Shape Features of the Lesion Habitat to Differentiate Brain Tumor Progression from Pseudoprogression on Routine Multiparametric MRI: A Multisite Study

References

1. 1.↵
   2. Wick W,
   3. Chinot OL,
   4. Bendszus M, et al

   . Evaluation of pseudoprogression rates and tumor progression patterns in a phase III trial of bevacizumab plus radiotherapy/temozolomide for newly diagnosed glioblastoma. Neuro Oncol 2016;18:1434–41 doi:10.1093/neuonc/now091 pmid:27515827

   CrossRefPubMed
2. 2.↵
   2. Clarke JL,
   3. Chang S

   . Pseudoprogression and pseudoresponse: challenges in brain tumor imaging. Curr Neurol Neurosci Rep 2009;9:241–46 doi:10.1007/s11910-009-0035-4 pmid:19348713

   CrossRefPubMedWeb of Science
3. 3.↵
   2. Parvez K,
   3. Parvez A,
   4. Zadeh G

   . The diagnosis and treatment of pseudoprogression, radiation necrosis and brain tumor recurrence. Int J Mol Sci 2014;15:11832–46 doi:10.3390/ijms150711832 pmid:24995696

   CrossRefPubMed
4. 4.↵
   2. Wen PY,
   3. Macdonald DR,
   4. Reardon DA, et al

   . Updated response assessment criteria for high-grade gliomas: Response Assessment in Neuro-Oncology working group. J Clin Oncol 2010;28:1963–72 doi:10.1200/JCO.2009.26.3541 pmid:20231676

   Abstract/FREE Full Text
5. 5.↵
   2. Provenzale JM,
   3. Ison C,
   4. DeLong D

   . Bidimensional measurements in brain tumors: assessment of interobserver variability. AJR Am J Roentgenol 2009;193:W515–22 doi:10.2214/AJR.09.2615 pmid:19933626

   CrossRefPubMedWeb of Science
6. 6.↵
   2. Dempsey MF,
   3. Condon BR,
   4. Hadley DM

   . Measurement of tumor “size” in recurrent malignant glioma: 1D, 2D, or 3D? AJNR Am J Neuroradiol 2005;26:770–76 pmid:15814919

   Abstract/FREE Full Text
7. 7.↵
   2. Mitsuya K,
   3. Nakasu Y,
   4. Horiguchi S, et al

   . Perfusion weighted magnetic resonance imaging to distinguish the recurrence of metastatic brain tumors from radiation necrosis after stereotactic radiosurgery. J Neurooncol 2010;99:81–88 doi:10.1007/s11060-009-0106-z pmid:20058049

   CrossRefPubMed
8. 8.↵
   2. Elias AE,
   3. Carlos RC,
   4. Smith EA, et al

   . MR spectroscopy using normalized and non-normalized metabolite ratios for differentiating recurrent brain tumor from radiation injury. Acad Radiol 2011;18:1101–08 doi:10.1016/j.acra.2011.05.006 pmid:21820634

   CrossRefPubMed
9. 9.↵
   2. Asao C,
   3. Korogi Y,
   4. Kitajima M, et al

   . Diffusion-weighted imaging of radiation-induced brain injury for differentiation from tumor recurrence. AJNR Am J Neuroradiol 2005;26:1455–60 pmid:15956515

   Abstract/FREE Full Text
10. 10.↵
    2. Brandsma D,
    3. Stalpers L,
    4. Taal W, et al

    . Clinical features, mechanisms, and management of pseudoprogression in malignant gliomas. Lancet Oncol 2008;9:453–61 doi:10.1016/S1470-2045(08)70125-6 pmid:18452856

    CrossRefPubMedWeb of Science
11. 11.↵
    2. Kickingereder P,
    3. Burth S,
    4. Wick A, et al

    . Radiomic profiling of glioblastoma: identifying an imaging predictor of patient survival with improved performance over established clinical and radiologic risk models. Radiology 2016;280:880–89 doi:10.1148/radiol.2016160845 pmid:27326665

    CrossRefPubMed
12. 12.↵
    2. Yang D,
    3. Rao G,
    4. Martinez J, et al

    . Evaluation of tumor-derived MRI-texture features for discrimination of molecular subtypes and prediction of 12-month survival status in glioblastoma. Med Phys 2015;42:6725–35 doi:10.1118/1.4934373 pmid:26520762

    CrossRefPubMed
13. 13.↵
    2. Tiwari P,
    3. Prasanna P,
    4. Wolansky L, et al

    . Computer-extracted texture features to distinguish cerebral radionecrosis from recurrent brain tumors on multiparametric MRI: a feasibility study. AJNR Am J Neuroradiol 2016;37:2231–36 doi:10.3174/ajnr.A4931 pmid:27633806

    Abstract/FREE Full Text
14. 14.↵
    2. Zhang Z,
    3. Yang J,
    4. Ho A, et al

