Cellular Density in Adult Glioma, Estimated with MR Imaging Data and a Machine Learning Algorithm, Has Prognostic Power Approaching World Health Organization Histologic Grading in a Cohort of 1181 Patients

References

1. 1.↵
   1. Louis DN,
   2. Perry A,
   3. Wesseling P, et al

   . The 2021 WHO Classification of Tumors of the Central Nervous System: a summary. Neuro Oncol 2021;23:1231– 51 doi:10.1093/neuonc/noab106 pmid:34185076

   CrossRefPubMed
2. 2.↵
   1. Louis DN,
   2. Perry A,
   3. Reifenberger G, et al

   . The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary. Acta Neuropathol 2016;131:803–20 doi:10.1007/s00401-016-1545-1 pmid:27157931

   CrossRefPubMed
3. 3.↵
   1. Huse JT

   . Establishing a robust molecular taxonomy for diffuse gliomas of adulthood. Surg Pathol Clin 2016;9:379–90 doi:10.1016/j.path.2016.04.005 pmid:27523967

   CrossRefPubMed
4. 4.↵
   1. Jackson RJ,
   2. Fuller GN,
   3. Abi-Said D, et al

   . Limitations of stereotactic biopsy in the initial management of gliomas. Neuro Oncol 2001;3:193–200 doi:10.1093/neuonc/3.3.193 pmid:11465400

   CrossRefPubMedWeb of Science
5. 5.↵
   1. Scott JN,
   2. Brasher PMA,
   3. Sevick RJ, et al

   . How often are nonenhancing supratentorial gliomas malignant? A population study. Neurology 2002;59:947–49 doi:10.1212/wnl.59.6.947 pmid:12297589

   CrossRefPubMed
6. 6.↵
   1. Gates ED,
   2. Weinberg JS,
   3. Prabhu SS, et al

   . Estimating local cellular density in glioma using MR imaging data. AJNR Am J Neuroradiol 2021;42:102–08 doi:10.3174/ajnr.A6884 pmid:33243897

   Abstract/FREE Full Text
7. 7.
   1. Chang PD,
   2. Malone HR,
   3. Bowden SG, et al

   . A multiparametric model for mapping cellularity in glioblastoma using radiographically localized biopsies. AJNR Am J Neuroradiol 2017;38:890–98 doi:10.3174/ajnr.A5112 pmid:28255030

   Abstract/FREE Full Text
8. 8.
   1. Gaw N,
   2. Hawkins-Daarud A,
   3. Hu LS, et al

   . Integration of machine learning and mechanistic models accurately predicts variation in cell density of glioblastoma using multiparametric MRI. Sci Rep 2019;9:10063 doi:10.1038/s41598-019-46296-4 pmid:31296889

   CrossRefPubMed
9. 9.↵
   1. Bobholz SA,
   2. Lowman AK,
   3. Brehler M, et al

   . Radio-pathomic maps of cell density identify glioma invasion beyond traditional MR imaging defined margins. AJNR Am J Neuroradiol .2022;43:682–88 doi:10.3174/ajnr.A7477 pmid:35422419

   Abstract/FREE Full Text
10. 10.↵
    1. Roy AG,
    2. Conjeti S,
    3. Navab N, et al

    . Inherent Brain Segmentation Quality Control from Fully ConvNet Monte Carlo Sampling. In: Proceedings Part 1 of the Medical Image Computing and Computer Assisted Intervention, MICCAI 2018, Granda, Spain. September 16–20, 2018
11. 11.↵
    1. Avants BB,
    2. Tustison NJ,
    3. Song G, et al

    . A reproducible evaluation of ANTs similarity metric performance in brain image registration. Neuroimage 2011;54:2033–44 doi:10.1016/j.neuroimage.2010.09.025 pmid:20851191

    CrossRefPubMed
12. 12.↵
    1. Avants BB,
    2. Tustison NJ,
    3. Wu J, et al

    . An open-source multivariate framework for n-tissue segmentation with evaluation on public data. Neuroinformatics 2011;9:381–400 doi:10.1007/s12021-011-9109-y pmid:21373993

    CrossRefPubMedWeb of Science
13. 13.↵
    1. Roetzer T,
    2. Leskovar K,
    3. Peter N, et al

    . Evaluating cellularity and structural connectivity on whole brain slides using a custom-made digital pathology pipeline. J Neurosci Methods 2019;311:215–21 doi:10.1016/j.jneumeth.2018.10.029 pmid:30365968

    CrossRefPubMed
14. 14.↵
    1. Gates ED,
    2. Celaya A,
    3. Suki D, et al

    . Technical note: an efficient MR image data quality screening dashboard. J Appl Clin Med Phys 2022;23:e13557 doi:10.1002/acm2.13557

    CrossRefPubMed
15. 15.↵
    1. Cox DR

    . Regression models and life-tables. J R Stat Soc Ser B Stat Methodol 1972;34:187–220 doi:10.1111/j.2517-6161.1972.tb00899.x

    CrossRef
16. 16.↵
    1. Harrell FE Jr.,
    2. Lee KL,
    3. Mark DB

    . Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat Med 1996;15:361–87 doi:10.1002/(SICI)1097-0258(19960229)15:4<361::AID-SIM168>3.0.CO;2-4 pmid:866886

