» Articles » PMID: 18711427

Response Criteria for Glioma

Overview
Specialty Oncology
Date 2008 Aug 20
PMID 18711427
Citations 107
Authors
Affiliations
Soon will be listed here.
Abstract

The current method for assessing the response to therapy of glial tumors was described by Macdonald et al. in 1990. Under this paradigm, response categorization is determined on the basis of changes in the cross-sectional area of a tumor on neuroimaging, coupled with clinical assessment of neurological status and corticosteroid utilization. These categories of response have certain limitations; for example, cross-sectional assessment is not as accurate as volumetric assessment, which is now feasible. Disentangling antitumor effects of therapies from their effects on blood-brain barrier permeability can be challenging. The use of insufficient response criteria might be overestimating the true benefits of drugs in early-stage studies, and, therefore, such therapies could mistakenly move forward into later phases, only to result in disappointment when overall survival is measured. We propose that studies report both radiographic and clinical response rates, use volumetric rather than cross-sectional area to measure lesion size, and incorporate findings from mechanistic imaging and blood biomarker studies more frequently, and also suggest that investigators recognize the limitations of imaging biomarkers as surrogate end points.

Citing Articles

Towards consistency in pediatric brain tumor measurements: Challenges, solutions, and the role of artificial intelligence-based segmentation.

Familiar A, Kazerooni A, Vossough A, Ware J, Bagheri S, Khalili N Neuro Oncol. 2024; 26(9):1557-1571.

PMID: 38769022 PMC: 11376457. DOI: 10.1093/neuonc/noae093.


Artificial intelligence in neuro-oncology.

Nakhate V, Gonzalez Castro L Front Neurosci. 2024; 17:1217629.

PMID: 38161802 PMC: 10755952. DOI: 10.3389/fnins.2023.1217629.


"A net for everyone": fully personalized and unsupervised neural networks trained with longitudinal data from a single patient.

Strack C, Pomykala K, Schlemmer H, Egger J, Kleesiek J BMC Med Imaging. 2023; 23(1):174.

PMID: 37907876 PMC: 10619304. DOI: 10.1186/s12880-023-01128-w.


Prediction of pseudoprogression in post-treatment glioblastoma using dynamic susceptibility contrast-derived oxygenation and microvascular transit time heterogeneity measures.

Park J, Kim H, Kim N, Borra R, Mouridsen K, Hansen M Eur Radiol. 2023; 34(5):3061-3073.

PMID: 37848773 DOI: 10.1007/s00330-023-10324-9.


Challenges and advances for glioma therapy based on inorganic nanoparticles.

Hu D, Xia M, Wu L, Liu H, Chen Z, Xu H Mater Today Bio. 2023; 20:100673.

PMID: 37441136 PMC: 10333687. DOI: 10.1016/j.mtbio.2023.100673.


References
1.
Kaplan R . Complexities, pitfalls, and strategies for evaluating brain tumor therapies. Curr Opin Oncol. 1998; 10(3):175-8. DOI: 10.1097/00001622-199805000-00001. View

2.
Vos M, Uitdehaag B, Barkhof F, Heimans J, Baayen H, Boogerd W . Interobserver variability in the radiological assessment of response to chemotherapy in glioma. Neurology. 2003; 60(5):826-30. DOI: 10.1212/01.wnl.0000049467.54667.92. View

3.
Hess K, Wong E, Jaeckle K, Kyritsis A, Levin V, PRADOS M . Response and progression in recurrent malignant glioma. Neuro Oncol. 2001; 1(4):282-8. PMC: 1920759. DOI: 10.1093/neuonc/1.4.282. View

4.
Ricci P, Karis J, Heiserman J, Fram E, Bice A, Drayer B . Differentiating recurrent tumor from radiation necrosis: time for re-evaluation of positron emission tomography?. AJNR Am J Neuroradiol. 1998; 19(3):407-13. PMC: 8338276. View

5.
Chamberlain M . MRI in patients with high-grade gliomas treated with bevacizumab and chemotherapy. Neurology. 2006; 67(11):2089. DOI: 10.1212/01.wnl.0000250628.10420.d8. View