al
    . Differentiating between glioblastoma and primary CNS lymphoma using combined whole-tumor histogram analysis of the normalized cerebral blood volume and the apparent diffusion coefficient. Magn Reson Med Sci 2019;18:53–61 doi:10.2463/mrms.mp.2017-0135 pmid:29848919
    CrossRefPubMed
  25. 25.
    1. Chen C,
    2. Zheng A,
    3. Ou X, et al
    . Comparison of radiomics-based machine-learning classifiers in diagnosis of glioblastoma from primary central nervous system lymphoma. Front Oncol 2020;10:1151doi:10.3389/fonc.2020.01151 pmid:33042784
    CrossRefPubMed
  26. 26.↵
    1. Chen Y,
    2. Li Z,
    3. Wu G, et al
    . Primary central nervous system lymphoma and glioblastoma differentiation based on conventional magnetic resonance imaging by high-throughput SIFT features. Int J Neurosci 2018;128:608–18 doi:10.1080/00207454.2017.1408613 pmid:29183170
    CrossRefPubMed
  27. 27.↵
    1. Eisenhut F,
    2. Schmidt MA,
    3. Putz F, et al
    . Classification of primary cerebral lymphoma and glioblastoma featuring dynamic susceptibility contrast and apparent diffusion coefficient. Brain Sci 2020;10:886 doi:10.3390/brainsci10110886 pmid:33233698
    CrossRefPubMed
  28. 28.↵
    1. Kang D,
    2. Park JE,
    3. Kim YH, 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;20:1251–61 doi:10.1093/neuonc/noy021 pmid:29438500
    CrossRefPubMed
  29. 29.↵
    1. Kickingereder P,
    2. Wiestler B,
    3. Sahm F, et al
    . Primary central nervous system lymphoma and atypical glioblastoma: multiparametric differentiation by using diffusion-, perfusion-, and susceptibility-weighted MR imaging. Radiology 2014;272:843–50 doi:10.1148/radiol.14132740 pmid:24814181
    CrossRefPubMedWeb of Science
  30. 30.↵
    1. Kim Y,
    2. Cho HH,
    3. Kim ST, et al
    . Radiomics features to distinguish glioblastoma from primary central nervous system lymphoma on multi-parametric MRI. Neuroradiology 2018;60:1297–1305 doi:10.1007/s00234-018-2091-4 pmid:30232517
    CrossRefPubMed
  31. 31.↵
    1. Kunimatsu A,
    2. Kunimatsu N,
    3. Yasaka K, et al
    . Machine learning-based texture analysis of contrast-enhanced MR imaging to differentiate between glioblastoma and primary central nervous system lymphoma. Magn Reson Med Sci 2019;18:44–52 doi:10.2463/mrms.mp.2017-0178 pmid:29769456
    CrossRefPubMed
  32. 32.↵
    1. Nakagawa M,
    2. Nakaura T,
    3. Namimoto T, et al
    . Machine learning based on multi-parametric magnetic resonance imaging to differentiate glioblastoma multiforme from primary cerebral nervous system lymphoma. Eur J Radiol 2018;108:147–54 doi:10.1016/j.ejrad.2018.09.017 pmid:30396648
    CrossRefPubMed
  33. 33.↵
    1. Park JE,
    2. Kim HS,
    3. 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;10:21485 doi:10.1038/s41598-020-78485-x pmid:33293590
    CrossRefPubMed
  34. 34.↵
    1. Shrot S,
    2. Salhov M,
    3. Dvorski N, et al
    . Application of MR morphologic, diffusion tensor, and perfusion imaging in the classification of brain tumors using machine learning scheme. Neuroradiology 2019;61:757–65 doi:10.1007/s00234-019-02195-z pmid:30949746
    CrossRefPubMed
  35. 35.↵
    1. Suh HB,
    2. Choi YS,
    3. Bae S, et al
    . Primary central nervous system lymphoma and atypical glioblastoma: differentiation using radiomics approach. Eur Radiol 2018;28:3832–39 doi:10.1007/s00330-018-5368-4 pmid:29626238
    CrossRefPubMed
  36. 36.↵
    1. Swinburne NC,
    2. Schefflein J,
    3. Sakai Y, et al
    . Machine learning for semi-automated classification of glioblastoma, brain metastasis and central nervous system lymphoma using magnetic resonance advanced imaging. Ann Transl Med 2019;7:232 doi:10.21037/atm.2018.08.05 pmid:31317002
    CrossRefPubMed
  37. 37.↵
    1. Xia W,
    2. Hu B,
    3. Li H, et al
    . Multiparametric-MRI-based radiomics model for differentiating primary central nervous system lymphoma from glioblastoma: development and cross-vendor validation. J Magn Reson Imaging 2021;53:242–50 doi:10.1002/jmri.27344 pmid:32864825
    CrossRefPubMed
  38. 38.↵
    1. Xiao DD,
    2. Yan PF,
