MRI measurement of brain tumor response: comparison of visual metric and automatic segmentation.
An automatic magnetic resonance imaging (MRI) multispectral segmentation method and a visual metric are compared for their effectiveness to measure tumor response to therapy. Automatic response measurements are important for multicenter clinical trials. A visual metric such as the product of the largest diameter and the largest perpendicular diameter of the tumor is a standard approach, and is currently used in the Radiation Treatment Oncology Group (RTOG) and the Eastern Cooperative Oncology Group (EGOG) clinical trials. In the standard approach, the tumor response is based on the percentage change in the visual metric and is categorized into cure, partial response, stable disease, or progression. Both visual and automatic methods are applied to six brain tumor cases (gliomas) of varying levels of segmentation difficulty. The analyzed data were serial multispectral MR images, collected using MR contrast enhancement. A fully automatic knowledge guided method (KG) was applied to the MRI multispectral data, while the visual metric was taken from the MRI films using the T1 gadolinium enhanced image, with repeat measurements done by two radiologists and two residents. Tumor measurements from both visual and automatic methods are compared to "ground truth," (GT) i.e., manually segmented tumor. The KG method was found to slightly overestimate tumor volume, but in a consistent manner, and the estimated tumor response compared very well to hand-drawn ground truth with a correlation coefficient of 0.96. In contrast, the visually estimated metric had a large variation between observers, particularly for difficult cases, where the tumor margins are not well delineated. The inter-observer variation for the measurement of the visual metric was only 16%, i.e., observers generally agreed on the lengths of the diameters. However, in 30% of the studied cases no consensus was found for the categorical tumor response measurement, indicating that the categories are very sensitive to variations in the diameter measurements. Moreover, the method failed to correctly identify the response in half of the cases. The data demonstrate that automatic 3D methods are clearly necessary for objective and clinically meaningful assessment of tumor volume in single or multicenter clinical trials.
Clarke
L P
LP
Department of Radiology, College of Medicine, University of South Florida, and the H. Lee Moffitt Cancer and Research Institute, Tampa 33612-4799, USA. clarke@rad.usf.edu
Velthuizen
R P
RP
Clark
M
M
Gaviria
J
J
Hall
L
L
Goldgof
D
D
Murtagh
R
R
Phuphanich
S
S
Brem
S
S
D000328:Adult; D000368:Aged; D016477:Artifacts; D001185:Artificial Intelligence; D001921:Brain / Q000473:pathology; D001932:Brain Neoplasms / Q000175:diagnosis / Q000473:pathology / Q000628:therapy*; D017024:Chemotherapy, Adjuvant; D002986:Clinical Trials as Topic; D003131:Combined Modality Therapy; D005103:Expert Systems; D005240:Feasibility Studies; D005260:Female; D005909:Glioblastoma / Q000175:diagnosis / Q000473:pathology / Q000628:therapy*; D006801:Humans; D007089:Image Enhancement / Q000295:instrumentation*; D007091:Image Processing, Computer-Assisted / Q000295:instrumentation*; D008279:Magnetic Resonance Imaging / Q000295:instrumentation*; D008297:Male; D008875:Middle Aged; D011878:Radiotherapy; D012680:Sensitivity and Specificity; D016896:Treatment Outcome