    . A predictive model for distinguishing radiation necrosis from tumor progression after gamma knife radiosurgery based on radiomic features from MR images. Eur Radiol 2018;28:2255–63 doi:10.1007/s00330-017-5154-8 pmid:29178031

    CrossRefPubMed
15. 15.↵
    2. Ellingson BM,
    3. Chung C,
    4. 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
16. 16.↵
    2. Goldberg-Zimring D,
    3. Talos IF,
    4. Bhagwat JG, et al

    . Statistical validation of brain tumor shape approximation via spherical harmonics for image-guided neurosurgery. Acad Radiol 2005;12:459–66 doi:10.1016/j.acra.2004.11.032 pmid:15831419

    CrossRefPubMed
17. 17.↵
    2. Kumar AJ,
    3. Leeds NE,
    4. Fuller GN, et al

    . Malignant gliomas: MR imaging spectrum of radiation therapy-and chemotherapy-induced necrosis of the brain after treatment. Radiology 2000;217:377–84 doi:10.1148/radiology.217.2.r00nv36377 pmid:11058631

    CrossRefPubMedWeb of Science
18. 18.↵
    2. Brandes AA,
    3. Tosoni A,
    4. Spagnolli F, et al

    . Disease progression or pseudoprogression after concomitant radiochemotherapy treatment: pitfalls in neurooncology. Neuro Oncol 2008;10:361–67 doi:10.1215/15228517-2008-008 pmid:18401015

    CrossRefPubMed
19. 19.↵
    2. Bharath K,
    3. Kurtek S,
    4. Rao A, et al

    . Radiologic image-based statistical shape analysis of brain tumors. Journal of the Royal Statistical Society: Series C (Applied Statistics).
20. 20.↵
    1. Jin D,
    2. Lin S

    2. Wu P,
    3. Xie K,
    4. Zheng Y, et al

    . Brain tumors classification based on 3D shape. In: Jin D, Lin S, eds. Advances in Future Computer and Control Systems. Berlin: Springer; 2012;160:277–83
21. 21.↵
    2. Gatenby RA,
    3. Grove O,
    4. Gillies RJ

    . Quantitative imaging in cancer evolution and ecology. Radiology 2013;269:8–15 doi:10.1148/radiol.13122697 pmid:24062559

    CrossRefPubMed
22. 22.↵
    2. Tustison NJ,
    3. Avants BB,
    4. 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
23. 23.↵
    2. Tao X,
    3. Chang MC

    . A skull stripping method using deformable surface and tissue classification. In: Medical Imaging: Image Processing. Ohio: International Society for Optics and Photonics; 2010;7623:76233L
24. 24.↵
    2. Pienaar R,
    3. Fischl B,
    4. Caviness V, et al

    . A methodology for analyzing curvature in the developing brain from preterm to adult. Int J Imaging Syst Technol 2008;18:42–68 doi:10.1002/ima.20138 pmid:19936261

    CrossRefPubMed
25. 25.↵
    2. Marcano-Cedeno A,
    3. Quintanilla-Domínguez J,
    4. Cortina-Januchs MG, et al

    . Feature selection using sequential forward selection and classification applying artificial metaplasticity neural network. In: Proceedings of the Annual Conference of the Institute of Electrical and Electronics Engineers Industrial Electronics Society. Glendale, Arizona; November 7–10, 2010;2845–50
26. 26.↵
    2. Guyon I,
    3. Weston J,
    4. Barnhill S, et al

    . Gene selection for cancer classification using support vector machines. Machine Learning 2002;46:389–422 doi:10.1023/A:1012487302797

    CrossRefWeb of Science
27. 27.↵
    2. Chen Y,
    3. Gunawan E,
    4. Low KS, et al

    . Effect of lesion morphology on microwave signature in 2-D ultra-wideband breast imaging. IEEE Trans Biomed Eng 2008;55:2011–21 doi:10.1109/TBME.2008.921136 pmid:18632364

    CrossRefPubMed
28. 28.↵
    2. Tsui PH,
    3. Liao YY,
    4. Chang CC, et al

    . Classification of benign and malignant breast tumors by 2-D analysis based on contour description and scatterer characterization. IEEE Trans Med Imaging 2010;29:513–22 doi:10.1109/TMI.2009.2037147 pmid:20129851

    CrossRefPubMed
29. 29.↵
    2. Zacharaki EI,
    3. Wang S,
    4. Chawla S, et al

    . Classification of brain tumor type and grade using MRI texture and shape in a machine learning scheme. Magn Reson Med 2009;62:1609–18 doi:10.1002/mrm.22147 pmid:19859947

    CrossRefPubMed
30. 30.↵
    2. Lee J,
    3. Nishikawa RM,
    4. Reiser I, et al

    . Local curvature analysis for classifying breast tumors: preliminary analysis in dedicated breast CT. Med Phys 2015;42:5479–89 pmid:26328996

    PubMed