    3. Wang YX, et al
    . Glioblastoma and primary central nervous system lymphoma: preoperative differentiation by using MRI-based 3D texture analysis. Clin Neurol Neurosurg 2018;173:84–90 doi:10.1016/j.clineuro.2018.08.004 pmid:30092408
    CrossRefPubMed
  39. 39.↵
    1. Yamasaki T,
    2. Chen T,
    3. Hirai T, et al
    . Classification of cerebral lymphomas and glioblastomas featuring luminance distribution analysis. Comput Math Methods Med 2013;2013:619658 doi:10.1155/2013/619658 pmid:23840280
    CrossRefPubMed
  40. 40.↵
    1. Yamashita K,
    2. Hiwatashi A,
    3. Togao O, et al
    . Diagnostic utility of intravoxel incoherent motion MR imaging in differentiating primary central nervous system lymphoma from glioblastoma multiforme. J Magn Reson Imaging 2016;44:1256–61 doi:10.1002/jmri.25261 pmid:27093558
    CrossRefPubMed
  41. 41.↵
    1. Yamashita K,
    2. Yoshiura T,
    3. Arimura H, et al
    . Performance evaluation of radiologists with artificial neural network for differential diagnosis of intra-axial cerebral tumors on MR images. AJNR Am J Neuroradiol 2008;29:1153–58 doi:10.3174/ajnr.A1037 pmid:18388216
    Abstract/FREE Full Text
  42. 42.↵
    1. Yamashita K,
    2. Yoshiura T,
    3. Hiwatashi A, et al
    . Differentiating primary CNS lymphoma from glioblastoma multiforme: assessment using arterial spin labeling, diffusion-weighted imaging, and (1)(8)F-fluorodeoxyglucose positron emission tomography. Neuroradiology 2013;55:135–43 doi:10.1007/s00234-012-1089-6 pmid:22961074
    CrossRefPubMed
  43. 43.↵
    1. Yun J,
    2. Park JE,
    3. Lee H, et al
    . Radiomic features and multilayer perceptron network classifier: a robust MRI classification strategy for distinguishing glioblastoma from primary central nervous system lymphoma. Sci Rep 2019;9:5746 doi:10.1038/s41598-019-42276-w pmid:30952930
    CrossRefPubMed
  44. 44.↵
    1. Zhou W,
    2. Wen J,
    3. Hua F, et al
    . (18)F-FDG PET/CT in immunocompetent patients with primary central nervous system lymphoma: differentiation from glioblastoma and correlation with DWI. Eur J Radiol 2018;104:26–32 doi:10.1016/j.ejrad.2018.04.020
    CrossRef
  45. 45.↵
    1. Wang S,
    2. Kim S,
    3. Chawla S, et al
    . Differentiation between glioblastomas, solitary brain metastases, and primary cerebral lymphomas using diffusion tensor and dynamic susceptibility contrast-enhanced MR imaging. AJNR Am J Neuroradiol 2011;32:507–14 doi:10.3174/ajnr.A2333 pmid:21330399
    Abstract/FREE Full Text
  46. 46.↵
    1. Lowe DG
    . Distinctive image features from scale-invariant keypoints. Int J Comput Vis 2004;60:91–110 doi:10.1023/B:VISI.0000029664.99615.94
    CrossRefPubMedWeb of Science
  47. 47.↵
    1. van Griethuysen JJM,
    2. Fedorov A,
    3. Parmar C, et al
    . Computational radiomics system to decode the radiographic phenotype. Cancer Res 2017;77:e104–07 doi:10.1158/0008-5472.CAN-17-0339 pmid:29092951
    Abstract/FREE Full Text
  48. 48.↵
    1. Bahar R,
    2. Merkaj S,
    3. Brim WR, et al
    . NIMG-23: machine learning methods in glioma grade prediction: a systematic review. Neuro-Oncology 2021;23:vi133 doi:10.1093/neuonc/noab196.523
    CrossRef
  49. 49.↵
    1. Brim WR,
    2. Jekel L,
    3. Petersen GC, et al
    . OTHR-12. The development of machine learning algorithms for the differentiation of glioma and brain metastases: a systematic review. Neuro-Oncology Advances 2021;3:iii17 doi:10.1093/noajnl/vdab071.067
    CrossRef
  50. 50.↵
    1. Lee JG,
    2. Jun S,
    3. Cho YW, et al
    . Deep learning in medical imaging: general overview. Korean J Radiology 2017;18:570–84 doi:10.3348/kjr.2017.18.4.570 pmid:28670152
    CrossRefPubMed
  51. 51.↵
    1. Takahashi R,
    2. Kajikawa Y
    . Computer-aided diagnosis: a survey with bibliometric analysis. Int J Med Inform 2017;101:58–67 doi:10.1016/j.ijmedinf.2017.02.004 pmid:28347448
    CrossRefPubMed
  52. 52.↵
    1. Booth TC,
    2. Williams M,
    3. Luis A, et al
    . Machine learning and glioma imaging biomarkers. Clin Radiol 2020;75:20–32 doi:10.1016/j.crad.2019.07.001 pmid:31371027
