Deep Learning MRI Models for the Differential Diagnosis of Tumefactive Demyelination versus IDH Wild-Type Glioblastoma

References

1. 1.↵
   1. Balloy G,
   2. Pelletier J,
   3. Suchet L, et al

   ; Société Francophone de la Sclérose en Plaques. Inaugural tumor-like multiple sclerosis: clinical presentation and medium-term outcome in 87 patients. J Neurol 2018;265:2251–59 doi:10.1007/s00415-018-8984-7 pmid:30054790

   CrossRefPubMed
2. 2.↵
   1. Frankl S,
   2. Viaene A,
   3. Vossough A, et al

   . Solitary tumefactive demyelinating lesions in children: clinical and magnetic resonance imaging features, pathologic characteristics, and outcomes. Pediatr Neurol 2024;157:141–50 doi:10.1016/j.pediatrneurol.2024.05.012 pmid:38917518

   CrossRefPubMed
3. 3.↵
   1. Pervin I,
   2. Ramanathan S,
   3. Cappelen-Smith C, et al

   . Clinical and radiological characteristics and outcomes of patients with recurrent or relapsing tumefactive demyelination. Mult Scler Relat Disord 2024;82:105408 doi:10.1016/j.msard.2023.105408 pmid:38219394

   CrossRefPubMed
4. 4.↵
   1. Tobin WO,
   2. Meyer FB,
   3. Keegan BM

   . Diagnostic yield and safety of cerebellar and brainstem parenchymal biopsy. World Neurosurg 2015;84:1973–76 doi:10.1016/j.wneu.2015.08.062 pmid:26342777

   CrossRefPubMed
5. 5.↵
   1. Tremblay MA,
   2. Villanueva-Meyer JE,
   3. Cha S, et al

   . Clinical and imaging correlation in patients with pathologically confirmed tumefactive demyelinating lesions. J Neurol Sci 2017;381:83–87 doi:10.1016/j.jns.2017.08.015 pmid:28991721

   CrossRefPubMed
6. 6.↵
   1. Lucchinetti CF,
   2. Gavrilova RH,
   3. Metz I, et al

   . Clinical and radiographic spectrum of pathologically confirmed tumefactive multiple sclerosis. Brain 2008;131:1759–75 doi:10.1093/brain/awn098 pmid:18535080

   CrossRefPubMedWeb of Science
7. 7.↵
   1. Fereidan-Esfahani M,
   2. Decker PA,
   3. Weigand SD, et al

   . Defining the natural history of tumefactive demyelination: a retrospective cohort of 257 patients. Ann Clin Transl Neurol 2023;10:1544–55 doi:10.1002/acn3.51844 pmid:37443413

   CrossRefPubMed
8. 8.↵
   1. Fereidan-Esfahani M,
   2. Decker PA,
   3. Eckel Passow JE, et al

   . Population-based incidence and clinico-radiological characteristics of tumefactive demyelination in Olmsted County, Minnesota, United States. Eur J Neurol 2022;29:782–89 doi:10.1111/ene.15182 pmid:34773343

   CrossRefPubMed
9. 9.↵
   1. Mouresan EF,
   2. Mentesidou E,
   3. Berglund A, et al

   . Clinical characteristics and long-term outcomes of late-onset multiple sclerosis: a Swedish nationwide study. Neurology 2024;102:e208051 doi:10.1212/WNL.0000000000208051 pmid:38394472

   CrossRefPubMed
10. 10.↵
    1. Tobin WO,
    2. Costanzi C,
    3. Guo Y, et al

    . Clinical-radiological-pathological spectrum of central nervous system-idiopathic inflammatory demyelinating disease in the elderly. Mult Scler 2017;23:1204–13 doi:10.1177/1352458516675748 pmid:27760861

    CrossRefPubMed
11. 11.↵
    1. Lin X,
    2. Yu WY,
    3. Liauw L, et al

    . Clinicoradiologic features distinguish tumefactive multiple sclerosis from CNS neoplasms. Neurol Clin Pract 2017;7:53–64 doi:10.1212/CPJ.0000000000000319 pmid:29849229

    Abstract/FREE Full Text
12. 12.↵
    1. Banks SA,
    2. Willrich MAV,
    3. Eckel-Passow JE, et al

    . Cerebrospinal fluid kappa free light chains in patients with tumefactive demyelination. Clin Chem Lab Med 2024;62:e189–92 doi:10.1515/cclm-2023-1416 pmid:38295388

