Semiautomated volumetric measurement on postcontrast MR imaging for analysis of recurrent and residual disease in glioblastoma multiforme

Abstract

Background and purpose: A limitation in postoperative monitoring of patients with glioblastoma is the lack of objective measures to quantify residual and recurrent disease. Automated computer-assisted volumetric analysis of contrast-enhancing tissue represents a potential tool to aid the radiologist in following these patients. In this study, we hypothesize that computer-assisted volumetry will show increased precision and speed over conventional 1D and 2D techniques in assessing residual and/or recurrent tumor.

Materials and methods: This retrospective study included patients with native glioblastomas with MR imaging performed at 24-48 hours following resection and 2-4 months postoperatively. 1D and 2D measurements were performed by 2 neuroradiologists with Certificates of Added Qualification. Volumetry was performed by using manual segmentation and computer-assisted volumetry, which combines region-based active contours and a level set approach. Tumor response was assessed by using established 1D, 2D, and volumetric standards. Manual and computer-assisted volumetry segmentation times were compared. Interobserver correlation was determined among 1D, 2D, and volumetric techniques.

Results: Twenty-nine patients were analyzed. Discrepancy in disease status between 1D and 2D compared with computer-assisted volumetry was 10.3% (3/29) and 17.2% (5/29), respectively. The mean time for segmentation between manual and computer-assisted volumetry techniques was 9.7 minutes and <1 minute, respectively (P < .01). Interobserver correlation was highest for volumetric measurements (0.995; 95% CI, 0.990-0.997) compared with 1D (0.826; 95% CI, 0.695-0.904) and 2D (0.905; 95% CI, 0.828-0.948) measurements.

Conclusions: Computer-assisted volumetry provides a reproducible and faster volumetric assessment of enhancing tumor burden, which has implications for monitoring disease progression and quantification of tumor burden in treatment trials.

Fig 1.

Comparison of linear 1D measurements (A) and CAV analysis (B) in a 64-year-old man with glioblastoma multiforme 11 weeks following resection. Our readers found volumetric analysis preferable, given the irregularity of recurrence.

Fig 2.

Disease status categorization in a 38-year-old woman with glioblastoma following resection, at 24 hours and at 12 weeks. 1D measurement found the contrast enhancement on initial postoperative imaging nonmeasurable (A) and subsequently labeled this case disease progression on follow-up imaging (B). CAV measurement labeled this case stable disease between baseline (C) and follow-up imaging (D).

Fig 3.

Disease status categorization in an 81-year-old man with glioblastoma following resection at 24 hours and at 12 weeks. 1D measurement labeled this case partial response between baseline (A) and follow-up imaging (B). CAV measurement labeled this case stable disease between baseline (C) and follow-up imaging (D).