Identification of Multiclass Pediatric Low-Grade Neuroepithelial Tumor Molecular Subtype with ADC MR Imaging and Machine Learning

References

1. 1.↵
   1. Ostrom QT,
   2. Price M,
   3. Neff C, et al

   . CBTRUS Statistical Report: Primary Brain and Other Central Nervous System Tumors Diagnosed in the United States in 2015-2019. Neuro Oncol 2022;24:v1–95 doi:10.1093/neuonc/noac202 pmid:36196752

   CrossRefPubMed
2. 2.↵
   1. Ryall S,
   2. Tabori U,
   3. Hawkins C

   . Pediatric low-grade glioma in the era of molecular diagnostics. Acta Neuropathol Commun 2020;8:30 doi:10.1186/s40478-020-00902-z pmid:32164789

   CrossRefPubMed
3. 3.↵
   1. Bale TA,
   2. Rosenblum MK

   . The 2021 WHO Classification of Tumors of the Central Nervous System: an update on pediatric low‐grade gliomas and glioneuronal tumors. Brain Pathol 2022;32:e13060 doi:10.1111/bpa.13060 pmid:35218102

   CrossRefPubMed
4. 4.↵
   1. Plant-Fox AS,
   2. O’Halloran K,
   3. Goldman S

   . Pediatric brain tumors: the era of molecular diagnostics, targeted and immune-based therapeutics, and a focus on long term neurologic sequelae. Curr Probl Cancer 2021;45:100777 doi:10.1016/j.currproblcancer.2021.100777 pmid:34303558

   CrossRefPubMed
5. 5.↵
   1. Armstrong GT,
   2. Conklin HM,
   3. Huang S, et al

   . Survival and long-term health and cognitive outcomes after low-grade glioma. Neuro Oncol 2011;13:223–34 doi:10.1093/neuonc/noq178 pmid:21177781

   CrossRefPubMed
6. 6.↵
   1. AlRayahi J,
   2. Zapotocky M,
   3. Ramaswamy V, et al

   . Pediatric brain tumor genetics: what radiologists need to know. RadioGraphics 2018;38:2102–22 doi:10.1148/rg.2018180109 pmid:30422762

   CrossRefPubMed
7. 7.↵
   1. Chalil A,
   2. Ramaswamy V

   . Low-grade gliomas in children. J Child Neurol 2016;31:517–22 doi:10.1177/0883073815599259 pmid:26286938

   CrossRefPubMed
8. 8.↵
   1. Engelhardt S,
   2. Behling F,
   3. Beschorner R, et al

   . Frequent FGFR1 hotspot alterations in driver-unknown low-grade glioma and mixed neuronal-glial tumors. J Cancer Res Clin Oncol 2022;148:857–66 doi:10.1007/s00432-021-03906-x pmid:35018490

   CrossRefPubMed
9. 9.↵
   1. Bag AK,
   2. Chiang J,
   3. Patay Z

   . Radiohistogenomics of pediatric low-grade neuroepithelial tumors. Neuroradiology 2021;63:1185–213 doi:10.1007/s00234-021-02691-1 pmid:33779771

   CrossRefPubMed
10. 10.↵
    1. Grob ST,
    2. Nobre L,
    3. Campbell KR, et al

    . Clinical and molecular characterization of a multi-institutional cohort of pediatric spinal cord low-grade gliomas. Neurooncol Adv 2020;2:vdaa103 doi:10.1093/noajnl/vdaa103 pmid:33063010

    CrossRefPubMed
11. 11.↵
    1. Mhatre R,
    2. Sugur HS,
    3. Nandeesh BN, et al

    . MN1 rearrangement in astroblastoma: study of eight cases and review of literature. Brain Tumor Pathol 2019;36:112–20 doi:10.1007/s10014-019-00346-x pmid:31111274

    CrossRefPubMed
12. 12.↵
    1. Moreira DC,
    2. Lam CG,
    3. Bhakta N, et al

    . Tackling pediatric low-grade gliomas: a global perspective. JCO Glob Oncol 2023;9:e2300017 doi:10.1200/GO.23.00017 pmid:37043711

    CrossRefPubMed
13. 13.↵
    1. Vagvala S,
    2. Guenette JP,
    3. Jaimes C, et al

    . Imaging diagnosis and treatment selection for brain tumors in the era of molecular therapeutics. Cancer Imaging 2022;22:19 doi:10.1186/s40644-022-00455-5 pmid:35436952