    CrossRefPubMedWeb of Science
17. 17.↵
    1. Simon RM,
    2. Subramanian J,
    3. Li MC, et al

    . Using cross-validation to evaluate predictive accuracy of survival risk classifiers based on high-dimensional data. Brief Bioinform 2011;12:203–14 doi:10.1093/bib/bbr001 pmid:21324971

    CrossRefPubMed
18. 18.↵
    1. Lacroix M,
    2. Abi-Said D,
    3. Fourney DR, et al

    . A multivariate analysis of 416 patients with glioblastoma multiforme: prognosis, extent of resection, and survival. J Neurosurg 2001;95:190–98 doi:10.3171/jns.2001.95.2.0190 pmid:11780887, 11599835

    CrossRefPubMedWeb of Science
19. 19.↵
    1. Li YM,
    2. Suki D,
    3. Hess K, et al

    . The influence of maximum safe resection of glioblastoma on survival in 1229 patients: can we do better than gross-total resection? J Neurosurg 2016;124:977–88 doi:10.3171/2015.5.JNS142087 pmid:26495941

    CrossRefPubMed
20. 20.↵
    1. Benjamini Y,
    2. Hochberg Y

    . Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B Stat Methodol 1995;57:289–300 doi:10.1111/j.2517-6161.1995.tb02031.x

    CrossRefPubMed
21. 21.↵
    1. Therneau TM,
    2. Watson DA

    . The Concordance Statistic and the Cox Model: Technical Report 85. Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota; 2017. https://www.mayo.edu/research/departments-divisions/department-health-sciences-research/division-biomedical-statistics-informatics/publications/technical-reports.
22. 22.↵
    1. Harrell FE Jr.,
    2. Califf RM,
    3. Pryor DB, et al

    . Evaluating the yield of medical tests. JAMA 1982;247:2543–46 doi:10.1001/jama.1982.03320430047030 pmid:7069920

    CrossRefPubMedWeb of Science
23. 23.↵
    1. Sahm F,
    2. Capper D,
    3. Jeibmann A, et al

    . Addressing diffuse glioma as a systemic brain disease with single-cell analysis. Arch Neurol 2012;69:523–26 doi:10.1001/archneurol.2011.2910 pmid:22158715

    CrossRefPubMedWeb of Science
24. 24.↵
    1. Ellingson BM,
    2. Malkin MG,
    3. Rand SD, et al

    . Validation of functional diffusion maps (fDMs) as a biomarker for human glioma cellularity. J Magn Reson Imaging 2010;31:538–48 doi:10.1002/jmri.22068 pmid:20187195

    CrossRefPubMedWeb of Science
25. 25.↵
    1. Latysheva A,
    2. Geier OM,
    3. Hope TR, et al

    . Diagnostic utility of restriction spectrum imaging in the characterization of the peritumoral brain zone in glioblastoma: analysis of overall and progression-free survival. Eur J Radiol 2020;132:109289 doi:10.1016/j.ejrad.2020.109289 pmid:33002815

    CrossRefPubMed
26. 26.↵
    1. Ohgaki H,
    2. Kleihues P

    . Epidemiology and etiology of gliomas. Acta Neuropathol 2005;109:93–108 doi:10.1007/s00401-005-0991-y pmid:15685439

    CrossRefPubMedWeb of Science
27. 27.↵
    1. Stupp R,
    2. Mason WP,
    3. van den Bent MJ, et al

    . Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. N Engl J Med 2005;352:987–96 doi:10.1056/NEJMoa043330 pmid:15758009

    CrossRefPubMedWeb of Science
28. 28.
    1. Hartmann C,
    2. Hentschel B,
    3. Simon M, et al

    ; German Glioma Network. Long-term survival in primary glioblastoma with versus without isocitrate dehydrogenase mutations. Clin Cancer Res 2013;19:5146–57 doi:10.1158/1078-0432.CCR-13-0017 pmid:23918605

    Abstract/FREE Full Text
29. 29.↵
    1. Reifenberger G,
    2. Wirsching HG,
    3. Knobbe-Thomsen CB, et al

    . Advances in the molecular genetics of gliomas: implications for classification and therapy. Nat Rev Clin Oncol 2017;14:434–52 doi:10.1038/nrclinonc.2016.204 pmid:28031556

    CrossRefPubMed
30. 30.↵
    1. Van den Bent MJ,
    2. Hoang-Xuan K,
    3. Brandes AA, et al

    . Long-term follow-up results of EORTC 26951: a randomized phase III study on adjuvant PCV chemotherapy in anaplastic oligodendroglial tumors (AOD). J Clin Oncol 2012;30(18_Suppl):2–2 doi:10.1200/jco.2012.30.18_suppl.2

    FREE Full Text
31. 31.↵
    1. Yan H,
    2. Parsons DW,
    3. Jin G, et al

    . IDH1 and IDH2 mutations in gliomas. N Engl J Med 2009;360:765–73 doi:10.1056/NEJMoa0808710 pmid:19228619

    CrossRefPubMedWeb of Science
32. 32.↵
    1. Wang T,
    2. Lei Y,
    3. Fu Y, et al

    . A review on medical imaging synthesis using deep learning and its clinical applications. J Appl Clin Med Phys 2021;22:11–36 doi:10.1002/acm2.13121 pmid:33305538

    CrossRefPubMed