A Radiomic “Warning-Sign” of Progression on Brain MRI in Individuals with MS

References

1. 1.↵
   1. McNamara C,
   2. Sugrue G,
   3. Murray B, et al

   . Current and emerging therapies in multiple sclerosis: implications for the radiologist, Part 1: mechanisms, efficacy, and safety. AJNR Am J Neuroradiol 2017;38:1664–71 doi:10.3174/ajnr.A5147 pmid:28408630

   Abstract/FREE Full TextGoogle Scholar
2. 2.↵
   1. McNamara C,
   2. Sugrue G,
   3. Murray B, et al

   . Current and emerging therapies in multiple sclerosis: implications for the radiologist, Part 2: surveillance for treatment complications and disease progression. AJNR Am J Neuroradiol 2017;38:1672–80 doi:10.3174/ajnr.A5148 pmid:28428206

   Abstract/FREE Full TextGoogle Scholar
3. 3.↵
   1. Commowick O,
   2. Cervenansky F,
   3. Cotton F, et al

   . MSSEG-2 challenge proceedings: multiple sclerosis new lesions segmentation challenge using a data management and processing infrastructure. In: International Conference on Medical Image Computing and Computer Assisted Intervention. September 27 to October 1, 2021; Virtual

   Google Scholar
4. 4.↵
   1. Yang J,
   2. Hamade M,
   3. Wu Q, et al

   . Current and future biomarkers in multiple sclerosis. Int J Mol Sci 2022;23:5877 doi:10.3390/ijms23115877 pmid:35682558

   CrossRefPubMedGoogle Scholar
5. 5.↵
   1. Martin D,
   2. Tong E,
   3. Kelly B, et al

   . Current perspectives of artificial intelligence in pediatric neuroradiology: an overview. Front Radiol 2021;1:713681 doi:10.3389/fradi.2021.713681 pmid:37492174

   CrossRefPubMedGoogle Scholar
6. 6.↵
   1. Kelly BS,
   2. Judge C,
   3. Bollard SM, et al

   . Radiology artificial intelligence: a systematic review and evaluation of methods (RAISE). Eur Radiol 2022;32:8054 doi:10.1007/s00330-022-08784-6 pmid:35593961

   CrossRefPubMedGoogle Scholar
7. 7.↵
   1. Diaz-Hurtado M,
   2. Martínez-Heras E,
   3. Solana E, et al

   . Recent advances in the longitudinal segmentation of multiple sclerosis lesions on magnetic resonance imaging: a review. Neuroradiology 2022;64:2103–17 doi:10.1007/s00234-022-03019-3 pmid:35864180

   CrossRefPubMedGoogle Scholar
8. 8.↵
   1. Lladó X,
   2. Ganiler O,
   3. Oliver A, et al

   . Automated detection of multiple sclerosis lesions in serial brain MRI. Neuroradiology 2012;54:787–807 doi:10.1007/s00234-011-0992-6 pmid:22179659

   CrossRefPubMedGoogle Scholar
9. 9.↵
   1. Filippi M,
   2. Preziosa P,
   3. Arnold DL, et al

   . Present and future of the diagnostic work-up of multiple sclerosis: the imaging perspective. J Neurol 2023;270:1286–99 doi:10.1007/s00415-022-11488-y pmid:36427168

   CrossRefPubMedGoogle Scholar
10. 10.↵
    1. Havas J,
    2. Leray E,
    3. Rollot F, et al

    . Predictive medicine in multiple sclerosis: a systematic review. Mult Scler Relat Disord 2020;40:101928 doi:10.1016/j.msard.2020.101928 pmid:32004856

    CrossRefPubMedGoogle Scholar
11. 11.↵
    1. Lorefice L,
    2. Pitzalis M,
    3. Murgia F, et al

    . Omics approaches to understanding the efficacy and safety of disease-modifying treatments in multiple sclerosis. Front Genet 2023;14:1076421 doi:10.3389/fgene.2023.1076421 pmid:36793897

    CrossRefPubMedGoogle Scholar
12. 12.↵
    1. Brummer T,
    2. Muthuraman M,
    3. Steffen F, et al

