A new metric for detecting change in slowly evolving brain tumors: Validation in meningioma patients

Neurosurgery

Volumn 68, Issue SUPPL. 1, 2011, Pages

  Pohl, Kilian M ^a   Konukoglu, Ender ^b   Novellas, Sebastian ^c   Ayache, Nicholas ^c   Fedorov, Andriy ^d   Talos, Ion Florin ^d   Golby, Alexandra ^d   Wells, William M ^d   Kikinis, Ron ^d   Black, Peter M ^d  

^a UNIVERSITY OF PENNSYLVANIA   (United States)

^b MICROSOFT RESEARCH   (United Kingdom)

^c INRIA   (France)

^d BRIGHAM AND WOMEN S HOSPITAL   (United States)

Author keywords

Automatic change detection; Growth rate; Longitudinal studies; Meningioma; Slowly evolving pathologies; Statistical modeling; Time series analysis

Indexed keywords

ARTICLE; CALIBRATION; CLINICAL ARTICLE; COMPUTER PROGRAM; FEMALE; HUMAN; MALE; MENINGIOMA; METRIC SYSTEM; NUCLEAR MAGNETIC RESONANCE IMAGING; PRIORITY JOURNAL; SENSITIVITY ANALYSIS; TUMOR GROWTH; TUMOR VOLUME;

BRAIN NEOPLASMS; DIAGNOSIS, COMPUTER-ASSISTED; DIAGNOSTIC IMAGING; DISEASE PROGRESSION; FEMALE; HUMANS; LONGITUDINAL STUDIES; MALE; MENINGEAL NEOPLASMS; MENINGIOMA; REPRODUCIBILITY OF RESULTS; SOFTWARE; TIME FACTORS;

EID: 79951769309     PISSN: 0148396X     EISSN: None     Source Type: Journal    
DOI: 10.1227/NEU.0b013e31820783d5     Document Type: Article

References (27)

1. CBTRUS 2007-2008: Primary Brain Tumors in the United States Statistical Report 2000-2004: Central Brain Tumor Registry of the United States.
2. Claus EB, Bondy ML, Schildkraut JM, Wiemels JL, Wrensch M, Black PM. Epidemiology of intracranial meningioma. Neurosurgery. 2005;57(6):1088-1095.
3. Perry A, Louis D, Scheithauer B, Budka H, von Diemling A. Meningiomas. Lyon: International Agency for Research on Cancer; 2007.
4. Nakasu S, Fukami T, Nakajima M, Watanabe K, Ichikawa M, Matsuda M. Growth pattern changes of meningiomas: long-term analysis. Neurosurgery. 2005;56(5):946-955.
5. Bindal R, Goodman JM, Kawasaki A, Purvin V, Kuzma B. The natural history of untreated skull base meningiomas. Surg Neurol. 2003;59(2):87-92. (Pubitemid 36338255)
6. Van Havenbergh T, Carvalho G, Tatagiba M, Plets C, Samii M. Natural history of petroclival meningiomas. Neurosurgery. 2003;52(1):55-64. (Pubitemid 36098653)
7. Yano S, Kuratsu J-I. Indications for surgery in patients with asymptomatic meningiomas based on an extensive experience. J Neurosurg. 2006; 105(4):538-543. (Pubitemid 44525882)
8. O'Leary S, Adams WM, Parrish RW, Mukonoweshuro W. A typical imaging appearances of intracranial meningiomas. Clin Radiol. 2007;62(1):10-17. (Pubitemid 44824748)
9. Viola P, Wells WM. Alignment by maximization of mutual information. Int J Comput Vis. 1997;24(2):137-154. (Pubitemid 127506551)
10. Clatz O, Sermesant M, Bondiau PY, et al. Realistic simulation of the 3-D growth of brain tumors in MR images coupling diffusion and biomechanical deformation. IEEE Trans Med Imaging. 2005;24(10):1334-1346. (Pubitemid 41463570)
11. Louis DN, Ohgaki H, Wiestler OD, Cavenee WK. Classification of Tumors of the Central Nervous System. Lyon: World Health Organization; 2007.
12. James K, Eisenhauer E, Christian M, et al. Measuring response in solid tumors: unidimensional versus bidimensional measurement. J Natl Cancer Inst. 1999;91(6):523-528. (Pubitemid 29157060)
13. Therasse P, Arbuck SG, Eisenhauer EA, et al. New guidelines to evaluate the response to treatment in solid tumors. J Natl Cancer Inst. 2000;92(3):205-216. (Pubitemid 30099779)
14. Zeidman L, Ankenbrandt W, Du H, Paleologos N, Vick N. Growth rate of non-operated meningiomas. J Neurol. 2008;255(6):891-895.
15. Commins DL, Atkinson RD, Burnett ME. Review of meningioma histopathology. Neurosurg Focus. 2007;23(4):E3.
16. McHugh K, Kao S. Response evaluation criteria in solid tumours (RECIST): problems and need for modifications in paediatric oncology? Br J Radiol. 2003;76(907):433-436. (Pubitemid 36903355)
17. Hashiba T, Hashimoto N, Izumoto S, et al. Serial volumetric assessment of the natural history and growth pattern of incidentally discovered meningiomas. J Neurosurg. 2009;110(4):675-684.
18. Sorensen AG, Patel S, Harmath C, et al. Comparison of diameter and perimeter methods for tumor volume calculation. J Clin Oncol. 2001;19(2):551-557. (Pubitemid 32112870)
19. Warren KE, Patronas N, Aikin AA, Albert PS, Balis FM. Comparison of one-, two-, and three-dimensional measurements of childhood brain tumors. J Natl Cancer Inst. 2001;93(18):1401-1405. (Pubitemid 32936817)
20. Hopper K, Kasales C, Van Slyke M, Schwartz T, TenHave T, Jozefiak J. Analysis of interobserver and intraobserver variability in CT tumor measurements. Am J Roentgenol. 1996;167(4):851-854. (Pubitemid 26322230)
21. Liu J, Udupa J, Odhner D, Hackney D, Moonis G. A system for brain tumor volume estimation via MR imaging and fuzzy connectedness. Comput Med Imaging Graph. 2005;29(1):21-34. (Pubitemid 40248631)
22. Prastawa M, Bullitt E, Moon N, Leemput KV, Gerig G. Automatic brain tumor segmentation by subject specific modification of atlas priors. Acad Radiol. 2003;10:1431-1348.
23. Gerig G, Welti D, Guttmann CRG, Colchester ACF, Szekely G. Exploring the discrimination power of the time domain for segmentation and characterization of active lesions in serial MR data. Med Image Anal. 2000;4(1):31-42.
24. Meier DS, Weiner HL, Guttmann CRG. MR Imaging intensity modeling of damage and repair in multiple sclerosis: relationship of short-term lesion recovery to progression and disability. AJNR Am J Neuroradiol. 2007; 28(10):1956-1963. (Pubitemid 350174742)
25. Thompson PM, Hayashi KM, Sowell ER, et al. Mapping cortical change in Alzheimer's disease, brain development, and schizophrenia. NeuroImage. 2004;23(Suppl 1):S2-S18. (Pubitemid 39421703)
26. Rey D, Subsol G, Delingette H, Ayache N. Automatic detection and segmentation of evolving processes in 3D medical images: application to multiple sclerosis. Med Image Anal. 2002;6(2):163-179. (Pubitemid 35282824)
27. Angelini ED, Atif J, Delon J, Mandonnet E, Duffau H, Capelle L. Detection of glioma evolution on longitudinal MRI studies. IEEE Intl Symp Biomed Imaging. 2007:49-52. (Pubitemid 350141022)