    CrossRefPubMed
  53. 53.↵
    1. Mongan J,
    2. Moy L,
    3. Kahn CE Jr..
    Checklist for Artificial Intelligence in Medical Imaging (CLAIM): a guide for authors and reviewers. Radiol Artif Intell 2020;2:e200029 doi:10.1148/ryai.2020200029 pmid:33937821
    CrossRefPubMed
  54. 54.↵
    1. Park JE,
    2. Kim D,
    3. Kim HS, et al
    . Quality of science and reporting of radiomics in oncologic studies: room for improvement according to radiomics quality score and TRIPOD statement. Eur Radiol 2020;30:523–36 doi:10.1007/s00330-019-06360-z pmid:31350588
    CrossRefPubMed
  55. 55.↵
    1. Jekel L,
    2. Brim WR,
    3. Petersen GC, et al
    . OTHR-15. Assessment of TRIPOD adherence in articles developing machine learning models for differentiation of glioma from brain metastasis. Neuro-oncology Advances 2021;3:17–18 doi:10.1093/noajnl/vdab071.070
    CrossRef
  56. 56.↵
    1. Merkaj S,
    2. Bahar R,
    3. Brim W, et al
    . NIMG-35: machine learning glioma grade prediction literature: a TRIPOD analysis of reporting quality. Neuro-Oncology 2021;23:vi136 doi:10.1093/neuonc/noab196.535
    CrossRef
  57. 57.↵
    1. Scherer RW,
    2. Saldanha IJ
    . How should systematic reviewers handle conference abstracts? A view from the trenches. Syst Rev 2019;8:264 doi:10.1186/s13643-019-1188-0 pmid:31699124
    CrossRefPubMed
  58. 58.↵
    1. Scherer RW,
    2. Sieving PC,
    3. Ervin AM, et al
    . Can we depend on investigators to identify and register randomized controlled trials? PLoS One 2012;7:e44183 doi:10.1371/journal.pone.0044183 pmid:22984474
    CrossRefPubMed
  59. 59.↵
    1. Bhandari AP,
    2. Liong R,
    3. Koppen J, et al
    . Noninvasive determination of IDH and 1p19q status of lower-grade gliomas using MRI radiomics: a systematic review. AJNR Am J Neuroradiol 2021;42:94–101 doi:10.3174/ajnr.A6875 pmid:33243896
    Abstract/FREE Full Text
  60. 60.↵
    1. Collins GS,
    2. Moons KG
    . Reporting of artificial intelligence prediction models. Lancet 2019;393:1577–79 doi:10.1016/S0140-6736(19)30037-6 pmid:31007185
    CrossRefPubMed
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American Journal of Neuroradiology: 43 (4)
American Journal of Neuroradiology
Vol. 43, Issue 4
1 Apr 2022
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Machine Learning in Differentiating Gliomas from Primary CNS Lymphomas: A Systematic Review, Reporting Quality, and Risk of Bias Assessment
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G.I. Cassinelli Petersen, J. Shatalov, T. Verma, W.R. Brim, H. Subramanian, A. Brackett, R.C. Bahar, S. Merkaj, T. Zeevi, L.H. Staib, J. Cui, A. Omuro, R.A. Bronen, A. Malhotra, M.S. Aboian
Machine Learning in Differentiating Gliomas from Primary CNS Lymphomas: A Systematic Review, Reporting Quality, and Risk of Bias Assessment
American Journal of Neuroradiology Apr 2022, 43 (4) 526-533; DOI: 10.3174/ajnr.A7473

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Machine Learning in Differentiating Gliomas from Primary CNS Lymphomas: A Systematic Review, Reporting Quality, and Risk of Bias Assessment
G.I. Cassinelli Petersen, J. Shatalov, T. Verma, W.R. Brim, H. Subramanian, A. Brackett, R.C. Bahar, S. Merkaj, T. Zeevi, L.H. Staib, J. Cui, A. Omuro, R.A. Bronen, A. Malhotra, M.S. Aboian
American Journal of Neuroradiology Apr 2022, 43 (4) 526-533; DOI: 10.3174/ajnr.A7473
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