    CrossRefPubMed
14. 14.↵
    1. Ryall S,
    2. Zapotocky M,
    3. Fukuoka K, et al

    . Integrated molecular and clinical analysis of 1,000 pediatric low-grade gliomas. Cancer Cell 2020;37:569–83.e5 doi:10.1016/j.ccell.2020.03.011 pmid:32289278

    CrossRefPubMed
15. 15.↵
    1. Nobre L,
    2. Zapotocky M,
    3. Ramaswamy V, et al

    . Outcomes of BRAF V600E pediatric gliomas treated with targeted BRAF inhibition. JCO Precis Oncol 2020;4:561–71 doi:10.1200/PO.19.00298 pmid:32923898

    CrossRefPubMed
16. 16.↵
    1. Trasolini A,
    2. Erker C,
    3. Cheng S, et al

    . MR imaging of pediatric low-grade gliomas: pretherapeutic differentiation of BRAF V600E mutation, BRAF musion, and wild-type tumors in patients without neurofibromatosis-1. AJNR Am J Neuroradiol 2022;43:1196–201 doi:10.3174/ajnr.A7574 pmid:35863783

    Abstract/FREE Full Text
17. 17.↵
    1. Wagner MW,
    2. Hainc N,
    3. Khalvati F, et al

    . Radiomics of pediatric low-grade gliomas: toward a pretherapeutic differentiation of BRAF-mutated and BRAF-fused tumors. AJNR Am J Neuroradiol 2021;42:759–65 doi:10.3174/ajnr.A6998 pmid:33574103

    Abstract/FREE Full Text
18. 18.↵
    1. Madhogarhia R,
    2. Haldar D,
    3. Bagheri S, et al

    . Radiomics and radiogenomics in pediatric neuro-oncology: a review. Neurooncol Adv 2022;4:vdac083 doi:10.1093/noajnl/vdac083 pmid:35795472

    CrossRefPubMed
19. 19.↵
    1. Zhang M,
    2. Wong SW,
    3. Wright JN, et al

    . MRI radiogenomics of pediatric medulloblastoma: a multicenter study. Radiology 2022;304:406–16 doi:10.1148/radiol.212137 pmid:35438562

    CrossRefPubMed
20. 20.↵
    1. Guerrini F,
    2. Paolicchi M,
    3. Ghio F, et al

    . The Droplet Digital PCR: a new valid molecular approach for the assessment of B-RAF V600E mutation in hairy cell leukemia. Front Pharmacol 2016;7:363 doi:10.3389/fphar.2016.00363 pmid:27790140

    CrossRefPubMed
21. 21.↵
    1. Fedorov A,
    2. Beichel R,
    3. Kalpathy-Cramer J, et al

    . 3D slicer as an image computing platform for the quantitative imaging network. Magn Reson Imaging 2012;30:1323–41 doi:10.1016/j.mri.2012.05.001 pmid:22770690

    CrossRefPubMedWeb of Science
22. 22.↵
    1. Parmar C,
    2. Velazquez ER,
    3. Leijenaar R, et al

    . Robust radiomics feature quantification using semiautomatic volumetric segmentation. PloS One 2014;9:e102107 doi:10.1371/journal.pone.0102107 pmid:25025374

    CrossRefPubMed
23. 23.↵
    1. Li J,
    2. Liu S,
    3. Qin Y, et al

    . High-order radiomics features based on T2 FLAIR MRI predict multiple glioma immunohistochemical features: a more precise and personalized gliomas management. PLoS One 2020;15:e0227703 doi:10.1371/journal.pone.0227703 pmid:31968004

    CrossRefPubMed
24. 24.↵
    1. Tustison NJ,
    2. Avants BB,
    3. Cook PA, et al

    . N4ITK: improved N3 bias correction. IEEE Trans Med Imaging 2010;29:1310–20 doi:10.1109/TMI.2010.2046908 pmid:20378467

    CrossRefPubMedWeb of Science
25. 25.↵
    1. Aerts HJ,
    2. Velazquez ER,
    3. Leijenaar RTH, et al

    . Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 2014;5:4006–09 doi:10.1038/ncomms5006 pmid:24892406

    CrossRefPubMed
26. 26.↵
    1. Chaddad A,
    2. Kucharczyk MJ,
    3. Daniel P, et al

    . Radiomics in glioblastoma: current status and challenges facing clinical implementation. Front Oncol 2019;9:374 doi:10.3389/fonc.2019.00374 pmid:31165039