    CrossRefPubMed
13. 13.↵
    1. Fereidan-Esfahani M,
    2. Tobin WO

    . Cyclophosphamide in treatment of tumefactive multiple sclerosis. Mult Scler Relat Disord 2021;47:102627 doi:10.1016/j.msard.2020.102627 pmid:33246262

    CrossRefPubMed
14. 14.↵
    1. Horbinski C,
    2. Nabors LB,
    3. Portnow J, et al

    . NCCN Guidelines(R) Insights: Central Nervous System Cancers, Version 2.2022. J Natl Compr Canc Netw 2023;21:12–20 doi:10.6004/jnccn.2023.0002 pmid:36634606

    CrossRefPubMed
15. 15.↵
    1. Miller RC,
    2. Lachance DH,
    3. Lucchinetti CF, et al

    . Multiple sclerosis, brain radiotherapy, and risk of neurotoxicity: the Mayo Clinic experience. Int J Radiat Oncol Biol Phys 2006;66:1178–86 doi:10.1016/j.ijrobp.2006.06.014 pmid:16965867

    CrossRefPubMedWeb of Science
16. 16.↵
    1. Abou Zeid N,
    2. Pirko I,
    3. Erickson B, et al

    . Diffusion-weighted imaging characteristics of biopsy-proven demyelinating brain lesions. Neurology 2012;78:1655–62 doi:10.1212/WNL.0b013e3182574f66 pmid:22573639

    CrossRefPubMed
17. 17.↵
    1. Mabray MC,
    2. Cohen BA,
    3. Villanueva-Meyer JE, et al

    . Performance of apparent diffusion coefficient values and conventional MRI features in differentiating tumefactive demyelinating lesions from primary brain neoplasms. AJR Am J Roentgenol 2015;205:1075–85 doi:10.2214/AJR.14.13970 pmid:26496556

    CrossRefPubMed
18. 18.↵
    1. Cacciaguerra L,
    2. Morris P,
    3. Tobin WO, et al

    . Tumefactive demyelination in MOG Ab-associated disease, multiple sclerosis, and AQP-4-IgG-positive neuromyelitis optica spectrum disorder. Neurology 2023;100:e1418–e1432 doi:10.1212/WNL.0000000000206820 pmid:36690455

    Abstract/FREE Full Text
19. 19.↵
    1. Koelblinger C,
    2. Fruehwald-Pallamar J,
    3. Kubin K, et al

    . Atypical idiopathic inflammatory demyelinating lesions (IIDL): conventional and diffusion-weighted MR imaging (DWI) findings in 42 cases. Eur J Radiology 2013;82:1996–2004 doi:10.1016/j.ejrad.2013.07.026 pmid:23993757

    CrossRefPubMed
20. 20.↵
    1. Schwartz KM,
    2. Erickson BJ,
    3. Lucchinetti C

    . Pattern of T2 hypointensity associated with ring-enhancing brain lesions can help to differentiate pathology. Neuroradiology 2006;48:143–49 doi:10.1007/s00234-005-0024-5 pmid:16447037

    CrossRefPubMedWeb of Science
21. 21.↵
    1. Zhang Y,
    2. Liang K,
    3. He J, et al

    . Deep learning with data enhancement for the differentiation of solitary and multiple cerebral glioblastoma, lymphoma, and tumefactive demyelinating lesion. Front Oncol 2021;11:665891 doi:10.3389/fonc.2021.665891 pmid:34490082

    CrossRefPubMed
22. 22.↵
    1. Ryu WS,
    2. Kang YR,
    3. Noh YG, et al

    . Acute infarct segmentation on diffusion-weighted imaging using deep learning algorithm and RAPID MRI. J Stroke 2023;25:425–29 doi:10.5853/jos.2023.02145 pmid:37813675

    CrossRefPubMed
23. 23.↵
    1. Lee J,
    2. Burkett BJ,
    3. Min HK, et al

    . Synthesizing images of tau pathology from cross-modal neuroimaging using deep learning. Brain 2024;147:980–95 doi:10.1093/brain/awad346 pmid:37804318

    CrossRefPubMed
24. 24.↵
    1. Wang W,
    2. Zhao Y,
    3. Teng L, et al

    . Neuropathologist-level integrated classification of adult-type diffuse gliomas using deep learning from whole-slide pathological images. Nat Commun 2023;14:6359 doi:10.1038/s41467-023-41195-9 pmid:37821431

    CrossRefPubMed
25. 25.↵
    1. Isensee F,
    2. Jaeger PF,
    3. Kohl SAA, et al

    . nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat Methods 2021;18:203–11 doi:10.1038/s41592-020-01008-z pmid:33288961

    CrossRefPubMed
26. 26.↵
    1. Isensee F,
    2. Schell M,
    3. Pflueger I, et al