    . Improved prediction of early cognitive impairment in multiple sclerosis combining blood and imaging biomarkers. Brain Commun 2022;4:fcac153 doi:10.1093/braincomms/fcac153 pmid:35813883

    CrossRefPubMedGoogle Scholar
13. 13.↵
    1. Storelli L,
    2. Azzimonti M,
    3. Gueye M, et al

    . A deep learning approach to predicting disease progression in multiple sclerosis using magnetic resonance imaging. Invest Radiol 2022;57:423–32 doi:10.1097/RLI.0000000000000854 pmid:35093968

    CrossRefPubMedGoogle Scholar
14. 14.↵
    1. Santos DP,
    2. dos Dietzel M,
    3. Baessler B

    . A decade of radiomics research: are images really data or just patterns in the noise? Eur Radiol 2021;31:1–4 doi:10.1007/s00330-020-07108-w pmid:32767103

    CrossRefPubMedGoogle Scholar
15. 15.↵
    1. Huang EP,
    2. O'Connor JP,
    3. McShane LM, et al

    . Criteria for the translation of radiomics into clinically useful tests. Nat Rev Clin Oncol 2023;20:69–82 doi:10.1038/s41571-022-00707-0 pmid:36443594

    CrossRefPubMedGoogle Scholar
16. 16.↵
    1. Liu Y,
    2. Dong D,
    3. Zhang L, et al

    . Radiomics in multiple sclerosis and neuromyelitis optica spectrum disorder. Eur Radiol 2019;29:4670–77 doi:10.1007/s00330-019-06026-w pmid:30770971

    CrossRefPubMedGoogle Scholar
17. 17.↵
    1. Peng Y,
    2. Zheng Y,
    3. Tan Z, 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 pmid:34052741

    CrossRefPubMedGoogle Scholar
18. 18.↵
    1. Luo X,
    2. Piao S,
    3. Li H, et al

    . Multi-lesion radiomics model for discrimination of relapsing-remitting multiple sclerosis and neuropsychiatric systemic lupus erythematosus. Eur Radiol 2022;32:5700–10 doi:10.1007/s00330-022-08653-2 pmid:35243524

    CrossRefPubMedGoogle Scholar
19. 19.↵
    1. Pontillo G,
    2. Tommasin S,
    3. Cuocolo R, et al

    . A combined radiomics and machine learning approach to overcome the clinicoradiologic paradox in multiple sclerosis. AJNR Am J Neuroradiol 2021;42:1927–33 doi:10.3174/ajnr.A7274 pmid:34531195

    Abstract/FREE Full TextGoogle Scholar
20. 20.↵
    1. Rajpurkar P,
    2. Lungren MP

    . The current and future state of AI interpretation of medical images. N Engl J Med 2023;388:1981–90 doi:10.1056/NEJMra2301725 pmid:37224199

    CrossRefPubMedGoogle Scholar
21. 21.↵
    1. Acosta JN,
    2. Falcone GJ,
    3. Rajpurkar P

    . The need for medical artificial intelligence that incorporates prior images. Radiology 2022;304:283–88 doi:10.1148/radiol.212830 pmid:35438563

    CrossRefPubMedGoogle Scholar
22. 22.↵
    1. Sushentsev N,
    2. Rundo L,
    3. Abrego L, et al

    . Time series radiomics for the prediction of prostate cancer progression in patients on active surveillance. Eur Radiol 2023;33:3792–800 doi:10.1007/s00330-023-09438-x pmid:36749370

    CrossRefPubMedGoogle Scholar
23. 23.↵
    1. Kelly BS,
    2. Mathur P,
    3. Plesniar J, et al

    . Using deep learning–derived image features in radiologic time series to make personalised predictions: proof of concept in colonic transit data. Eur Radiol 2023;33:8376–86 doi:10.1007/s00330-023-09769-9 pmid:37284869

    CrossRefPubMedGoogle Scholar
24. 24.↵
    1. Moskowitz CS,
    2. Welch ML,
    3. Jacobs MA, et al