    CrossRefPubMed
27. 27.↵
    1. Eun Park J,
    2. Sung Kim H

    . Radiomics as a quantitative imaging biomarker: practical considerations and the current standpoint in neuro-oncologic studies. Nucl Med Mol Imaging 2018;52:99–108 doi:10.1007/s13139-017-0512-7 pmid:29662558

    CrossRefPubMed
28. 28.↵
    1. Breiman L

    . Bagging predictors. Mach Learn 1996;24:123–40 doi:10.1007/BF00058655

    CrossRefWeb of Science
29. 29.↵
    1. Wagner M,
    2. Namdar K,
    3. Alqabbani A, et al

    . Dataset size sensitivity analysis of machine learning classifiers to differentiate molecular markers of pediatric low-grade gliomas based on MRI. Research Square 2021 Sept 17 [Epub ahead of print] doi:10.21203/rs.3.rs-883606/v1

    CrossRef
30. 30.↵
    1. Faulkner C,
    2. Ellis HP,
    3. Shaw A, et al

    . BRAF fusion analysis in pilocytic astrocytomas: KIAA1549-BRAF 15-9 fusions are more frequent in the midline than within the cerebellum. J Neuropathol Exp Neurol 2015;74:867–72 doi:10.1097/NEN.0000000000000226 pmid:26222501

    CrossRefPubMed
31. 31.↵
    1. Di Nunno V,
    2. Gatto L,
    3. Tosoni A, et al

    . Implications of BRAF V600E mutation in gliomas: molecular considerations, prognostic value and treatment evolution. Front Oncol 2022;12:1067252 doi:10.3389/fonc.2022.1067252 pmid:36686797

    CrossRefPubMed
32. 32.↵
    1. Koh DM,
    2. Collins DJ

    . Diffusion-weighted MRI in the body: applications and challenges in oncology. AJR Am J Roentgenol 2007;188:1622–35 doi:10.2214/AJR.06.1403 pmid:17515386

    CrossRefPubMedWeb of Science
33. 33.↵
    1. Guo AC,
    2. Cummings TJ,
    3. Dash RC, et al

    . Lymphomas and high-grade astrocytomas: comparison of water diffusibility and histologic characteristics. Radiology 2002;224:177–83 doi:10.1148/radiol.2241010637 pmid:12091680

    CrossRefPubMedWeb of Science
34. 34.↵
    1. Doskaliyev A,
    2. Yamasaki F,
    3. Ohtaki M, et al

    . Lymphomas and glioblastomas: Differences in the apparent diffusion coefficient evaluated with high b-value diffusion-weighted magnetic resonance imaging at 3T. Eur J Radiol 2012;81:339–44 doi:10.1016/j.ejrad.2010.11.005 pmid:21129872

    CrossRefPubMed
35. 35.↵
    1. Shrot S,
    2. Kerpel A,
    3. Belenky J, et al

    . MR imaging characteristics and ADC histogram metrics for differentiating molecular subgroups of pediatric low-grade gliomas. AJNR Am J Neuroradiol 2022;43:1356–62 doi:10.3174/ajnr.A7614 pmid:36007944

    Abstract/FREE Full Text
36. 36.↵
    1. Kolossváry M,
    2. Karády J,
    3. Kikuchi Y, et al

    . Radiomics versus visual and histogram-based assessment to identify atheromatous lesions at coronary CT angiography: an ex vivo study. Radiology 2019;293:89–96 doi:10.1148/radiol.2019190407 pmid:31385755

    CrossRefPubMed
37. 37.↵
    1. Capobianco E,
    2. Deng J

    . Radiomics at a glance: a few lessons learned from learning approaches. Cancers (Basel) 2020;12:2453 doi:10.3390/cancers12092453 pmid:32872466

    CrossRefPubMed
38. 38.↵
    1. Namdar K,
    2. Wagner MW,
    3. Ertl-Wagner BB,
    4. Khalvati F

    . Open-radiomics: a collection of standardized datasets and a technical protocol for reproducible radiomics machine learning pipelines. arXiv July 29 [Epub ahead of print]. https://arxiv.org/abs/2207.14776
39. 39.↵
    1. Lam LH,
    2. Do DT,
    3. Diep DT, et al

    . Molecular subtype classification of low-grade gliomas using magnetic resonance imaging-based radiomics and machine learning. NMR Biomed 2022;35:e4792 doi:10.1002/nbm.4792 pmid:35767281

