TY - JOUR
T1 - Quantitative tumor segmentation for evaluation of extent of glioblastoma resection to facilitate multisite clinical trials
AU - Cordova, James S.
AU - Schreibmann, Eduard
AU - Hadjipanayis, Costas G.
AU - Guo, Ying
AU - Hui-Kuo, Shu G.
AU - Shim, Hyunsuk
AU - Holder, Chad A.
N1 - Funding Information:
Address all correspondence to: Chad A. Holder, MD or Hyunsuk Shim, PhD, Winship Cancer Institute, C5018, 1365C Clifton Rd, Atlanta, GA 30322. E-mail: [email protected], [email protected] 1Work was supported by NIH U01CA172027 (to H.S.) and a predoctoral fellowship T32GM008602 (to J.S.C.). 2This article refers to supplementary materials, which are designated by Tables W1 to W6 and Figures W1 to W3 and are available online at www.transonc.com. Received 12 December 2013; Revised 15 January 2014; Accepted 16 January 2014 Copyright © 2014 Neoplasia Press, Inc. All rights reserved 1944-7124/14/$25.00 DOI 10.1593/tlo.13835
PY - 2014/2
Y1 - 2014/2
N2 - Standard-of-care therapy for glioblastomas, the most common and aggressive primary adult brain neoplasm, is maximal safe resection, followed by radiation and chemotherapy. Because maximizing resection may be beneficial for these patients, improving tumor extent of resection (EOR) with methods such as intraoperative 5-aminolevulinic acid fluorescence-guided surgery (FGS) is currently under evaluation. However, it is difficult to reproducibly judge EOR in these studies due to the lack of reliable tumor segmentation methods, especially for postoperative magnetic resonance imaging (MRI) scans. Therefore, a reliable, easily distributable segmentation method is needed to permit valid comparison, especially across multiple sites. We report a segmentation method that combines versatile region-of-interest blob generation with automated clustering methods. We applied this to glioblastoma cases undergoing FGS and matched controls to illustrate the method's reliability and accuracy. Agreement and interrater variability between segmentations were assessed using the concordance correlation coefficient, and spatial accuracy was determined using the Dice similarity index and mean Euclidean distance. Fuzzy C-means clustering with three classes was the best performing method, generating volumes with high agreement with manual contouring and high interrater agreement preoperatively and postoperatively. The proposed segmentation method allows tumor volume measurements of contrast-enhanced T 1-weighted images in the unbiased, reproducible fashion necessary for quantifying EOR in multicenter trials.
AB - Standard-of-care therapy for glioblastomas, the most common and aggressive primary adult brain neoplasm, is maximal safe resection, followed by radiation and chemotherapy. Because maximizing resection may be beneficial for these patients, improving tumor extent of resection (EOR) with methods such as intraoperative 5-aminolevulinic acid fluorescence-guided surgery (FGS) is currently under evaluation. However, it is difficult to reproducibly judge EOR in these studies due to the lack of reliable tumor segmentation methods, especially for postoperative magnetic resonance imaging (MRI) scans. Therefore, a reliable, easily distributable segmentation method is needed to permit valid comparison, especially across multiple sites. We report a segmentation method that combines versatile region-of-interest blob generation with automated clustering methods. We applied this to glioblastoma cases undergoing FGS and matched controls to illustrate the method's reliability and accuracy. Agreement and interrater variability between segmentations were assessed using the concordance correlation coefficient, and spatial accuracy was determined using the Dice similarity index and mean Euclidean distance. Fuzzy C-means clustering with three classes was the best performing method, generating volumes with high agreement with manual contouring and high interrater agreement preoperatively and postoperatively. The proposed segmentation method allows tumor volume measurements of contrast-enhanced T 1-weighted images in the unbiased, reproducible fashion necessary for quantifying EOR in multicenter trials.
UR - http://www.scopus.com/inward/record.url?scp=84902528505&partnerID=8YFLogxK
U2 - 10.1593/tlo.13835
DO - 10.1593/tlo.13835
M3 - Article
AN - SCOPUS:84902528505
SN - 1944-7124
VL - 7
SP - 40
EP - 47
JO - Translational Oncology
JF - Translational Oncology
IS - 1
ER -