Radio-Pathomic Maps of Cell Density Identify Brain Tumor Invasion beyond Traditional MRI-Defined Margins

References

1. 1.↵
   1. Ostrom QT,
   2. Bauchet L,
   3. Davis FG, et al

   . The epidemiology of glioma in adults: a “state of the science” review. Neuro Oncol 2014;16:896–913 doi:10.1093/neuonc/nou087 pmid:24842956

   CrossRefPubMed
2. 2.↵
   1. Ellingson BM,
   2. Wen PY,
   3. Cloughesy TF

   . Evidence and context of use for contrast enhancement as a surrogate of disease burden and treatment response in malignant glioma. Neuro Oncol 2018;20:457–71 doi:10.1093/neuonc/nox193 pmid:29040703

   CrossRefPubMed
3. 3.↵
   1. Lasocki A,
   2. Gaillard F

   . Non-contrast-enhancing tumor: a new frontier in glioblastoma research. AJNR Am J Neuroradiol. 2019;40:758–65 doi:10.3174/ajnr.A6025 pmid:30948373

   Abstract/FREE Full Text
4. 4.↵
   1. Hudak AM,
   2. Peng L,
   3. Marquez de la Plata C, et al

   . Cytotoxic and vasogenic cerebral oedema in traumatic brain injury: assessment with FLAIR and DWI imaging. Brain Inj 2014;28:1602–09 doi:10.3109/02699052.2014.936039 pmid:25058428

   CrossRefPubMed
5. 5.↵
   1. Hawkins-Daarud A,
   2. Rockne RC,
   3. Anderson ARA, et al

   . Modeling tumor-associated edema in gliomas during anti-angiogenic therapy and its impact on imageable tumor. Front Oncol 2013;3:66 doi:10.3389/fonc.2013.00066 pmid:23577324

   CrossRefPubMed
6. 6.↵
   1. Garrett MD,
   2. Yanagihara TK,
   3. Yeh R, et al

   . Monitoring radiation treatment effects in glioblastoma: FLAIR volume as significant predictor of survival. Tomography 2017;3:131–37 doi:10.18383/j.tom.2017.00009 pmid:30042977

   CrossRefPubMed
7. 7.↵
   1. Eidel O,
   2. Neumann JO,
   3. Burth S, et al

   . Automatic analysis of cellularity in glioblastoma and correlation with ADC using trajectory analysis and automatic nuclei counting. PLoS One 2016;11:e0160250 doi:10.1371/journal.pone.0160250 pmid:27467557

   CrossRefPubMed
8. 8.↵
   1. Chenevert TL,
   2. Malyarenko DI,
   3. Galbán CJ., et al

   . Comparison of voxel-wise and histogram analyses of glioma ADC maps for prediction of early therapeutic change. Tomography 2019;5:7–14 doi:10.18383/j.tom.2018.00049 pmid:30854437

   CrossRefPubMed
9. 9.↵
   1. LaViolette PS,
   2. Mickevicius NJ,
   3. Cochran EJ, et al

   . Precise ex vivo histological validation of heightened cellularity and diffusion-restricted necrosis in regions of dark apparent diffusion coefficient in 7 cases of high-grade glioma. Neuro Oncol 2014;16:1599–606 doi:10.1093/neuonc/nou142 pmid:25059209

   CrossRefPubMed
10. 10.↵
    1. Surov A,
    2. Meyer HJ,
    3. Wienke A

    . Correlation between apparent diffusion coefficient (ADC) and cellularity is different in several tumors: a meta-analysis. Oncotarget 2017;8:59492–99 doi:10.18632/oncotarget.17752 pmid:28938652

    CrossRefPubMed
11. 11.↵
    1. Nguyen HS,
    2. Milbach N,
    3. Hurrell SL, et al

    . Progressing bevacizumab-induced diffusion restriction is associated with coagulative necrosis surrounded by viable tumor and decreased overall survival in patients with recurrent glioblastoma. AJNR Am J Neuroradiol 2016;37:2201–08 doi:10.3174/ajnr.A4898 pmid:27492073

    Abstract/FREE Full Text
12. 12.↵
    1. Elder JB,
    2. Huntoon K,
    3. Otero J, et al

    . Histologic findings associated with laser interstitial thermotherapy for glioblastoma multiforme. Diagn Pathol 2019;14:19 doi:10.1186/s13000-019-0794-4 pmid:30767775

    CrossRefPubMed
13. 13.↵
    1. Hentschel SJ,
    2. Lang FF

    . Current surgical management of glioblastoma. Cancer J 2003;9:113–25 doi:10.1097/00130404-200303000-00007 pmid:12784877

    CrossRefPubMed
14. 14.↵
    1. Rong Y,
    2. Durden DL,
    3. Van Meir EG, et al

    . “ Pseudopalisading” necrosis in glioblastoma: a familiar morphologic feature that links vascular pathology, hypoxia, and angiogenesis. J Neuropathol Exp Neurol 2006;65:529–39 doi:10.1097/00005072-200606000-00001 pmid:16783163

    CrossRefPubMed
15. 15.↵
    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
16. 16.↵
    1. Bobholz SA,
    2. Lowman AK,
    3. Barrington A, et al

    . Radiomic features of multiparametric MRI present stable associations with analogous histological features in patients with brain cancer. Tomography 2020;6:160–69 doi:10.18383/j.tom.2019.00029 pmid:32548292