    CrossRefPubMed
40. 40.↵
    1. Manoharan N,
    2. Liu KX,
    3. Mueller S,
    4. Haas-Kogan DA,
    5. Bandopadhayay P

    . Pediatric low-grade glioma: Targeted therapeutics and clinical trials in the molecular era. Neoplasia 2023;36:100857 doi:10.1016/j.neo.2022.100857 pmid:36566593

    CrossRefPubMed
41. 41.↵
    1. Ramaglia A,
    2. Tortora D,
    3. Mankad K, et al

    . Role of diffusion weighted imaging for differentiating cerebral pilocytic astrocytoma and ganglioglioma BRAF V600E-mutant from wild type. Neuroradiology 2020;62:71–80 doi:10.1007/s00234-019-02304-y pmid:31667545

    CrossRefPubMed
42. 42.↵
    1. Kan P,
    2. Liu JK,
    3. Hedlund G, et al

    . The role of diffusion-weighted magnetic resonance imaging in pediatric brain tumors. Childs Nerv Syst 2006;22:1435–39 doi:10.1007/s00381-006-0229-x pmid:17021722

    CrossRefPubMedWeb of Science
43. 43.↵
    1. Erickson BJ,
    2. Korfiatis P,
    3. Akkus Z, et al

    . Machine learning for medical imaging. Radiographics 2017;37:505–15 doi:10.1148/rg.2017160130 pmid:28212054

    CrossRefPubMed
44. 44.↵
    1. Li Y,
    2. Kim MM,
    3. Wahl DR, et al

    . Survival prediction analysis in glioblastoma with diffusion kurtosis imaging. Front Oncol 2021;11:690036 doi:10.3389/fonc.2021.690036 pmid:34336676

    CrossRefPubMed
45. 45.↵
    1. Wu CC,
    2. Jain R,
    3. Radmanesh A, et al

    . Predicting genotype and survival in glioma using standard clinical MR imaging apparent diffusion coefficient images: a pilot study from The Cancer Genome Atlas. AJNR Am J Neuroradiol 2018;39:1814–20 doi:10.3174/ajnr.A5794 pmid:30190259

    Abstract/FREE Full Text
46. 46.↵
    1. Ho CY,
    2. Mobley BC,
    3. Gordish-Dressman H, et al

    . A clinicopathologic study of diencephalic pediatric low-grade gliomas with BRAF V600 mutation. Acta Neuropatho 2015;130:575–85 doi:10.1007/s00401-015-1467-3 pmid:26264609

    CrossRefPubMed
47. 47.↵
    1. Cho HH,
    2. Lee SH,
    3. Hak Kim J, et al

    . Classification of the glioma grading using radiomics analysis. PeerJ 2018;6:e5982 doi:10.7717/peerj.5982 pmid:30498643

    CrossRefPubMed
48. 48.↵
    1. Widhiarso W,
    2. Yohannes Y,
    3. Prakarsah C

    . Brain tumor classification using gray level co-occurrence matrix and convolutional neural network. Indonesian J Electron Instrum Syst 2018;8:179–90 doi:10.22146/ijeis.34713

    CrossRef
49. 49.↵
    1. Chekouo T,
    2. Mohammed S,
    3. Rao A

    . A Bayesian 2D functional linear model for gray-level co-occurrence matrices in texture analysis of lower grade gliomas. Neuroimage Clin 2020;28:102437 doi:10.1016/j.nicl.2020.102437 pmid:33035963

    CrossRefPubMed
50. 50.↵
    1. Zachariah RM,
    2. Priya PS,
    3. Pendem S

    . Classification of low- and high-grade gliomas using radiomic analysis of multiple sequences of MRI brain. J Cancer Res Ther 2023;19:435–46 doi:10.4103/jcrt.jcrt_1581_22 pmid:37313916

    CrossRefPubMed
51. 51.↵
    1. Xu J,
    2. Lai M,
    3. Li S, et al

    . Radiomics features based on MRI predict BRAF V600E mutation in pediatric low-grade gliomas: a non-invasive method for molecular diagnosis. Clin Neurol Neurosurg 2022;222:107478 doi:10.1016/j.clineuro.2022.107478 pmid:36244075

    CrossRefPubMed
52. 52.↵
    1. Mårtensson G,
    2. Ferreira D,
    3. Granberg T, et al

    . The reliability of a deep learning model in clinical out-of-distribution MRI data: amulticohort study. Med Image Anal 2020;66:101714 doi:10.1016/j.media.2020.101714 pmid:33007638

    CrossRefPubMed