    . Automated brain extraction of multisequence MRI using artificial neural networks. Hum Brain Mapp 2019;40:4952–64 doi:10.1002/hbm.24750 pmid:31403237

    CrossRefPubMed
27. 27.↵
    1. Pati S,
    2. Baid U,
    3. Edwards B, et al

    . The federated tumor segmentation (FeTS) tool: an open-source solution to further solid tumor research. Phys Med Biol 2022;67: doi:10.1088/1361-6560/ac9449 pmid:36137534

    CrossRefPubMed
28. 28.↵
    1. Rohlfing T,
    2. Zahr NM,
    3. Sullivan EV, et al

    . The SRI24 multichannel atlas of normal adult human brain structure. Hum Brain Mapp 2010;31:798–819 doi:10.1002/hbm.20906 pmid:20017133

    CrossRefPubMedWeb of Science
29. 29.↵
    1. Cardoso MJ,
    2. Li W,
    3. Brown R, et al

    . MONAI: An Open-Source Framework for Deep Learning in Healthcare. arXiv.org 2022. https://doi.org/10.48550/arXiv.2211.02701. Accessed May 1, 2024
30. 30.↵
    1. Hastie T,
    2. Tibshirani R,
    3. Friedman JH

    . The Elements of Statistical Learning: Data Mining, Inference, and Prediction. New York: Springer-Verlag; 2009.
31. 31.↵
    1. Kolakshyapati M,
    2. Hashizume A,
    3. Ochi K, et al

    . Usefulness of histogram-profile analysis in ring-enhancing intracranial lesions. World Neurosurg 2019;131:e226–36 doi:10.1016/j.wneu.2019.07.123 pmid:31349079

    CrossRefPubMed
32. 32.↵
    1. Verma RK,
    2. Wiest R,
    3. Locher C, et al

    . Differentiating enhancing multiple sclerosis lesions, glioblastoma, and lymphoma with dynamic texture parameters analysis (DTPA): a feasibility study. Med Phys 2017;44:4000–08 doi:10.1002/mp.12356 pmid:28543071

    CrossRefPubMed
33. 33.↵
    1. Ikeguchi R,
    2. Shimizu Y,
    3. Abe K, et al

    . Proton magnetic resonance spectroscopy differentiates tumefactive demyelinating lesions from gliomas. Mult Scler Relat Disord 2018;26:77–84 doi:10.1016/j.msard.2018.08.025 pmid:30237108

    CrossRefPubMed
34. 34.↵
    1. Hashimoto S,
    2. Inaji M,
    3. Nariai T, et al

    . Usefulness of [(11)C] methionine PET in the differentiation of tumefactive multiple sclerosis from high grade astrocytoma. Neurol Med Chir (Tokyo) 2019;59:176–83 doi:10.2176/nmc.oa.2018-0287 pmid:30996153

    CrossRefPubMed
35. 35.↵
    1. Kebir S,
    2. Rauschenbach L,
    3. Weber M, et al

    . Machine learning-based differentiation between multiple sclerosis and glioma WHO II degrees -IV degrees using O-(2-[18F] fluoroethyl)-L-tyrosine positron emission tomography. J Neurooncol 2021;152:325–32 doi:10.1007/s11060-021-03701-1 pmid:33502678

    CrossRefPubMed
36. 36.↵
    1. Yasuda S,
    2. Yano H,
    3. Kimura A, et al

    . Frontal tumefactive demyelinating lesion mimicking glioblastoma differentiated by methionine positron emission tomography. World Neurosurg 2018;119:244–48 doi:10.1016/j.wneu.2018.08.027 pmid:30114544

    CrossRefPubMed
37. 37.↵
    1. Kim DS,
    2. Na DG,
    3. Kim KH, et al

    . Distinguishing tumefactive demyelinating lesions from glioma or central nervous system lymphoma: added value of unenhanced CT compared with conventional contrast-enhanced MR imaging. Radiology 2009;251:467–75 doi:10.1148/radiol.2512072071 pmid:19261924

    CrossRefPubMedWeb of Science
38. 38.↵
    1. Ransohoff DF

    . Bias as a threat to the validity of cancer molecular-marker research. Nat Rev Cancer 2005;5:142–49 doi:10.1038/nrc1550 pmid:15685197

    CrossRefPubMedWeb of Science
39. 39.↵
    1. Huang G,
    2. Liu Z,
    3. van der Maaten L, et al

    . Densely connected convolutional networks. arXiv.org 2016. https://doi,org/10.48550/arXiv.1608.06993. Accessed May 1, 2024