    CrossRefPubMed
17. 17.↵
    1. McGarry SD,
    2. Hurrell SL,
    3. Kaczmarowski AL, et al

    . Magnetic resonance imaging-based radiomic profiles predict patient prognosis in newly diagnosed glioblastoma before therapy. Tomography 2016;2:223–28 doi:10.18383/j.tom.2016.00250 pmid:27774518

    CrossRefPubMed
18. 18.↵
    1. Jenkinson M,
    2. Bannister P,
    3. Brady M, et al

    . Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage 2002;17:825–41 doi:10.1006/nimg.2002.1132 pmid:12377157

    CrossRefPubMedWeb of Science
19. 19.↵
    1. Jenkinson M,
    2. Beckmann CF,
    3. Behrens TE, et al

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

    CrossRefPubMedWeb of Science
20. 20.↵
    1. Jenkinson M,
    2. Smith S

    . A global optimisation method for robust affine registration of brain images. Med Image Anal 2001;5:143–56 doi:10.1016/S1361-8415(01)00036-6 pmid:11516708

    CrossRefPubMedWeb of Science
21. 21.↵
    1. Ellingson BM,
    2. Aftab DT,
    3. Schwab GM, et al

    . Volumetric response quantified using T1 subtraction predicts long-term survival benefit from cabozantinib monotherapy in recurrent glioblastoma. Neuro Oncol 2018;20:1411–18 doi:10.1093/neuonc/noy054 pmid:29660005

    CrossRefPubMed
22. 22.↵
    1. Ellingson BM,
    2. Kim HJ,
    3. Woodworth DC, et al

    . Recurrent glioblastoma treated with bevacizumab: contrast-enhanced T1-weighted subtraction maps improve tumor delineation and aid prediction of survival in a multicenter clinical trial. Radiology 2014;271:200–10 doi:10.1148/radiol.13131305 pmid:24475840

    CrossRefPubMedWeb of Science
23. 23.↵
    1. Ruifrok AC,
    2. Johnston DA

    . Quantification of histochemical staining by color deconvolution. Anal Quant Cytol Histol 2001;23:291–99 pmid:11531144

    PubMedWeb of Science
24. 24.↵
    1. Bukowy JD,
    2. Foss H,
    3. McGarry SD, et al

    . Accurate segmentation of prostate cancer histomorphometric features using a weakly supervised convolutional neural network. J Med Imaging (Bellingham) 2020;7:057501 doi:10.1117/1.JMI.7.5.057501 pmid:33062803

    CrossRefPubMed
25. 25.↵
    1. McGarry SD,
    2. Hurrell SL,
    3. Iczkowski KA, et al

    . Radio-pathomic maps of epithelium and lumen density predict the location of high-grade prostate cancer. Int J Radiat Oncol Biol Phys 2018;101:1179–87 doi:10.1016/j.ijrobp.2018.04.044 pmid:29908785

    CrossRefPubMed
26. 26.↵
    1. McGarry SD,
    2. Bukowy JD,
    3. Iczkowski KA, et al

    . Gleason probability maps: a radiomics tool for mapping prostate cancer likelihood in MRI space. Tomography 2019;5:127–34 doi:10.18383/j.tom.2018.00033 pmid:30854450

    CrossRefPubMed
27. 27.↵
    1. Breiman L

    . Random forests. Mach Learn 2001;45:5–32 doi:10.1023/A:1010933404324

    CrossRefPubMedWeb of Science
28. 28.↵
    1. Breiman L

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

    CrossRefWeb of Science
29. 29.↵
    1. Ellingson BM,
    2. Chung C,
    3. Pope WB, et al

    . Pseudoprogression, radionecrosis, inflammation or true tumor progression? Challenges associated with glioblastoma response assessment in an evolving therapeutic landscape. J Neurooncol 2017;134:495–504 doi:10.1007/s11060-017-2375-2 pmid:28382534

    CrossRefPubMed
30. 30.↵
    1. Zikou A,
    2. Sioka C,
    3. Alexiou GA, et al

    . Radiation necrosis, pseudoprogression, pseudoresponse, and tumor recurrence: imaging challenges for the evaluation of treated gliomas. Contrast Media Mol Imaging 2018;2018:6828396 doi:10.1155/2018/6828396 pmid:30627060

    CrossRefPubMed
31. 31.↵
    1. Friedmann-Morvinski D

    . Glioblastoma heterogeneity and cancer cell plasticity. Crit Rev Oncog 2014;19:327–36 doi:10.1615/critrevoncog.2014011777 pmid:25404148

    CrossRefPubMed
32. 32.↵
    1. Perrin SL,
    2. Samuel MS,
    3. Koszyca B, et al

    . Glioblastoma heterogeneity and the tumour microenvironment: implications for preclinical research and development of new treatments. Biochem Soc Trans 2019;47:625–38 doi:10.1042/BST20180444 pmid:30902924

    Abstract/FREE Full Text
33. 33.↵
    1. Qazi MA,
    2. Vora P,
    3. Venugopal C, et al

    . Intratumoral heterogeneity: pathways to treatment resistance and relapse in human glioblastoma. Ann Oncol Off Oncol 2017;28:1448–56 doi:10.1093/annonc/mdx169 pmid:28407030

    CrossRefPubMed