    . Radiomic analysis: study design, statistical analysis, and other bias mitigation strategies. Radiology 2022;304:265–73 doi:10.1148/radiol.211597 pmid:35579522

    CrossRefPubMedGoogle Scholar
25. 25.↵
    1. Kocak B,
    2. Baessler B,
    3. Bakas S, et al

    . CheckList for EvaluAtion of Radiomics research (CLEAR): a step-by-step reporting guideline for authors and reviewers endorsed by ESR and EuSoMII. Insights Imaging 2023;14:75 doi:10.1186/s13244-023-01415-8 pmid:37142815

    CrossRefPubMedGoogle Scholar
26. 26.↵
    1. Kelly B,
    2. Kirwan A,
    3. Quinn M, et al

    . The ethical matrix as a method for involving people living with disease and the wider public (PPI) in near-term artificial intelligence research. Radiography (Lond) 2023;29(Suppl 1):S103–11 doi:10.1016/j.radi.2023.03.009 pmid:37062673

    CrossRefPubMedGoogle Scholar
27. 27.↵
    1. Mongan J,
    2. Moy L,
    3. Kahn CE

    . 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

    CrossRefPubMedGoogle Scholar
28. 28.↵
    1. Kocak B,
    2. Chepelev LL,
    3. Chu LC, et al

    . Assessment of RadiomIcS rEsearch (ARISE): a brief guide for authors, reviewers, and readers from the Scientific Editorial Board of European Radiology. Eur Radiol 2023;33:7556–60 doi:10.1007/s00330-023-09768-w pmid:37358612

    CrossRefPubMedGoogle Scholar
29. 29.↵
    1. Pham N,
    2. Hill V,
    3. Rauschecker A, et al

    . Critical appraisal of artificial intelligence–enabled imaging tools using the levels of evidence system. AJNR Am J Neuroradiol 2023;44:E21–28 doi:10.3174/ajnr.A7850 pmid:37080722

    Abstract/FREE Full TextGoogle Scholar
30. 30.↵
    1. Jenkinson M,
    2. Beckmann CF,
    3. Behrens TEJ, et al

    . FSL. Neuroimage 2012;62:782–90 doi:10.1016/j.neuroimage.2011.09.015 pmid:21979382

    CrossRefPubMedWeb of ScienceGoogle Scholar
31. 31.↵
    1. Kamnitsas K,
    2. Ferrante E,
    3. Parisot S, et al

    . Brainlesion: glioma, multiple sclerosis, stroke and traumatic brain injuries. In: Second International Workshop, BrainLes 2016, with the Challenges on BRATS, ISLES and mTOP 2016, Held in Conjunction with MICCAI 2016. Athens, Greece. October 17, 2016

    Google Scholar
32. 32.↵
    1. Yushkevich PA,
    2. Gao Y,
    3. Gerig G, et al

    . ITK-SNAP: an interactive tool for semi-automatic segmentation of multi-modality biomedical images. Annu Int Conf IEEE Eng Med Biol Soc 2016;2016:3342–45 doi:10.1109/EMBC.2016.7591443 pmid:28269019

    CrossRefPubMedGoogle Scholar
33. 33.↵
    1. Rovira À,
    2. Wattjes MP,
    3. Tintoré M, et al

    ; MAGNIMS study group. MAGNIMS consensus guidelines on the use of MRI in multiple sclerosis: clinical implementation in the diagnostic process. Nat Rev Neurol 2015;11:471–82 doi:10.1038/nrneurol.2015.106 pmid:26149978

    CrossRefPubMedGoogle Scholar
34. 34.↵
    1. Griethuysen JJM,
    2. van 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

    CrossRefPubMedGoogle Scholar
35. 35.↵
    1. Maier-Hein L,
    2. Reinke A,
    3. Christodoulou E, et al

    . Metrics reloaded: pitfalls and recommendations for image analysis validation. ArXiv 2023. https://arxiv.org/abs/2206.01653v1. Accessed November 1, 2023

    Google Scholar
36. 36.↵
    1. Filippi M,
    2. Preziosa P,
    3. Rocca MA

    . Brain mapping in multiple sclerosis: lessons learned about the human brain. Neuroimage 2019;190:32–45 doi:10.1016/j.neuroimage.2017.09.021 pmid:28917696

    CrossRefPubMedGoogle Scholar
37. 37.↵
    1. Wang L,
    2. Kelly B,
    3. Lee EH, et al

    . Multi-classifier-based identification of COVID-19 from chest computed tomography using generalizable and interpretable radiomics features. Eur J Radiol 2021;136:109552 doi:10.1016/j.ejrad.2021.109552 pmid:33497881

    CrossRefPubMedGoogle Scholar
38. 38.↵
    1. Ardakani AA,
    2. Bureau NJ,
    3. Ciaccio EJ, et al

    . Interpretation of radiomics features: a pictorial review. Comput Methods Programs Biomed 2022;215:106609 doi:10.1016/j.cmpb.2021.106609 pmid:34990929

    CrossRefPubMedGoogle Scholar
39. 39.↵
    1. Kelly B,
    2. Martinez M,
    3. Do H, et al

    . DEEP MOVEMENT: deep learning of movie files for management of endovascular thrombectomy. Eur Radiol 2023;33:5728–39 doi:10.1007/s00330-023-09478-3 pmid:36847835

    CrossRefPubMedGoogle Scholar
40. 40.↵
    1. Brex PA,
    2. Ciccarelli O,
    3. O'Riordan JI, et al

    . A longitudinal study of abnormalities on MRI and disability from multiple sclerosis. N Engl J Med 2002;346:158–64 doi:10.1056/NEJMoa011341 pmid:11796849

    CrossRefPubMedWeb of ScienceGoogle Scholar
41. 41.↵
    1. Todea AR,
    2. Melie‐Garcia L,
    3. Barakovic M, et al

    ; Swiss MS Cohort Study. A multicenter longitudinal MRI study assessing LeMan‐PV Software accuracy in the detection of white matter lesions in multiple sclerosis patients. J Magn Reson Imaging 2023;58:864–76 doi:10.1002/jmri.28618 pmid:36708267

    CrossRefPubMedGoogle Scholar
42. 42.↵
    1. Yu AC,
    2. Mohajer B,
    3. Eng J

    . External validation of deep learning algorithms for radiologic diagnosis: a systematic review. Radiol Artif Intell 2022;4:e210064 doi:10.1148/ryai.210064 pmid:35652114

    CrossRefPubMedGoogle Scholar
43. 43.↵
    1. Sica GT

    . Bias in research studies. Radiology 2006;238:780–89 doi:10.1148/radiol.2383041109 pmid:16505391

    CrossRefPubMedWeb of ScienceGoogle Scholar
44. 44.↵
    1. Martí-Bonmatí L,
    2. Alberich-Bayarri Á,
    3. Ladenstein R, et al

    . PRIMAGE project: predictive in silico multiscale analytics to support childhood cancer personalised evaluation empowered by imaging biomarkers. Eur Radiol Exp 2020;4:22 doi:10.1186/s41747-020-00150-9 pmid:32246291

    CrossRefPubMedGoogle Scholar
45. 45.↵
    European Society of Radiology (ESR). Medical imaging in personalised medicine: a white paper of the research committee of the European Society of Radiology (ESR). Insights Imaging 2015;6:141–55 doi:10.1007/s13244-015-0394-0 pmid:25763994

    CrossRefPubMedGoogle Scholar
46. 46.↵
    1. Yap MH,
    2. Goyal M,
    3. Osman F, et al

    . Breast ultrasound region of interest detection and lesion localisation. Artif Intell Med 2020;107:101880 doi:10.1016/j.artmed.2020.101880 pmid:32828439

    CrossRefPubMedGoogle Scholar
47. 47.↵
    1. Akinci D’Antonoli T,
    2. Stanzione A,
    3. Bluethgen C, et al

    . Large language models in radiology: fundamentals, applications, ethical considerations, risks, and future directions. Diagn Interv Radiol 2023 October 3. [Epub ahead of print] doi:10.4274/dir.2023.232417 pmid:37789676

    CrossRefPubMedGoogle Scholar